mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
402cf9b0ee
Refactor services folder/module structure.
**Motivation**
While working on our services I've repeatedly encountered circular imports and a general lack of clarity regarding where to put things. The structure introduced goes a long way towards resolving those issues, setting us up for a clean structure going forward.
**Services**
Services are now in their own folder with a few files:
- `services/{service_name}/__init__.py`: init as needed, mostly empty now
- `services/{service_name}/{service_name}_base.py`: the base class for the service
- `services/{service_name}/{service_name}_{impl_type}.py`: the default concrete implementation of the service - typically one of `sqlite`, `default`, or `memory`
- `services/{service_name}/{service_name}_common.py`: any common items - models, exceptions, utilities, etc
Though it's a bit verbose to have the service name both as the folder name and the prefix for files, I found it is _extremely_ confusing to have all of the base classes just be named `base.py`. So, at the cost of some verbosity when importing things, I've included the service name in the filename.
There are some minor logic changes. For example, in `InvocationProcessor`, instead of assigning the model manager service to a variable to be used later in the file, the service is used directly via the `Invoker`.
**Shared**
Things that are used across disparate services are in `services/shared/`:
- `default_graphs.py`: previously in `services/`
- `graphs.py`: previously in `services/`
- `paginatation`: generic pagination models used in a few services
- `sqlite`: the `SqliteDatabase` class, other sqlite-specific things
316 lines
11 KiB
Python
316 lines
11 KiB
Python
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
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from typing import Any, Optional
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from invokeai.app.invocations.model import ModelInfo
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from invokeai.app.services.invocation_processor.invocation_processor_common import ProgressImage
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from invokeai.app.services.session_queue.session_queue_common import (
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BatchStatus,
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EnqueueBatchResult,
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SessionQueueItem,
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SessionQueueStatus,
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)
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from invokeai.app.util.misc import get_timestamp
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from invokeai.backend.model_management.models.base import BaseModelType, ModelType, SubModelType
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class EventServiceBase:
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queue_event: str = "queue_event"
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"""Basic event bus, to have an empty stand-in when not needed"""
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def dispatch(self, event_name: str, payload: Any) -> None:
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pass
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def __emit_queue_event(self, event_name: str, payload: dict) -> None:
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"""Queue events are emitted to a room with queue_id as the room name"""
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payload["timestamp"] = get_timestamp()
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self.dispatch(
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event_name=EventServiceBase.queue_event,
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payload=dict(event=event_name, data=payload),
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)
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# Define events here for every event in the system.
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# This will make them easier to integrate until we find a schema generator.
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def emit_generator_progress(
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self,
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queue_id: str,
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queue_item_id: int,
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queue_batch_id: str,
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graph_execution_state_id: str,
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node: dict,
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source_node_id: str,
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progress_image: Optional[ProgressImage],
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step: int,
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order: int,
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total_steps: int,
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) -> None:
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"""Emitted when there is generation progress"""
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self.__emit_queue_event(
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event_name="generator_progress",
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payload=dict(
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queue_id=queue_id,
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queue_item_id=queue_item_id,
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queue_batch_id=queue_batch_id,
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graph_execution_state_id=graph_execution_state_id,
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node_id=node.get("id"),
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source_node_id=source_node_id,
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progress_image=progress_image.dict() if progress_image is not None else None,
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step=step,
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order=order,
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total_steps=total_steps,
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),
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)
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def emit_invocation_complete(
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self,
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queue_id: str,
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queue_item_id: int,
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queue_batch_id: str,
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graph_execution_state_id: str,
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result: dict,
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node: dict,
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source_node_id: str,
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) -> None:
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"""Emitted when an invocation has completed"""
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self.__emit_queue_event(
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event_name="invocation_complete",
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payload=dict(
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queue_id=queue_id,
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queue_item_id=queue_item_id,
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queue_batch_id=queue_batch_id,
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graph_execution_state_id=graph_execution_state_id,
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node=node,
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source_node_id=source_node_id,
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result=result,
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),
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)
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def emit_invocation_error(
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self,
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queue_id: str,
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queue_item_id: int,
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queue_batch_id: str,
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graph_execution_state_id: str,
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node: dict,
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source_node_id: str,
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error_type: str,
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error: str,
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) -> None:
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"""Emitted when an invocation has completed"""
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self.__emit_queue_event(
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event_name="invocation_error",
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payload=dict(
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queue_id=queue_id,
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queue_item_id=queue_item_id,
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queue_batch_id=queue_batch_id,
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graph_execution_state_id=graph_execution_state_id,
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node=node,
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source_node_id=source_node_id,
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error_type=error_type,
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error=error,
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),
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)
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def emit_invocation_started(
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self,
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queue_id: str,
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queue_item_id: int,
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queue_batch_id: str,
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graph_execution_state_id: str,
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node: dict,
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source_node_id: str,
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) -> None:
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"""Emitted when an invocation has started"""
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self.__emit_queue_event(
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event_name="invocation_started",
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payload=dict(
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queue_id=queue_id,
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queue_item_id=queue_item_id,
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queue_batch_id=queue_batch_id,
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graph_execution_state_id=graph_execution_state_id,
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node=node,
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source_node_id=source_node_id,
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),
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)
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def emit_graph_execution_complete(
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self, queue_id: str, queue_item_id: int, queue_batch_id: str, graph_execution_state_id: str
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) -> None:
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"""Emitted when a session has completed all invocations"""
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self.__emit_queue_event(
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event_name="graph_execution_state_complete",
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payload=dict(
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queue_id=queue_id,
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queue_item_id=queue_item_id,
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queue_batch_id=queue_batch_id,
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graph_execution_state_id=graph_execution_state_id,
