The InvokeAI Download Queue#

The DownloadQueueService provides a multithreaded parallel download queue for arbitrary URLs, with queue prioritization, event handling, and restart capabilities.

Simple Example#

from invokeai.app.services.download import DownloadQueueService, TqdmProgress

download_queue = DownloadQueueService()
for url in ['https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/assets/a-painting-of-a-fire.png?raw=true',
            'https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/assets/birdhouse.png?raw=true',
            'https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/assets/missing.png',
            'https://civitai.com/api/download/models/152309?type=Model&format=SafeTensor',
            ]:

    # urls start downloading as soon as download() is called
    download_queue.download(source=url,
                            dest='/tmp/downloads',
                            on_progress=TqdmProgress().update
                            )

download_queue.join()  # wait for all downloads to finish
for job in download_queue.list_jobs():
    print(job.model_dump_json(exclude_none=True, indent=4),"\n")

Output:

{
    "source": "https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/assets/a-painting-of-a-fire.png?raw=true",
    "dest": "/tmp/downloads",
    "id": 0,
    "priority": 10,
    "status": "completed",
    "download_path": "/tmp/downloads/a-painting-of-a-fire.png",
    "job_started": "2023-12-04T05:34:41.742174",
    "job_ended": "2023-12-04T05:34:42.592035",
    "bytes": 666734,
    "total_bytes": 666734
} 

{
    "source": "https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/assets/birdhouse.png?raw=true",
    "dest": "/tmp/downloads",
    "id": 1,
    "priority": 10,
    "status": "completed",
    "download_path": "/tmp/downloads/birdhouse.png",
    "job_started": "2023-12-04T05:34:41.741975",
    "job_ended": "2023-12-04T05:34:42.652841",
    "bytes": 774949,
    "total_bytes": 774949
}

{
    "source": "https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/assets/missing.png",
    "dest": "/tmp/downloads",
    "id": 2,
    "priority": 10,
    "status": "error",
    "job_started": "2023-12-04T05:34:41.742079",
    "job_ended": "2023-12-04T05:34:42.147625",
    "bytes": 0,
    "total_bytes": 0,
    "error_type": "HTTPError(Not Found)",
    "error": "Traceback (most recent call last):\n  File \"/home/lstein/Projects/InvokeAI/invokeai/app/services/download/download_default.py\", line 182, in _download_next_item\n    self._do_download(job)\n  File \"/home/lstein/Projects/InvokeAI/invokeai/app/services/download/download_default.py\", line 206, in _do_download\n    raise HTTPError(resp.reason)\nrequests.exceptions.HTTPError: Not Found\n"
}

{
    "source": "https://civitai.com/api/download/models/152309?type=Model&format=SafeTensor",
    "dest": "/tmp/downloads",
    "id": 3,
    "priority": 10,
    "status": "completed",
    "download_path": "/tmp/downloads/xl_more_art-full_v1.safetensors",
    "job_started": "2023-12-04T05:34:42.147645",
    "job_ended": "2023-12-04T05:34:43.735990",
    "bytes": 719020768,
    "total_bytes": 719020768
}

The API#

The default download queue is DownloadQueueService, an implementation of ABC DownloadQueueServiceBase. It juggles multiple background download requests and provides facilities for interrogating and cancelling the requests. Access to a current or past download task is mediated via DownloadJob objects which report the current status of a job request

The Queue Object#

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

queue = DownloadQueueService(event_bus=events)

DownloadQueueService() takes three optional arguments:

Argument	Type	Default	Description
`max_parallel_dl`	int	5	Maximum number of simultaneous downloads allowed
`event_bus`	EventServiceBase	None	System-wide FastAPI event bus for reporting download events
`requests_session`	requests.sessions.Session	None	An alternative requests Session object to use for the download

max_parallel_dl specifies how many download jobs are allowed to run simultaneously. Each will run in a different thread of execution.

event_bus is an EventServiceBase, typically the one created at InvokeAI startup. If present, download events are periodically emitted on this bus to allow clients to follow download progress.

requests_session is a url library requests Session object. It is used for testing.

The Job object#

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.

Two job types are defined. DownloadJob and MultiFileDownloadJob. The former is a pydantic object with the following fields:

Field	Type	Default	Description
Fields passed in at job creation time
`source`	AnyHttpUrl		Where to download from
`dest`	Path		Where to download to
`access_token`	str		[optional] string containing authentication token for access
`on_start`	Callable		[optional] callback when the download starts
`on_progress`	Callable		[optional] callback called at intervals during download progress
`on_complete`	Callable		[optional] callback called after successful download completion
`on_error`	Callable		[optional] callback called after an error occurs
`id`	int	auto assigned	Job ID, an integer >= 0
`priority`	int	10	Job priority. Lower priorities run before higher priorities

Fields updated over the course of the download task
`status`	DownloadJobStatus		Status code
`download_path`	Path		Path to the location of the downloaded file
`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
`bytes`	int	0	Bytes downloaded so far
`total_bytes`	int	0	Total size of the file at the remote site
`error_type`	str		String version of the exception that caused an error during download
`error`	str		String version of the traceback associated with an error
`cancelled`	bool	False	Set to true if the job was cancelled by the caller

When you create a job, you can assign it a priority. If multiple jobs are queued, the job with the lowest priority runs first.

