[Loading] Make sure loading edge cases work (huggingface#1192)

* [Loading] Make edge cases work

* up

* finish

* up

Author: patrickvonplaten

src/diffusers/pipeline_flax_utils.py

@@ -55,6 +55,8 @@
         "PreTrainedTokenizerFast": ["save_pretrained", "from_pretrained"],
         "FlaxPreTrainedModel": ["save_pretrained", "from_pretrained"],
         "FeatureExtractionMixin": ["save_pretrained", "from_pretrained"],
+        "ProcessorMixin": ["save_pretrained", "from_pretrained"],
+        "ImageProcessingMixin": ["save_pretrained", "from_pretrained"],
     },
 }
 
@@ -172,8 +174,8 @@ def save_pretrained(self, save_directory: Union[str, os.PathLike], params: Union
             for library_name, library_classes in LOADABLE_CLASSES.items():
                 library = importlib.import_module(library_name)
                 for base_class, save_load_methods in library_classes.items():
-                    class_candidate = getattr(library, base_class)
-                    if issubclass(model_cls, class_candidate):
+                    class_candidate = getattr(library, base_class, None)
+                    if class_candidate is not None and issubclass(model_cls, class_candidate):
                         # if we found a suitable base class in LOADABLE_CLASSES then grab its save method
                         save_method_name = save_load_methods[0]
                         break
@@ -387,11 +389,11 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
                     library = importlib.import_module(library_name)
                     class_obj = getattr(library, class_name)
                     importable_classes = LOADABLE_CLASSES[library_name]
-                    class_candidates = {c: getattr(library, c) for c in importable_classes.keys()}
+                    class_candidates = {c: getattr(library, c, None) for c in importable_classes.keys()}
 
                     expected_class_obj = None
                     for class_name, class_candidate in class_candidates.items():
-                        if issubclass(class_obj, class_candidate):
+                        if class_candidate is not None and issubclass(class_obj, class_candidate):
                             expected_class_obj = class_candidate
 
                     if not issubclass(passed_class_obj[name].__class__, expected_class_obj):
@@ -425,12 +427,12 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
                 class_obj = import_flax_or_no_model(library, class_name)
 
                 importable_classes = LOADABLE_CLASSES[library_name]
-                class_candidates = {c: getattr(library, c) for c in importable_classes.keys()}
+                class_candidates = {c: getattr(library, c, None) for c in importable_classes.keys()}
 
             if loaded_sub_model is None and sub_model_should_be_defined:
                 load_method_name = None
                 for class_name, class_candidate in class_candidates.items():
-                    if issubclass(class_obj, class_candidate):
+                    if class_candidate is not None and issubclass(class_obj, class_candidate):
                         load_method_name = importable_classes[class_name][1]
 
                 load_method = getattr(class_obj, load_method_name)

src/diffusers/pipeline_utils.py

@@ -74,6 +74,8 @@
         "PreTrainedTokenizerFast": ["save_pretrained", "from_pretrained"],
         "PreTrainedModel": ["save_pretrained", "from_pretrained"],
         "FeatureExtractionMixin": ["save_pretrained", "from_pretrained"],
+        "ProcessorMixin": ["save_pretrained", "from_pretrained"],
+        "ImageProcessingMixin": ["save_pretrained", "from_pretrained"],
     },
 }
 
@@ -190,8 +192,8 @@ def save_pretrained(self, save_directory: Union[str, os.PathLike]):
             for library_name, library_classes in LOADABLE_CLASSES.items():
                 library = importlib.import_module(library_name)
                 for base_class, save_load_methods in library_classes.items():
-                    class_candidate = getattr(library, base_class)
-                    if issubclass(model_cls, class_candidate):
+                    class_candidate = getattr(library, base_class, None)
+                    if class_candidate is not None and issubclass(model_cls, class_candidate):
                         # if we found a suitable base class in LOADABLE_CLASSES then grab its save method
                         save_method_name = save_load_methods[0]
                         break
@@ -543,11 +545,11 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
                     library = importlib.import_module(library_name)
                     class_obj = getattr(library, class_name)
                     importable_classes = LOADABLE_CLASSES[library_name]
-                    class_candidates = {c: getattr(library, c) for c in importable_classes.keys()}
+                    class_candidates = {c: getattr(library, c, None) for c in importable_classes.keys()}
 
