diff --git a/tests/backend/model_manager/load/model_cache/torch_module_autocast/custom_modules/test_all_custom_modules.py b/tests/backend/model_manager/load/model_cache/torch_module_autocast/custom_modules/test_all_custom_modules.py
index 875b95da07..789a83f9f0 100644
--- a/tests/backend/model_manager/load/model_cache/torch_module_autocast/custom_modules/test_all_custom_modules.py
+++ b/tests/backend/model_manager/load/model_cache/torch_module_autocast/custom_modules/test_all_custom_modules.py
@@ -15,6 +15,7 @@ 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
 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.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 (
@@ -282,6 +283,7 @@ PatchUnderTest = tuple[list[tuple[BaseLayerPatch, float]], torch.Tensor]
         "multiple_loras",
         "concatenated_lora",
         "flux_control_lora",
+        "single_lokr",
     ]
 )
 def patch_under_test(request: pytest.FixtureRequest) -> PatchUnderTest:
@@ -350,6 +352,20 @@ def patch_under_test(request: pytest.FixtureRequest) -> PatchUnderTest:
 
         input = torch.randn(1, patched_in_features)
         return ([(lora_layer, 0.7)], input)
+    elif layer_type == "single_lokr":
+        lokr_layer = LoKRLayer(
+            w1=torch.randn(rank, rank),
+            w1_a=None,
+            w1_b=None,
+            w2=torch.randn(out_features // rank, in_features // rank),
+            w2_a=None,
+            w2_b=None,
+            t2=None,
+            alpha=1.0,
+            bias=torch.randn(out_features),
+        )
+        input = torch.randn(1, in_features)
+        return ([(lokr_layer, 0.7)], input)
     else:
         raise ValueError(f"Unsupported layer_type: {layer_type}")