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32
docs/installation/INSTALL_DOCKER.md
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@ -1,4 +1,10 @@
# Before you begin
---
title: Docker
---
# :fontawesome-brands-docker: Docker
## Before you begin
- For end users: Install Stable Diffusion locally using the instructions for
your OS.
@ -6,7 +12,7 @@
deployment to other environments (on-premises or cloud), follow these
instructions. For general use, install locally to leverage your machine's GPU.
# Why containers?
## Why containers?
They provide a flexible, reliable way to build and deploy Stable Diffusion.
You'll also use a Docker volume to store the largest model files and image
@ -26,11 +32,11 @@ development purposes it's fine. Once you're done with development tasks on your
laptop you can build for the target platform and architecture and deploy to
another environment with NVIDIA GPUs on-premises or in the cloud.
# Installation on a Linux container
## Installation on a Linux container
## Prerequisites
### Prerequisites
### Get the data files
#### Get the data files
Go to
[Hugging Face](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original),
@ -44,14 +50,14 @@ cd ~/Downloads
wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth
```
### Install [Docker](https://github.com/santisbon/guides#docker)
#### Install [Docker](https://github.com/santisbon/guides#docker)
On the Docker Desktop app, go to Preferences, Resources, Advanced. Increase the
CPUs and Memory to avoid this
[Issue](https://github.com/invoke-ai/InvokeAI/issues/342). You may need to
increase Swap and Disk image size too.
## Setup
### Setup
Set the fork you want to use and other variables.
@ -132,9 +138,9 @@ docker run -it \
$TAG_STABLE_DIFFUSION
```
# Usage (time to have fun)
## Usage (time to have fun)
## Startup
### Startup
If you're on a **Linux container** the `invoke` script is **automatically
started** and the output dir set to the Docker volume you created earlier.
@ -158,7 +164,7 @@ invoke> -h
invoke> q
```
## Text to Image
### Text to Image
For quick (but bad) image results test with 5 steps (default 50) and 1 sample
image. This will let you know that everything is set up correctly.
@ -188,7 +194,7 @@ volume):
docker cp dummy:/data/000001.928403745.png /Users/<your-user>/Pictures
```
## Image to Image
### Image to Image
You can also do text-guided image-to-image translation. For example, turning a
sketch into a detailed drawing.
@ -225,7 +231,7 @@ If you're on a Linux container on your Mac
invoke> "A fantasy landscape, trending on artstation" -I /data/sketch-mountains-input.jpg --strength 0.75 --steps 50 -n1
```
## Web Interface
### Web Interface
You can use the `invoke` script with a graphical web interface. Start the web
server with:
@ -238,7 +244,7 @@ If it's running on your Mac point your Mac web browser to http://127.0.0.1:9090
Press Control-C at the command line to stop the web server.
## Notes
### Notes
Some text you can add at the end of the prompt to make it very pretty:

73
docs/installation/INSTALL_LINUX.md
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@ -26,38 +26,36 @@ title: Linux
3. Copy the InvokeAI source code from GitHub:
```
(base) ~$ git clone https://github.com/invoke-ai/InvokeAI.git
```
```bash
(base) ~$ git clone https://github.com/invoke-ai/InvokeAI.git
```
This will create InvokeAI folder where you will follow the rest of the steps.
This will create InvokeAI folder where you will follow the rest of the steps.
4. Enter the newly-created InvokeAI folder. From this step forward make sure that you are working in the InvokeAI directory!
```
(base) ~$ cd InvokeAI
(base) ~/InvokeAI$
```
```bash
(base) ~$ cd InvokeAI
(base) ~/InvokeAI$
```
5. Use anaconda to copy necessary python packages, create a new python
environment named `ldm` and activate the environment.
environment named `invokeai` and activate the environment.
```bash
(base) ~/InvokeAI$ conda env create
(base) ~/InvokeAI$ conda activate invokeai
(invokeai) ~/InvokeAI$
```
```
(base) ~/InvokeAI$ conda env create
(base) ~/InvokeAI$ conda activate ldm
(ldm) ~/InvokeAI$
```
After these steps, your command prompt will be prefixed by `(ldm)` as shown
After these steps, your command prompt will be prefixed by `(invokeai)` as shown
above.
