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In order for InvokeAI to run at full speed, you will need a graphics card with a supported GPU. InvokeAI supports NVidia cards via the CUDA driver on Windows and Linux, and AMD cards via the ROCm driver on Linux.


Linux and Windows Install#

If you have used your system for other graphics-intensive tasks, such as gaming, you may very well already have the CUDA drivers installed. To confirm, open up a command-line window and type:


If this command produces a status report on the GPU(s) installed on your system, CUDA is installed and you have no more work to do. If instead you get "command not found", or similar, then the driver will need to be installed.

We strongly recommend that you install the CUDA Toolkit package directly from NVIDIA. Do not try to install Ubuntu's nvidia-cuda-toolkit package. It is out of date and will cause conflicts among the NVIDIA driver and binaries.

Go to CUDA Toolkit Downloads, and use the target selection wizard to choose your operating system, hardware platform, and preferred installation method (e.g. "local" versus "network").

This will provide you with a downloadable install file or, depending on your choices, a recipe for downloading and running a install shell script. Be sure to read and follow the full installation instructions.

After an install that seems successful, you can confirm by again running nvidia-smi from the command line.

Linux Install with a Runtime Container#

On Linux systems, an alternative to installing CUDA Toolkit directly on your system is to run an NVIDIA software container that has the CUDA libraries already in place. This is recommended if you are already familiar with containerization technologies such as Docker.

For downloads and instructions, visit the NVIDIA CUDA Container Runtime Site

cuDNN Installation for 40/30 Series Optimization* (Optional)#

  1. Find the InvokeAI folder
  2. Click on .venv folder - e.g., YourInvokeFolderHere\.venv
  3. Click on Lib folder - e.g., YourInvokeFolderHere\.venv\Lib
  4. Click on site-packages folder - e.g., YourInvokeFolderHere\.venv\Lib\site-packages
  5. Click on Torch directory - e.g., YourInvokeFolderHere\InvokeAI\.venv\Lib\site-packages\torch
  6. Click on the lib folder - e.g., YourInvokeFolderHere\.venv\Lib\site-packages\torch\lib
  7. Copy everything inside the folder and save it elsewhere as a backup.
  8. Go to
  9. Login or create an Account.
  10. Choose the newer version of cuDNN. Note: There are two versions, 11.x or 12.x for the differents architectures(Turing,Maxwell Etc...) of GPUs. You can find which version you should download from this link.
  11. Download the latest version and extract it from the download location
  12. Find the bin folder E\cudnn-windows-x86_64-Whatever Version\bin
  13. Copy and paste the .dll files into YourInvokeFolderHere\.venv\Lib\site-packages\torch\lib Make sure to copy, and not move the files
  14. If prompted, replace any existing files

Notes: * If no change is seen or any issues are encountered, follow the same steps as above and paste the torch/lib backup folder you made earlier and replace it. If you didn't make a backup, you can also uninstall and reinstall torch through the command line to repair this folder. * This optimization is intended for the newer version of graphics card (40/30 series) but results have been seen with older graphics card.

Torch Installation#

When installing torch and torchvision manually with pip, remember to provide the argument --extra-index-url as described in the Manual Installation Guide.


Linux Install#

AMD GPUs are only supported on Linux platforms due to the lack of a Windows ROCm driver at the current time. Also be aware that support for newer AMD GPUs is spotty. Your mileage may vary.

It is possible that the ROCm driver is already installed on your machine. To test, open up a terminal window and issue the following command:


If you get a table labeled "ROCm System Management Interface" the driver is installed and you are done. If you get "command not found," then the driver needs to be installed.

Go to AMD's ROCm Downloads Guide and scroll to the Installation Methods section. Find the subsection for the install method for your preferred Linux distribution, and issue the commands given in the recipe.

Annoyingly, the official AMD site does not have a recipe for the most recent version of Ubuntu, 22.04. However, this community-contributed recipe is reported to work well.

After installation, please run rocm-smi a second time to confirm that the driver is present and the GPU is recognized. You may need to do a reboot in order to load the driver.

Linux Install with a ROCm-docker Container#

If you are comfortable with the Docker containerization system, then you can build a ROCm docker file. The source code and installation recipes are available Here

Torch Installation#

When installing torch and torchvision manually with pip, remember to provide the argument --extra-index-url as described in the Manual Installation Guide.

This will be done automatically for you if you use the installer script.

Be aware that the torch machine learning library does not seamlessly interoperate with all AMD GPUs and you may experience garbled images, black images, or long startup delays before rendering commences. Most of these issues can be solved by Googling for workarounds. If you have a problem and find a solution, please post an Issue so that other users benefit and we can update this document.

Last update: November 14, 2023
Created: August 24, 2022