fixes to tests

tests/lora/test_lora_layers_flux.py

@@ -330,7 +330,8 @@ def test_lora_parameter_expanded_shapes(self):
         }
         with CaptureLogger(logger) as cap_logger:
             pipe.load_lora_weights(lora_state_dict, "adapter-1")
-            self.assertTrue(check_if_lora_correctly_set(pipe.transformer), "Lora not correctly set in denoiser")
+
+        self.assertTrue(check_if_lora_correctly_set(pipe.transformer), "Lora not correctly set in denoiser")
 
         lora_out = pipe(**inputs, generator=torch.manual_seed(0))[0]
 
@@ -339,6 +340,7 @@ def test_lora_parameter_expanded_shapes(self):
         self.assertTrue(pipe.transformer.config.in_channels == 2 * in_features)
         self.assertTrue(cap_logger.out.startswith("Expanding the nn.Linear input/output features for module"))
 
+        # Testing opposite direction where the LoRA params are zero-padded.
         components, _, _ = self.get_dummy_components(FlowMatchEulerDiscreteScheduler)
         pipe = self.pipeline_class(**components)
         pipe = pipe.to(torch_device)
@@ -349,15 +351,21 @@ def test_lora_parameter_expanded_shapes(self):
             "transformer.x_embedder.lora_A.weight": dummy_lora_A.weight,
             "transformer.x_embedder.lora_B.weight": dummy_lora_B.weight,
         }
-        # We should error out because lora input features is less than original. We only
-        # support expanding the module, not shrinking it
-        with self.assertRaises(NotImplementedError):
+        with CaptureLogger(logger) as cap_logger:
             pipe.load_lora_weights(lora_state_dict, "adapter-1")
 
-    def test_lora_expanding_shape_with_normal_lora_raises_error(self):
-        # TODO: This test checks if an error is raised when a lora expands shapes (like control loras) but
-        # another lora with correct shapes is loaded. This is not supported at the moment and should raise an error.
-        # When we do support it, this test should be removed. Context: https://github.com/huggingface/diffusers/issues/10180
+        self.assertTrue(check_if_lora_correctly_set(pipe.transformer), "Lora not correctly set in denoiser")
+
+        lora_out = pipe(**inputs, generator=torch.manual_seed(0))[0]
+
+        self.assertFalse(np.allclose(original_out, lora_out, rtol=1e-4, atol=1e-4))
+        self.assertTrue(pipe.transformer.x_embedder.weight.data.shape[1] == 2 * in_features)
+        self.assertTrue(pipe.transformer.config.in_channels == 2 * in_features)
+        self.assertTrue("The following LoRA modules were zero padded to match the state dict of" in cap_logger.out)
+
+    def test_normal_lora_with_expanded_lora_raises_error(self):
+        # Test the following situation. Load a regular LoRA (such as the ones trained on Flux.1-Dev). And then
+        # load shape expanded LoRA (such as Control LoRA).
         components, _, _ = self.get_dummy_components(FlowMatchEulerDiscreteScheduler)
 
         # Change the transformer config to mimic a real use case.