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Introduction to the Model Manager V2#

The Model Manager is responsible for organizing the various machine learning models used by InvokeAI. It consists of a series of interdependent services that together handle the full lifecycle of a model. These are the:

  • ModelRecordServiceBase Responsible for managing model metadata and configuration information. Among other things, the record service tracks the type of the model, its provenance, and where it can be found on disk.

  • ModelInstallServiceBase A service for installing models to disk. It uses DownloadQueueServiceBase to download models and their metadata, and ModelRecordServiceBase to store that information. It is also responsible for managing the InvokeAI models directory and its contents.

  • DownloadQueueServiceBase A multithreaded downloader responsible for downloading models from a remote source to disk. The download queue has special methods for downloading repo_id folders from Hugging Face, as well as discriminating among model versions in Civitai, but can be used for arbitrary content.

  • ModelLoadServiceBase Responsible for loading a model from disk into RAM and VRAM and getting it ready for inference.

Location of the Code#

The four main services can be found in invokeai/app/services in the following directories:

  • invokeai/app/services/model_records/
  • invokeai/app/services/model_install/
  • invokeai/app/services/downloads/
  • invokeai/app/services/model_load/

Code related to the FastAPI web API can be found in invokeai/app/api/routers/

What's in a Model? The ModelRecordService#

The ModelRecordService manages the model's metadata. It supports a hierarchy of pydantic metadata "config" objects, which become increasingly specialized to support particular model types.


All model metadata classes inherit from this pydantic class. it provides the following fields:

Field Name Type Description
key str Unique identifier for the model
name str Name of the model (not unique)
model_type ModelType The type of the model
model_format ModelFormat The format of the model (e.g. "diffusers"); also used as a Union discriminator
base_model BaseModelType The base model that the model is compatible with
path str Location of model on disk
hash str Hash of the model
description str Human-readable description of the model (optional)
source str Model's source URL or repo id (optional)

The key is a unique 32-character random ID which was generated at install time. The hash field stores a hash of the model's contents at install time obtained by sampling several parts of the model's files using the imohash library. Over the course of the model's lifetime it may be transformed in various ways, such as changing its precision or converting it from a .safetensors to a diffusers model.

ModelType, ModelFormat and BaseModelType are string enums that are defined in invokeai.backend.model_manager.config. They are also imported by, and can be reexported from,

from import ModelType, ModelFormat, BaseModelType

The path field can be absolute or relative. If relative, it is taken to be relative to the models_dir setting in the user's invokeai.yaml file.


This adds support for checkpoint configurations, and adds the following field:

Field Name Type Description
config str Path to the checkpoint's config file

config is the path to the checkpoint's config file. If relative, it is taken to be relative to the InvokeAI root directory (e.g. configs/stable-diffusion/v1-inference.yaml)


This adds support for "main" Stable Diffusion models, and adds these fields:

Field Name Type Description
vae str Path to a VAE to use instead of the burnt-in one
variant ModelVariantType Model variant type, such as "inpainting"

vae can be an absolute or relative path. If relative, its base is taken to be the models_dir directory.

variant is an enumerated string class with values normal, inpaint and depth. If needed, it can be imported if needed from either or invokeai.backend.model_manager.config.


Field Name Type Description
prediction_type SchedulerPredictionType Scheduler prediction type to use, e.g. "epsilon"
upcast_attention bool Model requires its attention module to be upcast

The SchedulerPredictionType enum can be imported from either or invokeai.backend.model_manager.config.

Other config classes#

There are a series of such classes each discriminated by their ModelFormat, including LoRAConfig, IPAdapterConfig, and so forth. These are rarely needed outside the model manager's internal code, but available in invokeai.backend.model_manager.config if needed. There is also a Union of all ModelConfig classes, called AnyModelConfig that can be imported from the same file.

Limitations of the Data Model#

The config hierarchy has a major limitation in its handling of the base model type. Each model can only be compatible with one base model, which breaks down in the event of models that are compatible with two or more base models. For example, SD-1 VAEs also work with SD-2 models. A partial workaround is to use BaseModelType.Any, which indicates that the model is compatible with any of the base models. This works OK for some models, such as the IP Adapter image encoders, but is an all-or-nothing proposition.

Reading and Writing Model Configuration Records#

The ModelRecordService provides the ability to retrieve model configuration records from SQL or YAML databases, update them, and write them back.

A application-wide ModelRecordService is created during API initialization and can be retrieved within an invocation from the InvocationContext object:

store =

or from elsewhere in the code by accessing

Creating a ModelRecordService#

To create a new ModelRecordService database or open an existing one, you can directly create either a ModelRecordServiceSQL or a ModelRecordServiceFile object:

from import ModelRecordServiceSQL, ModelRecordServiceFile

store = ModelRecordServiceSQL.from_connection(connection, lock)
store = ModelRecordServiceSQL.from_db_file('/path/to/sqlite_database.db')
store = ModelRecordServiceFile.from_db_file('/path/to/database.yaml')

The from_connection() form is only available from the ModelRecordServiceSQL class, and is used to manage records in a previously-opened SQLITE3 database using a sqlite3.connection object and a threading.lock object. It is intended for the specific use case of storing the record information in the main InvokeAI database, usually databases/invokeai.db.

The from_db_file() methods can be used to open new connections to the named database files. If the file doesn't exist, it will be created and initialized.

