mirror of
https://github.com/invoke-ai/InvokeAI
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06429028c8
Turns out they are just different enough in purpose that the implementations would be rather unintuitive. I've made a separate ObjectSerializer service to handle tensors and conditioning. Refined the class a bit too.
153 lines
6.5 KiB
Python
153 lines
6.5 KiB
Python
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
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from logging import Logger
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import torch
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from invokeai.app.services.item_storage.item_storage_memory import ItemStorageMemory
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from invokeai.app.services.object_serializer.object_serializer_ephemeral_disk import ObjectSerializerEphemeralDisk
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from invokeai.app.services.object_serializer.object_serializer_forward_cache import ObjectSerializerForwardCache
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from invokeai.app.services.shared.sqlite.sqlite_util import init_db
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from invokeai.backend.model_manager.metadata import ModelMetadataStore
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from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningFieldData
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from invokeai.backend.util.logging import InvokeAILogger
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from invokeai.version.invokeai_version import __version__
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from ..services.board_image_records.board_image_records_sqlite import SqliteBoardImageRecordStorage
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from ..services.board_images.board_images_default import BoardImagesService
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from ..services.board_records.board_records_sqlite import SqliteBoardRecordStorage
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from ..services.boards.boards_default import BoardService
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from ..services.config import InvokeAIAppConfig
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from ..services.download import DownloadQueueService
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from ..services.image_files.image_files_disk import DiskImageFileStorage
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from ..services.image_records.image_records_sqlite import SqliteImageRecordStorage
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from ..services.images.images_default import ImageService
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from ..services.invocation_cache.invocation_cache_memory import MemoryInvocationCache
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from ..services.invocation_processor.invocation_processor_default import DefaultInvocationProcessor
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from ..services.invocation_queue.invocation_queue_memory import MemoryInvocationQueue
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from ..services.invocation_services import InvocationServices
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from ..services.invocation_stats.invocation_stats_default import InvocationStatsService
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from ..services.invoker import Invoker
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from ..services.model_install import ModelInstallService
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from ..services.model_manager.model_manager_default import ModelManagerService
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from ..services.model_records import ModelRecordServiceSQL
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from ..services.names.names_default import SimpleNameService
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from ..services.session_processor.session_processor_default import DefaultSessionProcessor
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from ..services.session_queue.session_queue_sqlite import SqliteSessionQueue
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from ..services.shared.graph import GraphExecutionState
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from ..services.urls.urls_default import LocalUrlService
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from ..services.workflow_records.workflow_records_sqlite import SqliteWorkflowRecordsStorage
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from .events import FastAPIEventService
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# TODO: is there a better way to achieve this?
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def check_internet() -> bool:
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"""
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Return true if the internet is reachable.
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It does this by pinging huggingface.co.
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"""
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import urllib.request
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host = "http://huggingface.co"
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try:
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urllib.request.urlopen(host, timeout=1)
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return True
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except Exception:
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return False
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logger = InvokeAILogger.get_logger()
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class ApiDependencies:
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"""Contains and initializes all dependencies for the API"""
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invoker: Invoker
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@staticmethod
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def initialize(config: InvokeAIAppConfig, event_handler_id: int, logger: Logger = logger) -> None:
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logger.info(f"InvokeAI version {__version__}")
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logger.info(f"Root directory = {str(config.root_path)}")
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logger.debug(f"Internet connectivity is {config.internet_available}")
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output_folder = config.output_path
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if output_folder is None:
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raise ValueError("Output folder is not set")
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image_files = DiskImageFileStorage(f"{output_folder}/images")
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db = init_db(config=config, logger=logger, image_files=image_files)
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configuration = config
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logger = logger
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board_image_records = SqliteBoardImageRecordStorage(db=db)
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board_images = BoardImagesService()
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board_records = SqliteBoardRecordStorage(db=db)
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boards = BoardService()
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events = FastAPIEventService(event_handler_id)
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graph_execution_manager = ItemStorageMemory[GraphExecutionState]()
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image_records = SqliteImageRecordStorage(db=db)
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images = ImageService()
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invocation_cache = MemoryInvocationCache(max_cache_size=config.node_cache_size)
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tensors = ObjectSerializerForwardCache(ObjectSerializerEphemeralDisk[torch.Tensor](output_folder / "tensors"))
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conditioning = ObjectSerializerForwardCache(
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ObjectSerializerEphemeralDisk[ConditioningFieldData](output_folder / "conditioning")
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)
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model_manager = ModelManagerService(config, logger)
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model_record_service = ModelRecordServiceSQL(db=db)
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download_queue_service = DownloadQueueService(event_bus=events)
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metadata_store = ModelMetadataStore(db=db)
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model_install_service = ModelInstallService(
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app_config=config,
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record_store=model_record_service,
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download_queue=download_queue_service,
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metadata_store=metadata_store,
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event_bus=events,
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)
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names = SimpleNameService()
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performance_statistics = InvocationStatsService()
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processor = DefaultInvocationProcessor()
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queue = MemoryInvocationQueue()
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session_processor = DefaultSessionProcessor()
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session_queue = SqliteSessionQueue(db=db)
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urls = LocalUrlService()
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workflow_records = SqliteWorkflowRecordsStorage(db=db)
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services = InvocationServices(
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board_image_records=board_image_records,
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board_images=board_images,
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board_records=board_records,
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boards=boards,
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configuration=configuration,
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events=events,
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graph_execution_manager=graph_execution_manager,
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image_files=image_files,
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image_records=image_records,
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images=images,
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invocation_cache=invocation_cache,
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logger=logger,
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model_manager=model_manager,
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model_records=model_record_service,
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download_queue=download_queue_service,
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model_install=model_install_service,
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names=names,
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performance_statistics=performance_statistics,
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processor=processor,
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queue=queue,
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session_processor=session_processor,
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session_queue=session_queue,
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urls=urls,
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workflow_records=workflow_records,
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tensors=tensors,
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conditioning=conditioning,
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)
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ApiDependencies.invoker = Invoker(services)
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db.clean()
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@staticmethod
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def shutdown() -> None:
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if ApiDependencies.invoker:
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ApiDependencies.invoker.stop()
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