invokeAI-docs

:material-tune-variant: InvokeAI Configuration

Intro

Runtime settings, including the location of files and directories, memory usage, and performance, are managed via the invokeai.yaml config file or environment variables. A subset of settings may be set via commandline arguments.

Settings sources are used in this order:

• CLI args
• Environment variables
• invokeai.yaml settings
• Fallback: defaults

InvokeAI Root Directory

On startup, InvokeAI searches for its “root” directory. This is the directory that contains models, images, the database, and so on. It also contains a configuration file called invokeai.yaml.

InvokeAI searches for the root directory in this order:

1. The --root <path> CLI arg.
2. The environment variable INVOKEAI_ROOT.
3. The directory containing the currently active virtual environment.
4. Fallback: a directory in the current user’s home directory named invokeai.

InvokeAI Configuration File

Inside the root directory, we read settings from the invokeai.yaml file.

It has two sections - one for internal use and one for user settings:

# Internal metadata - do not edit:
schema_version: 4

# Put user settings here - see https://invoke-ai.github.io/InvokeAI/features/CONFIGURATION/:
host: 0.0.0.0 # serve the app on your local network
models_dir: D:\invokeai\models # store models on an external drive
precision: float16 # always use fp16 precision

The settings in this file will override the defaults. You only need to change this file if the default for a particular setting doesn’t work for you.

You’ll find an example file next to invokeai.yaml that shows the default values.

Some settings, like Model Marketplace API Keys, require the YAML to be formatted correctly. Here is a basic guide to YAML files.

Custom Config File Location

You can use any config file with the --config CLI arg. Pass in the path to the invokeai.yaml file you want to use.

Note that environment variables will trump any settings in the config file.

Environment Variables

All settings may be set via environment variables by prefixing INVOKEAI_ to the variable name. For example, INVOKEAI_HOST would set the host setting.

For non-primitive values, pass a JSON-encoded string:

export INVOKEAI_REMOTE_API_TOKENS='[{"url_regex":"modelmarketplace", "token": "12345"}]'

We suggest using invokeai.yaml, as it is more user-friendly.

CLI Args

A subset of settings may be specified using CLI args:

• --root: specify the root directory
• --config: override the default invokeai.yaml file location

All Settings

Following the table are additional explanations for certain settings.

::: invokeai.app.services.config.config_default.InvokeAIAppConfig options: heading_level: 4 members: false show_docstring_description: false group_by_category: true show_category_heading: false

Model Marketplace API Keys

Some model marketplaces require an API key to download models. You can provide a URL pattern and appropriate token in your invokeai.yaml file to provide that API key.

The pattern can be any valid regex (you may need to surround the pattern with quotes):

remote_api_tokens:
  # Any URL containing `models.com` will automatically use `your_models_com_token`
  - url_regex: models.com
    token: your_models_com_token
  # Any URL matching this contrived regex will use `some_other_token`
  - url_regex: '^[a-z]{3}whatever.*\.com$'
    token: some_other_token

The provided token will be added as a Bearer token to the network requests to download the model files. As far as we know, this works for all model marketplaces that require authorization.

Model Hashing

Models are hashed during installation, providing a stable identifier for models across all platforms. Hashing is a one-time operation.

hashing_algorithm: blake3_single # default value

You might want to change this setting, depending on your system:

• blake3_single (default): Single-threaded - best for spinning HDDs, still OK for SSDs
• blake3_multi: Parallelized, memory-mapped implementation - best for SSDs, terrible for spinning disks
• random: Skip hashing entirely - fastest but of course no hash

During the first startup after upgrading to v4, all of your models will be hashed. This can take a few minutes.

Most common algorithms are supported, like md5, sha256, and sha512. These are typically much, much slower than either of the BLAKE3 variants.

Path Settings

These options set the paths of various directories and files used by InvokeAI. Any user-defined paths should be absolute paths.

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 a method 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 severities
• plain - same as above, but monochrome text only
• syslog - the log level and error message only, allowing the syslog system to attach the time and date
• legacy - a format similar to the one used by the legacy 2.3 InvokeAI releases.

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