diff --git a/src/transformers/feature_extraction_utils.py b/src/transformers/feature_extraction_utils.py
index ed7a0978b4e1..f359fd9e8d00 100644
--- a/src/transformers/feature_extraction_utils.py
+++ b/src/transformers/feature_extraction_utils.py
@@ -67,11 +67,18 @@ class BatchFeature(UserDict):
         tensor_type (`Union[None, str, TensorType]`, *optional*):
             You can give a tensor_type here to convert the lists of integers in PyTorch/Numpy Tensors at
             initialization.
+        skip_tensor_conversion (`list[str]` or `set[str]`, *optional*):
+            List or set of keys that should NOT be converted to tensors, even when `tensor_type` is specified.
     """
 
-    def __init__(self, data: Optional[dict[str, Any]] = None, tensor_type: Union[None, str, TensorType] = None):
+    def __init__(
+        self,
+        data: Optional[dict[str, Any]] = None,
+        tensor_type: Union[None, str, TensorType] = None,
+        skip_tensor_conversion: Optional[Union[list[str], set[str]]] = None,
+    ):
         super().__init__(data)
-        self.convert_to_tensors(tensor_type=tensor_type)
+        self.convert_to_tensors(tensor_type=tensor_type, skip_tensor_conversion=skip_tensor_conversion)
 
     def __getitem__(self, item: str) -> Any:
         """
@@ -110,6 +117,14 @@ def _get_is_as_tensor_fns(self, tensor_type: Optional[Union[str, TensorType]] =
             import torch
 
             def as_tensor(value):
+                if torch.is_tensor(value):
+                    return value
+
+                # stack list of tensors if tensor_type is PyTorch (# torch.tensor() does not support list of tensors)
+                if isinstance(value, (list, tuple)) and len(value) > 0 and torch.is_tensor(value[0]):
+                    return torch.stack(value)
+
+                # convert list of numpy arrays to numpy array (stack) if tensor_type is Numpy
                 if isinstance(value, (list, tuple)) and len(value) > 0:
                     if isinstance(value[0], np.ndarray):
                         value = np.array(value)
@@ -138,7 +153,11 @@ def as_tensor(value, dtype=None):
             is_tensor = is_numpy_array
         return is_tensor, as_tensor
 
-    def convert_to_tensors(self, tensor_type: Optional[Union[str, TensorType]] = None):
+    def convert_to_tensors(
+        self,
+        tensor_type: Optional[Union[str, TensorType]] = None,
+        skip_tensor_conversion: Optional[Union[list[str], set[str]]] = None,
+    ):
         """
         Convert the inner content to tensors.
 
@@ -146,6 +165,8 @@ def convert_to_tensors(self, tensor_type: Optional[Union[str, TensorType]] = Non
             tensor_type (`str` or [`~utils.TensorType`], *optional*):
                 The type of tensors to use. If `str`, should be one of the values of the enum [`~utils.TensorType`]. If
                 `None`, no modification is done.
+            skip_tensor_conversion (`list[str]` or `set[str]`, *optional*):
+                List or set of keys that should NOT be converted to tensors, even when `tensor_type` is specified.
         """
         if tensor_type is None:
             return self
@@ -154,18 +175,26 @@ def convert_to_tensors(self, tensor_type: Optional[Union[str, TensorType]] = Non
 
         # Do the tensor conversion in batch
         for key, value in self.items():
+            # Skip keys explicitly marked for no conversion
+            if skip_tensor_conversion and key in skip_tensor_conversion:
+                continue
+
             try:
                 if not is_tensor(value):
                     tensor = as_tensor(value)
-
                     self[key] = tensor
-            except:  # noqa E722
+            except Exception as e:
                 if key == "overflowing_values":
-                    raise ValueError("Unable to create tensor returning overflowing values of different lengths. ")
+                    raise ValueError(
+                        f"Unable to create tensor for '{key}' with overflowing values of different lengths. "
+                        f"Original error: {str(e)}"
+                    ) from e
                 raise ValueError(
-                    "Unable to create tensor, you should probably activate padding "
-                    "with 'padding=True' to have batched tensors with the same length."
-                )
+                    f"Unable to convert output '{key}' (type: {type(value).__name__}) to tensor: {str(e)}\n"
+                    f"You can try:\n"
+                    f"  1. Use padding=True to ensure all outputs have the same shape\n"
+                    f"  2. Set return_tensors=None to return Python objects instead of tensors"
+                ) from e
 