As a convenience, ModelRecordServiceBase offers two methods, from_db_file and open, which will return either a SQL or File implementation depending on the context. The former looks at the file extension to determine whether to open the file as a SQL database (".db") or as a file database (".yaml"). If the file exists, but is either the wrong type or does not contain the expected schema metainformation, then an appropriate AssertionError will be raised:

store = ModelRecordServiceBase.from_db_file('/path/to/a/file.{yaml,db}')

The method is specifically designed for use in the InvokeAI web server. Its signature is:

def open(
    config: InvokeAIAppConfig, 
    conn: Optional[sqlite3.Connection] = None, 
    lock: Optional[threading.Lock] = None
    ) -> Union[ModelRecordServiceSQL, ModelRecordServiceFile]:

The way it works is as follows:

  1. Retrieve the value of the model_config_db option from the user's invokeai.yaml config file.
  2. If model_config_db is auto (the default), then:
  3. Use the values of conn and lock to return a ModelRecordServiceSQL object opened on the passed connection and lock.
  4. Open up a new connection to databases/invokeai.db if conn and/or lock are missing (see note below).
  5. If model_config_db is a Path, then use from_db_file to return the appropriate type of ModelRecordService.
  6. If model_config_db is None, then retrieve the legacy conf_path option from invokeai.yaml and use the Path indicated there. This will default to configs/models.yaml.

So a typical startup pattern would be:

import sqlite3
from import lock
from import ModelRecordServiceBase
from import InvokeAIAppConfig

config = InvokeAIAppConfig.get_config()
db_conn = sqlite3.connect(config.db_path.as_posix(), check_same_thread=False)
store =, db_conn, lock)

Fetching a Model's Configuration from ModelRecordServiceBase#

Configurations can be retrieved in several ways.

get_model(key) -> AnyModelConfig#

The basic functionality is to call the record store object's get_model() method with the desired model's unique key. It returns the appropriate subclass of ModelConfigBase:

model_conf = store.get_model('f13dd932c0c35c22dcb8d6cda4203764')

>> '/tmp/models/ckpts/v1-5-pruned-emaonly.safetensors'

If the key is unrecognized, this call raises an UnknownModelException.

exists(key) -> AnyModelConfig#

Returns True if a model with the given key exists in the databsae.

search_by_path(path) -> AnyModelConfig#

Returns the configuration of the model whose path is path. The path is matched using a simple string comparison and won't correctly match models referred to by different paths (e.g. using symbolic links).

search_by_name(name, base, type) -> List[AnyModelConfig]#

This method searches for models that match some combination of name, BaseType and ModelType. Calling without any arguments will return all the models in the database.

all_models() -> List[AnyModelConfig]#

Return all the model configs in the database. Exactly equivalent to calling search_by_name() with no arguments.

search_by_tag(tags) -> List[AnyModelConfig]#

tags is a list of strings. This method returns a list of model configs that contain all of the given tags. Examples:

# find all models that are marked as both SFW and as generating
# background scenery
configs = store.search_by_tag(['sfw', 'scenery'])

Note that only tags are not searchable in this way. Other fields can be searched using a filter:

commercializable_models = [x for x in store.all_models() \
                           if x.license.contains('allowCommercialUse=Sell')]

version() -> str#

Returns the version of the database, currently at 3.2

model_info_by_name(name, base_model, model_type) -> ModelConfigBase#

This method exists to ease the transition from the previous version of the model manager, in which get_model() took the three arguments shown above. This looks for a unique model identified by name, base model and model type and returns it.

The method will generate a DuplicateModelException if there are more than one models that share the same type, base and name. While unlikely, it is certainly possible to have a situation in which the user had added two models with the same name, base and type, one located at path /foo/my_model and the other at /bar/my_model. It is strongly recommended to search for models using search_by_name(), which can return multiple results, and then to select the desired model and pass its key to get_model().

Writing model configs to the database#

Several methods allow you to create and update stored model config records.

add_model(key, config) -> AnyModelConfig#

Given a key and a configuration, this will add the model's configuration record to the database. config can either be a subclass of ModelConfigBase (i.e. any class listed in AnyModelConfig), or a dict of key/value pairs. In the latter case, the correct configuration class will be picked by Pydantic's discriminated union mechanism.

If successful, the method will return the appropriate subclass of ModelConfigBase. It will raise a DuplicateModelException if a model with the same key is already in the database, or an InvalidModelConfigException if a dict was passed and Pydantic experienced a parse or validation error.

update_model(key, config) -> AnyModelConfig#

Given a key and a configuration, this will update the model configuration record in the database. config can be either a instance of ModelConfigBase, or a sparse dict containing the fields to be updated. This will return an AnyModelConfig on success, or raise InvalidModelConfigException or UnknownModelException exceptions on failure.

Model installation#

The ModelInstallService class implements the ModelInstallServiceBase abstract base class, and provides a one-stop shop for all your model install needs. It provides the following functionality:

  • Registering a model config record for a model already located on the local filesystem, without moving it or changing its path.

  • Installing a model alreadiy located on the local filesystem, by moving it into the InvokeAI root directory under the models folder (or wherever config parameter models_dir specifies).

  • Probing of models to determine their type, base type and other key information.

  • Interface with the InvokeAI event bus to provide status updates on the download, installation and registration process.

  • Downloading a model from an arbitrary URL and installing it in models_dir.

  • Special handling for HuggingFace repo_ids to recursively download the contents of the repository, paying attention to alternative variants such as fp16.

