Fix BaseOutput initialization from dict (#570)

* Fix BaseOutput initialization from dict

* style

* Simplify post-init, add tests

* remove debug

src/diffusers/utils/outputs.py

@@ -59,10 +59,17 @@ class BaseOutput(OrderedDict):
         if not len(class_fields):
             raise ValueError(f"{self.__class__.__name__} has no fields.")
 
-        for field in class_fields:
-            v = getattr(self, field.name)
-            if v is not None:
-                self[field.name] = v
+        first_field = getattr(self, class_fields[0].name)
+        other_fields_are_none = all(getattr(self, field.name) is None for field in class_fields[1:])
+
+        if other_fields_are_none and isinstance(first_field, dict):
+            for key, value in first_field.items():
+                self[key] = value
+        else:
+            for field in class_fields:
+                v = getattr(self, field.name)
+                if v is not None:
+                    self[field.name] = v
 
     def __delitem__(self, *args, **kwargs):
         raise Exception(f"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.")

tests/test_outputs.py

+import unittest
+from dataclasses import dataclass
+from typing import List, Union
+
+import numpy as np
+
+import PIL.Image
+from diffusers.utils.outputs import BaseOutput
+
+
+@dataclass
+class CustomOutput(BaseOutput):
+    images: Union[List[PIL.Image.Image], np.ndarray]
+
+
+class ConfigTester(unittest.TestCase):
+    def test_outputs_single_attribute(self):
+        outputs = CustomOutput(images=np.random.rand(1, 3, 4, 4))
+
+        # check every way of getting the attribute
+        assert isinstance(outputs.images, np.ndarray)
+        assert outputs.images.shape == (1, 3, 4, 4)
+        assert isinstance(outputs["images"], np.ndarray)
+        assert outputs["images"].shape == (1, 3, 4, 4)
+        assert isinstance(outputs[0], np.ndarray)
+        assert outputs[0].shape == (1, 3, 4, 4)
+
+        # test with a non-tensor attribute
+        outputs = CustomOutput(images=[PIL.Image.new("RGB", (4, 4))])
+
+        # check every way of getting the attribute
+        assert isinstance(outputs.images, list)
+        assert isinstance(outputs.images[0], PIL.Image.Image)
+        assert isinstance(outputs["images"], list)
+        assert isinstance(outputs["images"][0], PIL.Image.Image)
+        assert isinstance(outputs[0], list)
+        assert isinstance(outputs[0][0], PIL.Image.Image)
+
+    def test_outputs_dict_init(self):
+        # test output reinitialization with a `dict` for compatibility with `accelerate`
+        outputs = CustomOutput({"images": np.random.rand(1, 3, 4, 4)})
+
+        # check every way of getting the attribute
+        assert isinstance(outputs.images, np.ndarray)
+        assert outputs.images.shape == (1, 3, 4, 4)
+        assert isinstance(outputs["images"], np.ndarray)
+        assert outputs["images"].shape == (1, 3, 4, 4)
+        assert isinstance(outputs[0], np.ndarray)
+        assert outputs[0].shape == (1, 3, 4, 4)
+
+        # test with a non-tensor attribute
+        outputs = CustomOutput({"images": [PIL.Image.new("RGB", (4, 4))]})
+
+        # check every way of getting the attribute
+        assert isinstance(outputs.images, list)
+        assert isinstance(outputs.images[0], PIL.Image.Image)
+        assert isinstance(outputs["images"], list)
+        assert isinstance(outputs["images"][0], PIL.Image.Image)
+        assert isinstance(outputs[0], list)
+        assert isinstance(outputs[0][0], PIL.Image.Image)