mirror of
https://github.com/invoke-ai/InvokeAI
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154 lines
5.1 KiB
Python
154 lines
5.1 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.models.image import ProgressImage
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from invokeai.app.util.misc import get_timestamp
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from invokeai.app.services.model_manager_service import BaseModelType, ModelType, SubModelType, ModelInfo
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class EventServiceBase:
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session_event: str = "session_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_session_event(self, event_name: str, payload: dict) -> None:
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payload["timestamp"] = get_timestamp()
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self.dispatch(
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event_name=EventServiceBase.session_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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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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total_steps: int,
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) -> None:
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"""Emitted when there is generation progress"""
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self.__emit_session_event(
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event_name="generator_progress",
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payload=dict(
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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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progress_image=progress_image.dict() if progress_image is not None else None,
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step=step,
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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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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_session_event(
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event_name="invocation_complete",
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payload=dict(
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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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graph_execution_state_id: str,
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node: dict,
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source_node_id: 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_session_event(
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event_name="invocation_error",
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payload=dict(
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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=error,
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),
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)
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def emit_invocation_started(
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self, graph_execution_state_id: str, node: dict, 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_session_event(
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event_name="invocation_started",
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payload=dict(
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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(self, graph_execution_state_id: str) -> None:
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"""Emitted when a session has completed all invocations"""
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self.__emit_session_event(
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event_name="graph_execution_state_complete",
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payload=dict(
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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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graph_execution_state_id: str,
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node: dict,
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source_node_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_session_event(
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event_name="model_load_started",
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payload=dict(
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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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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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graph_execution_state_id: str,
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node: dict,
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source_node_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_session_event(
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event_name="model_load_completed",
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payload=dict(
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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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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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model_info=model_info,
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),
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)
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