Improve loading pipe (#4009)

* improve loading subcomponents

* Add test for logging

* improve loading subcomponents

* make style

* make style

* fix

* finish

src/diffusers/pipelines/pipeline_utils.py

@@ -556,6 +556,7 @@ class DiffusionPipeline(ConfigMixin):
         model_index_dict.pop("_class_name", None)
         model_index_dict.pop("_diffusers_version", None)
         model_index_dict.pop("_module", None)
+        model_index_dict.pop("_name_or_path", None)
 
         expected_modules, optional_kwargs = self._get_signature_keys(self)
 
@@ -1013,7 +1014,7 @@ class DiffusionPipeline(ConfigMixin):
         from diffusers import pipelines
 
         # 6. Load each module in the pipeline
-        for name, (library_name, class_name) in init_dict.items():
+        for name, (library_name, class_name) in tqdm(init_dict.items(), desc="Loading pipeline components..."):
             # 6.1 - now that JAX/Flax is an official framework of the library, we might load from Flax names
             if class_name.startswith("Flax"):
                 class_name = class_name[4:]
@@ -1055,6 +1056,9 @@ class DiffusionPipeline(ConfigMixin):
                     low_cpu_mem_usage=low_cpu_mem_usage,
                     cached_folder=cached_folder,
                 )
+                logger.info(
+                    f"Loaded {name} as {class_name} from `{name}` subfolder of {pretrained_model_name_or_path}."
+                )
 
             init_kwargs[name] = loaded_sub_model  # UNet(...), # DiffusionSchedule(...)
 
@@ -1073,8 +1077,15 @@ class DiffusionPipeline(ConfigMixin):
 
         # 8. Instantiate the pipeline
         model = pipeline_class(**init_kwargs)
+
+        # 9. Save where the model was instantiated from
+        model.register_to_config(_name_or_path=pretrained_model_name_or_path)
         return model
 
+    @property
+    def name_or_path(self) -> str:
+        return getattr(self.config, "_name_or_path", None)
+
     @classmethod
     def download(cls, pretrained_model_name, **kwargs) -> Union[str, os.PathLike]:
         r"""

tests/pipelines/test_pipelines.py

@@ -821,7 +821,12 @@ class CustomPipelineTests(unittest.TestCase):
             pipe_new = CustomPipeline.from_pretrained(tmpdirname)
             pipe_new.save_pretrained(tmpdirname)
 
-        assert dict(pipe_new.config) == dict(pipe.config)
+        conf_1 = dict(pipe.config)
+        conf_2 = dict(pipe_new.config)
+
+        del conf_2["_name_or_path"]
+
+        assert conf_1 == conf_2
 
     @slow
     @require_torch_gpu
@@ -1363,6 +1368,18 @@ class PipelineFastTests(unittest.TestCase):
             assert sd.config.safety_checker != (None, None)
             assert sd.config.feature_extractor != (None, None)
 
+    def test_name_or_path(self):
+        model_path = "hf-internal-testing/tiny-stable-diffusion-torch"
+        sd = DiffusionPipeline.from_pretrained(model_path)
+
+        assert sd.name_or_path == model_path
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            sd.save_pretrained(tmpdirname)
+            sd = DiffusionPipeline.from_pretrained(tmpdirname)
+
+            assert sd.name_or_path == tmpdirname
+
     def test_warning_no_variant_available(self):
         variant = "fp16"
         with self.assertWarns(FutureWarning) as warning_context:

tests/pipelines/test_pipelines_common.py

@@ -17,7 +17,7 @@ from diffusers.image_processor import VaeImageProcessor
 from diffusers.schedulers import KarrasDiffusionSchedulers
 from diffusers.utils import logging
 from diffusers.utils.import_utils import is_accelerate_available, is_accelerate_version, is_xformers_available
-from diffusers.utils.testing_utils import require_torch, torch_device
+from diffusers.utils.testing_utils import CaptureLogger, require_torch, torch_device
 
 
 def to_np(tensor):
@@ -298,9 +298,19 @@ class PipelineTesterMixin:
         inputs = self.get_dummy_inputs(torch_device)
         output = pipe(**inputs)[0]
 
+        logger = logging.get_logger("diffusers.pipelines.pipeline_utils")
+        logger.setLevel(diffusers.logging.INFO)
+
         with tempfile.TemporaryDirectory() as tmpdir:
             pipe.save_pretrained(tmpdir)
+
+            with CaptureLogger(logger) as cap_logger:
                 pipe_loaded = self.pipeline_class.from_pretrained(tmpdir)
+
+            for name in pipe_loaded.components.keys():
+                if name not in pipe_loaded._optional_components:
+                    assert name in str(cap_logger)
+
             pipe_loaded.to(torch_device)
             pipe_loaded.set_progress_bar_config(disable=None)