         return self
 
diff --git a/src/transformers/image_processing_utils_fast.py b/src/transformers/image_processing_utils_fast.py
index 762b7160f155..e1e6d935aa10 100644
--- a/src/transformers/image_processing_utils_fast.py
+++ b/src/transformers/image_processing_utils_fast.py
@@ -932,7 +932,6 @@ def _preprocess(
         if do_pad:
             processed_images = self.pad(processed_images, pad_size=pad_size, disable_grouping=disable_grouping)
 
-        processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
         return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors)
 
     def to_dict(self):
diff --git a/src/transformers/models/beit/image_processing_beit_fast.py b/src/transformers/models/beit/image_processing_beit_fast.py
index 5d89120283a5..b739a1fab579 100644
--- a/src/transformers/models/beit/image_processing_beit_fast.py
+++ b/src/transformers/models/beit/image_processing_beit_fast.py
@@ -163,7 +163,6 @@ def _preprocess(
             processed_images_grouped[shape] = stacked_images
 
         processed_images = reorder_images(processed_images_grouped, grouped_images_index)
-        processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
 
         return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors)
 
diff --git a/src/transformers/models/bridgetower/image_processing_bridgetower_fast.py b/src/transformers/models/bridgetower/image_processing_bridgetower_fast.py
index 76a76b4b0a47..b4ca1caf0d3d 100644
--- a/src/transformers/models/bridgetower/image_processing_bridgetower_fast.py
+++ b/src/transformers/models/bridgetower/image_processing_bridgetower_fast.py
@@ -251,10 +251,8 @@ def _preprocess(
             processed_images, processed_masks = self.pad(
                 processed_images, return_mask=True, disable_grouping=disable_grouping
             )
-            processed_masks = torch.stack(processed_masks, dim=0) if return_tensors else processed_masks
             data["pixel_mask"] = processed_masks
 
-        processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
         data["pixel_values"] = processed_images
 
         return BatchFeature(data=data, tensor_type=return_tensors)
diff --git a/src/transformers/models/cohere2_vision/image_processing_cohere2_vision_fast.py b/src/transformers/models/cohere2_vision/image_processing_cohere2_vision_fast.py
index afdd683e2312..70aa8d71ef19 100644
--- a/src/transformers/models/cohere2_vision/image_processing_cohere2_vision_fast.py
+++ b/src/transformers/models/cohere2_vision/image_processing_cohere2_vision_fast.py
@@ -262,7 +262,6 @@ def _preprocess(
             processed_images_grouped[shape] = stacked_images
 
         processed_images = reorder_images(processed_images_grouped, grouped_images_index)
-        processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
 
         return BatchFeature(
             data={"pixel_values": processed_images, "num_patches": num_patches}, tensor_type=return_tensors
diff --git a/src/transformers/models/convnext/image_processing_convnext_fast.py b/src/transformers/models/convnext/image_processing_convnext_fast.py
index 035b92f8b7d2..fb122b41f2ca 100644
--- a/src/transformers/models/convnext/image_processing_convnext_fast.py
+++ b/src/transformers/models/convnext/image_processing_convnext_fast.py
@@ -162,7 +162,6 @@ def _preprocess(
             processed_images_grouped[shape] = stacked_images
 
         processed_images = reorder_images(processed_images_grouped, grouped_images_index)
-        processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
 
         return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors)
 
diff --git a/src/transformers/models/deepseek_vl/image_processing_deepseek_vl_fast.py b/src/transformers/models/deepseek_vl/image_processing_deepseek_vl_fast.py
index 6eaa15d827d9..aab51fdf9679 100644
--- a/src/transformers/models/deepseek_vl/image_processing_deepseek_vl_fast.py
+++ b/src/transformers/models/deepseek_vl/image_processing_deepseek_vl_fast.py
@@ -171,7 +171,6 @@ def _preprocess(
             processed_images_grouped[shape] = stacked_images
 
         processed_images = reorder_images(processed_images_grouped, grouped_images_index)
-        processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
 
