Manual Installation

Please check the system requirements page to make sure your hardware is capable of running the desired models.

Are you in the right place?

If you want to use Invoke locally, you should probably use the launcher instead.

If you want to contribute to InvokeAI or run the app on the main branch, follow the developer installation guide instead.

Walkthrough

We’ll use uv to install python and create a virtual environment, then install the invokeai package. uv is a modern, very fast alternative to pip.

The following commands vary depending on the version of Invoke being installed and the system onto which it is being installed.

1. Install uv as described in its docs. We suggest using the standalone installer method.

Run uv --version to confirm that uv is installed and working. After installation, you may need to restart your terminal to get access to uv.

2. Create a directory for your installation, typically in your home directory (e.g. ~/invokeai or $Home/invokeai):

    mkdir $Home/invokeai
    
    cd $Home/invokeai
    ```
    
    ```
    mkdir ~/invokeai
    
    cd ~/invokeai
    ```

03. Create a virtual environment in that directory:
    
    ```
    uv venv --relocatable --prompt invoke --python 3.12 --python-preference only-managed .venv
    ```

This command creates a portable virtual environment at `.venv` complete with a portable python 3.12. It doesn’t matter if your system has no python installed, or has a different version - `uv` will handle everything.

04. Activate the virtual environment:

.venv\Scripts\activate
```

```
source .venv/bin/activate
```

5. Choose a version to install.
6. Determine the package specifier to use when installing. This is a performance optimization.
   • If you have an Nvidia 20xx series GPU or older, use invokeai[xformers].
   • If you have an Nvidia 30xx series GPU or newer, or do not have an Nvidia GPU, use invokeai.
7. Determine the torch backend to use for installation, if any. This is necessary to get the right version of torch installed. This is achieved by using UV’s built in torch support.

Use:

```
--torch-backend=cu128
```

On `x86_64`, use:

```
--torch-backend=cpu
```

On ARM64 (`aarch64`, e.g. Raspberry Pi 5), do **not** use a torch backend — PyTorch’s `cpu` index has no ARM64 torchvision wheels. The default PyPI wheels are CPU-only on ARM64 and work out of the box.

Use:

```
--torch-backend=rocm7.1
```

8. Install the invokeai package. Substitute the package specifier and version.

    uv pip install <PACKAGE_SPECIFIER>==<VERSION> --python 3.12 --python-preference only-managed --force-reinstall
    ```

09. Deactivate and reactivate your venv so that the invokeai-specific commands become available in the environment:

deactivate

.venv\Scripts\activate
```

```
deactivate && source .venv/bin/activate
```

10. Run the application, specifying the directory you created earlier as the root directory:

    invokeai-web --root ~/invokeai
    ```
    
    If you run Invoke on a headless server, you might want to install and run Invoke on the command line.

You can create your own scripts for this by copying the handful of commands in this guide. `uv`’s [`pip` interface docs](https://docs.astral.sh/uv/reference/cli/#uv-pip-install) may be useful.