Rename ConcatenatedLoRALayer to MergedLayerPatch. And other minor cle…

…anup.

invokeai/app/invocations/flux_text_encoder.py

@@ -98,7 +98,7 @@ def _t5_encode(self, context: InvocationContext) -> torch.Tensor:
                 raise ValueError(f"Unsupported model format: {t5_encoder_config.format}")
 
             # Apply LoRA models to the T5 encoder.
-            # Note: We apply the LoRA after the transformer has been moved to its target device for faster patching.
+            # Note: We apply the LoRA after the encoder has been moved to its target device for faster patching.
             exit_stack.enter_context(
                 LayerPatcher.apply_smart_model_patches(
                     model=t5_text_encoder,

invokeai/backend/patches/layers/concatenated_lora_layer.py → invokeai/backend/patches/layers/merged_layer_patch.py

@@ -13,8 +13,8 @@ class Range:
     end: int
 
 
-class ConcatenatedLoRALayer(BaseLayerPatch):
-    """A patch layer that is composed of multiple sub-layers concatenated together.
+class MergedLayerPatch(BaseLayerPatch):
+    """A patch layer that is composed of multiple sub-layers merged together.
 
     This class was created to handle a special case with FLUX LoRA models. In the BFL FLUX model format, the attention
     Q, K, V matrices are concatenated along the first dimension. In the diffusers LoRA format, the Q, K, V matrices are

invokeai/backend/patches/lora_conversions/flux_diffusers_lora_conversion_utils.py

@@ -3,7 +3,7 @@
 import torch
 
 from invokeai.backend.patches.layers.base_layer_patch import BaseLayerPatch
-from invokeai.backend.patches.layers.concatenated_lora_layer import ConcatenatedLoRALayer, Range
+from invokeai.backend.patches.layers.merged_layer_patch import MergedLayerPatch, Range
 from invokeai.backend.patches.layers.utils import any_lora_layer_from_state_dict
 from invokeai.backend.patches.lora_conversions.flux_lora_constants import FLUX_LORA_TRANSFORMER_PREFIX
 from invokeai.backend.patches.model_patch_raw import ModelPatchRaw
@@ -113,7 +113,7 @@ def add_qkv_lora_layer_if_present(
 
             dim_0_offset += src_weight_shape[0]
 
-        layers[dst_qkv_key] = ConcatenatedLoRALayer(sub_layers, sub_layer_ranges)
+        layers[dst_qkv_key] = MergedLayerPatch(sub_layers, sub_layer_ranges)
 
     # time_text_embed.timestep_embedder -> time_in.
     add_lora_layer_if_present("time_text_embed.timestep_embedder.linear_1", "time_in.in_layer")

tests/backend/model_manager/load/model_cache/torch_module_autocast/custom_modules/test_all_custom_modules.py

@@ -13,10 +13,10 @@
 )
 from invokeai.backend.patches.layer_patcher import LayerPatcher
 from invokeai.backend.patches.layers.base_layer_patch import BaseLayerPatch
-from invokeai.backend.patches.layers.concatenated_lora_layer import ConcatenatedLoRALayer, Range
 from invokeai.backend.patches.layers.flux_control_lora_layer import FluxControlLoRALayer
 from invokeai.backend.patches.layers.lokr_layer import LoKRLayer
 from invokeai.backend.patches.layers.lora_layer import LoRALayer
+from invokeai.backend.patches.layers.merged_layer_patch import MergedLayerPatch, Range
 from invokeai.backend.util.original_weights_storage import OriginalWeightsStorage
 from tests.backend.model_manager.load.model_cache.torch_module_autocast.custom_modules.test_custom_invoke_linear_8_bit_lt import (
     build_linear_8bit_lt_layer,
@@ -328,7 +328,7 @@ def patch_under_test(request: pytest.FixtureRequest) -> PatchUnderTest:
     elif layer_type == "concatenated_lora":
         sub_layer_out_features = [16, 16, 32]
 
-        # Create a ConcatenatedLoRA layer.
+        # Create a MergedLayerPatch.
         sub_layers: list[LoRALayer] = []
         sub_layer_ranges: list[Range] = []
         dim_0_offset = 0
@@ -339,10 +339,10 @@ def patch_under_test(request: pytest.FixtureRequest) -> PatchUnderTest:
             sub_layers.append(LoRALayer(up=up, mid=None, down=down, alpha=1.0, bias=bias))
             sub_layer_ranges.append(Range(dim_0_offset, dim_0_offset + out_features))
             dim_0_offset += out_features
-        concatenated_lora_layer = ConcatenatedLoRALayer(sub_layers, sub_layer_ranges)
+        merged_layer_patch = MergedLayerPatch(sub_layers, sub_layer_ranges)
 
         input = torch.randn(1, in_features)
-        return ([(concatenated_lora_layer, 0.7)], input)
+        return ([(merged_layer_patch, 0.7)], input)
     elif layer_type == "flux_control_lora":
         # Create a FluxControlLoRALayer.
         patched_in_features = 40