  • Saving tags and other metadata about the model into the invokeai database when fetching from a repo that provides that type of information, (currently only HuggingFace).

Initializing the installer#

A default installer is created at InvokeAI api startup time and stored in and can also be retrieved from an invocation's context argument with

In the event you wish to create a new installer, you may use the following initialization pattern:

from import InvokeAIAppConfig
from import ModelRecordServiceSQL
from import ModelInstallService
from import DownloadQueueService
from import SqliteDatabase
from invokeai.backend.util.logging import InvokeAILogger

config = InvokeAIAppConfig.get_config()

logger = InvokeAILogger.get_logger(config=config)
db = SqliteDatabase(config, logger)
record_store = ModelRecordServiceSQL(db)
queue = DownloadQueueService()

installer = ModelInstallService(app_config=config, 

The full form of ModelInstallService() takes the following required parameters:

Argument Type Description
app_config InvokeAIAppConfig InvokeAI app configuration object
record_store ModelRecordServiceBase Config record storage database
download_queue DownloadQueueServiceBase Download queue object
session Optional[requests.Session] Swap in a different Session object (usually for debugging)

Once initialized, the installer will provide the following methods:

install_job = installer.heuristic_import(source, [config], [access_token])#

This is a simplified interface to the installer which takes a source string, an optional model configuration dictionary and an optional access token.

The source is a string that can be any of these forms

  1. A path on the local filesystem (C:\\users\\fred\\model.safetensors)
  2. A Url pointing to a single downloadable model file (
  3. A HuggingFace repo_id with any of the following formats:
  4. model/name -- entire model
  5. model/name:fp32 -- entire model, using the fp32 variant
  6. model/name:fp16:vae -- vae submodel, using the fp16 variant
  7. model/name::vae -- vae submodel, using default precision
  8. model/name:fp16:path/to/model.safetensors -- an individual model file, fp16 variant
  9. model/name::path/to/model.safetensors -- an individual model file, default variant

Note that by specifying a relative path to the top of the HuggingFace repo, you can download and install arbitrary models files.

The variant, if not provided, will be automatically filled in with fp32 if the user has requested full precision, and fp16 otherwise. If a variant that does not exist is requested, then the method will install whatever HuggingFace returns as its default revision.

config is an optional dict of values that will override the autoprobed values for model type, base, scheduler prediction type, and so forth. See Model configuration and probing for details.

access_token is an optional access token for accessing resources that need authentication.

The method will return a ModelInstallJob. This object is discussed at length in the following section.

install_job = installer.import_model()#

The import_model() method is the core of the installer. The following illustrates basic usage:

from import (

source1 = LocalModelSource(path='/opt/models/sushi.safetensors')   # a local safetensors file
source2 = LocalModelSource(path='/opt/models/sushi_diffusers')     # a local diffusers folder

source3 = HFModelSource(repo_id='runwayml/stable-diffusion-v1-5')  # a repo_id
source4 = HFModelSource(repo_id='runwayml/stable-diffusion-v1-5', subfolder='vae')  # a subfolder within a repo_id
source5 = HFModelSource(repo_id='runwayml/stable-diffusion-v1-5', variant='fp16')   # a named variant of a HF model
source6 = HFModelSource(repo_id='runwayml/stable-diffusion-v1-5', subfolder='OrangeMix/OrangeMix1.ckpt')   # path to an individual model file

source7 = URLModelSource(url='')       # model located at a URL
source8 = URLModelSource(url='', access_token='letmein') # with an access token

for source in [source1, source2, source3, source4, source5, source6, source7]:
   install_job = installer.install_model(source)

source2job = installer.wait_for_installs(timeout=120)
for source in sources:
    job = source2job[source]
 if job.complete:
  model_config = job.config_out
  model_key = model_config.key
  print(f"{source} installed as {model_key}")
 elif job.errored:
     print(f"{source}: {job.error_type}.\nStack trace:\n{job.error}")

As shown here, the import_model() method accepts a variety of sources, including local safetensors files, local diffusers folders, HuggingFace repo_ids with and without a subfolder designation, Civitai model URLs and arbitrary URLs that point to checkpoint files (but not to folders).

Each call to import_model() return a ModelInstallJob job, an object which tracks the progress of the install.

If a remote model is requested, the model's files are downloaded in parallel across a multiple set of threads using the download queue. During the download process, the ModelInstallJob is updated to provide status and progress information. After the files (if any) are downloaded, the remainder of the installation runs in a single serialized background thread. These are the model probing, file copying, and config record database update steps.

Multiple install jobs can be queued up. You may block until all install jobs are completed (or errored) by calling the wait_for_installs() method as shown in the code example. wait_for_installs() will return a dict that maps the requested source to its job. This object can be interrogated to determine its status. If the job errored out, then the error type and details can be recovered from job.error_type and job.error.

The full list of arguments to import_model() is as follows:

Argument Type Default Description
source ModelSource None The source of the model, Path, URL or repo_id
config Dict[str, Any] None Override all or a portion of model's probed attributes

The next few sections describe the various types of ModelSource that can be passed to import_model().

config can be used to override all or a portion of the configuration attributes returned by the model prober. See the section below for details.


This is used for a model that is located on a locally-accessible Posix filesystem, such as a local disk or networked fileshare.

Argument Type Default Description
path str Path None
inplace bool False If set, the model file(s) will be left in their location; otherwise they will be copied into the InvokeAI root's models directory


This is used for a single-file model that is accessible via a URL. The fields are:

Argument Type Default Description
url AnyHttpUrl None The URL for the model file.
access_token str None An access token needed to gain access to this file.