         return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors)
 
diff --git a/src/transformers/models/deepseek_vl_hybrid/image_processing_deepseek_vl_hybrid_fast.py b/src/transformers/models/deepseek_vl_hybrid/image_processing_deepseek_vl_hybrid_fast.py
index 0a1efea13cff..ef2ee384b736 100644
--- a/src/transformers/models/deepseek_vl_hybrid/image_processing_deepseek_vl_hybrid_fast.py
+++ b/src/transformers/models/deepseek_vl_hybrid/image_processing_deepseek_vl_hybrid_fast.py
@@ -207,9 +207,6 @@ def _preprocess(
             )
             high_res_processed_images_grouped[shape] = stacked_high_res_images
         high_res_processed_images = reorder_images(high_res_processed_images_grouped, grouped_high_res_images_index)
-        high_res_processed_images = (
-            torch.stack(high_res_processed_images, dim=0) if return_tensors else high_res_processed_images
-        )
 
         resized_images_grouped = {}
         for shape, stacked_high_res_padded_images in high_res_padded_images.items():
@@ -233,7 +230,6 @@ def _preprocess(
             )
             processed_images_grouped[shape] = stacked_images
         processed_images = reorder_images(processed_images_grouped, grouped_resized_images_index)
-        processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
 
         return BatchFeature(
             data={"pixel_values": processed_images, "high_res_pixel_values": high_res_processed_images},
diff --git a/src/transformers/models/deepseek_vl_hybrid/modular_deepseek_vl_hybrid.py b/src/transformers/models/deepseek_vl_hybrid/modular_deepseek_vl_hybrid.py
index 4501ed7810d2..622458328977 100644
--- a/src/transformers/models/deepseek_vl_hybrid/modular_deepseek_vl_hybrid.py
+++ b/src/transformers/models/deepseek_vl_hybrid/modular_deepseek_vl_hybrid.py
@@ -888,9 +888,6 @@ def _preprocess(
             )
             high_res_processed_images_grouped[shape] = stacked_high_res_images
         high_res_processed_images = reorder_images(high_res_processed_images_grouped, grouped_high_res_images_index)
-        high_res_processed_images = (
-            torch.stack(high_res_processed_images, dim=0) if return_tensors else high_res_processed_images
-        )
 
         resized_images_grouped = {}
         for shape, stacked_high_res_padded_images in high_res_padded_images.items():
@@ -914,7 +911,6 @@ def _preprocess(
             )
             processed_images_grouped[shape] = stacked_images
         processed_images = reorder_images(processed_images_grouped, grouped_resized_images_index)
-        processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
 
         return BatchFeature(
             data={"pixel_values": processed_images, "high_res_pixel_values": high_res_processed_images},
diff --git a/src/transformers/models/depth_pro/image_processing_depth_pro_fast.py b/src/transformers/models/depth_pro/image_processing_depth_pro_fast.py
index bc621e0ffc26..93cf889d43ee 100644
--- a/src/transformers/models/depth_pro/image_processing_depth_pro_fast.py
+++ b/src/transformers/models/depth_pro/image_processing_depth_pro_fast.py
@@ -94,7 +94,6 @@ def _preprocess(
             processed_images_grouped[shape] = stacked_images
 
         processed_images = reorder_images(processed_images_grouped, grouped_images_index)
-        processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
 
         return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors)
 
diff --git a/src/transformers/models/dinov3_vit/image_processing_dinov3_vit_fast.py b/src/transformers/models/dinov3_vit/image_processing_dinov3_vit_fast.py
index 7c080485ed00..3f3a2334ab5c 100644
--- a/src/transformers/models/dinov3_vit/image_processing_dinov3_vit_fast.py
+++ b/src/transformers/models/dinov3_vit/image_processing_dinov3_vit_fast.py
@@ -88,7 +88,6 @@ def _preprocess(
             processed_images_grouped[shape] = stacked_images
 
         processed_images = reorder_images(processed_images_grouped, grouped_images_index)
-        processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
 
         return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors)
 
