Merge branch 'main' into get-device-mps

Author: sayakpaul

src/diffusers/loaders/lora_base.py

@@ -470,7 +470,7 @@ def _func_optionally_disable_offloading(_pipeline):
             for _, component in _pipeline.components.items():
                 if not isinstance(component, nn.Module) or not hasattr(component, "_hf_hook"):
                     continue
-            remove_hook_from_module(component, recurse=is_sequential_cpu_offload)
+                remove_hook_from_module(component, recurse=is_sequential_cpu_offload)
 
     return (is_model_cpu_offload, is_sequential_cpu_offload, is_group_offload)
 

tests/lora/utils.py

@@ -2510,3 +2510,34 @@ def test_group_offloading_inference_denoiser(self, offload_type, use_stream):
                 # materializes the test methods on invocation which cannot be overridden.
                 return
         self._test_group_offloading_inference_denoiser(offload_type, use_stream)
+
+    @require_torch_accelerator
+    def test_lora_loading_model_cpu_offload(self):
+        components, _, denoiser_lora_config = self.get_dummy_components(self.scheduler_classes[0])
+        _, _, inputs = self.get_dummy_inputs(with_generator=False)
+        pipe = self.pipeline_class(**components)
+        pipe = pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+
+        denoiser = pipe.transformer if self.unet_kwargs is None else pipe.unet
+        denoiser.add_adapter(denoiser_lora_config)
+        self.assertTrue(check_if_lora_correctly_set(denoiser), "Lora not correctly set in denoiser.")
+
+        output_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            modules_to_save = self._get_modules_to_save(pipe, has_denoiser=True)
+            lora_state_dicts = self._get_lora_state_dicts(modules_to_save)
+            self.pipeline_class.save_lora_weights(
+                save_directory=tmpdirname, safe_serialization=True, **lora_state_dicts
+            )
+            # reinitialize the pipeline to mimic the inference workflow.
+            components, _, denoiser_lora_config = self.get_dummy_components(self.scheduler_classes[0])
+            pipe = self.pipeline_class(**components)
+            pipe.enable_model_cpu_offload(device=torch_device)
+            pipe.load_lora_weights(tmpdirname)
+            denoiser = pipe.transformer if self.unet_kwargs is None else pipe.unet
+            self.assertTrue(check_if_lora_correctly_set(denoiser), "Lora not correctly set in denoiser.")
+
+        output_lora_loaded = pipe(**inputs, generator=torch.manual_seed(0))[0]
+        self.assertTrue(np.allclose(output_lora, output_lora_loaded, atol=1e-3, rtol=1e-3))