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Requirements#

Invoke runs on Windows 10+, macOS 14+ and Linux (Ubuntu 20.04+ is well-tested).

Hardware#

Hardware requirements vary significantly depending on model and image output size. The requirements below are rough guidelines.

  • All Apple Silicon (M1, M2, etc) Macs work, but 16GB+ memory is recommended.
  • AMD GPUs are supported on Linux only. The VRAM requirements are the same as Nvidia GPUs.

Hardware Requirements (Windows/Linux)

  • GPU: Nvidia 10xx series or later, 4GB+ VRAM.
  • Memory: At least 8GB RAM.
  • Disk: 10GB for base installation plus 30GB for models.
  • GPU: Nvidia 20xx series or later, 8GB+ VRAM.
  • Memory: At least 16GB RAM.
  • Disk: 10GB for base installation plus 100GB for models.
  • GPU: Nvidia 20xx series or later, 10GB+ VRAM.
  • Memory: At least 32GB RAM.
  • Disk: 10GB for base installation plus 200GB for models.

tmpfs on Linux

If your temporary directory is mounted as a tmpfs, ensure it has sufficient space.

Python#

The launcher installs python for you

You don't need to do this if you are installing with the Invoke Launcher.

Invoke requires python 3.10 or 3.11. If you don't already have one of these versions installed, we suggest installing 3.11, as it will be supported for longer.

Check that your system has an up-to-date Python installed by running python3 --version in the terminal (Linux, macOS) or cmd/powershell (Windows).

Installing Python

  • Install python 3.11 with [an official installer].
  • The installer includes an option to add python to your PATH. Be sure to enable this. If you missed it, re-run the installer, choose to modify an existing installation, and tick that checkbox.
  • You may need to install [Microsoft Visual C++ Redistributable].
  • Install python 3.11 with [an official installer].
  • If model installs fail with a certificate error, you may need to run this command (changing the python version to match what you have installed): /Applications/Python\ 3.10/Install\ Certificates.command
  • If you haven't already, you will need to install the XCode CLI Tools by running xcode-select --install in a terminal.
  • Installing python varies depending on your system. On Ubuntu, you can use the deadsnakes PPA.
  • You'll need to install libglib2.0-0 and libgl1-mesa-glx for OpenCV to work. For example, on a Debian system: sudo apt update && sudo apt install -y libglib2.0-0 libgl1-mesa-glx

Drivers#

If you have an Nvidia or AMD GPU, you may need to manually install drivers or other support packages for things to work well or at all.

Nvidia#

Run nvidia-smi on your system's command line to verify that drivers and CUDA are installed. If this command fails, or doesn't report versions, you will need to install drivers.

Go to the CUDA Toolkit Downloads and carefully follow the instructions for your system to get everything installed.

Confirm that nvidia-smi displays driver and CUDA versions after installation.

Linux - via Nvidia Container Runtime#

An alternative to installing CUDA locally is to use the Nvidia Container Runtime to run the application in a container.

Windows - Nvidia cuDNN DLLs#

An out-of-date cuDNN library can greatly hamper performance on 30-series and 40-series cards. Check with the community on discord to compare your it/s if you think you may need this fix.

First, locate the destination for the DLL files and make a quick back up:

  1. Find your InvokeAI installation folder, e.g. C:\Users\Username\InvokeAI\.
  2. Open the .venv folder, e.g. C:\Users\Username\InvokeAI\.venv (you may need to show hidden files to see it).
  3. Navigate deeper to the torch package, e.g. C:\Users\Username\InvokeAI\.venv\Lib\site-packages\torch.
  4. Copy the lib folder inside torch and back it up somewhere.

Next, download and copy the updated cuDNN DLLs:

  1. Go to https://developer.nvidia.com/cudnn.
  2. Create an account if needed and log in.
  3. Choose the newest version of cuDNN that works with your GPU architecture. Consult the cuDNN support matrix to determine the correct version for your GPU.
  4. Download the latest version and extract it.
  5. Find the bin folder, e.g. cudnn-windows-x86_64-SOME_VERSION\bin.
  6. Copy and paste the .dll files into the lib folder you located earlier. Replace files when prompted.

If, after restarting the app, this doesn't improve your performance, either restore your back up or re-run the installer to reset torch back to its original state.

AMD#

Linux Only

AMD GPUs are supported on Linux only, due to ROCm (the AMD equivalent of CUDA) support being Linux only.

Bumps Ahead

While the application does run on AMD GPUs, there are occasional bumps related to spotty torch support.

Run rocm-smi on your system's command line verify that drivers and ROCm are installed. If this command fails, or doesn't report versions, you will need to install them.

Go to the ROCm Documentation and carefully follow the instructions for your system to get everything installed.

Confirm that rocm-smi displays driver and CUDA versions after installation.

Linux - via Docker Container#

An alternative to installing ROCm locally is to use a ROCm docker container to run the application in a container.