chore: import upstream snapshot with attribution

invokeai/backend/util/original_weights_storage.py

@@ -0,0 +1,39 @@
+from __future__ import annotations
+
+from typing import Dict, Iterator, Optional, Tuple
+
+import torch
+
+from invokeai.backend.util.devices import TorchDevice
+
+
+class OriginalWeightsStorage:
+    """A class for tracking the original weights of a model for patch/unpatch operations."""
+
+    def __init__(self, cached_weights: Optional[Dict[str, torch.Tensor]] = None):
+        # The original weights of the model.
+        self._weights: dict[str, torch.Tensor] = {}
+        # The keys of the weights that have been changed (via `save()`) during the lifetime of this instance.
+        self._changed_weights: set[str] = set()
+        if cached_weights:
+            self._weights.update(cached_weights)
+
+    def save(self, key: str, weight: torch.Tensor, copy: bool = True):
+        self._changed_weights.add(key)
+        if key in self._weights:
+            return
+
+        self._weights[key] = weight.detach().to(device=TorchDevice.CPU_DEVICE, copy=copy)
+
+    def get(self, key: str, copy: bool = False) -> Optional[torch.Tensor]:
+        weight = self._weights.get(key, None)
+        if weight is not None and copy:
+            weight = weight.clone()
+        return weight
+
+    def contains(self, key: str) -> bool:
+        return key in self._weights
+
+    def get_changed_weights(self) -> Iterator[Tuple[str, torch.Tensor]]:
+        for key in self._changed_weights:
+            yield key, self._weights[key]