mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
73 lines
2.8 KiB
Python
73 lines
2.8 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
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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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from invokeai.backend.util.original_weights_storage import OriginalWeightsStorage
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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(self, unet: UNet2DConditionModel, original_weights: OriginalWeightsStorage):
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"""A context manager for applying patches to the UNet model. The context manager's lifetime spans the entire
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diffusion process. Weight unpatching is handled upstream, and is achieved by saving unchanged weights by
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`original_weights.save` function. Note that this enables some performance optimization by avoiding redundant
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operations. All other patches (e.g. changes to tensor shapes, function monkey-patches, etc.) should be unpatched
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by this context manager.
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Args:
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unet (UNet2DConditionModel): The UNet model on execution device to patch.
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original_weights (OriginalWeightsStorage): A storage with copy of the model's original weights in CPU, for
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unpatching purposes. Extension should save tensor which being modified in this storage, also extensions
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can access original weights values.
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"""
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yield
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