fix(image_processor): maintain 3-tuple return contract in InpaintProcessor.preprocess when mask is None

src/diffusers/image_processor.py

@@ -14,6 +14,7 @@
 
 import math
 import warnings
+from typing import Any
 
 import numpy as np
 import PIL.Image
@@ -885,7 +886,7 @@ def preprocess(
         height: int | None = None,
         width: int | None = None,
         padding_mask_crop: int | None = None,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
+    ) -> tuple[torch.Tensor, torch.Tensor | None, dict[str, Any]]:
         """
         Preprocess the image and mask.
         """
@@ -894,7 +895,13 @@ def preprocess(
 
         # if mask is None, same behavior as regular image processor
         if mask is None:
-            return self._image_processor.preprocess(image, height=height, width=width)
+            processed_image = self._image_processor.preprocess(image, height=height, width=width)
+            postprocessing_kwargs = {
+                "crops_coords": None,
+                "original_image": None,
+                "original_mask": None,
+            }
+            return processed_image, None, postprocessing_kwargs
 
         if padding_mask_crop is not None:
             crops_coords = self._image_processor.get_crop_region(mask, width, height, pad=padding_mask_crop)

tests/others/test_image_processor.py

@@ -15,9 +15,10 @@
 
 import numpy as np
 import PIL.Image
+import pytest
 import torch
 
-from diffusers.image_processor import VaeImageProcessor
+from diffusers.image_processor import InpaintProcessor, VaeImageProcessor
 
 
 class TestImageProcessor:
@@ -306,3 +307,38 @@ def test_vae_image_processor_resize_np(self):
         assert out_np.shape == exp_np_shape, (
             f"resized image output shape '{out_np.shape}' didn't match expected shape '{exp_np_shape}'."
         )
+
+    @pytest.mark.parametrize(
+        "has_mask, padding_mask_crop",
+        [
+            (True, None),
+            (False, None),
+            (True, 8),
+        ],
+    )
+    def test_inpaint_processor_preprocess(self, has_mask, padding_mask_crop):
+        processor = InpaintProcessor()
+        image = PIL.Image.fromarray(np.zeros((64, 64, 3), dtype=np.uint8))
+        mask = PIL.Image.fromarray(np.ones((64, 64), dtype=np.uint8) * 255) if has_mask else None
+
+        kwargs = {"height": 64, "width": 64}
+        if padding_mask_crop is not None:
+            kwargs["padding_mask_crop"] = padding_mask_crop
+
+        out_img, out_mask, postprocessing_kwargs = processor.preprocess(image, mask=mask, **kwargs)
+
+        assert isinstance(out_img, torch.Tensor)
+        if has_mask:
+            assert isinstance(out_mask, torch.Tensor)
+        else:
+            assert out_mask is None
+        assert isinstance(postprocessing_kwargs, dict)
+
+        if padding_mask_crop is not None:
+            assert postprocessing_kwargs["crops_coords"] is not None
+            assert postprocessing_kwargs["original_image"] == image
+            assert postprocessing_kwargs["original_mask"] == mask
+        else:
+            assert postprocessing_kwargs["crops_coords"] is None
+            assert postprocessing_kwargs["original_image"] is None
+            assert postprocessing_kwargs["original_mask"] is None