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We ❤️ contributions from the open-source community! Everyone is welcome, and all types of participation –not just code– are valued and appreciated. Answering questions, helping others, reaching out, and improving the documentation are all immensely valuable to the community, so don't be afraid and get involved if you're up for it!
Everyone is encouraged to start by saying 👋 in our public Discord channel. We discuss the latest trends in diffusion models, ask questions, show off personal projects, help each other with contributions, or just hang out ☕.
Whichever way you choose to contribute, we strive to be part of an open, welcoming, and kind community. Please, read our code of conduct and be mindful to respect it during your interactions. We also recommend you become familiar with the ethical guidelines that guide our project and ask you to adhere to the same principles of transparency and responsibility.
We enormously value feedback from the community, so please do not be afraid to speak up if you believe you have valuable feedback that can help improve the library - every message, comment, issue, and pull request (PR) is read and considered.
You can contribute in many ways ranging from answering questions on issues to adding new diffusion models to the core library.
In the following, we give an overview of different ways to contribute, ranked by difficulty in ascending order. All of them are valuable to the community.
As said before, all contributions are valuable to the community. In the following, we will explain each contribution a bit more in detail.
For all contributions 4-9, you will need to open a PR. It is explained in detail how to do so in Opening a pull request.
Any question or comment related to the Diffusers library can be asked on the discussion forum or on Discord. Such questions and comments include (but are not limited to):
Every question that is asked on the forum or on Discord actively encourages the community to publicly share knowledge and might very well help a beginner in the future who has the same question you're having. Please do pose any questions you might have. In the same spirit, you are of immense help to the community by answering such questions because this way you are publicly documenting knowledge for everybody to learn from.
Please keep in mind that the more effort you put into asking or answering a question, the higher the quality of the publicly documented knowledge. In the same way, well-posed and well-answered questions create a high-quality knowledge database accessible to everybody, while badly posed questions or answers reduce the overall quality of the public knowledge database. In short, a high quality question or answer is precise, concise, relevant, easy-to-understand, accessible, and well-formatted/well-posed. For more information, please have a look through the How to write a good issue section.
NOTE about channels: The forum is much better indexed by search engines, such as Google. Posts are ranked by popularity rather than chronologically. Hence, it's easier to look up questions and answers that we posted some time ago. In addition, questions and answers posted in the forum can easily be linked to. In contrast, Discord has a chat-like format that invites fast back-and-forth communication. While it will most likely take less time for you to get an answer to your question on Discord, your question won't be visible anymore over time. Also, it's much harder to find information that was posted a while back on Discord. We therefore strongly recommend using the forum for high-quality questions and answers in an attempt to create long-lasting knowledge for the community. If discussions on Discord lead to very interesting answers and conclusions, we recommend posting the results on the forum to make the information more available for future readers.
The 🧨 Diffusers library is robust and reliable thanks to the users who notify us of the problems they encounter. So thank you for reporting an issue.
Remember, GitHub issues are reserved for technical questions directly related to the Diffusers library, bug reports, feature requests, or feedback on the library design.
In a nutshell, this means that everything that is not related to the code of the Diffusers library (including the documentation) should not be asked on GitHub, but rather on either the forum or Discord.
Please consider the following guidelines when opening a new issue:
python -c "import diffusers; print(diffusers.__version__)" is higher or matches the latest Diffusers version.New issues usually include the following.
A bug report should always have a reproducible code snippet and be as minimal and concise as possible. This means in more detail:
diffusers-cli env in your shell and copy-paste the displayed information to the issue.For more information, please have a look through the How to write a good issue section.
You can open a bug report here.
A world-class feature request addresses the following points:
You can open a feature request here.
Feedback about the library design and why it is good or not good helps the core maintainers immensely to build a user-friendly library. To understand the philosophy behind the current design philosophy, please have a look here. If you feel like a certain design choice does not fit with the current design philosophy, please explain why and how it should be changed. If a certain design choice follows the design philosophy too much, hence restricting use cases, explain why and how it should be changed. If a certain design choice is very useful for you, please also leave a note as this is great feedback for future design decisions.
You can open an issue about feedback here.
Technical questions are mainly about why certain code of the library was written in a certain way, or what a certain part of the code does. Please make sure to link to the code in question and please provide detail on why this part of the code is difficult to understand.
You can open an issue about a technical question here.
If the diffusion model community released a new model, pipeline, or scheduler that you would like to see in the Diffusers library, please provide the following information:
If you are willing to contribute to the model yourself, let us know so we can best guide you. Also, don't forget to tag the original author of the component (model, scheduler, pipeline, etc.) by GitHub handle if you can find it.
You can open a request for a model/pipeline/scheduler here.
Answering issues on GitHub might require some technical knowledge of Diffusers, but we encourage everybody to give it a try even if you are not 100% certain that your answer is correct. Some tips to give a high-quality answer to an issue:
Also, many issues tend to be simply off-topic, duplicates of other issues, or irrelevant. It is of great help to the maintainers if you can answer such issues, encouraging the author of the issue to be more precise, provide the link to a duplicated issue or redirect them to the forum or Discord.