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),
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)
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def emit_model_load_started(
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self,
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queue_id: str,
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queue_item_id: int,
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queue_batch_id: str,
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graph_execution_state_id: str,
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model_name: str,
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base_model: BaseModelType,
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model_type: ModelType,
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submodel: SubModelType,
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) -> None:
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"""Emitted when a model is requested"""
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self.__emit_queue_event(
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event_name="model_load_started",
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payload=dict(
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queue_id=queue_id,
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queue_item_id=queue_item_id,
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queue_batch_id=queue_batch_id,
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graph_execution_state_id=graph_execution_state_id,
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model_name=model_name,
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base_model=base_model,
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model_type=model_type,
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submodel=submodel,
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),
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)
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def emit_model_load_completed(
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self,
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queue_id: str,
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queue_item_id: int,
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queue_batch_id: str,
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graph_execution_state_id: str,
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model_name: str,
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base_model: BaseModelType,
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model_type: ModelType,
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submodel: SubModelType,
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model_info: ModelInfo,
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) -> None:
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"""Emitted when a model is correctly loaded (returns model info)"""
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self.__emit_queue_event(
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event_name="model_load_completed",
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payload=dict(
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queue_id=queue_id,
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queue_item_id=queue_item_id,
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queue_batch_id=queue_batch_id,
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graph_execution_state_id=graph_execution_state_id,
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model_name=model_name,
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base_model=base_model,
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model_type=model_type,
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submodel=submodel,
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hash=model_info.hash,
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location=str(model_info.location),
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precision=str(model_info.precision),
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),
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)
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def emit_session_retrieval_error(
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self,
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queue_id: str,
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queue_item_id: int,
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queue_batch_id: str,
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graph_execution_state_id: str,
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error_type: str,
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error: str,
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) -> None:
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"""Emitted when session retrieval fails"""
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self.__emit_queue_event(
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event_name="session_retrieval_error",
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payload=dict(
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queue_id=queue_id,
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queue_item_id=queue_item_id,
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queue_batch_id=queue_batch_id,
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graph_execution_state_id=graph_execution_state_id,
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error_type=error_type,
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error=error,
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),
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)
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def emit_invocation_retrieval_error(
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self,
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queue_id: str,
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queue_item_id: int,
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queue_batch_id: str,
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graph_execution_state_id: str,
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node_id: str,
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error_type: str,
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error: str,
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) -> None:
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"""Emitted when invocation retrieval fails"""
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self.__emit_queue_event(
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event_name="invocation_retrieval_error",
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payload=dict(
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queue_id=queue_id,
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queue_item_id=queue_item_id,
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queue_batch_id=queue_batch_id,
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graph_execution_state_id=graph_execution_state_id,
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node_id=node_id,
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error_type=error_type,
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error=error,
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),
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)
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def emit_session_canceled(
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self,
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queue_id: str,
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queue_item_id: int,
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queue_batch_id: str,
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graph_execution_state_id: str,
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) -> None:
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"""Emitted when a session is canceled"""
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self.__emit_queue_event(
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event_name="session_canceled",
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payload=dict(
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queue_id=queue_id,
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queue_item_id=queue_item_id,
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queue_batch_id=queue_batch_id,
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graph_execution_state_id=graph_execution_state_id,
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),
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)
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def emit_queue_item_status_changed(
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self,
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session_queue_item: SessionQueueItem,
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batch_status: BatchStatus,
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queue_status: SessionQueueStatus,
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) -> None:
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"""Emitted when a queue item's status changes"""
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self.__emit_queue_event(
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event_name="queue_item_status_changed",
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payload=dict(
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queue_id=queue_status.queue_id,
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queue_item=dict(
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queue_id=session_queue_item.queue_id,
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item_id=session_queue_item.item_id,
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status=session_queue_item.status,
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batch_id=session_queue_item.batch_id,
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session_id=session_queue_item.session_id,
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error=session_queue_item.error,
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created_at=str(session_queue_item.created_at) if session_queue_item.created_at else None,
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updated_at=str(session_queue_item.updated_at) if session_queue_item.updated_at else None,
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started_at=str(session_queue_item.started_at) if session_queue_item.started_at else None,
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completed_at=str(session_queue_item.completed_at) if session_queue_item.completed_at else None,
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),
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batch_status=batch_status.dict(),
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queue_status=queue_status.dict(),
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),
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)
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def emit_batch_enqueued(self, enqueue_result: EnqueueBatchResult) -> None:
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"""Emitted when a batch is enqueued"""
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self.__emit_queue_event(
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event_name="batch_enqueued",
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payload=dict(
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queue_id=enqueue_result.queue_id,
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batch_id=enqueue_result.batch.batch_id,
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enqueued=enqueue_result.enqueued,
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),
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)
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def emit_queue_cleared(self, queue_id: str) -> None:
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"""Emitted when the queue is cleared"""
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self.__emit_queue_event(
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event_name="queue_cleared",
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payload=dict(queue_id=queue_id),
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)
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