6. Load a couple of small machine-learning models required by stable diffusion:
```
(ldm) ~/InvokeAI$ python3 scripts/preload_models.py
```
```bash
(invokeai) ~/InvokeAI$ python3 scripts/preload_models.py
```
!!! note
@ -69,7 +67,7 @@ This will create InvokeAI folder where you will follow the rest of the steps.
- For running with the released weights, you will first need to set up an acount
with [Hugging Face](https://huggingface.co).
- Use your credentials to log in, and then point your browser [here](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original.)
- Use your credentials to log in, and then point your browser [here](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original).
- You may be asked to sign a license agreement at this point.
- Click on "Files and versions" near the top of the page, and then click on the
file named "sd-v1-4.ckpt". You'll be taken to a page that prompts you to click
@ -79,34 +77,33 @@ This will create InvokeAI folder where you will follow the rest of the steps.
This will create a symbolic link from the stable-diffusion model.ckpt file, to
the true location of the `sd-v1-4.ckpt` file.
```
(ldm) ~/InvokeAI$ mkdir -p models/ldm/stable-diffusion-v1
(ldm) ~/InvokeAI$ ln -sf /path/to/sd-v1-4.ckpt models/ldm/stable-diffusion-v1/model.ckpt
```
```bash
(invokeai) ~/InvokeAI$ mkdir -p models/ldm/stable-diffusion-v1
(invokeai) ~/InvokeAI$ ln -sf /path/to/sd-v1-4.ckpt models/ldm/stable-diffusion-v1/model.ckpt
```
8. Start generating images!
```
# for the pre-release weights use the -l or --liaon400m switch
(ldm) ~/InvokeAI$ python3 scripts/invoke.py -l
```bash
# for the pre-release weights use the -l or --liaon400m switch
(invokeai) ~/InvokeAI$ python3 scripts/invoke.py -l
# for the post-release weights do not use the switch
(ldm) ~/InvokeAI$ python3 scripts/invoke.py
# for the post-release weights do not use the switch
(invokeai) ~/InvokeAI$ python3 scripts/invoke.py
# for additional configuration switches and arguments, use -h or --help
(ldm) ~/InvokeAI$ python3 scripts/invoke.py -h
```
# for additional configuration switches and arguments, use -h or --help
(invokeai) ~/InvokeAI$ python3 scripts/invoke.py -h
```
9. Subsequently, to relaunch the script, be sure to run "conda activate ldm" (step 5, second command), enter the `InvokeAI` directory, and then launch the invoke script (step 8). If you forget to activate the ldm environment, the script will fail with multiple `ModuleNotFound` errors.
9. Subsequently, to relaunch the script, be sure to run "conda activate invokeai" (step 5, second command), enter the `InvokeAI` directory, and then launch the invoke script (step 8). If you forget to activate the 'invokeai' environment, the script will fail with multiple `ModuleNotFound` errors.
## Updating to newer versions of the script
This distribution is changing rapidly. If you used the `git clone` method (step 5) to download the InvokeAI directory, then to update to the latest and greatest version, launch the Anaconda window, enter `InvokeAI` and type:
```
(ldm) ~/InvokeAI$ git pull
```bash
(invokeai) ~/InvokeAI$ git pull
(invokeai) ~/InvokeAI$ conda env update -f environment.yml
```
This will bring your local copy into sync with the remote one.

189
docs/installation/INSTALL_MAC.md
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@ -2,6 +2,8 @@
title: macOS
---
# :fontawesome-brands-apple: macOS
Invoke AI runs quite well on M1 Macs and we have a number of M1 users
in the community.
@ -24,100 +26,130 @@ First you need to download a large checkpoint file.
3. Accept the terms and click Access Repository
4. Download [sd-v1-4.ckpt (4.27 GB)](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/blob/main/sd-v1-4.ckpt) and note where you have saved it (probably the Downloads folder). You may want to move it somewhere else for longer term storage - SD needs this file to run.