diff --git a/src/transformers/models/donut/image_processing_donut_fast.py b/src/transformers/models/donut/image_processing_donut_fast.py
index 9a150f4df75f..f27c9491cb59 100644
--- a/src/transformers/models/donut/image_processing_donut_fast.py
+++ b/src/transformers/models/donut/image_processing_donut_fast.py
@@ -231,7 +231,6 @@ def _preprocess(
             processed_images_grouped[shape] = stacked_images
 
         processed_images = reorder_images(processed_images_grouped, grouped_images_index)
-        processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
 
         return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors)
 
diff --git a/src/transformers/models/dpt/image_processing_dpt_fast.py b/src/transformers/models/dpt/image_processing_dpt_fast.py
index ba0a6d28c56c..06fd884afaf7 100644
--- a/src/transformers/models/dpt/image_processing_dpt_fast.py
+++ b/src/transformers/models/dpt/image_processing_dpt_fast.py
@@ -225,8 +225,7 @@ def _preprocess(
             processed_images_grouped[shape] = stacked_images
 
         processed_images = reorder_images(processed_images_grouped, grouped_images_index)
-        processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
-        return BatchFeature(data={"pixel_values": processed_images})
+        return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors)
 
     def post_process_semantic_segmentation(self, outputs, target_sizes: Optional[list[tuple]] = None):
         """
diff --git a/src/transformers/models/dpt/modular_dpt.py b/src/transformers/models/dpt/modular_dpt.py
index d99160653557..ea7a789a536d 100644
--- a/src/transformers/models/dpt/modular_dpt.py
+++ b/src/transformers/models/dpt/modular_dpt.py
@@ -228,8 +228,7 @@ def _preprocess(
             processed_images_grouped[shape] = stacked_images
 
         processed_images = reorder_images(processed_images_grouped, grouped_images_index)
-        processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
-        return BatchFeature(data={"pixel_values": processed_images})
+        return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors)
 
     def post_process_depth_estimation(
         self,
diff --git a/src/transformers/models/efficientloftr/image_processing_efficientloftr_fast.py b/src/transformers/models/efficientloftr/image_processing_efficientloftr_fast.py
index 74a5d4577c91..1584e2a782ad 100644
--- a/src/transformers/models/efficientloftr/image_processing_efficientloftr_fast.py
+++ b/src/transformers/models/efficientloftr/image_processing_efficientloftr_fast.py
@@ -153,9 +153,8 @@ def _preprocess(
         stacked_pairs = [torch.stack(pair, dim=0) for pair in image_pairs]
 
         # Return in same format as slow processor
-        image_pairs = torch.stack(stacked_pairs, dim=0) if return_tensors else stacked_pairs
 
-        return BatchFeature(data={"pixel_values": image_pairs})
+        return BatchFeature(data={"pixel_values": stacked_pairs}, tensor_type=return_tensors)
 
     def post_process_keypoint_matching(
         self,
diff --git a/src/transformers/models/efficientnet/image_processing_efficientnet_fast.py b/src/transformers/models/efficientnet/image_processing_efficientnet_fast.py
index 5f3439aaa273..93e1237f061c 100644
--- a/src/transformers/models/efficientnet/image_processing_efficientnet_fast.py
+++ b/src/transformers/models/efficientnet/image_processing_efficientnet_fast.py
@@ -178,7 +178,6 @@ def _preprocess(
             processed_images_grouped[shape] = stacked_images
 
         processed_images = reorder_images(processed_images_grouped, grouped_images_index)
-        processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
 
         return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors)
 
diff --git a/src/transformers/models/eomt/image_processing_eomt_fast.py b/src/transformers/models/eomt/image_processing_eomt_fast.py
index b6bf7f3aa13e..ec85d98f7d39 100644
--- a/src/transformers/models/eomt/image_processing_eomt_fast.py
+++ b/src/transformers/models/eomt/image_processing_eomt_fast.py
@@ -162,8 +162,7 @@ def _preprocess_image_like_inputs(
         )
         ignore_index = kwargs.pop("ignore_index", None)
         images_kwargs = kwargs.copy()
-        processed_images, patch_offsets = self._preprocess(images, **images_kwargs)
-        outputs = BatchFeature({"pixel_values": processed_images})
+        outputs = self._preprocess(images, **images_kwargs)
 
         if segmentation_maps is not None:
             processed_segmentation_maps = self._prepare_image_like_inputs(
@@ -183,9 +182,9 @@ def _preprocess_image_like_inputs(
                 }
             )
 