The AnyHttpUrl class can be imported from pydantic.networks.

Ordinarily, no metadata is retrieved from these sources. However, there is special-case code in the installer that looks for HuggingFace and fetches the corresponding model metadata from the corresponding repo.


HuggingFace has the most complicated ModelSource structure:

Argument Type Default Description
repo_id str None The ID of the desired model.
variant ModelRepoVariant ModelRepoVariant('fp16') The desired variant.
subfolder Path None Look for the model in a subfolder of the repo.
access_token str None An access token needed to gain access to a subscriber's-only model.

The repo_id is the repository ID, such as stabilityai/sdxl-turbo.

The variant is one of the various diffusers formats that HuggingFace supports and is used to pick out from the hodgepodge of files that in a typical HuggingFace repository the particular components needed for a complete diffusers model. ModelRepoVariant is an enum that can be imported from invokeai.backend.model_manager and has the following values:

Name String Value
ModelRepoVariant.DEFAULT "default"
ModelRepoVariant.FP16 "fp16"
ModelRepoVariant.FP32 "fp32"
ModelRepoVariant.ONNX "onnx"
ModelRepoVariant.OPENVINO "openvino"
ModelRepoVariant.FLAX "flax"

You can also pass the string forms to variant directly. Note that InvokeAI may not be able to load and run all variants. At the current time, specifying ModelRepoVariant.DEFAULT will retrieve model files that are unqualified, e.g. pytorch_model.safetensors rather than pytorch_model.fp16.safetensors. These are usually the 32-bit safetensors forms of the model.

If subfolder is specified, then the requested model resides in a subfolder of the main model repository. This is typically used to fetch and install VAEs.

Some models require you to be registered with HuggingFace and logged in. To download these files, you must provide an access_token. Internally, if no access token is provided, then HfFolder.get_token() will be called to fill it in with the cached one.

Monitoring the install job process#

When you create an install job with import_model(), it launches the download and installation process in the background and returns a ModelInstallJob object for monitoring the process.

The ModelInstallJob class has the following structure:

Attribute Type Description
id int Integer ID for this job
status InstallStatus An enum of [waiting, downloading, running, completed, error and cancelled]
config_in dict Overriding configuration values provided by the caller
config_out AnyModelConfig After successful completion, contains the configuration record written to the database
inplace boolean True if the caller asked to install the model in place using its local path
source ModelSource The local path, remote URL or repo_id of the model to be installed
local_path Path If a remote model, holds the path of the model after it is downloaded; if a local model, same as source
error_type str Name of the exception that led to an error status
error str Traceback of the error

If the event_bus argument was provided, events will also be broadcast to the InvokeAI event bus. The events will appear on the bus as an event of type EventServiceBase.model_event, a timestamp and the following event names:


For remote models only, model_install_downloading events will be issued at regular intervals as the download progresses. The event's payload contains the following keys:

Key Type Description
source str String representation of the requested source
local_path str String representation of the path to the downloading model (usually a temporary directory)
bytes int How many bytes downloaded so far
total_bytes int Total size of all the files that make up the model
parts List[Dict] Information on the progress of the individual files that make up the model

The parts is a list of dictionaries that give information on each of the components pieces of the download. The dictionary's keys are source, local_path, bytes and total_bytes, and correspond to the like-named keys in the main event.

Note that downloading events will not be issued for local models, and that downloading events occur before the running event.


model_install_running is issued when all the required downloads have completed (if applicable) and the model probing, copying and registration process has now started.

The payload will contain the key source.


model_install_completed is issued once at the end of a successful installation. The payload will contain the keys source, total_bytes and key, where key is the ID under which the model has been registered.


model_install_error is emitted if the installation process fails for some reason. The payload will contain the keys source, error_type and error. error_type is a short message indicating the nature of the error, and error is the long traceback to help debug the problem.


model_install_cancelled is issued if the model installation is cancelled, or if one or more of its files' downloads are cancelled. The payload will contain source.

Following the model status#

You may poll the ModelInstallJob object returned by import_model() to ascertain the state of the install. The job status can be read from the job's status attribute, an InstallStatus enum which has the enumerated values WAITING, DOWNLOADING, RUNNING, COMPLETED, ERROR and CANCELLED.

For convenience, install jobs also provided the following boolean properties: waiting, downloading, running, complete, errored and cancelled, as well as in_terminal_state. The last will return True if the job is in the complete, errored or cancelled states.

Model configuration and probing#

The install service uses the invokeai.backend.model_manager.probe module during import to determine the model's type, base type, and other configuration parameters. Among other things, it assigns a default name and description for the model based on probed fields.

When downloading remote models is implemented, additional configuration information, such as list of trigger terms, will be retrieved from the HuggingFace and Civitai model repositories.

