diff --git a/src/diffusers/loaders/lora_base.py b/src/diffusers/loaders/lora_base.py
index ec23d6e09d22..6c383b1c3347 100644
--- a/src/diffusers/loaders/lora_base.py
+++ b/src/diffusers/loaders/lora_base.py
@@ -921,7 +921,7 @@ def get_active_adapters(self) -> list[str]:
 
         for component in self._lora_loadable_modules:
             model = getattr(self, component, None)
-            if model is not None and issubclass(model.__class__, ModelMixin):
+            if model is not None and issubclass(model.__class__, (ModelMixin, PreTrainedModel)):
                 for module in model.modules():
                     if isinstance(module, BaseTunerLayer):
                         active_adapters = module.active_adapters
diff --git a/tests/pipelines/testing_utils/lora.py b/tests/pipelines/testing_utils/lora.py
index 87d1da8fac52..661d2908b9ec 100644
--- a/tests/pipelines/testing_utils/lora.py
+++ b/tests/pipelines/testing_utils/lora.py
@@ -700,6 +700,18 @@ def test_simple_inference_with_text_denoiser_multi_adapter_weighted(self, base_p
             msg="output with no lora and output with lora disabled should give same results",
         )
 
+    def test_get_active_adapters_text_encoder_only(self):
+        """
+        Tests that get_active_adapters correctly reports active adapters when only the text encoder has a LoRA attached.
+        """
+        if not self.text_encoder_components:
+            pytest.skip("Skipping test as text encoder LoRAs are not currently supported.")
+
+        pipe = self.get_pipeline().to(torch_device)
+        self.add_adapters_to_pipeline(pipe, components=self.text_encoder_components, adapter_name="default")
+
+        assert pipe.get_active_adapters() == ["default"]
+
     def test_get_adapters(self):
         pipe = self.get_pipeline().to(torch_device)