InvokeAI/docs/contributing/LOCAL_DEVELOPMENT.md

Local Development

If you are looking to contribute you will need to have a local development environment. See the Developer Install for full details.

Broadly this involves cloning the repository, installing the pre-reqs, and InvokeAI (in editable form). Assuming this is working, choose your area of focus.

Documentation

We use mkdocs for our documentation with the material theme. Documentation is written in markdown files under the ./docs folder and then built into a static website for hosting with GitHub Pages at invoke-ai.github.io/InvokeAI.

To contribute to the documentation you'll need to install the dependencies. Note the use of ".

pip install ".[docs]"

Now, to run the documentation locally with hot-reloading for changes made.

mkdocs serve

You'll then be prompted to connect to http://127.0.0.1:8080 in order to access.

Backend

The backend is contained within the ./invokeai/backend and ./invokeai/app directories. To get started please install the development dependencies.

From the root of the repository run the following command. Note the use of ".

pip install ".[dev,test]"

These are optional groups of packages which are defined within the pyproject.toml and will be required for testing the changes you make to the code.

Running Tests

We use pytest for our test suite. Tests can be found under the ./tests folder and can be run with a single pytest command. Optionally, to review test coverage you can append --cov.

pytest --cov

Test outcomes and coverage will be reported in the terminal. In addition a more detailed report is created in both XML and HTML format in the ./coverage folder. The HTML one in particular can help identify missing statements requiring tests to ensure coverage. This can be run by opening ./coverage/html/index.html.

For example.

pytest --cov; open ./coverage/html/index.html

??? info "HTML coverage report output"

![html-overview](../assets/contributing/html-overview.png)

![html-detail](../assets/contributing/html-detail.png)

Reloading Changes

Experimenting with changes to the Python source code is a drag if you have to re-start the server — and re-load those multi-gigabyte models — after every change.

For a faster development workflow, add the --dev_reload flag when starting the server. The server will watch for changes to all the Python files in the invokeai directory and apply those changes to the running server on the fly.

This will allow you to avoid restarting the server (and reloading models) in most cases, but there are some caveats; see the jurigged documentation for details.

Front End

--8<-- "invokeai/frontend/web/README.md"

Developing InvokeAI in VSCode

VSCode offers some nice tools:

• python debugger
• automatic venv activation
• remote dev (e.g. run InvokeAI on a beefy linux desktop while you type in comfort on your macbook)

Setup

You'll need the Python and Pylance extensions installed first.

It's also really handy to install the Jupyter extensions:

• Jupyter
• Jupyter Cell Tags
• Jupyter Notebook Renderers
• Jupyter Slide Show

InvokeAI workspace

Creating a VSCode workspace for working on InvokeAI is highly recommended. It can hold InvokeAI-specific settings and configs.

To make a workspace:

• Open the InvokeAI repo dir in VSCode
• File > Save Workspace As > save it outside the repo

Default python interpreter (i.e. automatic virtual environment activation)

• Use command palette to run command Preferences: Open Workspace Settings (JSON)
• Add python.defaultInterpreterPath to settings, pointing to your venv's python

Should look something like this:

{
  // I like to have all InvokeAI-related folders in my workspace
  "folders": [
    {
      // repo root
      "path": "InvokeAI"
    },
    {
      // InvokeAI root dir, where `invokeai.yaml` lives
      "path": "/path/to/invokeai_root"
    }
  ],
  "settings": {
    // Where your InvokeAI `venv`'s python executable lives
    "python.defaultInterpreterPath": "/path/to/invokeai_root/.venv/bin/python"
  }
}

Now when you open the VSCode integrated terminal, or do anything that needs to run python, it will automatically be in your InvokeAI virtual environment.

Bonus: When you create a Jupyter notebook, when you run it, you'll be prompted for the python interpreter to run in. This will default to your venv python, and so you'll have access to the same python environment as the InvokeAI app.

This is super handy.

Debugging configs with launch.json

Debugging configs are managed in a launch.json file. Like most VSCode configs, these can be scoped to a workspace or folder.

Follow the official guide to set up your launch.json and try it out.

Now we can create the InvokeAI debugging configs:

{
  // Use IntelliSense to learn about possible attributes.
  // Hover to view descriptions of existing attributes.
  // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
  "version": "0.2.0",
  "configurations": [
    {
      // Run the InvokeAI backend & serve the pre-built UI
      "name": "InvokeAI Web",
      "type": "python",
      "request": "launch",
      "program": "scripts/invokeai-web.py",
      "args": [
        // Your InvokeAI root dir (where `invokeai.yaml` lives)
        "--root",
        "/path/to/invokeai_root",
        // Access the app from anywhere on your local network
        "--host",
        "0.0.0.0"
      ],
      "justMyCode": true
    },
    {
      // Run the nodes-based CLI
      "name": "InvokeAI CLI",
      "type": "python",
      "request": "launch",
      "program": "scripts/invokeai-cli.py",
      "justMyCode": true
    },
    {
      // Run tests
      "name": "InvokeAI Test",
      "type": "python",
      "request": "launch",
      "module": "pytest",
      "args": ["--capture=no"],
      "justMyCode": true
    },
    {
      // Run a single test
      "name": "InvokeAI Single Test",
      "type": "python",
      "request": "launch",
      "module": "pytest",
      "args": [
        // Change this to point to the specific test you are working on
        "tests/nodes/test_invoker.py"
      ],
      "justMyCode": true
    },
    {
      // This is the default, useful to just run a single file
      "name": "Python: File",
      "type": "python",
      "request": "launch",
      "program": "${file}",
      "justMyCode": true
    }
  ]
}

You'll see these configs in the debugging configs drop down. Running them will start InvokeAI with attached debugger, in the correct environment, and work just like the normal app.

Enjoy debugging InvokeAI with ease (not that we have any bugs of course).

Remote dev

This is very easy to set up and provides the same very smooth experience as local development. Environments and debugging, as set up above, just work, though you'd need to recreate the workspace and debugging configs on the remote.

Consult the official guide to get it set up.

Suggest using VSCode's included settings sync so that your remote dev host has all the same app settings and extensions automagically.

One remote dev gotcha

I've found the automatic port forwarding to be very flakey. You can disable it in Preferences: Open Remote Settings (ssh: hostname). Search for remote.autoForwardPorts and untick the box.

To forward ports very reliably, use SSH on the remote dev client (e.g. your macbook). Here's how to forward both backend API port (9090) and the frontend live dev server port (5173):

ssh \
    -L 9090:localhost:9090 \
    -L 5173:localhost:5173 \
    user@remote-dev-host

The forwarding stops when you close the terminal window, so suggest to do this outside the VSCode integrated terminal in case you need to restart VSCode for an extension update or something

Now, on your remote dev client, you can open localhost:9090 and access the UI, now served from the remote dev host, just the same as if it was running on the client.