While that is downloading, open Terminal and run the following
commands one at a time, reading the comments and taking care to run
the appropriate command for your Mac's architecture (Intel or M1).
While that is downloading, open Terminal and run the following commands one at a time, reading the comments and taking care to run the appropriate command for your Mac's architecture (Intel or M1).
Do not just copy and paste the whole thing into your terminal!
!!! todo "Homebrew"
```bash
# Install brew (and Xcode command line tools):
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
If you have no brew installation yet (otherwise skip):
# Now there are two options to get the Python (miniconda) environment up and running:
# 1. Alongside pyenv
# 2. Standalone
#
# If you don't know what we are talking about, choose 2.
#
# If you are familiar with python environments, you'll know there are other options
# for setting up the environment - you are on your own if you go one of those routes.
##### BEGIN TWO DIFFERENT OPTIONS #####
```bash title="install brew (and Xcode command line tools)"
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
```
### BEGIN OPTION 1: Installing alongside pyenv ###
brew install pyenv-virtualenv # you might have this from before, no problem
pyenv install anaconda3-2022.05
pyenv virtualenv anaconda3-2022.05
eval "$(pyenv init -)"
pyenv activate anaconda3-2022.05
### END OPTION 1 ###
!!! todo "Conda Installation"
Now there are two different ways to set up the Python (miniconda) environment:
### BEGIN OPTION 2: Installing standalone ###
# Install cmake, protobuf, and rust:
brew install cmake protobuf rust
1. Standalone
2. with pyenv
# BEGIN ARCHITECTURE-DEPENDENT STEP #
# For M1: install miniconda (M1 arm64 version):
curl https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-arm64.sh -o Miniconda3-latest-MacOSX-arm64.sh
/bin/bash Miniconda3-latest-MacOSX-arm64.sh
If you don't know what we are talking about, choose Standalone. If you are familiar with python environments, choose "with pyenv"
# For Intel: install miniconda (Intel x86-64 version):
curl https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh -o Miniconda3-latest-MacOSX-x86_64.sh
/bin/bash Miniconda3-latest-MacOSX-x86_64.sh
# END ARCHITECTURE-DEPENDENT STEP #
=== "Standalone"
### END OPTION 2 ###
```bash title="Install cmake, protobuf, and rust"
brew install cmake protobuf rust
```
##### END TWO DIFFERENT OPTIONS #####
Then clone the InvokeAI repository:
```bash title="Clone the InvokeAI repository:
# Clone the Invoke AI repo
git clone https://github.com/invoke-ai/InvokeAI.git
cd InvokeAI
```
Choose the appropriate architecture for your system and install miniconda:
# Clone the Invoke AI repo
git clone https://github.com/invoke-ai/InvokeAI.git
cd InvokeAI
=== "M1 arm64"
### WAIT FOR THE CHECKPOINT FILE TO DOWNLOAD, THEN PROCEED ###
# We will leave the big checkpoint wherever you stashed it for long-term storage,
# and make a link to it from the repo's folder. This allows you to use it for
# other repos, and if you need to delete Invoke AI, you won't have to download it again.
```bash title="Install miniconda for M1 arm64"
curl https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-arm64.sh \
-o Miniconda3-latest-MacOSX-arm64.sh
/bin/bash Miniconda3-latest-MacOSX-arm64.sh
```
# Make the directory in the repo for the symlink
mkdir -p models/ldm/stable-diffusion-v1/
=== "Intel x86_64"
# This is the folder where you put the checkpoint file `sd-v1-4.ckpt`
PATH_TO_CKPT="$HOME/Downloads"
```bash title="Install miniconda for Intel"
curl https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh \
-o Miniconda3-latest-MacOSX-x86_64.sh
/bin/bash Miniconda3-latest-MacOSX-x86_64.sh
```
# Create a link to the checkpoint
ln -s "$PATH_TO_CKPT/sd-v1-4.ckpt" models/ldm/stable-diffusion-v1/model.ckpt
=== "with pyenv"
# BEGIN ARCHITECTURE-DEPENDENT STEP #
# For M1: Create the environment & install packages
PIP_EXISTS_ACTION=w CONDA_SUBDIR=osx-arm64 conda env create -f environment-mac.yml
```bash
brew install pyenv-virtualenv
pyenv install anaconda3-2022.05
pyenv virtualenv anaconda3-2022.05
eval "$(pyenv init -)"
pyenv activate anaconda3-2022.05
```
# For Intel: Create the environment & install packages
PIP_EXISTS_ACTION=w CONDA_SUBDIR=osx-64 conda env create -f environment-mac.yml
# END ARCHITECTURE-DEPENDENT STEP #
!!! todo "Clone the Invoke AI repo"
# Activate the environment (you need to do this every time you want to run SD)
conda activate invokeai
```bash
git clone https://github.com/invoke-ai/InvokeAI.git
cd InvokeAI
```
# This will download some bits and pieces and make take a while
python scripts/preload_models.py
!!! todo "Wait until the checkpoint-file download finished, then proceed"