The probed values can be overriden by providing a dictionary in the optional config argument passed to import_model(). You may provide overriding values for any of the model's configuration attributes. Here is an example of setting the SchedulerPredictionType and name for an sd-2 model:

install_job = installer.import_model(
      name='stable diffusion 2 base model',

Other installer methods#

This section describes additional methods provided by the installer class.

jobs = installer.wait_for_installs([timeout])#

Block until all pending installs are completed or errored and then returns a list of completed jobs. The optional timeout argument will return from the call if jobs aren't completed in the specified time. An argument of 0 (the default) will block indefinitely.

jobs = installer.wait_for_job(job, [timeout])#

Like wait_for_installs(), but block until a specific job has completed or errored, and then return the job. The optional timeout argument will return from the call if the job doesn't complete in the specified time. An argument of 0 (the default) will block indefinitely.

jobs = installer.list_jobs()#

Return a list of all active and complete ModelInstallJobs.

jobs = installer.get_job_by_source(source)#

Return a list of ModelInstallJob corresponding to the indicated model source.

jobs = installer.get_job_by_id(id)#

Return a list of ModelInstallJob corresponding to the indicated model id.

jobs = installer.cancel_job(job)#

Cancel the indicated job.


Remove jobs that are in a terminal state (i.e. complete, errored or cancelled) from the job list returned by list_jobs() and get_job().

installer.app_config, installer.record_store, installer.event_bus#

Properties that provide access to the installer's InvokeAIAppConfig, ModelRecordServiceBase and EventServiceBase objects.

key = installer.register_path(model_path, config), key = installer.install_path(model_path, config)#

These methods bypass the download queue and directly register or install the model at the indicated path, returning the unique ID for the installed model.

Both methods accept a Path object corresponding to a checkpoint or diffusers folder, and an optional dict of config attributes to use to override the values derived from model probing.

The difference between register_path() and install_path() is that the former creates a model configuration record without changing the location of the model in the filesystem. The latter makes a copy of the model inside the InvokeAI models directory before registering it.


This will remove the model config record for the model at key, and is equivalent to installer.record_store.del_model(key)


This is similar to unregister() but has the additional effect of conditionally deleting the underlying model file(s) if they reside within the InvokeAI models directory


This method is similar to unregister(), but also unconditionally deletes the corresponding model weights file(s), regardless of whether they are inside or outside the InvokeAI models hierarchy.

path = installer.download_and_cache(remote_source, [access_token], [timeout])#

This utility routine will download the model file located at source, cache it, and return the path to the cached file. It does not attempt to determine the model type, probe its configuration values, or register it with the models database.

You may provide an access token if the remote source requires authorization. The call will block indefinitely until the file is completely downloaded, cancelled or raises an error of some sort. If you provide a timeout (in seconds), the call will raise a TimeoutError exception if the download hasn't completed in the specified period.

You may use this mechanism to request any type of file, not just a model. The file will be stored in a subdirectory of INVOKEAI_ROOT/models/.cache. If the requested file is found in the cache, its path will be returned without redownloading it.

Be aware that the models cache is cleared of infrequently-used files and directories at regular intervals when the size of the cache exceeds the value specified in Invoke's convert_cache configuration variable.


The start method is called by the API intialization routines when the API starts up. Its effect is to call sync_to_config() to synchronize the model record store database with what's currently on disk.

Get on line: The Download Queue#

InvokeAI can download arbitrary files using a multithreaded background download queue. Internally, the download queue is used for installing models located at remote locations. The queue is implemented by the DownloadQueueService defined in However, most of the implementation is spread out among several files in invokeai/backend/model_manager/download/*

A default download queue is located in However, you can create additional instances if you need to isolate your queue from the main one.

A job for every task#

The queue operates on a series of download job objects. These objects specify the source and destination of the download, and keep track of the progress of the download. Jobs come in a variety of shapes and colors as they are progressively specialized for particular download task.

The basic job is the DownloadJobBase, a pydantic object with the following fields:

Field Type Default Description
id int Job ID, an integer >= 0
priority int 10 Job priority. Lower priorities run before higher priorities
source str Where to download from (specialized types used in subclasses)
destination Path Where to download to
status DownloadJobStatus Idle Job's status (see below)
event_handlers List[DownloadEventHandler] Event handlers (see below)
job_started float Timestamp for when the job started running
job_ended float Timestamp for when the job completed or errored out
job_sequence int A counter that is incremented each time a model is dequeued
error Exception A copy of the Exception that caused an error during download

When you create a job, you can assign it a priority. If multiple jobs are queued, the job with the lowest priority runs first. (Don't blame me! The Unix developers came up with this convention.)

Every job has a source and a destination. source is a string in the base class, but subclassses redefine it more specifically.

The destination must be the Path to a file or directory on the local filesystem. If the Path points to a new or existing file, then the source will be stored under that filename. If the Path ponts to an existing directory, then the downloaded file will be stored inside the directory, usually using the name assigned to it at the remote site in the content-disposition http field.

When the job is submitted, it is assigned a numeric id. The id can then be used to control the job, such as starting, stopping and cancelling its download.

The status field is updated by the queue to indicate where the job is in its lifecycle. Values are defined in the string enum DownloadJobStatus, a symbol available from Possible values are:

Value String Value Description
IDLE idle Job created, but not submitted to the queue
ENQUEUED enqueued Job is patiently waiting on the queue
RUNNING running Job is running!
PAUSED paused Job was paused and can be restarted
COMPLETED completed Job has finished its work without an error
ERROR error Job encountered an error and will not run again
CANCELLED cancelled Job was cancelled and will not run (again)

job_started, job_ended and job_sequence indicate when the job was started (using a python timestamp), when it completed, and the order in which it was taken off the queue. These are mostly used for debugging and performance testing.

In case of an error, the Exception that caused the error will be placed in the error field, and the job's status will be set to DownloadJobStatus.ERROR.

