Suggested changes

Co-Authored-By: Ryan Dick <14897797+RyanJDick@users.noreply.github.com>

invokeai/backend/lora.py

@@ -71,6 +71,9 @@ class LoRALayerBase:
             self.bias = self.bias.to(device=device, dtype=dtype)
 
     def check_keys(self, values: Dict[str, torch.Tensor], known_keys: Set[str]):
+        """Log a warning if values contains unhandled keys."""
+        # {"alpha", "bias_indices", "bias_values", "bias_size"} are hard-coded, because they are handled by
+        # `LoRALayerBase`. Sub-classes should provide the known_keys that they handled.
         all_known_keys = known_keys | {"alpha", "bias_indices", "bias_values", "bias_size"}
         unknown_keys = set(values.keys()) - all_known_keys
         if unknown_keys:
@@ -232,7 +235,6 @@ class LoKRLayer(LoRALayerBase):
         else:
             self.rank = None  # unscaled
 
-        # Although lokr_t1 not used in algo, it still defined in LoKR weights
         self.check_keys(
             values,
             {
@@ -242,7 +244,6 @@ class LoKRLayer(LoRALayerBase):
                 "lokr_w2",
                 "lokr_w2_a",
                 "lokr_w2_b",
-                "lokr_t1",
                 "lokr_t2",
             },
         )

invokeai/backend/stable_diffusion/extensions/base.py

@@ -2,7 +2,7 @@ from __future__ import annotations
 
 from contextlib import contextmanager
 from dataclasses import dataclass
-from typing import TYPE_CHECKING, Callable, Dict, List, Optional
+from typing import TYPE_CHECKING, Callable, Dict, List, Optional, Set, Tuple
 
 import torch
 from diffusers import UNet2DConditionModel
@@ -56,5 +56,17 @@ class ExtensionBase:
         yield None
 
     @contextmanager
-    def patch_unet(self, unet: UNet2DConditionModel, cached_weights: Optional[Dict[str, torch.Tensor]] = None):
-        yield None
+    def patch_unet(
+        self, unet: UNet2DConditionModel, cached_weights: Optional[Dict[str, torch.Tensor]] = None
+    ) -> Tuple[Set[str], Dict[str, torch.Tensor]]:
+        """Apply patches to UNet model. This function responsible for restoring all changes except weights,
+        changed weights should only be reported in return.
+        Return contains 2 values:
+        - Set of cached weights, just keys from cached_weights dictionary
+        - Dict of not cached weights that should be copies on the cpu device
+
+        Args:
+            unet (UNet2DConditionModel): The UNet model on execution device to patch.
+            cached_weights (Optional[Dict[str, torch.Tensor]]): Read-only copy of the model's state dict in CPU, for caches purposes.
+        """
+        yield set(), {}

invokeai/backend/stable_diffusion/extensions/freeu.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 from contextlib import contextmanager
-from typing import TYPE_CHECKING, Dict, Optional
+from typing import TYPE_CHECKING, Dict, Optional, Set, Tuple
 
 import torch
 from diffusers import UNet2DConditionModel
@@ -21,7 +21,9 @@ class FreeUExt(ExtensionBase):
         self._freeu_config = freeu_config
 
     @contextmanager
-    def patch_unet(self, unet: UNet2DConditionModel, cached_weights: Optional[Dict[str, torch.Tensor]] = None):
+    def patch_unet(
+        self, unet: UNet2DConditionModel, cached_weights: Optional[Dict[str, torch.Tensor]] = None
+    ) -> Tuple[Set[str], Dict[str, torch.Tensor]]:
         unet.enable_freeu(
             b1=self._freeu_config.b1,
             b2=self._freeu_config.b2,
@@ -30,6 +32,6 @@ class FreeUExt(ExtensionBase):
         )
 
         try:
-            yield
+            yield set(), {}
         finally:
             unet.disable_freeu()

invokeai/backend/stable_diffusion/extensions/lora.py

@@ -28,7 +28,9 @@ class LoRAExt(ExtensionBase):
         self._weight = weight
 
     @contextmanager
-    def patch_unet(self, unet: UNet2DConditionModel, cached_weights: Optional[Dict[str, torch.Tensor]] = None):
+    def patch_unet(
+        self, unet: UNet2DConditionModel, cached_weights: Optional[Dict[str, torch.Tensor]] = None
+    ) -> Tuple[Set[str], Dict[str, torch.Tensor]]:
         lora_model = self._node_context.models.load(self._model_id).model
         modified_cached_weights, modified_weights = self.patch_model(
             model=unet,
@@ -49,14 +51,14 @@ class LoRAExt(ExtensionBase):
         lora: LoRAModelRaw,
         lora_weight: float,
         cached_weights: Optional[Dict[str, torch.Tensor]] = None,
-    ):
+    ) -> Tuple[Set[str], Dict[str, torch.Tensor]]:
         """
         Apply one or more LoRAs to a model.
         :param model: The model to patch.
         :param lora: LoRA model to patch in.
         :param lora_weight: LoRA patch weight.
         :param prefix: A string prefix that precedes keys used in the LoRAs weight layers.
-        :cached_weights: Read-only copy of the model's state dict in CPU, for unpatching purposes.
+        :param cached_weights: Read-only copy of the model's state dict in CPU, for unpatching purposes.
         """
         if cached_weights is None:
             cached_weights = {}

invokeai/backend/stable_diffusion/extensions_manager.py

@@ -70,13 +70,12 @@ class ExtensionsManager:
         modified_weights: Dict[str, torch.Tensor] = {}
         modified_cached_weights: Set[str] = set()
 
-        exit_stack = ExitStack()
         try:
+            with ExitStack() as exit_stack:
                 for ext in self._extensions:
-                res = exit_stack.enter_context(ext.patch_unet(unet, cached_weights))
-                if res is None:
-                    continue
-                ext_modified_cached_weights, ext_modified_weights = res
+                    ext_modified_cached_weights, ext_modified_weights = exit_stack.enter_context(
+                        ext.patch_unet(unet, cached_weights)
+                    )
 
                     modified_cached_weights.update(ext_modified_cached_weights)
                     # store only first returned weight for each key, because
@@ -89,7 +88,6 @@ class ExtensionsManager:
                 yield None
 
         finally:
-            exit_stack.close()
             with torch.no_grad():
                 for param_key in modified_cached_weights:
                     unet.get_parameter(param_key).copy_(cached_weights[param_key])