# Run SD!
python scripts/dream.py
```
# or run the web interface!
python scripts/invoke.py --web
We will leave the big checkpoint wherever you stashed it for long-term storage,
and make a link to it from the repo's folder. This allows you to use it for
other repos, or if you need to delete Invoke AI, you won't have to download it again.
# The original scripts should work as well.
python scripts/orig_scripts/txt2img.py \
--prompt "a photograph of an astronaut riding a horse" \
--plms
```
```{.bash .annotate}
# Make the directory in the repo for the symlink
mkdir -p models/ldm/stable-diffusion-v1/
Note, `export PIP_EXISTS_ACTION=w` is a precaution to fix `conda env
create -f environment-mac.yml` never finishing in some situations. So
it isn't required but wont hurt.
# This is the folder where you put the checkpoint file `sd-v1-4.ckpt`
PATH_TO_CKPT="$HOME/Downloads" # (1)!
# Create a link to the checkpoint
ln -s "$PATH_TO_CKPT/sd-v1-4.ckpt" models/ldm/stable-diffusion-v1/model.ckpt
```
1. replace `$HOME/Downloads` with the Location where you actually stored the Checkppoint (`sd-v1-4.ckpt`)
!!! todo "Create the environment & install packages"
=== "M1 Mac"
```bash
PIP_EXISTS_ACTION=w CONDA_SUBDIR=osx-arm64 conda env create -f environment-mac.yml
```
=== "Intel x86_64 Mac"
```bash
PIP_EXISTS_ACTION=w CONDA_SUBDIR=osx-64 conda env create -f environment-mac.yml
```
```bash
# Activate the environment (you need to do this every time you want to run SD)
conda activate invokeai
# This will download some bits and pieces and make take a while
(invokeai) python scripts/preload_models.py
# Run SD!
(invokeai) python scripts/dream.py
# or run the web interface!
(invokeai) python scripts/invoke.py --web
# The original scripts should work as well.
(invokeai) python scripts/orig_scripts/txt2img.py \
--prompt "a photograph of an astronaut riding a horse" \
--plms
```
!!! info
`export PIP_EXISTS_ACTION=w` is a precaution to fix `conda env
create -f environment-mac.yml` never finishing in some situations. So
it isn't required but wont hurt.
---
## Common problems
@ -157,7 +189,6 @@ conda install \
-n invokeai
```
If it takes forever to run `conda env create -f environment-mac.yml`, try this:
```bash
@ -169,12 +200,12 @@ conda clean \
Or you could try to completley reset Anaconda:
```bash
conda update \
--force-reinstall \
-y \
-n base \
-c defaults conda
```bash
conda update \
--force-reinstall \
-y \
-n base \
-c defaults conda
```
---

65
docs/installation/INSTALL_WINDOWS.md
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@ -39,7 +39,7 @@ in the wiki
4. Run the command:
```bash
```batch
git clone https://github.com/invoke-ai/InvokeAI.git
```
@ -48,17 +48,21 @@ in the wiki
5. Enter the newly-created InvokeAI folder. From this step forward make sure that you are working in the InvokeAI directory!
