InvokeAI/invokeai/app/api/dependencies.py
psychedelicious a514c9e28b feat(backend): update workflows handling
Update workflows handling for Workflow Library.

**Updated Workflow Storage**

"Embedded Workflows" are workflows associated with images, and are now only stored in the image files. "Library Workflows" are not associated with images, and are stored only in DB.

This works out nicely. We have always saved workflows to files, but recently began saving them to the DB in addition to in image files. When that happened, we stopped reading workflows from files, so all the workflows that only existed in images were inaccessible. With this change, access to those workflows is restored, and no workflows are lost.

**Updated Workflow Handling in Nodes**

Prior to this change, workflows were embedded in images by passing the whole workflow JSON to a special workflow field on a node. In the node's `invoke()` function, the node was able to access this workflow and save it with the image. This (inaccurately) models workflows as a property of an image and is rather awkward technically.

A workflow is now a property of a batch/session queue item. It is available in the InvocationContext and therefore available to all nodes during `invoke()`.

**Database Migrations**

Added a `SQLiteMigrator` class to handle database migrations. Migrations were needed to accomodate the DB-related changes in this PR. See the code for details.

The `images`, `workflows` and `session_queue` tables required migrations for this PR, and are using the new migrator. Other tables/services are still creating tables themselves. A followup PR will adapt them to use the migrator.

**Other/Support Changes**

- Add a `has_workflow` column to `images` table to indicate that the image has an embedded workflow.
- Add handling for retrieving the workflow from an image in python. The image file must be fetched, the workflow extracted, and then sent to client, avoiding needing the browser to parse the image file. With the `has_workflow` column, the UI knows if there is a workflow to be fetched, and only fetches when the user requests to load the workflow.
- Add route to get the workflow from an image
- Add CRUD service/routes for the library workflows
- `workflow_images` table and services removed (no longer needed now that embedded workflows are not in the DB)
2023-11-29 12:42:10 +11:00

135 lines
5.6 KiB
Python

# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
from logging import Logger
from invokeai.backend.util.logging import InvokeAILogger
from invokeai.version.invokeai_version import __version__
from ..services.board_image_records.board_image_records_sqlite import SqliteBoardImageRecordStorage
from ..services.board_images.board_images_default import BoardImagesService
from ..services.board_records.board_records_sqlite import SqliteBoardRecordStorage
from ..services.boards.boards_default import BoardService
from ..services.config import InvokeAIAppConfig
from ..services.image_files.image_files_disk import DiskImageFileStorage
from ..services.image_records.image_records_sqlite import SqliteImageRecordStorage
from ..services.images.images_default import ImageService
from ..services.invocation_cache.invocation_cache_memory import MemoryInvocationCache
from ..services.invocation_processor.invocation_processor_default import DefaultInvocationProcessor
from ..services.invocation_queue.invocation_queue_memory import MemoryInvocationQueue
from ..services.invocation_services import InvocationServices
from ..services.invocation_stats.invocation_stats_default import InvocationStatsService
from ..services.invoker import Invoker
from ..services.item_storage.item_storage_sqlite import SqliteItemStorage
from ..services.latents_storage.latents_storage_disk import DiskLatentsStorage
from ..services.latents_storage.latents_storage_forward_cache import ForwardCacheLatentsStorage
from ..services.model_manager.model_manager_default import ModelManagerService
from ..services.model_records import ModelRecordServiceSQL
from ..services.names.names_default import SimpleNameService
from ..services.session_processor.session_processor_default import DefaultSessionProcessor
from ..services.session_queue.session_queue_sqlite import SqliteSessionQueue
from ..services.shared.default_graphs import create_system_graphs
from ..services.shared.graph import GraphExecutionState, LibraryGraph
from ..services.shared.sqlite.sqlite_database import SqliteDatabase
from ..services.urls.urls_default import LocalUrlService
from ..services.workflow_records.workflow_records_sqlite import SqliteWorkflowRecordsStorage
from .events import FastAPIEventService
# TODO: is there a better way to achieve this?
def check_internet() -> bool:
"""
Return true if the internet is reachable.
It does this by pinging huggingface.co.
"""
import urllib.request
host = "http://huggingface.co"
try:
urllib.request.urlopen(host, timeout=1)
return True
except Exception:
return False
logger = InvokeAILogger.get_logger()
class ApiDependencies:
"""Contains and initializes all dependencies for the API"""
invoker: Invoker
@staticmethod
def initialize(config: InvokeAIAppConfig, event_handler_id: int, logger: Logger = logger):
logger.info(f"InvokeAI version {__version__}")
logger.info(f"Root directory = {str(config.root_path)}")
logger.debug(f"Internet connectivity is {config.internet_available}")
output_folder = config.output_path
db = SqliteDatabase(config, logger)
configuration = config
logger = logger
board_image_records = SqliteBoardImageRecordStorage(db=db)
board_images = BoardImagesService()
board_records = SqliteBoardRecordStorage(db=db)
boards = BoardService()
events = FastAPIEventService(event_handler_id)
graph_execution_manager = SqliteItemStorage[GraphExecutionState](db=db, table_name="graph_executions")
graph_library = SqliteItemStorage[LibraryGraph](db=db, table_name="graphs")
image_files = DiskImageFileStorage(f"{output_folder}/images")
image_records = SqliteImageRecordStorage(db=db)
images = ImageService()
invocation_cache = MemoryInvocationCache(max_cache_size=config.node_cache_size)
latents = ForwardCacheLatentsStorage(DiskLatentsStorage(f"{output_folder}/latents"))
model_manager = ModelManagerService(config, logger)
model_record_service = ModelRecordServiceSQL(db=db)
names = SimpleNameService()
performance_statistics = InvocationStatsService()
processor = DefaultInvocationProcessor()
queue = MemoryInvocationQueue()
session_processor = DefaultSessionProcessor()
session_queue = SqliteSessionQueue(db=db)
urls = LocalUrlService()
workflow_records = SqliteWorkflowRecordsStorage(db=db)
services = InvocationServices(
board_image_records=board_image_records,
board_images=board_images,
board_records=board_records,
boards=boards,
configuration=configuration,
events=events,
graph_execution_manager=graph_execution_manager,
graph_library=graph_library,
image_files=image_files,
image_records=image_records,
images=images,
invocation_cache=invocation_cache,
latents=latents,
logger=logger,
model_manager=model_manager,
model_records=model_record_service,
names=names,
performance_statistics=performance_statistics,
processor=processor,
queue=queue,
session_processor=session_processor,
session_queue=session_queue,
urls=urls,
workflow_records=workflow_records,
)
create_system_graphs(services.graph_library)
db.run_migrations()
db.clean()
ApiDependencies.invoker = Invoker(services)
@staticmethod
def shutdown():
if ApiDependencies.invoker:
ApiDependencies.invoker.stop()