-            processed_segmentation_maps, _ = self._preprocess(
+            processed_segmentation_maps = self._preprocess(
                 images=processed_segmentation_maps, **segmentation_maps_kwargs
-            )
+            ).pixel_values
             processed_segmentation_maps = processed_segmentation_maps.squeeze(1).to(torch.int64)
             # Convert to list of binary masks and labels
             mask_labels, class_labels = [], []
@@ -208,8 +207,8 @@ def _preprocess_image_like_inputs(
             outputs["mask_labels"] = mask_labels
             outputs["class_labels"] = class_labels
 
-        if patch_offsets:
-            outputs["patch_offsets"] = [torch.tensor(offsets) for offsets in patch_offsets]
+        if outputs.patch_offsets:
+            outputs["patch_offsets"] = [torch.tensor(offsets) for offsets in outputs.patch_offsets]
 
         return outputs
 
@@ -274,11 +273,13 @@ def _preprocess(
                 stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std
             )
             processed_images_grouped[shape] = stacked_images
-        images = reorder_images(processed_images_grouped, grouped_images_index)
+        processed_images = reorder_images(processed_images_grouped, grouped_images_index)
 
-        processed_images = torch.stack(images, dim=0) if return_tensors else images
-
-        return processed_images, patch_offsets
+        return BatchFeature(
+            data={"pixel_values": processed_images, "patch_offsets": patch_offsets},
+            tensor_type=return_tensors,
+            skip_tensor_conversion=["patch_offsets"],
+        )
 
     def merge_image_patches(
         self,
diff --git a/src/transformers/models/flava/image_processing_flava_fast.py b/src/transformers/models/flava/image_processing_flava_fast.py
index 0dfbd07f17a7..8959bf3c2bb8 100644
--- a/src/transformers/models/flava/image_processing_flava_fast.py
+++ b/src/transformers/models/flava/image_processing_flava_fast.py
@@ -306,7 +306,6 @@ def _preprocess_image(
             processed_images_grouped[shape] = stacked_images
 
         processed_images = reorder_images(processed_images_grouped, grouped_images_index)
-        processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
 
         return processed_images
 
@@ -397,7 +396,6 @@ def _preprocess(
                 mask_group_max_aspect_ratio=mask_group_max_aspect_ratio,
             )
             masks = [mask_generator() for _ in range(len(images))]
-            masks = torch.stack(masks, dim=0) if return_tensors else masks
             data["bool_masked_pos"] = masks
 
         return BatchFeature(data=data, tensor_type=return_tensors)
diff --git a/src/transformers/models/fuyu/image_processing_fuyu.py b/src/transformers/models/fuyu/image_processing_fuyu.py
index e86352af1bf5..c1a2d492c5df 100644
--- a/src/transformers/models/fuyu/image_processing_fuyu.py
+++ b/src/transformers/models/fuyu/image_processing_fuyu.py
@@ -94,7 +94,7 @@ class FuyuBatchFeature(BatchFeature):
     The outputs dictionary from the processors contains a mix of tensors and lists of tensors.
     """
 
-    def convert_to_tensors(self, tensor_type: Optional[Union[str, TensorType]] = None):
+    def convert_to_tensors(self, tensor_type: Optional[Union[str, TensorType]] = None, **kwargs):
         """
         Convert the inner content to tensors.
 
diff --git a/src/transformers/models/gemma3/image_processing_gemma3_fast.py b/src/transformers/models/gemma3/image_processing_gemma3_fast.py
index bfb58be2a8e1..a6ad4d9c67a2 100644
--- a/src/transformers/models/gemma3/image_processing_gemma3_fast.py
+++ b/src/transformers/models/gemma3/image_processing_gemma3_fast.py
@@ -231,7 +231,6 @@ def _preprocess(
             processed_images_grouped[shape] = stacked_images
 