```
cd InvokeAI
```
```batch
cd InvokeAI
```
6. Run the following two commands:
```
conda env create (step 6a)
conda activate ldm (step 6b)
```
This will install all python requirements and activate the "ldm" environment
```batch title="step 6a"
conda env create
```
```batch title="step 6b"
conda activate invokeai
```
This will install all python requirements and activate the "invokeai" environment
which sets PATH and other environment variables properly.
Note that the long form of the first command is `conda env create -f environment.yml`. If the
@ -67,7 +71,7 @@ conda activate ldm (step 6b)
7. Run the command:
```bash
```batch
python scripts\preload_models.py
```
@ -79,45 +83,44 @@ conda activate ldm (step 6b)
8. Now you need to install the weights for the big stable diffusion model.
- For running with the released weights, you will first need to set up an acount with Hugging Face (https://huggingface.co).
- Use your credentials to log in, and then point your browser at https://huggingface.co/CompVis/stable-diffusion-v-1-4-original.
- You may be asked to sign a license agreement at this point.
- Click on "Files and versions" near the top of the page, and then click on the file named `sd-v1-4.ckpt`. You'll be taken to a page that
prompts you to click the "download" link. Now save the file somewhere safe on your local machine.
- The weight file is >4 GB in size, so
downloading may take a while.
1. For running with the released weights, you will first need to set up an acount with Hugging Face (https://huggingface.co).
2. Use your credentials to log in, and then point your browser at https://huggingface.co/CompVis/stable-diffusion-v-1-4-original.
3. You may be asked to sign a license agreement at this point.
4. Click on "Files and versions" near the top of the page, and then click on the file named `sd-v1-4.ckpt`. You'll be taken to a page that
prompts you to click the "download" link. Now save the file somewhere safe on your local machine.
5. The weight file is >4 GB in size, so
downloading may take a while.
Now run the following commands from **within the InvokeAI directory** to copy the weights file to the right place:
Now run the following commands from **within the InvokeAI directory** to copy the weights file to the right place:
```
mkdir -p models\ldm\stable-diffusion-v1
copy C:\path\to\sd-v1-4.ckpt models\ldm\stable-diffusion-v1\model.ckpt
```
```batch
mkdir -p models\ldm\stable-diffusion-v1
copy C:\path\to\sd-v1-4.ckpt models\ldm\stable-diffusion-v1\model.ckpt
```
Please replace `C:\path\to\sd-v1.4.ckpt` with the correct path to wherever you stashed this file. If you prefer not to copy or move the .ckpt file,
you may instead create a shortcut to it from within `models\ldm\stable-diffusion-v1\`.
Please replace `C:\path\to\sd-v1.4.ckpt` with the correct path to wherever you stashed this file. If you prefer not to copy or move the .ckpt file,
you may instead create a shortcut to it from within `models\ldm\stable-diffusion-v1\`.
9. Start generating images!
```bash
# for the pre-release weights
```batch title="for the pre-release weights"
python scripts\invoke.py -l
```
# for the post-release weights
```batch title="for the post-release weights"
python scripts\invoke.py
```
10. Subsequently, to relaunch the script, first activate the Anaconda command window (step 3),enter the InvokeAI directory (step 5, `cd \path\to\InvokeAI`), run `conda activate ldm` (step 6b), and then launch the invoke script (step 9).
10. Subsequently, to relaunch the script, first activate the Anaconda command window (step 3),enter the InvokeAI directory (step 5, `cd \path\to\InvokeAI`), run `conda activate invokeai` (step 6b), and then launch the invoke script (step 9).
!!! tip "Tildebyte has written an alternative"
**Note:** Tildebyte has written an alternative
["Easy peasy Windows install"](https://github.com/invoke-ai/InvokeAI/wiki/Easy-peasy-Windows-install)
which uses the Windows Powershell and pew. If you are having trouble with
Anaconda on Windows, give this a try (or try it first!)
---
This distribution is changing rapidly. If you used the `git clone` method (step 5) to download the InvokeAI directory, then to update to the latest and greatest version, launch the Anaconda window, enter `InvokeAI`, and type:
This distribution is changing rapidly. If you used the `git clone` method
(step 5) to download the stable-diffusion directory, then to update to the
latest and greatest version, launch the Anaconda window, enter