YAML Config | InvokeAI Documentation

YAML Config

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 command-line arguments.

Settings sources are used in this order:

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: 4.0.2

# Put user settings here - see https://invoke.ai/configuration/invokeai-yaml/: 
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.

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.

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:

Path Settings

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

Multi-GPU Generation

On a machine with more than one GPU, InvokeAI can run several generation sessions at the same time — one per GPU — instead of processing the queue one job at a time.
This is controlled by the generation_devices setting:

generation_devices: auto # default value
Value Behavior
auto Use every available CUDA GPU, running one generation session per GPU concurrently. This is the default.
[cuda:0,cuda:1] Use the specific devices listed, one session per device. Useful for reserving a GPU for other work.
[cuda:0] Use a single specific device. Generation runs serially, as it did before multi-GPU support.

Text Encoder Offload to Idle GPUs

When more than one GPU is configured for generation but not all of them are busy, InvokeAI can run a session’s text/prompt encoder on a currently-idle GPU instead of the GPU running its denoise pipeline. This avoids evicting the denoise model from VRAM just to make room for the encoder, and lets the cached encoder be reused across generations — making repeated generations noticeably smoother.

This is controlled by the offload_text_encoders_to_idle_gpus setting:

offload_text_encoders_to_idle_gpus: true # default value

Image Subfolder Strategy

By default, generated images are stored in a single flat directory under outputs/images/. The image_subfolder_strategy setting lets you organize newly-created images into subfolders automatically. You can edit this setting in invokeai.yaml or, as an admin user, in the Settings panel.

image_subfolder_strategy: flat # default value

Available strategies:

Strategy Example Path Description
flat outputs/images/abc123.png Store images directly in the images directory.
date outputs/images/2026/03/17/abc123.png Organize images by creation date.
type outputs/images/general/abc123.png Organize images by image category.
hash outputs/images/ab/abc123.png Use the first two characters of the image UUID for filesystem performance with large collections.

Changing this setting only affects newly-created images. Existing images remain in their current locations unless you run Image Storage Maintenance.

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

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.

CLI Args

A subset of settings may be specified using CLI args:

Low-VRAM Mode

See the Low-VRAM mode docs for details on enabling this feature.

All Settings

The full settings reference is below. Additional explanations for selected settings appear earlier on this page.