add warning in the test.

Author: sayakpaul

tests/lora/test_lora_layers_sdxl.py

@@ -33,8 +33,10 @@
     StableDiffusionXLPipeline,
     T2IAdapter,
 )
+from diffusers.utils import logging
 from diffusers.utils.import_utils import is_accelerate_available
 from diffusers.utils.testing_utils import (
+    CaptureLogger,
     load_image,
     nightly,
     numpy_cosine_similarity_distance,
@@ -620,12 +622,16 @@ def test_integration_logits_for_dora_lora(self):
         pipeline.load_lora_weights("hf-internal-testing/dora-trained-on-kohya")
         pipeline.enable_model_cpu_offload()
 
-        images = pipeline(
-            "photo of ohwx dog",
-            num_inference_steps=10,
-            generator=torch.manual_seed(0),
-            output_type="np",
-        ).images
+        logger = logging.get_logger("diffusers.loaders.lora_pipeline")
+        logger.setLevel(30)
+        with CaptureLogger(logger) as cap_logger:
+            images = pipeline(
+                "photo of ohwx dog",
+                num_inference_steps=10,
+                generator=torch.manual_seed(0),
+                output_type="np",
+            ).images
+        assert "It seems like you are using a DoRA checkpoint" in cap_logger.out
 
         predicted_slice = images[0, -3:, -3:, -1].flatten()
         expected_slice_scale = np.array([0.1817, 0.0697, 0.2346, 0.0900, 0.1261, 0.2279, 0.1767, 0.1991, 0.2886])