[LoRA] make set_adapters() robust on silent failures.

src/diffusers/loaders/lora_base.py

@@ -661,8 +661,20 @@ def set_adapters(
         adapter_names: Union[List[str], str],
         adapter_weights: Optional[Union[float, Dict, List[float], List[Dict]]] = None,
     ):
-        adapter_names = [adapter_names] if isinstance(adapter_names, str) else adapter_names
+        if isinstance(adapter_weights, dict):
+            components_passed = set(adapter_weights.keys())
+            lora_components = set(self._lora_loadable_modules)
+
+            invalid_components = sorted(components_passed - lora_components)
+            if invalid_components:
+                logger.warning(
+                    f"The following components in `adapter_weights` are not part of the pipeline: {invalid_components}. "
+                    f"Available components that are LoRA-compatible: {self._lora_loadable_modules}. So, weights belonging "
+                    "to the invalid components will be removed and ignored."
+                )
+                adapter_weights = {k: v for k, v in adapter_weights.items() if k not in invalid_components}
 
+        adapter_names = [adapter_names] if isinstance(adapter_names, str) else adapter_names
         adapter_weights = copy.deepcopy(adapter_weights)
 
         # Expand weights into a list, one entry per adapter
@@ -697,12 +709,6 @@ def set_adapters(
             for adapter_name, weights in zip(adapter_names, adapter_weights):
                 if isinstance(weights, dict):
                     component_adapter_weights = weights.pop(component, None)
-
-                    if component_adapter_weights is not None and not hasattr(self, component):
-                        logger.warning(
-                            f"Lora weight dict contains {component} weights but will be ignored because pipeline does not have {component}."
-                        )
-
                     if component_adapter_weights is not None and component not in invert_list_adapters[adapter_name]:
                         logger.warning(
                             (

tests/lora/test_lora_layers_cogvideox.py

@@ -155,3 +155,7 @@ def test_simple_inference_with_text_lora_fused(self):
     @unittest.skip("Text encoder LoRA is not supported in CogVideoX.")
     def test_simple_inference_with_text_lora_save_load(self):
         pass
+
+    @unittest.skip("Not supported in CogVideoX.")
+    def test_simple_inference_with_text_denoiser_multi_adapter_block_lora(self):
+        pass

tests/lora/test_lora_layers_flux.py

@@ -262,6 +262,10 @@ def test_lora_expansion_works_for_extra_keys(self):
             "LoRA should lead to different results.",
         )
 
+    @unittest.skip("Not supported in Flux.")
+    def test_simple_inference_with_text_denoiser_block_scale(self):
+        pass
+
     @unittest.skip("Not supported in Flux.")
     def test_simple_inference_with_text_denoiser_block_scale_for_all_dict_options(self):
         pass
@@ -270,6 +274,10 @@ def test_simple_inference_with_text_denoiser_block_scale_for_all_dict_options(se
     def test_modify_padding_mode(self):
         pass
 
+    @unittest.skip("Not supported in Flux.")
+    def test_simple_inference_with_text_denoiser_multi_adapter_block_lora(self):
+        pass
+
 
 class FluxControlLoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
     pipeline_class = FluxControlPipeline
@@ -783,6 +791,10 @@ def test_lora_unload_with_parameter_expanded_shapes_and_no_reset(self):
         self.assertTrue(pipe.transformer.x_embedder.weight.data.shape[1] == in_features * 2)
         self.assertTrue(pipe.transformer.config.in_channels == in_features * 2)
 
+    @unittest.skip("Not supported in Flux.")
+    def test_simple_inference_with_text_denoiser_block_scale(self):
+        pass
+
     @unittest.skip("Not supported in Flux.")
     def test_simple_inference_with_text_denoiser_block_scale_for_all_dict_options(self):
         pass
@@ -791,6 +803,10 @@ def test_simple_inference_with_text_denoiser_block_scale_for_all_dict_options(se
     def test_modify_padding_mode(self):
         pass
 
+    @unittest.skip("Not supported in Flux.")
+    def test_simple_inference_with_text_denoiser_multi_adapter_block_lora(self):
+        pass
+
 
