diff --git a/docs/index.md b/docs/index.md
index c4e7ac264c..6a4b3d841c 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -116,7 +116,7 @@ images in full-precision mode:
`invoke.py` with the `--precision=float32` flag:
```bash
- (invokeai) ~/InvokeAI$ python scripts/invoke.py --full_precision
+ (invokeai) ~/InvokeAI$ python scripts/invoke.py --precision=float32
```
## :octicons-package-dependencies-24: Installation
diff --git a/docs/installation/010_INSTALL_AUTOMATED.md b/docs/installation/010_INSTALL_AUTOMATED.md
index f500095524..b24c5f8ea9 100644
--- a/docs/installation/010_INSTALL_AUTOMATED.md
+++ b/docs/installation/010_INSTALL_AUTOMATED.md
@@ -118,9 +118,7 @@ experimental versions later.
Terminal, including InvokeAI. This package is provided
directly by Apple. To install, open a terminal window and run `xcode-select --install`. You will get a macOS system popup guiding you through the
install. If you already have them installed, you will instead see some
- output in the Terminal advising you that the tools are already installed.
- More information can be found at [FreeCode
- Camp](https://www.freecodecamp.org/news/install-xcode-command-line-tools/)
+ output in the Terminal advising you that the tools are already installed. More information can be found at [FreeCode Camp](https://www.freecodecamp.org/news/install-xcode-command-line-tools/)
3. The InvokeAI installer is distributed as a ZIP files. Go to the
[latest release](https://github.com/invoke-ai/InvokeAI/releases/latest),
diff --git a/docs/installation/030_INSTALL_CUDA_AND_ROCM.md b/docs/installation/030_INSTALL_CUDA_AND_ROCM.md
index db138a4785..54dded0fde 100644
--- a/docs/installation/030_INSTALL_CUDA_AND_ROCM.md
+++ b/docs/installation/030_INSTALL_CUDA_AND_ROCM.md
@@ -8,27 +8,111 @@ title: NVIDIA Cuda / AMD ROCm
</figure>
+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.
+
## :simple-nvidia: CUDA
-### Container Runtime
+### Linux and Windows Install
-Fancy Site: https://developer.nvidia.com/nvidia-container-runtime
+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:
-GitHub Source: https://github.com/NVIDIA/nvidia-container-runtime/
-(where the Fancy Site also links ot xD)
+```
+nvidia-smi
+```
-Maybe the most simple way to get InvokeAI running with NVIDIA CUDA will be the official
-NVIDIA Container Runtime (not confirmed, but got told by a friend) that the Runtime even
-works when you do not have the actual Drivers installed, since it is mounting the GPU
-into the Container
+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 11.7
+Downloads](https://developer.nvidia.com/cuda-11-7-0-download-archive),
+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 the driver directly on
+your system is to run an NVIDIA software container that has the driver
+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](https://developer.nvidia.com/nvidia-container-runtime)
## :simple-amd: ROCm
-### ROCm-docker
+### Linux Install
-GitHub Source: https://github.com/RadeonOpenCompute/ROCm-docker/blob/master/quick-start.md
+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.
-Yeah - I am sorry, but since I am not into PC-Masterrace, I had no better Idea than
-looking up if there is a container runtime for ROCm as well xD
+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:
-So please forgive me, but at least the page isn't empty anymore 🙈
+```
+rocm-smi
+```
+
+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](https://rocmdocs.amd.com/en/latest/Installation_Guide/Installation_new.html#installation-methods)
+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](https://novaspirit.github.io/amdgpu-rocm-ubu22/) 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](https://github.com/RadeonOpenCompute/ROCm-docker/blob/master/quick-start.md)
+
+### Torch Installation
+
+When installing torch and torchvision manually with `pip`, remember to provide
+the argument `--extra-index-url
+https://download.pytorch.org/whl/rocm5.2` as described in the [Manual
+Installation Guide](020_INSTALL_MANUAL.md).
+
+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](https://github.com/invoke-ai/InvokeAI/issues) so that other
+users benefit and we can update this document.