Every job has a source and a dest. source is a pydantic.networks AnyHttpUrl object. The dest is a path on the local filesystem that specifies the destination for the downloaded object. Its semantics are described below.

When the job is submitted, it is assigned a numeric id. The id can then be used to fetch the job object from the queue.

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 invokeai.app.services.download_manager. Possible values are:

Value	String Value	Description
`WAITING`	waiting	Job is on the queue but not yet running
`RUNNING`	running	The download is started
`COMPLETED`	completed	Job has finished its work without an error
`ERROR`	error	Job encountered an error and will not run again

job_started and job_ended indicate when the job was started (using a python timestamp) and when it completed.

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

A cancelled job will have status DownloadJobStatus.ERROR and an error_type field of "DownloadJobCancelledException". In addition, the job's cancelled property will be set to True.

The MultiFileDownloadJob is used for diffusers model downloads, which contain multiple files and directories under a common root:

Field	Type	Default	Description
Fields passed in at job creation time
`download_parts`	Set[DownloadJob]		Component download jobs
`dest`	Path		Where to download to
`on_start`	Callable		[optional] callback when the download starts
`on_progress`	Callable		[optional] callback called at intervals during download progress
`on_complete`	Callable		[optional] callback called after successful download completion
`on_error`	Callable		[optional] callback called after an error occurs
`id`	int	auto assigned	Job ID, an integer >= 0
Fields updated over the course of the download task
`status`	DownloadJobStatus		Status code
`download_path`	Path		Path to the root of the downloaded files
`bytes`	int	0	Bytes downloaded so far
`total_bytes`	int	0	Total size of the file at the remote site
`error_type`	str		String version of the exception that caused an error during download
`error`	str		String version of the traceback associated with an error
`cancelled`	bool	False	Set to true if the job was cancelled by the caller

Note that the MultiFileDownloadJob does not support the priority, job_started, job_ended or content_type attributes. You can get these from the individual download jobs in download_parts.

Callbacks#

Download jobs can be associated with a series of callbacks, each with the signature Callable[["DownloadJob"], None]. The callbacks are assigned using optional arguments on_start, on_progress, on_complete and on_error. When the corresponding event occurs, the callback wil be invoked and passed the job. The callback will be run in a try: context in the same thread as the download job. Any exceptions that occur during execution of the callback will be caught and converted into a log error message, thereby allowing the download to continue.

`TqdmProgress`#

The invokeai.app.services.download.download_default module defines a class named TqdmProgress which can be used as an on_progress handler to display a completion bar in the console. Use as follows:

from invokeai.app.services.download import TqdmProgress

download_queue.download(source='http://some.server.somewhere/some_file',
                        dest='/tmp/downloads',
                        on_progress=TqdmProgress().update
                        )

Events#

If the queue was initialized with the InvokeAI event bus (the case when using ApiDependencies.invoker.services.download_queue), then download events will also be issued on the bus. The events are:

download_started -- This is issued when a job is taken off the queue and a request is made to the remote server for the URL headers, but before any data has been downloaded. The event payload will contain the keys source and download_path. The latter contains the path that the URL will be downloaded to.
download_progress -- This is issued periodically as the download runs. The payload contains the keyssource,download_path,current_bytesandtotal_bytes`. The latter two fields can be used to display the percent complete.
download_complete -- This is issued when the download completes successfully. The payload contains the keys source, download_path and total_bytes.
download_error -- This is issued when the download stops because of an error condition. The payload contains the fields error_type and error. The former is the text representation of the exception, and the latter is a traceback showing where the error occurred.

Job control#

To create a job call the queue's download() method. You can list all jobs using list_jobs(), fetch a single job by its with id_to_job(), cancel a running job with cancel_job(), cancel all running jobs with cancel_all_jobs(), and wait for all jobs to finish with join().

job = queue.download(source, dest, priority, access_token, on_start, on_progress, on_complete, on_cancelled, on_error)#

Create a new download job and put it on the queue, returning the DownloadJob object.

multifile_job = queue.multifile_download(parts, dest, access_token, on_start, on_progress, on_complete, on_cancelled, on_error)#

This is similar to download(), but instead of taking a single source, it accepts a parts argument consisting of a list of RemoteModelFile objects. Each part corresponds to a URL/Path pair, where the URL is the location of the remote file, and the Path is the destination.

RemoteModelFile can be imported from invokeai.backend.model_manager.metadata, and consists of a url/path pair. Note that the path must be relative.

The method returns a MultiFileDownloadJob.

from invokeai.backend.model_manager.metadata import RemoteModelFile
remote_file_1 = RemoteModelFile(url='http://www.foo.bar/my/pytorch_model.safetensors'',
                                path='my_model/textencoder/pytorch_model.safetensors'
                          )
remote_file_2 = RemoteModelFile(url='http://www.bar.baz/vae.ckpt',
                                path='my_model/vae/diffusers_model.safetensors'
                          )
job = queue.multifile_download(parts=[remote_file_1, remote_file_2],
                               dest='/tmp/downloads',
                               on_progress=TqdmProgress().update)
queue.wait_for_job(job)
print(f"The files were downloaded to {job.download_path}")

jobs = queue.list_jobs()#

Return a list of all active and inactive DownloadJobs.

job = queue.id_to_job(id)#

Return the job corresponding to given ID.