InvokeAI/invokeai/backend/stable_diffusion/extensions/base.py

61 lines
1.8 KiB
Python

from __future__ import annotations
from contextlib import contextmanager
from dataclasses import dataclass
from typing import TYPE_CHECKING, Callable, Dict, List
import torch
from diffusers import UNet2DConditionModel
if TYPE_CHECKING:
from invokeai.backend.stable_diffusion.denoise_context import DenoiseContext
from invokeai.backend.stable_diffusion.extension_callback_type import ExtensionCallbackType
@dataclass
class CallbackMetadata:
callback_type: ExtensionCallbackType
order: int
@dataclass
class CallbackFunctionWithMetadata:
metadata: CallbackMetadata
function: Callable[[DenoiseContext], None]
def callback(callback_type: ExtensionCallbackType, order: int = 0):
def _decorator(function):
function._ext_metadata = CallbackMetadata(
callback_type=callback_type,
order=order,
)
return function
return _decorator
class ExtensionBase:
def __init__(self):
self._callbacks: Dict[ExtensionCallbackType, List[CallbackFunctionWithMetadata]] = {}
# Register all of the callback methods for this instance.
for func_name in dir(self):
func = getattr(self, func_name)
metadata = getattr(func, "_ext_metadata", None)
if metadata is not None and isinstance(metadata, CallbackMetadata):
if metadata.callback_type not in self._callbacks:
self._callbacks[metadata.callback_type] = []
self._callbacks[metadata.callback_type].append(CallbackFunctionWithMetadata(metadata, func))
def get_callbacks(self):
return self._callbacks
@contextmanager
def patch_extension(self, ctx: DenoiseContext):
yield None
@contextmanager
def patch_unet(self, state_dict: Dict[str, torch.Tensor], unet: UNet2DConditionModel):
yield None