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How to contribute to diffusers?

Everyone is welcome to contribute, and we value everybody's contribution. Code is thus not the only way to help the community. Answering questions, helping others, reaching out and improving the documentations are immensely valuable to the community.

It also helps us if you spread the word: reference the library from blog posts on the awesome projects it made possible, shout out on Twitter every time it has helped you, or simply star the repo to say "thank you".

Whichever way you choose to contribute, please be mindful to respect our code of conduct.

You can contribute in so many ways!

There are 4 ways you can contribute to diffusers:

Fixing outstanding issues with the existing code;
Implementing new diffusion pipelines, new schedulers or new models
Contributing to the examples or to the documentation;
Submitting issues related to bugs or desired new features.

In particular there is a special Good First Issue listing. It will give you a list of open Issues that are open to anybody to work on. Just comment in the issue that you'd like to work on it. In that same listing you will also find some Issues with Good Second Issue label. These are typically slightly more complicated than the Issues with just Good First Issue label. But if you feel you know what you're doing, go for it.

All are equally valuable to the community.

Submitting a new issue or feature request

Do your best to follow these guidelines when submitting an issue or a feature request. It will make it easier for us to come back to you quickly and with good feedback.

Did you find a bug?

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.

First, we would really appreciate it if you could make sure the bug was not already reported (use the search bar on Github under Issues).

Do you want to implement a new diffusion pipeline / diffusion model?

Awesome! Please provide the following information:

Short description of the diffusion pipeline and link to the paper;
Link to the implementation if it is open-source;
Link to the model weights if they are available.

If you are willing to contribute the model yourself, let us know so we can best guide you.

Do you want a new feature (that is not a model)?

A world-class feature request addresses the following points:

Motivation first:

Is it related to a problem/frustration with the library? If so, please explain why. Providing a code snippet that demonstrates the problem is best.
Is it related to something you would need for a project? We'd love to hear about it!
Is it something you worked on and think could benefit the community? Awesome! Tell us what problem it solved for you.

Write a full paragraph describing the feature;
Provide a code snippet that demonstrates its future use;
In case this is related to a paper, please attach a link;
Attach any additional information (drawings, screenshots, etc.) you think may help.

If your issue is well written we're already 80% of the way there by the time you post it.

Start contributing! (Pull Requests)

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

Create a new branch to hold your development changes:
```
$ git checkout -b a-descriptive-name-for-my-changes
```
Do not work on the main branch.
Set up a development environment by running the following command in a virtual environment:
```
$ pip install -e ".[dev]"
```
(If diffusers was already installed in the virtual environment, remove it with pip uninstall diffusers before reinstalling it in editable mode with the -e flag.)

To run the full test suite, you might need the additional dependency on transformers and datasets which requires a separate source install:
```
$ git clone https://github.com/huggingface/transformers
$ cd transformers
$ pip install -e .
```
```
$ git clone https://github.com/huggingface/datasets
$ cd datasets
$ pip install -e .
```
If you have already cloned that repo, you might need to git pull to get the most recent changes in the datasets library.
Develop the features on your branch.

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
```
You can also run the full 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
```
For more information about tests, check out the dedicated documentation

🧨 Diffusers relies on black 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
```
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 fetch upstream
$ git rebase upstream/main
```
Push the changes to your account using:
```
$ git push -u origin a-descriptive-name-for-my-changes
```
Once you are satisfied (and the checklist below is happy too), 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.

Checklist

The title of your pull request should be a summary of its contribution;
If your pull request addresses an issue, please mention the issue number in the pull request description to make sure they are linked (and people consulting the issue know you are working on it);
To indicate a work in progress please prefix the title with [WIP]. These are useful to avoid duplicated work, and to differentiate it from PRs ready to be merged;
Make sure existing tests pass;
Add high-coverage tests. No quality testing = no merge.
- If you are adding new @slow tests, make sure they pass using RUN_SLOW=1 python -m pytest tests/test_my_new_model.py.
- If you are adding a new tokenizer, write tests, and make sure RUN_SLOW=1 python -m pytest tests/test_tokenization_{your_model_name}.py passes. CircleCI does not run the slow tests, but github actions does every night!
All public methods must have informative docstrings that work nicely with sphinx. See modeling_bert.py for an example.
Due to the rapidly growing repository, it is important to make sure that no files that would significantly weigh down the repository are added. This includes images, videos and other non-text files. We prefer to leverage a hf.co hosted dataset like the ones hosted on hf-internal-testing in which to place these files and reference them by URL. We recommend putting them in the following dataset: huggingface/documentation-images. 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.

Tests

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 (sans the pip install line)!

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/

This means unittest is fully supported. Here's how to run tests with unittest:

$ python -m unittest discover -s tests -t . -v
$ python -m unittest discover -s examples -t examples -v

Style guide

For documentation strings, 🧨 Diffusers follows the google style.

This guide was heavily inspired by the awesome scikit-learn guide to contributing.

Syncing forked main with upstream (HuggingFace) main

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:

When possible, avoid syncing with the upstream using a branch and PR on the forked repository. Instead merge directly into the forked main.
If a PR is absolutely necessary, use the following steps after checking out your branch:

$ 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

12 KiB Raw Blame History