If you have verified that the issued bug report is correct and requires a correction in the source code, please have a look at the next sections.
For all of the following contributions, you will need to open a PR. It is explained in detail how to do so in the Opening a pull request section.
Good first issues are marked by the Good first issue label. Usually, the issue already explains how a potential solution should look so that it is easier to fix. If the issue hasn't been closed and you would like to try to fix this issue, you can just leave a message "I would like to try this issue.". There are usually three scenarios:
A good library always has good documentation! The official documentation is often one of the first points of contact for new users of the library, and therefore contributing to the documentation is a highly valuable contribution.
Contributing to the library can have many forms:
Anything displayed on the official Diffusers doc page is part of the official documentation and can be corrected, adjusted in the respective documentation source.
Please have a look at this page on how to verify changes made to the documentation locally.
Pipelines are usually the first point of contact between the Diffusers library and the user. Pipelines are examples of how to use Diffusers models and schedulers. We support two types of pipelines:
Both official and community pipelines follow the same design and consist of the same type of components.
Official pipelines are tested and maintained by the core maintainers of Diffusers. Their code resides in src/diffusers/pipelines. In contrast, community pipelines are contributed and maintained purely by the community and are not tested. They reside in examples/community and while they can be accessed via the PyPI diffusers package, their code is not part of the PyPI distribution.
The reason for the distinction is that the core maintainers of the Diffusers library cannot maintain and test all possible ways diffusion models can be used for inference, but some of them may be of interest to the community. Officially released diffusion pipelines, such as Stable Diffusion are added to the core src/diffusers/pipelines package which ensures high quality of maintenance, no backward-breaking code changes, and testing. More bleeding edge pipelines should be added as community pipelines. If usage for a community pipeline is high, the pipeline can be moved to the official pipelines upon request from the community. This is one of the ways we strive to be a community-driven library.
To add a community pipeline, one should add a .py file to examples/community and adapt the examples/community/README.md to include an example of the new pipeline.
An example can be seen here.
Community pipeline PRs are only checked at a superficial level and ideally they should be maintained by their original authors.
Contributing a community pipeline is a great way to understand how Diffusers models and schedulers work. Having contributed a community pipeline is usually the first stepping stone to contributing an official pipeline to the core package.
Diffusers examples are a collection of training scripts that reside in examples.
We support two types of training examples:
Research training examples are located in examples/research_projects whereas official training examples include all folders under examples except the research_projects and community folders.
The official training examples are maintained by the Diffusers' core maintainers whereas the research training examples are maintained by the community.
This is because of the same reasons put forward in 6. Contribute a community pipeline for official pipelines vs. community pipelines: It is not feasible for the core maintainers to maintain all possible training methods for diffusion models.
If the Diffusers core maintainers and the community consider a certain training paradigm to be too experimental or not popular enough, the corresponding training code should be put in the research_projects folder and maintained by the author.
Both official training and research examples consist of a directory that contains one or more training scripts, a requirements.txt file, and a README.md file. In order for the user to make use of the
training examples, it is required to clone the repository:
git clone https://github.com/huggingface/diffusers
as well as to install all additional dependencies required for training:
cd diffusers
pip install -r examples/<your-example-folder>/requirements.txt
Therefore when adding an example, the requirements.txt file shall define all pip dependencies required for your training example so that once all those are installed, the user can run the example's training script. See, for example, the DreamBooth requirements.txt file.
Training examples of the Diffusers library should adhere to the following philosophy:
python <your-example>.py --args.To contribute an example, it is highly recommended to look at already existing examples such as dreambooth to get an idea of how they should look like.
We strongly advise contributors to make use of the Accelerate library as it's tightly integrated
with Diffusers.
Once an example script works, please make sure to add a comprehensive README.md that states how to use the example exactly. This README should include:
If you are contributing to the official training examples, please also make sure to add a test to examples/test_examples.py. This is not necessary for non-official training examples.
Good second issues are marked by the Good second issue label. Good second issues are usually more complicated to solve than Good first issues. The issue description usually gives less guidance on how to fix the issue and requires a decent understanding of the library by the interested contributor. If you are interested in tackling a good second issue, feel free to open a PR to fix it and link the PR to the issue. If you see that a PR has already been opened for this issue but did not get merged, have a look to understand why it wasn't merged and try to open an improved PR. Good second issues are usually more difficult to get merged compared to good first issues, so don't hesitate to ask for help from the core maintainers. If your PR is almost finished the core maintainers can also jump into your PR and commit to it in order to get it merged.
Pipelines, models, and schedulers are the most important pieces of the Diffusers library. They provide easy access to state-of-the-art diffusion technologies and thus allow the community to build powerful generative AI applications.
By adding a new model, pipeline, or scheduler you might enable a new powerful use case for any of the user interfaces relying on Diffusers which can be of immense value for the whole generative AI ecosystem.
