mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
73 lines
2.5 KiB
Python
73 lines
2.5 KiB
Python
from __future__ import annotations
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from contextlib import contextmanager
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from dataclasses import dataclass
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from typing import TYPE_CHECKING, Callable, Dict, List, Optional, Set, Tuple
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import torch
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from diffusers import UNet2DConditionModel
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if TYPE_CHECKING:
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from invokeai.backend.stable_diffusion.denoise_context import DenoiseContext
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from invokeai.backend.stable_diffusion.extension_callback_type import ExtensionCallbackType
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@dataclass
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class CallbackMetadata:
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callback_type: ExtensionCallbackType
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order: int
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@dataclass
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class CallbackFunctionWithMetadata:
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metadata: CallbackMetadata
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function: Callable[[DenoiseContext], None]
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def callback(callback_type: ExtensionCallbackType, order: int = 0):
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def _decorator(function):
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function._ext_metadata = CallbackMetadata(
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callback_type=callback_type,
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order=order,
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)
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return function
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return _decorator
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class ExtensionBase:
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def __init__(self):
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self._callbacks: Dict[ExtensionCallbackType, List[CallbackFunctionWithMetadata]] = {}
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# Register all of the callback methods for this instance.
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for func_name in dir(self):
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func = getattr(self, func_name)
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metadata = getattr(func, "_ext_metadata", None)
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if metadata is not None and isinstance(metadata, CallbackMetadata):
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if metadata.callback_type not in self._callbacks:
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self._callbacks[metadata.callback_type] = []
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self._callbacks[metadata.callback_type].append(CallbackFunctionWithMetadata(metadata, func))
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def get_callbacks(self):
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return self._callbacks
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@contextmanager
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def patch_extension(self, ctx: DenoiseContext):
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yield None
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@contextmanager
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def patch_unet(
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self, unet: UNet2DConditionModel, cached_weights: Optional[Dict[str, torch.Tensor]] = None
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) -> Tuple[Set[str], Dict[str, torch.Tensor]]:
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"""Apply patches to UNet model. This function responsible for restoring all changes except weights,
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changed weights should only be reported in return.
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Return contains 2 values:
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- Set of cached weights, just keys from cached_weights dictionary
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- Dict of not cached weights that should be copies on the cpu device
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Args:
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unet (UNet2DConditionModel): The UNet model on execution device to patch.
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cached_weights (Optional[Dict[str, torch.Tensor]]): Read-only copy of the model's state dict in CPU, for caches purposes.
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"""
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yield set(), {}
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