InvokeAI/invokeai/app/api/dependencies.py
psychedelicious 9bd78823a3 refactor(events): use pydantic schemas for events
Our events handling and implementation has a couple pain points:
- Adding or removing data from event payloads requires changes wherever the events are dispatched from.
- We have no type safety for events and need to rely on string matching and dict access when interacting with events.
- Frontend types for socket events must be manually typed. This has caused several bugs.

`fastapi-events` has a neat feature where you can create a pydantic model as an event payload, give it an `__event_name__` attr, and then dispatch the model directly.

This allows us to eliminate a layer of indirection and some unpleasant complexity:
- Event handler callbacks get type hints for their event payloads, and can use `isinstance` on them if needed.
- Event payload construction is now the responsibility of the event itself (a pydantic model), not the service. Every event model has a `build` class method, encapsulating this logic. The build methods are provided as few args as possible. For example, `InvocationStartedEvent.build()` gets the invocation instance and queue item, and can choose the data it wants to include in the event payload.
- Frontend event types may be autogenerated from the OpenAPI schema. We use the payload registry feature of `fastapi-events` to collect all payload models into one place, making it trivial to keep our schema and frontend types in sync.

This commit moves the backend over to this improved event handling setup.
2024-05-27 09:06:02 +10:00

144 lines
6.0 KiB
Python

# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
from logging import Logger
import torch
from invokeai.app.services.object_serializer.object_serializer_disk import ObjectSerializerDisk
from invokeai.app.services.object_serializer.object_serializer_forward_cache import ObjectSerializerForwardCache
from invokeai.app.services.shared.sqlite.sqlite_util import init_db
from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningFieldData
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.bulk_download.bulk_download_default import BulkDownloadService
from ..services.config import InvokeAIAppConfig
from ..services.download import DownloadQueueService
from ..services.events.events_fastapievents import FastAPIEventService
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_services import InvocationServices
from ..services.invocation_stats.invocation_stats_default import InvocationStatsService
from ..services.invoker import Invoker
from ..services.model_images.model_images_default import ModelImageFileStorageDisk
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, DefaultSessionRunner
from ..services.session_queue.session_queue_sqlite import SqliteSessionQueue
from ..services.urls.urls_default import LocalUrlService
from ..services.workflow_records.workflow_records_sqlite import SqliteWorkflowRecordsStorage
# 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) -> None:
logger.info(f"InvokeAI version {__version__}")
logger.info(f"Root directory = {str(config.root_path)}")
output_folder = config.outputs_path
if output_folder is None:
raise ValueError("Output folder is not set")
image_files = DiskImageFileStorage(f"{output_folder}/images")
model_images_folder = config.models_path
db = init_db(config=config, logger=logger, image_files=image_files)
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)
bulk_download = BulkDownloadService()
image_records = SqliteImageRecordStorage(db=db)
images = ImageService()
invocation_cache = MemoryInvocationCache(max_cache_size=config.node_cache_size)
tensors = ObjectSerializerForwardCache(
ObjectSerializerDisk[torch.Tensor](output_folder / "tensors", ephemeral=True)
)
conditioning = ObjectSerializerForwardCache(
ObjectSerializerDisk[ConditioningFieldData](output_folder / "conditioning", ephemeral=True)
)
download_queue_service = DownloadQueueService(event_bus=events)
model_images_service = ModelImageFileStorageDisk(model_images_folder / "model_images")
model_manager = ModelManagerService.build_model_manager(
app_config=configuration,
model_record_service=ModelRecordServiceSQL(db=db),
download_queue=download_queue_service,
events=events,
)
names = SimpleNameService()
performance_statistics = InvocationStatsService()
session_processor = DefaultSessionProcessor(session_runner=DefaultSessionRunner())
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,
bulk_download=bulk_download,
configuration=configuration,
events=events,
image_files=image_files,
image_records=image_records,
images=images,
invocation_cache=invocation_cache,
logger=logger,
model_images=model_images_service,
model_manager=model_manager,
download_queue=download_queue_service,
names=names,
performance_statistics=performance_statistics,
session_processor=session_processor,
session_queue=session_queue,
urls=urls,
workflow_records=workflow_records,
tensors=tensors,
conditioning=conditioning,
)
ApiDependencies.invoker = Invoker(services)
db.clean()
@staticmethod
def shutdown() -> None:
if ApiDependencies.invoker:
ApiDependencies.invoker.stop()