After an error occurs, any partially downloaded files will be deleted from disk, unless preserve_partial_downloads was set to True at job creation time (or set to True any time before the error occurred). Note that since all InvokeAI model install operations involve downloading files to a temporary directory that has a limited lifetime, this flag is not used by the model installer.

There are a series of subclasses of DownloadJobBase that provide support for specific types of downloads. These are:


This subclass redefines source to be a filesystem Path. It is used to move a file or directory from the source to the destination paths in the background using a uniform event-based infrastructure.


This subclass adds the following fields to the job:

Field Type Default Description
bytes int 0 bytes downloaded so far
total_bytes int 0 total size to download
access_token Any None an authorization token to present to the remote source

The job will start out with 0/0 in its bytes/total_bytes fields. Once it starts running, total_bytes will be populated from information provided in the HTTP download header (if available), and the number of bytes downloaded so far will be progressively incremented.


This is a subclass of DownloadJobBase. It redefines source to be a Pydantic AnyHttpUrl object, which enforces URL validation checking on the field.

Note that the installer service defines an additional subclass of DownloadJobRemoteSource that accepts HuggingFace repo_ids in addition to URLs. This is discussed later in this document.

Event handlers#

While a job is being downloaded, the queue will emit events at periodic intervals. A typical series of events during a successful download session will look like this:

  • enqueued
  • running
  • running
  • running
  • completed

There will be a single enqueued event, followed by one or more running events, and finally one completed, error or cancelled events.

It is possible for a caller to pause download temporarily, in which case the events may look something like this:

  • enqueued
  • running
  • running
  • paused
  • running
  • completed

The download queue logs when downloads start and end (unless quiet is set to True at initialization time) but doesn't log any progress events. You will probably want to be alerted to events during the download job and provide more user feedback. In order to intercept and respond to events you may install a series of one or more event handlers in the job. Whenever the job's status changes, the chain of event handlers is traversed and executed in the same thread that the download job is running in.

Event handlers have the signature Callable[["DownloadJobBase"], None], i.e.

def handler(job: DownloadJobBase):

A typical handler will examine job.status and decide if there's something to be done. This can include cancelling or erroring the job, but more typically is used to report on the job status to the user interface or to perform certain actions on successful completion of the job.

Event handlers can be attached to a job at creation time. In addition, you can create a series of default handlers that are attached to the queue object itself. These handlers will be executed for each job after the job's own handlers (if any) have run.

During a download, running events are issued every time roughly 1% of the file is transferred. This is to provide just enough granularity to update a tqdm progress bar smoothly.

Handlers can be added to a job after the fact using the job's add_event_handler method:


All handlers can be cleared using the job's clear_event_handlers() method. Note that it might be a good idea to pause the job before altering its handlers.

Creating a download queue object#

The DownloadQueueService constructor takes the following arguments:

Argument Type Default Description
event_handlers List[DownloadEventHandler] [] Event handlers
max_parallel_dl int 5 Maximum number of simultaneous downloads allowed
requests_session requests.sessions.Session None An alternative requests Session object to use for the download
quiet bool False Do work quietly without issuing log messages

A typical initialization sequence will look like:

from import DownloadQueueService

def log_download_event(job: DownloadJobBase):'job={}: status={job.status}')

queue = DownloadQueueService(

Event handlers can be provided to the queue at initialization time as shown in the example. These will be automatically appended to the handler list for any job that is submitted to this queue.

max_parallel_dl sets the number of simultaneous active downloads that are allowed. The default of five has not been benchmarked in any way, but seems to give acceptable performance.

requests_session can be used to provide a requests module Session object that will be used to stream remote URLs to disk. This facility was added for use in the module's unit tests to simulate a remote web server, but may be useful in other contexts.

quiet will prevent the queue from issuing any log messages at the INFO or higher levels.

Submitting a download job#

You can submit a download job to the queue either by creating the job manually and passing it to the queue's submit_download_job() method, or using the create_download_job() method, which will do the same thing on your behalf.

To use the former method, follow this example:

job = DownloadJobRemoteSource(
   event_handlers=[my_handler1, my_handler2], # if desired
queue.submit_download_job(job, start=True)

submit_download_job() takes just two arguments: the job to submit, and a flag indicating whether to immediately start the job (defaulting to True). If you choose not to start the job immediately, you can start it later by calling the queue's start_job() or start_all_jobs() methods, which are described later.

To have the queue create the job for you, follow this example instead:

job = queue.create_download_job(
   event_handlers=[my_handler1, my_handler2], # if desired

The filename argument forces the downloader to use the specified name for the file rather than the name provided by the remote source, and is equivalent to manually specifying a destination of `/tmp/models/my_model.safetensors' in the submitted job.

Here is the full list of arguments that can be provided to create_download_job():

Argument Type Default Description
source Union[str, Path, AnyHttpUrl] Download remote or local source
destdir Path Destination directory for downloaded file
filename Path None Filename for downloaded file
start bool True Enqueue the job immediately
priority int 10 Starting priority for this job
access_token str None Authorization token for this resource
event_handlers List[DownloadEventHandler] [] Event handlers for this job

Internally, create_download_job() has a little bit of internal logic that looks at the type of the source and selects the right subclass of DownloadJobBase to create and enqueue.

TODO: move this logic into its own method for overriding in subclasses.

Job control#

Prior to completion, jobs can be controlled with a series of queue method calls. Do not attempt to modify jobs by directly writing to their fields, as this is likely to lead to unexpected results.