                     expected_class_obj = None
                     for class_name, class_candidate in class_candidates.items():
-                        if issubclass(class_obj, class_candidate):
+                        if class_candidate is not None and issubclass(class_obj, class_candidate):
                             expected_class_obj = class_candidate
 
                     if not issubclass(passed_class_obj[name].__class__, expected_class_obj):
@@ -577,14 +579,15 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
             else:
                 # else we just import it from the library.
                 library = importlib.import_module(library_name)
+
                 class_obj = getattr(library, class_name)
                 importable_classes = LOADABLE_CLASSES[library_name]
-                class_candidates = {c: getattr(library, c) for c in importable_classes.keys()}
+                class_candidates = {c: getattr(library, c, None) for c in importable_classes.keys()}
 
             if loaded_sub_model is None and sub_model_should_be_defined:
                 load_method_name = None
                 for class_name, class_candidate in class_candidates.items():
-                    if issubclass(class_obj, class_candidate):
+                    if class_candidate is not None and issubclass(class_obj, class_candidate):
                         load_method_name = importable_classes[class_name][1]
 
                 if load_method_name is None:

tests/test_pipelines.py

@@ -88,6 +88,50 @@ def test_download_only_pytorch(self):
             # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_flax_model.msgpack
             assert not any(f.endswith(".msgpack") for f in files)
 
+    def test_download_no_safety_checker(self):
+        prompt = "hello"
+        pipe = StableDiffusionPipeline.from_pretrained(
+            "hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None
+        )
+        generator = torch.Generator(device=torch_device).manual_seed(0)
+        out = pipe(prompt, num_inference_steps=2, generator=generator, output_type="numpy").images
+
+        pipe_2 = StableDiffusionPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-torch")
+        generator_2 = torch.Generator(device=torch_device).manual_seed(0)
+        out_2 = pipe_2(prompt, num_inference_steps=2, generator=generator_2, output_type="numpy").images
+
+        assert np.max(np.abs(out - out_2)) < 1e-3
+
+    def test_load_no_safety_checker_explicit_locally(self):
+        prompt = "hello"
+        pipe = StableDiffusionPipeline.from_pretrained(
+            "hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None
+        )
+        generator = torch.Generator(device=torch_device).manual_seed(0)
+        out = pipe(prompt, num_inference_steps=2, generator=generator, output_type="numpy").images
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            pipe.save_pretrained(tmpdirname)
+            pipe_2 = StableDiffusionPipeline.from_pretrained(tmpdirname, safety_checker=None)
+            generator_2 = torch.Generator(device=torch_device).manual_seed(0)
+            out_2 = pipe_2(prompt, num_inference_steps=2, generator=generator_2, output_type="numpy").images
+
+        assert np.max(np.abs(out - out_2)) < 1e-3
+
+    def test_load_no_safety_checker_default_locally(self):
+        prompt = "hello"
+        pipe = StableDiffusionPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-torch")
+        generator = torch.Generator(device=torch_device).manual_seed(0)
+        out = pipe(prompt, num_inference_steps=2, generator=generator, output_type="numpy").images
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            pipe.save_pretrained(tmpdirname)
+            pipe_2 = StableDiffusionPipeline.from_pretrained(tmpdirname)
+            generator_2 = torch.Generator(device=torch_device).manual_seed(0)
+            out_2 = pipe_2(prompt, num_inference_steps=2, generator=generator_2, output_type="numpy").images
+
+        assert np.max(np.abs(out - out_2)) < 1e-3
+
 
 class CustomPipelineTests(unittest.TestCase):
     def test_load_custom_pipeline(self):