 @slow
 @nightly

tests/lora/test_lora_layers_mochi.py

@@ -136,3 +136,7 @@ def test_simple_inference_with_text_lora_fused(self):
     @unittest.skip("Text encoder LoRA is not supported in Mochi.")
     def test_simple_inference_with_text_lora_save_load(self):
         pass
+
+    @unittest.skip("Not supported in CogVideoX.")
+    def test_simple_inference_with_text_denoiser_multi_adapter_block_lora(self):
+        pass

tests/lora/test_lora_layers_sd3.py

@@ -30,6 +30,7 @@
 from diffusers.utils import load_image
 from diffusers.utils.import_utils import is_accelerate_available
 from diffusers.utils.testing_utils import (
+    is_flaky,
     nightly,
     numpy_cosine_similarity_distance,
     require_big_gpu_with_torch_cuda,
@@ -128,6 +129,10 @@ def test_simple_inference_with_text_denoiser_block_scale_for_all_dict_options(se
     def test_modify_padding_mode(self):
         pass
 
+    @is_flaky
+    def test_multiple_wrong_adapter_name_raises_error(self):
+        super().test_multiple_wrong_adapter_name_raises_error()
+
 
 @nightly
 @require_torch_gpu

tests/lora/test_lora_layers_sdxl.py

@@ -37,6 +37,7 @@
 from diffusers.utils.import_utils import is_accelerate_available
 from diffusers.utils.testing_utils import (
     CaptureLogger,
+    is_flaky,
     load_image,
     nightly,
     numpy_cosine_similarity_distance,
@@ -111,6 +112,10 @@ def tearDown(self):
         gc.collect()
         torch.cuda.empty_cache()
 
+    @is_flaky
+    def test_multiple_wrong_adapter_name_raises_error(self):
+        super().test_multiple_wrong_adapter_name_raises_error()
+
 
 @slow
 @nightly

tests/lora/utils.py

@@ -1135,6 +1135,43 @@ def test_wrong_adapter_name_raises_error(self):
         pipe.set_adapters("adapter-1")
         _ = pipe(**inputs, generator=torch.manual_seed(0))[0]
 
+    def test_multiple_wrong_adapter_name_raises_error(self):
+        scheduler_cls = self.scheduler_classes[0]
+        components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
+        pipe = self.pipeline_class(**components)
+        pipe = pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+        _, _, inputs = self.get_dummy_inputs(with_generator=False)
+
+        if "text_encoder" in self.pipeline_class._lora_loadable_modules:
+            pipe.text_encoder.add_adapter(text_lora_config, "adapter-1")
+            self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
+
+        denoiser = pipe.transformer if self.unet_kwargs is None else pipe.unet
+        denoiser.add_adapter(denoiser_lora_config, "adapter-1")
+        self.assertTrue(check_if_lora_correctly_set(denoiser), "Lora not correctly set in denoiser.")
+
+        if self.has_two_text_encoders or self.has_three_text_encoders:
+            if "text_encoder_2" in self.pipeline_class._lora_loadable_modules:
+                pipe.text_encoder_2.add_adapter(text_lora_config, "adapter-1")
+                self.assertTrue(
+                    check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
+                )
+
+        scale_with_wrong_components = {"foo": 0.0, "bar": 0.0, "tik": 0.0}
+        logger = logging.get_logger("diffusers.loaders.lora_base")
+        logger.setLevel(30)
+        with CaptureLogger(logger) as cap_logger:
+            pipe.set_adapters("adapter-1", adapter_weights=scale_with_wrong_components)
+
+        wrong_components = sorted(set(scale_with_wrong_components.keys()))
+        msg = f"The following components in `adapter_weights` are not part of the pipeline: {wrong_components}. "
+        self.assertTrue(msg in str(cap_logger.out))
+
+        # test this works.
+        pipe.set_adapters("adapter-1")
+        _ = pipe(**inputs, generator=torch.manual_seed(0))[0]
+
     def test_simple_inference_with_text_denoiser_block_scale(self):
         """
         Tests a simple inference with lora attached to text encoder and unet, attaches