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Configuration |
:material-tune-variant: InvokeAI Configuration
Intro
InvokeAI has numerous runtime settings which can be used to adjust many aspects of its operations, including the location of files and directories, memory usage, and performance. These settings can be viewed and customized in several ways:
- By editing settings in the
invokeai.yaml
file. - By setting environment variables.
- On the command-line, when InvokeAI is launched.
In addition, the most commonly changed settings are accessible
graphically via the invokeai-configure
script.
How the Configuration System Works
When InvokeAI is launched, the very first thing it needs to do is to find its "root" directory, which contains its configuration files, installed models, its database of images, and the folder(s) of generated images themselves. In this document, the root directory will be referred to as ROOT.
Finding the Root Directory
To find its root directory, InvokeAI uses the following recipe:
-
It first looks for the argument
--root <path>
on the command line it was launched from, and uses the indicated path if present. -
Next it looks for the environment variable INVOKEAI_ROOT, and uses the directory path found there if present.
-
If neither of these are present, then InvokeAI looks for the folder containing the
.venv
Python virtual environment directory for the currently active environment. This directory is checked for files expected inside the InvokeAI root before it is used. -
Finally, InvokeAI looks for a directory in the current user's home directory named
invokeai
.
Reading the InvokeAI Configuration File
Once the root directory has been located, InvokeAI looks for a file
named ROOT/invokeai.yaml
, and if present reads configuration values
from it. The top of this file looks like this:
InvokeAI:
Web Server:
host: localhost
port: 9090
allow_origins: []
allow_credentials: true
allow_methods:
- '*'
allow_headers:
- '*'
Features:
esrgan: true
internet_available: true
log_tokenization: false
patchmatch: true
restore: true
...
This lines in this file are used to establish default values for
Invoke's settings. In the above fragment, the Web Server's listening
port is set to 9090 by the port
setting.
You can edit this file with a text editor such as "Notepad" (do not use Word or any other word processor). When editing, be careful to maintain the indentation, and do not add extraneous text, as syntax errors will prevent InvokeAI from launching. A basic guide to the format of YAML files can be found here.
You can fix a broken invokeai.yaml
by deleting it and running the
configuration script again -- option [6] in the launcher, "Re-run the
configure script".
Reading Environment Variables
Next InvokeAI looks for defined environment variables in the format
INVOKEAI_<setting_name>
, for example INVOKEAI_port
. Environment
variable values take precedence over configuration file variables. On
a Macintosh system, for example, you could change the port that the
web server listens on by setting the environment variable this way:
export INVOKEAI_port=8000
invokeai-web
Please check out these Macintosh and Windows guides for setting temporary and permanent environment variables.
Reading the Command Line
Lastly, InvokeAI takes settings from the command line, which override
everything else. The command-line settings have the same name as the
corresponding configuration file settings, preceded by a --
, for
example --port 8000
.
If you are using the launcher (invoke.sh
or invoke.bat
) to launch
InvokeAI, then just pass the command-line arguments to the launcher:
invoke.bat --port 8000 --host 0.0.0.0
The arguments will be applied when you select the web server option (and the other options as well).
If, on the other hand, you prefer to launch InvokeAI directly from the
command line, you would first activate the virtual environment (known
as the "developer's console" in the launcher), and run invokeai-web
:
> C:\Users\Fred\invokeai\.venv\scripts\activate
(.venv) > invokeai-web --port 8000 --host 0.0.0.0
You can get a listing and brief instructions for each of the
command-line options by giving the --help
argument:
(.venv) > invokeai-web --help
usage: InvokeAI [-h] [--host HOST] [--port PORT] [--allow_origins [ALLOW_ORIGINS ...]] [--allow_credentials | --no-allow_credentials] [--allow_methods [ALLOW_METHODS ...]]
[--allow_headers [ALLOW_HEADERS ...]] [--esrgan | --no-esrgan] [--internet_available | --no-internet_available] [--log_tokenization | --no-log_tokenization]
[--patchmatch | --no-patchmatch] [--restore | --no-restore]
[--always_use_cpu | --no-always_use_cpu] [--free_gpu_mem | --no-free_gpu_mem] [--max_loaded_models MAX_LOADED_MODELS] [--max_cache_size MAX_CACHE_SIZE]
[--max_vram_cache_size MAX_VRAM_CACHE_SIZE] [--gpu_mem_reserved GPU_MEM_RESERVED] [--precision {auto,float16,float32,autocast}]
[--sequential_guidance | --no-sequential_guidance] [--xformers_enabled | --no-xformers_enabled] [--tiled_decode | --no-tiled_decode] [--root ROOT]
[--autoimport_dir AUTOIMPORT_DIR] [--lora_dir LORA_DIR] [--embedding_dir EMBEDDING_DIR] [--controlnet_dir CONTROLNET_DIR] [--conf_path CONF_PATH]
[--models_dir MODELS_DIR] [--legacy_conf_dir LEGACY_CONF_DIR] [--db_dir DB_DIR] [--outdir OUTDIR] [--from_file FROM_FILE]
[--use_memory_db | --no-use_memory_db] [--model MODEL] [--log_handlers [LOG_HANDLERS ...]] [--log_format {plain,color,syslog,legacy}]
[--log_level {debug,info,warning,error,critical}] [--version | --no-version]
The Configuration Settings
The config is managed by the InvokeAIAppConfig
class, which is a pydantic model. The below docs are autogenerated from the class.
When editing your invokeai.yaml
file, you'll need to put settings under their appropriate group. The group for each setting is denoted in the table below.
Following the table are additional explanations for certain settings.
::: invokeai.app.services.config.config_default.InvokeAIAppConfig options: heading_level: 3 members: false
Paths
These options set the paths of various directories and files used by
InvokeAI. Relative paths are interpreted relative to the root directory, so
if root is /home/fred/invokeai
and the path is
autoimport/main
, then the corresponding directory will be located at
/home/fred/invokeai/autoimport/main
.
Note that the autoimport directory will be searched recursively, allowing you to organize the models into folders and subfolders in any way you wish.
Logging
Several different log handler destinations are available, and multiple destinations are supported by providing a list:
log_handlers:
- console
- syslog=localhost
- file=/var/log/invokeai.log
-
console
is the default. It prints log messages to the command-line window from which InvokeAI was launched. -
syslog
is only available on Linux and Macintosh systems. It uses the operating system's "syslog" facility to write log file entries locally or to a remote logging machine.syslog
offers a variety of configuration options:
syslog=/dev/log` - log to the /dev/log device
syslog=localhost` - log to the network logger running on the local machine
syslog=localhost:512` - same as above, but using a non-standard port
syslog=fredserver,facility=LOG_USER,socktype=SOCK_DRAM`
- Log to LAN-connected server "fredserver" using the facility LOG_USER and datagram packets.
http
can be used to log to a remote web server. The server must be properly configured to receive and act on log messages. The option accepts the URL to the web server, and amethod
argument indicating whether the message should be submitted using the GET or POST method.
http=http://my.server/path/to/logger,method=POST
The log_format
option provides several alternative formats:
color
- default format providing time, date and a message, using text colors to distinguish different log severitiesplain
- same as above, but monochrome text onlysyslog
- the log level and error message only, allowing the syslog system to attach the time and datelegacy
- a format similar to the one used by the legacy 2.3 InvokeAI releases.