Diffusers has a couple of open feature requests for all three components - feel free to gloss over them if you don't know yet what specific component you would like to add:
Before adding any of the three components, it is strongly recommended that you give the Philosophy guide a read to better understand the design of any of the three components. Please be aware that we cannot merge model, scheduler, or pipeline additions that strongly diverge from our design philosophy as it will lead to API inconsistencies. If you fundamentally disagree with a design choice, please open a Feedback issue instead so that it can be discussed whether a certain design pattern/design choice shall be changed everywhere in the library and whether we shall update our design philosophy. Consistency across the library is very important for us.
Please make sure to add links to the original codebase/paper to the PR and ideally also ping the original author directly on the PR so that they can follow the progress and potentially help with questions.
If you are unsure or stuck in the PR, don't hesitate to leave a message to ask for a first review or help.
The better your issue is written, the higher the chances that it will be quickly resolved.
[WIP]. These
are useful to avoid duplicated work, and to differentiate it from PRs ready
to be merged;@slow tests, make sure they pass using
RUN_SLOW=1 python -m pytest tests/test_my_new_model.py.
CircleCI does not run the slow tests, but GitHub Actions does every night!pipeline_latent_diffusion.py for an example.dataset like
hf-internal-testing or huggingface/documentation-images to place these files.
If an external contribution, feel free to add the images to your PR and ask a Hugging Face member to migrate your images
to this dataset.Before writing code, we strongly advise you to search through the existing PRs or issues to make sure that nobody is already working on the same thing. If you are unsure, it is always a good idea to open an issue to get some feedback.
You will need basic git proficiency to be able to contribute to
🧨 Diffusers. git is not the easiest tool to use but it has the greatest
manual. Type git --help in a shell and enjoy. If you prefer books, Pro
Git is a very good reference.
Follow these steps to start contributing (supported Python versions):
Fork the repository by clicking on the 'Fork' button on the repository's page. This creates a copy of the code under your GitHub user account.
Clone your fork to your local disk, and add the base repository as a remote:
$ git clone git@github.com:<your GitHub handle>/diffusers.git
$ cd diffusers
$ git remote add upstream https://github.com/huggingface/diffusers.git
$ git checkout -b a-descriptive-name-for-my-changes
Do not work on the main branch.
$ pip install -e ".[dev]"
If you have already cloned the repo, you might need to git pull to get the most recent changes in the
library.
As you work on the features, you should make sure that the test suite passes. You should run the tests impacted by your changes like this:
$ pytest tests/<TEST_TO_RUN>.py
Before you run the tests, please make sure you install the dependencies required for testing. You can do so with this command:
$ pip install -e ".[test]"
You can also run the full test suite with the following command, but it takes a beefy machine to produce a result in a decent amount of time now that Diffusers has grown a lot. Here is the command for it:
$ make test
🧨 Diffusers relies on ruff and isort to format its source code
consistently. After you make changes, apply automatic style corrections and code verifications
that can't be automated in one go with:
$ make style
🧨 Diffusers also uses ruff and a few custom scripts to check for coding mistakes. Quality
control runs in CI, however, you can also run the same checks with:
$ make quality
Once you're happy with your changes, add changed files using git add and
make a commit with git commit to record your changes locally:
$ git add modified_file.py
$ git commit -m "A descriptive message about your changes."
It is a good idea to sync your copy of the code with the original repository regularly. This way you can quickly account for changes:
$ git pull upstream main
Push the changes to your account using:
$ git push -u origin a-descriptive-name-for-my-changes
Once you are satisfied, go to the webpage of your fork on GitHub. Click on 'Pull request' to send your changes to the project maintainers for review.
It's ok if maintainers ask you for changes. It happens to core contributors too! So everyone can see the changes in the Pull request, work in your local branch and push the changes to your fork. They will automatically appear in the pull request.
An extensive test suite is included to test the library behavior and several examples. Library tests can be found in the tests folder.
We like pytest and pytest-xdist because it's faster. From the root of the
repository, here's how to run tests with pytest for the library:
$ python -m pytest -n auto --dist=loadfile -s -v ./tests/
In fact, that's how make test is implemented!
You can specify a smaller set of tests in order to test only the feature you're working on.
By default, slow tests are skipped. Set the RUN_SLOW environment variable to
yes to run them. This will download many gigabytes of models — make sure you
have enough disk space and a good Internet connection, or a lot of patience!
$ RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./tests/
unittest is fully supported, here's how to run tests with it:
$ python -m unittest discover -s tests -t . -v
$ python -m unittest discover -s examples -t examples -v
To avoid pinging the upstream repository which adds reference notes to each upstream PR and sends unnecessary notifications to the developers involved in these PRs, when syncing the main branch of a forked repository, please, follow these steps:
$ git checkout -b your-branch-for-syncing
$ git pull --squash --no-commit upstream main
$ git commit -m '<your message without GitHub references>'
$ git push --set-upstream origin your-branch-for-syncing
For documentation strings, 🧨 Diffusers follows the Google style.
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