InvokeAI/invokeai/app/services/events.py
2023-07-03 12:17:45 -04:00

154 lines
5.1 KiB
Python

# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
from typing import Any, Optional
from invokeai.app.models.image import ProgressImage
from invokeai.app.util.misc import get_timestamp
from invokeai.app.services.model_manager_service import BaseModelType, ModelType, SubModelType, ModelInfo
class EventServiceBase:
session_event: str = "session_event"
"""Basic event bus, to have an empty stand-in when not needed"""
def dispatch(self, event_name: str, payload: Any) -> None:
pass
def __emit_session_event(self, event_name: str, payload: dict) -> None:
payload["timestamp"] = get_timestamp()
self.dispatch(
event_name=EventServiceBase.session_event,
payload=dict(event=event_name, data=payload),
)
# Define events here for every event in the system.
# This will make them easier to integrate until we find a schema generator.
def emit_generator_progress(
self,
graph_execution_state_id: str,
node: dict,
source_node_id: str,
progress_image: Optional[ProgressImage],
step: int,
total_steps: int,
) -> None:
"""Emitted when there is generation progress"""
self.__emit_session_event(
event_name="generator_progress",
payload=dict(
graph_execution_state_id=graph_execution_state_id,
node=node,
source_node_id=source_node_id,
progress_image=progress_image.dict() if progress_image is not None else None,
step=step,
total_steps=total_steps,
),
)
def emit_invocation_complete(
self,
graph_execution_state_id: str,
result: dict,
node: dict,
source_node_id: str,
) -> None:
"""Emitted when an invocation has completed"""
self.__emit_session_event(
event_name="invocation_complete",
payload=dict(
graph_execution_state_id=graph_execution_state_id,
node=node,
source_node_id=source_node_id,
result=result,
),
)
def emit_invocation_error(
self,
graph_execution_state_id: str,
node: dict,
source_node_id: str,
error: str,
) -> None:
"""Emitted when an invocation has completed"""
self.__emit_session_event(
event_name="invocation_error",
payload=dict(
graph_execution_state_id=graph_execution_state_id,
node=node,
source_node_id=source_node_id,
error=error,
),
)
def emit_invocation_started(
self, graph_execution_state_id: str, node: dict, source_node_id: str
) -> None:
"""Emitted when an invocation has started"""
self.__emit_session_event(
event_name="invocation_started",
payload=dict(
graph_execution_state_id=graph_execution_state_id,
node=node,
source_node_id=source_node_id,
),
)
def emit_graph_execution_complete(self, graph_execution_state_id: str) -> None:
"""Emitted when a session has completed all invocations"""
self.__emit_session_event(
event_name="graph_execution_state_complete",
payload=dict(
graph_execution_state_id=graph_execution_state_id,
),
)
def emit_model_load_started (
self,
graph_execution_state_id: str,
node: dict,
source_node_id: str,
model_name: str,
base_model: BaseModelType,
model_type: ModelType,
submodel: SubModelType,
) -> None:
"""Emitted when a model is requested"""
self.__emit_session_event(
event_name="model_load_started",
payload=dict(
graph_execution_state_id=graph_execution_state_id,
node=node,
source_node_id=source_node_id,
model_name=model_name,
base_model=base_model,
model_type=model_type,
submodel=submodel,
),
)
def emit_model_load_completed(
self,
graph_execution_state_id: str,
node: dict,
source_node_id: str,
model_name: str,
base_model: BaseModelType,
model_type: ModelType,
submodel: SubModelType,
model_info: ModelInfo,
) -> None:
"""Emitted when a model is correctly loaded (returns model info)"""
self.__emit_session_event(
event_name="model_load_completed",
payload=dict(
graph_execution_state_id=graph_execution_state_id,
node=node,
source_node_id=source_node_id,
model_name=model_name,
base_model=base_model,
model_type=model_type,
submodel=submodel,
model_info=model_info,
),
)