Any method that accepts a job argument may raise an UnknownJobIDException if the job has not yet been submitted to the queue or was not created by this queue.


This method will block until all the active jobs in the queue have reached a terminal state (completed, errored or cancelled).

queue.wait_for_job(job, [timeout])#

This method will block until the indicated job has reached a terminal state (completed, errored or cancelled). If the optional timeout is provided, the call will block for at most timeout seconds, and raise a TimeoutError otherwise.

jobs = queue.list_jobs()#

This will return a list of all jobs, including ones that have not yet been enqueued and those that have completed or errored out.

job = queue.id_to_job(int)#

This method allows you to recover a submitted job using its ID.


Remove completed and errored jobs from the job list.


If the job was submitted with start=False, then it can be started using this method.


This will temporarily pause the job, if possible. It can later be restarted and pick up where it left off using queue.start_job().


This will cancel the job if possible and clean up temporary files and other resources that it might have been using.

queue.start_all_jobs(), queue.pause_all_jobs(), queue.cancel_all_jobs()#

This will start/pause/cancel all jobs that have been submitted to the queue and have not yet reached a terminal state.

This Meta be Good: Model Metadata Storage#

The modules found under invokeai.backend.model_manager.metadata provide a straightforward API for fetching model metadatda from online repositories. Currently only HuggingFace is supported. However, the modules are easily extended for additional repos, provided that they have defined APIs for metadata access.

Metadata comprises any descriptive information that is not essential for getting the model to run. For example "author" is metadata, while "type", "base" and "format" are not. The latter fields are part of the model's config, as defined in invokeai.backend.model_manager.config.

Example Usage#

from invokeai.backend.model_manager.metadata import (
# to access the initialized sql database
from import ApiDependencies

hf = HuggingFaceMetadataFetch()

# fetch the metadata
model_metadata = hf.from_id("<repo_id>")

assert isinstance(model_metadata, HuggingFaceMetadata)

Structure of the Metadata objects#

There is a short class hierarchy of Metadata objects, all of which descend from the Pydantic BaseModel.


This is the common base class for metadata:

Field Name Type Description
name str Repository's name for the model
author str Model's author
tags Set[str] Model tags

Note that the model config record also has a name field. It is intended that the config record version be locally customizable, while the metadata version is read-only. However, enforcing this is expected to be part of the business logic.

Descendents of the base add additional fields.


This descends from ModelMetadataBase and adds the following fields:

Field Name Type Description
type Literal["huggingface"] Used for the discriminated union of metadata classes
id str HuggingFace repo_id
tag_dict Dict[str, Any] A dictionary of tag/value pairs provided in addition to tags
last_modified datetime Date of last commit of this model to the repo
files List[Path] List of the files in the model repo


This is a discriminated Union of HuggingFaceMetadata.

Fetching Metadata from Online Repos#

The HuggingFaceMetadataFetch class will retrieve metadata from its corresponding repository and return AnyModelRepoMetadata objects. Their base class ModelMetadataFetchBase is an abstract class that defines two methods: from_url() and from_id(). The former accepts the type of model URLs that the user will try to cut and paste into the model import form. The latter accepts a string ID in the format recognized by the repository of choice. Both methods return an AnyModelRepoMetadata.

The base class also has a class method from_json() which will take the JSON representation of a ModelMetadata object, validate it, and return the corresponding AnyModelRepoMetadata object.

When initializing one of the metadata fetching classes, you may provide a requests.Session argument. This allows you to customize the low-level HTTP fetch requests and is used, for instance, in the testing suite to avoid hitting the internet.

The HuggingFace fetcher subclass add additional repo-specific fetching methods:


This overrides its base class from_json() method to return a HuggingFaceMetadata object directly.

Metadata Storage#

The ModelConfigBase stores this response in the source_api_response field as a JSON blob.

The Lowdown on the ModelLoadService#

The ModelLoadService is responsible for loading a named model into memory so that it can be used for inference. Despite the fact that it does a lot under the covers, it is very straightforward to use.

An application-wide model loader is created at API initialization time and stored in However, you can create alternative instances if you wish.

Creating a ModelLoadService object#

The class is defined in It is initialized with an InvokeAIAppConfig object, from which it gets configuration information such as the user's desired GPU and precision, and with a previously-created ModelRecordServiceBase object, from which it loads the requested model's configuration information.

Here is a typical initialization pattern:

from import InvokeAIAppConfig
from import ModelLoadService, ModelLoaderRegistry

config = InvokeAIAppConfig.get_config()
ram_cache = ModelCache(
 max_cache_size=config.ram_cache_size, max_vram_cache_size=config.vram_cache_size, logger=logger
convert_cache = ModelConvertCache(
 cache_path=config.models_convert_cache_path, max_size=config.convert_cache_size
loader = ModelLoadService(

load_model(model_config, [submodel_type], [context]) -> LoadedModel#

The load_model() method takes an AnyModelConfig returned by ModelRecordService.get_model() and returns the corresponding loaded model. It loads the model into memory, gets the model ready for use, and returns a LoadedModel object.

The optional second argument, subtype is a SubModelType string enum, such as "vae". It is mandatory when used with a main model, and is used to select which part of the main model to load.

The optional third argument, context can be provided by an invocation to trigger model load event reporting. See below for details.

