fix issues with loading, add test for pipeline

src/diffusers/configuration_utils.py

@@ -190,7 +190,6 @@ def get_config_dict(
     def extract_init_dict(cls, config_dict, **kwargs):
         expected_keys = set(dict(inspect.signature(cls.__init__).parameters).keys())
         expected_keys.remove("self")
-        import ipdb; ipdb.set_trace()
         init_dict = {}
         for key in expected_keys:
             if key in kwargs:

src/diffusers/pipeline_utils.py

@@ -56,19 +56,23 @@ def register_modules(self, **kwargs):
             class_name = module.__class__.__name__
 
             register_dict = {name: (library, class_name)}
-            register_dict["_module"] = self.__module__
+
 
             # save model index config
             self.register(**register_dict)
 
             # set models
             setattr(self, name, module)
+
+        register_dict = {"_module" : self.__module__.split(".")[-1] + ".py"}
+        self.register(**register_dict)
 
     def save_pretrained(self, save_directory: Union[str, os.PathLike]):
         self.save_config(save_directory)
 
         model_index_dict = self._dict_to_save
         model_index_dict.pop("_class_name")
+        model_index_dict.pop("_module")
 
         for name, (library_name, class_name) in self._dict_to_save.items():
             importable_classes = LOADABLE_CLASSES[library_name]
@@ -98,12 +102,17 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
             cached_folder = pretrained_model_name_or_path
 
         config_dict = cls.get_config_dict(cached_folder)
+
         module = config_dict["_module"]
         class_name_ = config_dict["_class_name"]
-        class_obj = get_class_from_dynamic_module(cached_folder, module, class_name_, cached_folder)
+
+        if class_name_ == cls.__name__:
+            pipeline_class = cls
+        else:
+            pipeline_class = get_class_from_dynamic_module(cached_folder, module, class_name_, cached_folder)
+
 
-        init_dict, unused = class_obj.extract_init_dict(config_dict, **kwargs)
-        import ipdb; ipdb.set_trace()
+        init_dict, _ = pipeline_class.extract_init_dict(config_dict, **kwargs)
 
         init_kwargs = {}
 
@@ -132,6 +141,5 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
 
             init_kwargs[name] = loaded_sub_model  # UNet(...), # DiffusionSchedule(...)
 
-
-        model = class_obj(**init_kwargs)
+        model = pipeline_class(**init_kwargs)
         return model

tests/test_modeling_utils.py

@@ -22,6 +22,8 @@
 import torch
 
 from diffusers import GaussianDDPMScheduler, UNetModel
+from diffusers.pipeline_utils import DiffusionPipeline
+from models.vision.ddpm.modeling_ddpm import DDPM
 
 
 global_rng = random.Random()
@@ -199,3 +201,46 @@ def test_sample_fast(self):
         assert image.shape == (1, 3, 256, 256)
         image_slice = image[0, -1, -3:, -3:].cpu()
         assert (image_slice - torch.tensor([[0.1746, 0.5125, -0.7920], [-0.5734, -0.2910, -0.1984], [0.4090, -0.7740, -0.3941]])).abs().sum() < 1e-3
+
+
+class PipelineTesterMixin(unittest.TestCase):
+    def test_from_pretrained_save_pretrained(self):
+        # 1. Load models
+        model = UNetModel(ch=32, ch_mult=(1, 2), num_res_blocks=2, attn_resolutions=(16,), resolution=32)
+        schedular = GaussianDDPMScheduler(timesteps=10)
+
+        ddpm = DDPM(model, schedular)
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            ddpm.save_pretrained(tmpdirname)
+            new_ddpm = DDPM.from_pretrained(tmpdirname)
+
+        generator = torch.Generator()
+        generator = generator.manual_seed(669472945848556)
+
+        image = ddpm(generator)
+        generator = generator.manual_seed(669472945848556)
+        new_image = new_ddpm(generator)
+
+        assert (image - new_image).abs().sum() < 1e-5, "Models don't give the same forward pass"
+
+
+    @slow
+    def test_from_pretrained_hub(self):
+        model_path = "fusing/ddpm-cifar10"
+
+        ddpm = DDPM.from_pretrained(model_path)
+        ddpm_from_hub = DiffusionPipeline.from_pretrained(model_path)
+
+        ddpm.noise_scheduler.num_timesteps = 10
+        ddpm_from_hub.noise_scheduler.num_timesteps = 10
+
+
+        generator = torch.Generator(device=torch_device)
+        generator = generator.manual_seed(669472945848556)
+
+        image = ddpm(generator)
+        generator = generator.manual_seed(669472945848556)
+        new_image = ddpm_from_hub(generator)
+
+        assert (image - new_image).abs().sum() < 1e-5, "Models don't give the same forward pass"