Optimize weights handling

Author: StAlKeR7779

invokeai/backend/model_patcher.py

@@ -19,6 +19,7 @@
 from invokeai.backend.onnx.onnx_runtime import IAIOnnxRuntimeModel
 from invokeai.backend.stable_diffusion.extensions.lora import LoRAExt
 from invokeai.backend.textual_inversion import TextualInversionManager, TextualInversionModelRaw
+from invokeai.backend.util.original_weights_storage import OriginalWeightsStorage
 
 """
 loras = [
@@ -123,9 +124,7 @@ def apply_lora(
         :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.
         """
-        original_weights: Dict[str, torch.Tensor] = {}
-        if cached_weights:
-            original_weights.update(cached_weights)
+        original_weights = OriginalWeightsStorage(cached_weights)
         try:
             for lora_model, lora_weight in loras:
                 LoRAExt.patch_model(
@@ -141,7 +140,7 @@ def apply_lora(
 
         finally:
             with torch.no_grad():
-                for param_key, weight in original_weights.items():
+                for param_key, weight in original_weights.get_changed_weights():
                     model.get_parameter(param_key).copy_(weight)
 
     @classmethod

invokeai/backend/stable_diffusion/extensions/base.py

@@ -4,12 +4,12 @@
 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
+    from invokeai.backend.util.original_weights_storage import OriginalWeightsStorage
 
 
 @dataclass
@@ -56,17 +56,17 @@ def patch_extension(self, ctx: DenoiseContext):
         yield None
 
     @contextmanager
-    def patch_unet(self, unet: UNet2DConditionModel, original_weights: Dict[str, torch.Tensor]):
+    def patch_unet(self, unet: UNet2DConditionModel, original_weights: OriginalWeightsStorage):
         """A context manager for applying patches to the UNet model. The context manager's lifetime spans the entire
-        diffusion process. Weight unpatching is handled upstream, and is achieved by adding unsaved weights in
-        `original_weights` dict. Note that this enables some performance optimization by avoiding redundant operations.
-        All other patches (e.g. changes to tensor shapes, function monkey-patches, etc.) should be unpatched by this
-        context manager.
+        diffusion process. Weight unpatching is handled upstream, and is achieved by saving unchanged weights by
+        `original_weights.save` function. Note that this enables some performance optimization by avoiding redundant
+        operations. All other patches (e.g. changes to tensor shapes, function monkey-patches, etc.) should be unpatched
+        by this context manager.
 
         Args:
             unet (UNet2DConditionModel): The UNet model on execution device to patch.
-            original_weights (Dict[str, torch.Tensor]]): A read-only copy of the model's original weights in CPU, for
-                unpatching purposes. Extension can save tensor which being modified, if it is not saved yet, or can
-                access original weight value.
+            original_weights (OriginalWeightsStorage): A storage with copy of the model's original weights in CPU, for
+                unpatching purposes. Extension should save tensor which being modified in this storage, also extensions
+                can access original weights values.
         """
         yield

invokeai/backend/stable_diffusion/extensions/freeu.py

@@ -1,15 +1,15 @@
 from __future__ import annotations
 
 from contextlib import contextmanager
-from typing import TYPE_CHECKING, Dict
+from typing import TYPE_CHECKING
 
-import torch
 from diffusers import UNet2DConditionModel
 
 from invokeai.backend.stable_diffusion.extensions.base import ExtensionBase
 
 if TYPE_CHECKING:
     from invokeai.app.shared.models import FreeUConfig
+    from invokeai.backend.util.original_weights_storage import OriginalWeightsStorage
 
 
 class FreeUExt(ExtensionBase):
@@ -21,7 +21,7 @@ def __init__(
         self._freeu_config = freeu_config
 
