feat: add adaLN for custom module test

Author: simpletrontdip

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

@@ -14,6 +14,7 @@
 from invokeai.backend.patches.layer_patcher import LayerPatcher
 from invokeai.backend.patches.layers.base_layer_patch import BaseLayerPatch
 from invokeai.backend.patches.layers.flux_control_lora_layer import FluxControlLoRALayer
+from invokeai.backend.patches.layers.diffusers_ada_ln_lora_layer import DiffusersAdaLN_LoRALayer
 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
@@ -283,6 +284,7 @@ def test_inference_autocast_from_cpu_to_device(device: str, layer_under_test: La
         "multiple_loras",
         "concatenated_lora",
         "flux_control_lora",
+        "diffusers_adaLN_lora",
         "single_lokr",
     ]
 )
@@ -370,6 +372,16 @@ def patch_under_test(request: pytest.FixtureRequest) -> PatchUnderTest:
         )
         input = torch.randn(1, in_features)
         return ([(lokr_layer, 0.7)], input)
+    elif layer_type == "diffusers_adaLN_lora":
+        lora_layer = DiffusersAdaLN_LoRALayer(
+            up=torch.randn(out_features, rank),
+            mid=None,
+            down=torch.randn(rank, in_features),
+            alpha=1.0,
+            bias=torch.randn(out_features),
+        )
+        input = torch.randn(1, in_features)
+        return ([(lora_layer, 0.7)], input)
     else:
         raise ValueError(f"Unsupported layer_type: {layer_type}")