Fix overflow and dtype handling in rgblike_to_depthmap (NumPy + PyTorch)

src/diffusers/image_processor.py

@@ -1045,16 +1045,37 @@ def depth_pil_to_numpy(images: Union[List[PIL.Image.Image], PIL.Image.Image]) ->
     def rgblike_to_depthmap(image: Union[np.ndarray, torch.Tensor]) -> Union[np.ndarray, torch.Tensor]:
         r"""
         Convert an RGB-like depth image to a depth map.
-
-        Args:
-            image (`Union[np.ndarray, torch.Tensor]`):
-                The RGB-like depth image to convert.
-
-        Returns:
-            `Union[np.ndarray, torch.Tensor]`:
-                The corresponding depth map.
         """
-        return image[:, :, 1] * 2**8 + image[:, :, 2]
+        # 1. Cast the tensor to a larger integer type (e.g., int32)
+        #    to safely perform the multiplication by 256.
+        # 2. Perform the 16-bit combination: High-byte * 256 + Low-byte.
+        # 3. Cast the final result to the desired depth map type (uint16) if needed 
+        #    before returning, though leaving it as int32/int64 is often safer 
+        #    for return value from a library function.
+
+        if isinstance(image, torch.Tensor):
+            # Cast to a safe dtype (e.g., int32 or int64) for the calculation
+            image_safe = image.to(torch.int32) 
+
+            # Calculate the depth map
+            depth_map = image_safe[:, :, 1] * 256 + image_safe[:, :, 2]
+
+            # You may want to cast the final result to uint16, but casting to a 
+            # larger int type (like int32) is sufficient to fix the overflow.
+            # depth_map = depth_map.to(torch.uint16) # Uncomment if uint16 is strictly required
+            return depth_map
+
+        elif isinstance(image, np.ndarray):
+            # NumPy equivalent: Cast to a safe dtype (e.g., np.int32)
+            image_safe = image.astype(np.int32)
+
+            # Calculate the depth map
+            depth_map = image_safe[:, :, 1] * 256 + image_safe[:, :, 2]
+
+            # depth_map = depth_map.astype(np.uint16) # Uncomment if uint16 is strictly required
+            return depth_map
+        else:
+            raise TypeError("Input image must be a torch.Tensor or np.ndarray")
 
     def numpy_to_depth(self, images: np.ndarray) -> List[PIL.Image.Image]:
         r"""