     @contextmanager
-    def patch_unet(self, unet: UNet2DConditionModel, original_weights: Dict[str, torch.Tensor]):
+    def patch_unet(self, unet: UNet2DConditionModel, original_weights: OriginalWeightsStorage):
         unet.enable_freeu(
             b1=self._freeu_config.b1,
             b2=self._freeu_config.b2,

invokeai/backend/stable_diffusion/extensions/lora.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 from contextlib import contextmanager
-from typing import TYPE_CHECKING, Dict, Tuple
+from typing import TYPE_CHECKING, Tuple
 
 import torch
 from diffusers import UNet2DConditionModel
@@ -13,6 +13,7 @@
     from invokeai.app.invocations.model import ModelIdentifierField
     from invokeai.app.services.shared.invocation_context import InvocationContext
     from invokeai.backend.lora import LoRAModelRaw
+    from invokeai.backend.util.original_weights_storage import OriginalWeightsStorage
 
 
 class LoRAExt(ExtensionBase):
@@ -28,7 +29,7 @@ def __init__(
         self._weight = weight
 
     @contextmanager
-    def patch_unet(self, unet: UNet2DConditionModel, original_weights: Dict[str, torch.Tensor]):
+    def patch_unet(self, unet: UNet2DConditionModel, original_weights: OriginalWeightsStorage):
         lora_model = self._node_context.models.load(self._model_id).model
         self.patch_model(
             model=unet,
@@ -49,17 +50,20 @@ def patch_model(
         prefix: str,
         lora: LoRAModelRaw,
         lora_weight: float,
-        original_weights: Dict[str, torch.Tensor],
+        original_weights: OriginalWeightsStorage,
     ):
         """
         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.
-        :param original_weights: Dict of original weights, filled by weights which lora patches, used for unpatching.
+        :param original_weights: Storage with original weights, filled by weights which lora patches, used for unpatching.
         """
 
+        if lora_weight == 0:
+            return
+
         # assert lora.device.type == "cpu"
         for layer_key, layer in lora.layers.items():
             if not layer_key.startswith(prefix):
@@ -95,8 +99,7 @@ def patch_model(
                 module_param = module.get_parameter(param_name)
 
                 # save original weight
-                if param_key not in original_weights:
-                    original_weights[param_key] = module_param.detach().to(device=TorchDevice.CPU_DEVICE, copy=True)
+                original_weights.save(param_key, module_param)
 
                 if module_param.shape != lora_param_weight.shape:
                     # TODO: debug on lycoris

invokeai/backend/stable_diffusion/extensions_manager.py

@@ -7,6 +7,7 @@
 from diffusers import UNet2DConditionModel
 
 from invokeai.app.services.session_processor.session_processor_common import CanceledException
+from invokeai.backend.util.original_weights_storage import OriginalWeightsStorage
 
 if TYPE_CHECKING:
     from invokeai.backend.stable_diffusion.denoise_context import DenoiseContext
@@ -67,10 +68,7 @@ def patch_unet(self, unet: UNet2DConditionModel, cached_weights: Optional[Dict[s
         if self._is_canceled and self._is_canceled():
             raise CanceledException
 
-        original_weights: Dict[str, torch.Tensor] = {}
-        if cached_weights:
-            original_weights.update(cached_weights)
-
+        original_weights = OriginalWeightsStorage(cached_weights)
         try:
             with ExitStack() as exit_stack:
                 for ext in self._extensions:
@@ -80,5 +78,5 @@ def patch_unet(self, unet: UNet2DConditionModel, cached_weights: Optional[Dict[s
 
         finally:
             with torch.no_grad():
-                for param_key, weight in original_weights.items():
+                for param_key, weight in original_weights.get_changed_weights():
                     unet.get_parameter(param_key).copy_(weight)

invokeai/backend/util/original_weights_storage.py

@@ -0,0 +1,35 @@
+from __future__ import annotations
+
+from typing import Dict, Iterator, Optional, Tuple
+
+import torch
+
+from invokeai.backend.util.devices import TorchDevice
+
+
+class OriginalWeightsStorage:
+    def __init__(self, cached_weights: Optional[Dict[str, torch.Tensor]] = None):
+        self._weights = {}
+        self._changed_weights = 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]