The returned LoadedModel object contains a copy of the configuration record returned by the model record get_model() method, as well as the in-memory loaded model:

Attribute Name Type Description
config AnyModelConfig A copy of the model's configuration record for retrieving base type, etc.
model AnyModel The instantiated model (details below)
locker ModelLockerBase A context manager that mediates the movement of the model into VRAM

Because the loader can return multiple model types, it is typed to return AnyModel, a Union ModelMixin, torch.nn.Module, IAIOnnxRuntimeModel, IPAdapter, IPAdapterPlus, and EmbeddingModelRaw. ModelMixin is the base class of all diffusers models, EmbeddingModelRaw is used for LoRA and TextualInversion models. The others are obvious.

LoadedModel acts as a context manager. The context loads the model into the execution device (e.g. VRAM on CUDA systems), locks the model in the execution device for the duration of the context, and returns the model. Use it like this:

model_info = loader.get_model_by_key('f13dd932c0c35c22dcb8d6cda4203764', SubModelType('vae'))
with model_info as vae:
 image = vae.decode(latents)[0]

get_model_by_key() may raise any of the following exceptions:

  • UnknownModelException -- key not in database
  • ModelNotFoundException -- key in database but model not found at path
  • NotImplementedException -- the loader doesn't know how to load this type of model

Emitting model loading events#

When the context argument is passed to load_model_*(), it will retrieve the invocation event bus from the passed InvocationContext object to emit events on the invocation bus. The two events are "model_load_started" and "model_load_completed". Both carry the following payload:


Adding Model Loaders#

Model loaders are small classes that inherit from the ModelLoader base class. They typically implement one method _load_model() whose signature is:

def _load_model(
    model_path: Path,
    model_variant: Optional[ModelRepoVariant] = None,
    submodel_type: Optional[SubModelType] = None,
) -> AnyModel:

_load_model() will be passed the path to the model on disk, an optional repository variant (used by the diffusers loaders to select, e.g. the fp16 variant, and an optional submodel_type for main and onnx models.

To install a new loader, place it in invokeai/backend/model_manager/load/model_loaders. Inherit from ModelLoader and use the @ModelLoaderRegistry.register() decorator to indicate what type of models the loader can handle.

Here is a complete example from, which is able to load several different diffusers types:

from pathlib import Path
from typing import Optional

from invokeai.backend.model_manager import (
from .. import ModelLoader, ModelLoaderRegistry

@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.CLIPVision, format=ModelFormat.Diffusers)
@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.T2IAdapter, format=ModelFormat.Diffusers)
class GenericDiffusersLoader(ModelLoader):
    """Class to load simple diffusers models."""

    def _load_model(
        model_path: Path,
        model_variant: Optional[ModelRepoVariant] = None,
        submodel_type: Optional[SubModelType] = None,
    ) -> AnyModel:
        model_class = self._get_hf_load_class(model_path)
        if submodel_type is not None:
            raise Exception(f"There are no submodels in models of type {model_class}")
        variant = model_variant.value if model_variant else None
        result: AnyModel = model_class.from_pretrained(model_path, torch_dtype=self._torch_dtype, variant=variant)  # type: ignore
        return result

Note that a loader can register itself to handle several different model types. An exception will be raised if more than one loader tries to register the same model type.


Some models require conversion to diffusers format before they can be loaded. These loaders should override two additional methods:

_needs_conversion(self, config: AnyModelConfig, model_path: Path, dest_path: Path) -> bool
_convert_model(self, config: AnyModelConfig, model_path: Path, output_path: Path) -> Path:

The first method accepts the model configuration, the path to where the unmodified model is currently installed, and a proposed destination for the converted model. This method returns True if the model needs to be converted. It typically does this by comparing the last modification time of the original model file to the modification time of the converted model. In some cases you will also want to check the modification date of the configuration record, in the event that the user has changed something like the scheduler prediction type that will require the model to be re-converted. See for an example of this logic.

The second method accepts the model configuration, the path to the original model on disk, and the desired output path for the converted model. It does whatever it needs to do to get the model into diffusers format, and returns the Path of the resulting model. (The path should ordinarily be the same as output_path.)

The ModelManagerService object#

For convenience, the API provides a ModelManagerService object which gives a single point of access to the major model manager services. This object is created at initialization time and can be found in the global object, or in from within an invocation.

In the examples below, we have retrieved the manager using:

mm =

The following properties and methods will be available:

This retrieves the ModelRecordService associated with the manager. Example:

configs ='stable-diffusion-v1-5')


This retrieves the ModelInstallService associated with the manager. Example:

job = mm.install.heuristic_import(``)


This retrieves the ModelLoaderService associated with the manager. Example:

configs ='stable-diffusion-v1-5')
assert len(configs) > 0

loaded_model = mm.load.load_model(configs[0])

The model manager also offers a few convenience shortcuts for loading models:

mm.load_model_by_config(model_config, [submodel], [context]) -> LoadedModel#

Same as mm.load.load_model().

mm.load_model_by_attr(model_name, base_model, model_type, [submodel], [context]) -> LoadedModel#

This accepts the combination of the model's name, type and base, which it passes to the model record config store for retrieval. If a unique model config is found, this method returns a LoadedModel. It can raise the following exceptions:

UnknownModelException -- model with these attributes not known
NotImplementedException -- the loader doesn't know how to load this type of model
ValueError -- more than one model matches this combination of base/type/name

mm.load_model_by_key(key, [submodel], [context]) -> LoadedModel#

This method takes a model key, looks it up using the ModelRecordServiceBase object in, and passes the returned model configuration to load_model_by_config(). It may raise a NotImplementedException.