Rename peft -> lora in a bunch of places.

invokeai/app/invocations/flux_denoise.py

@@ -30,7 +30,7 @@ from invokeai.backend.flux.sampling_utils import (
     unpack,
 )
 from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
-from invokeai.backend.lora.peft_patcher import PeftPatcher
+from invokeai.backend.lora.lora_patcher import LoraPatcher
 from invokeai.backend.stable_diffusion.diffusers_pipeline import PipelineIntermediateState
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import FLUXConditioningInfo
 from invokeai.backend.util.devices import TorchDevice
@@ -192,7 +192,7 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         with (
             transformer_info.model_on_device() as (cached_weights, transformer),
             # Apply the LoRA after transformer has been moved to its target device for faster patching.
-            PeftPatcher.apply_peft_patches(
+            LoraPatcher.apply_lora_patches(
                 model=transformer,
                 patches=self._lora_iterator(context),
                 prefix="",

invokeai/backend/lora/conversions/flux_kohya_lora_conversion_utils.py

@@ -4,7 +4,7 @@ from typing import Any, Dict, TypeVar
 import torch
 
 from invokeai.backend.lora.layers.any_lora_layer import AnyLoRALayer
-from invokeai.backend.lora.layers.utils import peft_layer_from_state_dict
+from invokeai.backend.lora.layers.utils import any_lora_layer_from_state_dict
 from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
 
 # A regex pattern that matches all of the keys in the Kohya FLUX LoRA format.
@@ -41,7 +41,7 @@ def lora_model_from_flux_kohya_state_dict(state_dict: Dict[str, torch.Tensor]) -
     # Create LoRA layers.
     layers: dict[str, AnyLoRALayer] = {}
     for layer_key, layer_state_dict in grouped_state_dict.items():
-        layer = peft_layer_from_state_dict(layer_key, layer_state_dict)
+        layer = any_lora_layer_from_state_dict(layer_key, layer_state_dict)
         layers[layer_key] = layer
 
     # Create and return the LoRAModelRaw.

invokeai/backend/lora/conversions/sd_lora_conversion_utils.py

@@ -3,7 +3,7 @@ from typing import Dict
 import torch
 
 from invokeai.backend.lora.layers.any_lora_layer import AnyLoRALayer
-from invokeai.backend.lora.layers.utils import peft_layer_from_state_dict
+from invokeai.backend.lora.layers.utils import any_lora_layer_from_state_dict
 from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
 
 
@@ -12,7 +12,7 @@ def lora_model_from_sd_state_dict(state_dict: Dict[str, torch.Tensor]) -> LoRAMo
 
     layers: dict[str, AnyLoRALayer] = {}
     for layer_key, values in grouped_state_dict.items():
-        layer = peft_layer_from_state_dict(layer_key, values)
+        layer = any_lora_layer_from_state_dict(layer_key, values)
         layers[layer_key] = layer
 
     return LoRAModelRaw(layers=layers)

invokeai/backend/lora/layers/utils.py

@@ -11,7 +11,7 @@ from invokeai.backend.lora.layers.lora_layer import LoRALayer
 from invokeai.backend.lora.layers.norm_layer import NormLayer
 
 
-def peft_layer_from_state_dict(layer_key: str, state_dict: Dict[str, torch.Tensor]) -> AnyLoRALayer:
+def any_lora_layer_from_state_dict(layer_key: str, state_dict: Dict[str, torch.Tensor]) -> AnyLoRALayer:
     # Detect layers according to LyCORIS detection logic(`weight_list_det`)
     # https://github.com/KohakuBlueleaf/LyCORIS/tree/8ad8000efb79e2b879054da8c9356e6143591bad/lycoris/modules
 

invokeai/backend/lora/lora_patcher.py

@@ -8,29 +8,29 @@ from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.original_weights_storage import OriginalWeightsStorage
 
 
-class PeftPatcher:
+class LoraPatcher:
     @classmethod
     @torch.no_grad()
     @contextmanager
-    def apply_peft_patches(
+    def apply_lora_patches(
         cls,
         model: torch.nn.Module,
         patches: Iterator[Tuple[LoRAModelRaw, float]],
         prefix: str,
         cached_weights: Optional[Dict[str, torch.Tensor]] = None,
     ):
-        """Apply one or more PEFT patches to a model.
+        """Apply one or more LoRA patches to a model.
 
         :param model: The model to patch.
-        :param loras: An iterator that returns tuples of PEFT patches and associated weights. An iterator is used so
-            that the PEFT patches do not need to be loaded into memory all at once.
+        :param loras: An iterator that returns tuples of LoRA patches and associated weights. An iterator is used so
+            that the LoRA patches do not need to be loaded into memory all at once.
         :param prefix: The keys in the patches will be filtered to only include weights with this prefix.
         :cached_weights: Read-only copy of the model's state dict in CPU, for efficient unpatching purposes.
         """
         original_weights = OriginalWeightsStorage(cached_weights)
         try:
             for patch, patch_weight in patches:
-                cls._apply_peft_patch(
+                cls._apply_lora_patch(
                     model=model,
                     prefix=prefix,
                     patch=patch,
@@ -45,7 +45,7 @@ class PeftPatcher:
 
     @classmethod
     @torch.no_grad()
-    def _apply_peft_patch(
+    def _apply_lora_patch(
         cls,
         model: torch.nn.Module,
         prefix: str,

tests/backend/lora/conversions/test_flux_diffusers_lora_conversion_utils.py

@@ -5,13 +5,13 @@ from invokeai.backend.lora.conversions.flux_diffusers_lora_conversion_utils impo
     is_state_dict_likely_in_flux_diffusers_format,
     lora_model_from_flux_diffusers_state_dict,
 )
-from tests.backend.peft.conversions.lora_state_dicts.flux_lora_diffusers_format import (
+from tests.backend.lora.conversions.lora_state_dicts.flux_lora_diffusers_format import (
     state_dict_keys as flux_diffusers_state_dict_keys,
 )
-from tests.backend.peft.conversions.lora_state_dicts.flux_lora_kohya_format import (
+from tests.backend.lora.conversions.lora_state_dicts.flux_lora_kohya_format import (
     state_dict_keys as flux_kohya_state_dict_keys,
 )
-from tests.backend.peft.conversions.lora_state_dicts.utils import keys_to_mock_state_dict
+from tests.backend.lora.conversions.lora_state_dicts.utils import keys_to_mock_state_dict
 
 
 def test_is_state_dict_likely_in_flux_diffusers_format_true():

tests/backend/lora/conversions/test_flux_kohya_lora_conversion_utils.py

@@ -8,13 +8,13 @@ from invokeai.backend.lora.conversions.flux_kohya_lora_conversion_utils import (
     is_state_dict_likely_in_flux_kohya_format,
     lora_model_from_flux_kohya_state_dict,
 )
-from tests.backend.peft.conversions.lora_state_dicts.flux_lora_diffusers_format import (
+from tests.backend.lora.conversions.lora_state_dicts.flux_lora_diffusers_format import (
     state_dict_keys as flux_diffusers_state_dict_keys,
 )
-from tests.backend.peft.conversions.lora_state_dicts.flux_lora_kohya_format import (
+from tests.backend.lora.conversions.lora_state_dicts.flux_lora_kohya_format import (
     state_dict_keys as flux_kohya_state_dict_keys,
 )
-from tests.backend.peft.conversions.lora_state_dicts.utils import keys_to_mock_state_dict
+from tests.backend.lora.conversions.lora_state_dicts.utils import keys_to_mock_state_dict
 
 
 def test_is_state_dict_likely_in_flux_kohya_format_true():