         processed_images = reorder_images(processed_images_grouped, grouped_images_index)
-        processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
         return BatchFeature(
             data={"pixel_values": processed_images, "num_crops": num_crops}, tensor_type=return_tensors
         )
diff --git a/src/transformers/models/glpn/image_processing_glpn_fast.py b/src/transformers/models/glpn/image_processing_glpn_fast.py
index a906dc29c271..07f737a37f96 100644
--- a/src/transformers/models/glpn/image_processing_glpn_fast.py
+++ b/src/transformers/models/glpn/image_processing_glpn_fast.py
@@ -107,7 +107,6 @@ def _preprocess(
             processed_groups[shape] = stacked_images
 
         processed_images = reorder_images(processed_groups, grouped_index)
-        processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
         return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors)
 
     def post_process_depth_estimation(self, outputs, target_sizes=None):
diff --git a/src/transformers/models/got_ocr2/image_processing_got_ocr2_fast.py b/src/transformers/models/got_ocr2/image_processing_got_ocr2_fast.py
index 210a18a406be..be183ae79415 100644
--- a/src/transformers/models/got_ocr2/image_processing_got_ocr2_fast.py
+++ b/src/transformers/models/got_ocr2/image_processing_got_ocr2_fast.py
@@ -189,7 +189,6 @@ def _preprocess(
             processed_images_grouped[shape] = stacked_images
 
         processed_images = reorder_images(processed_images_grouped, grouped_images_index)
-        processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
 
         return BatchFeature(
             data={"pixel_values": processed_images, "num_patches": num_patches}, tensor_type=return_tensors
diff --git a/src/transformers/models/imagegpt/image_processing_imagegpt_fast.py b/src/transformers/models/imagegpt/image_processing_imagegpt_fast.py
index 1be050b5ecf9..44aff7c91245 100644
--- a/src/transformers/models/imagegpt/image_processing_imagegpt_fast.py
+++ b/src/transformers/models/imagegpt/image_processing_imagegpt_fast.py
@@ -164,12 +164,8 @@ def _preprocess(
 
             input_ids = reorder_images(input_ids_grouped, grouped_images_index)
 
-            return BatchFeature(
-                data={"input_ids": torch.stack(input_ids, dim=0) if return_tensors else input_ids},
-                tensor_type=return_tensors,
-            )
+            return BatchFeature(data={"input_ids": input_ids}, tensor_type=return_tensors)
 
-        pixel_values = torch.stack(pixel_values, dim=0) if return_tensors else pixel_values
         return BatchFeature(data={"pixel_values": pixel_values}, tensor_type=return_tensors)
 
     def to_dict(self):
diff --git a/src/transformers/models/instructblipvideo/video_processing_instructblipvideo.py b/src/transformers/models/instructblipvideo/video_processing_instructblipvideo.py
index f2c49925ef19..ba5933769af9 100644
--- a/src/transformers/models/instructblipvideo/video_processing_instructblipvideo.py
+++ b/src/transformers/models/instructblipvideo/video_processing_instructblipvideo.py
@@ -84,7 +84,6 @@ def _preprocess(
             processed_videos_grouped[shape] = stacked_videos
 
         processed_videos = reorder_videos(processed_videos_grouped, grouped_videos_index)
-        processed_videos = torch.stack(processed_videos, dim=0) if return_tensors else processed_videos
 
         return BatchFeature(data={"pixel_values": processed_videos}, tensor_type=return_tensors)
 
diff --git a/src/transformers/models/internvl/video_processing_internvl.py b/src/transformers/models/internvl/video_processing_internvl.py
index a544bb08815a..de82e04602dc 100644
--- a/src/transformers/models/internvl/video_processing_internvl.py
+++ b/src/transformers/models/internvl/video_processing_internvl.py
@@ -140,7 +140,6 @@ def _preprocess(
             processed_videos_grouped[shape] = stacked_videos
 
         processed_videos = reorder_videos(processed_videos_grouped, grouped_videos_index)
-        processed_videos = torch.stack(processed_videos, dim=0) if return_tensors else processed_videos
 
         return BatchFeature(data={"pixel_values_videos": processed_videos}, tensor_type=return_tensors)
 
diff --git a/src/transformers/models/janus/image_processing_janus_fast.py b/src/transformers/models/janus/image_processing_janus_fast.py
index b8e032786bf4..0a176ca9818e 100644
--- a/src/transformers/models/janus/image_processing_janus_fast.py
+++ b/src/transformers/models/janus/image_processing_janus_fast.py
@@ -180,7 +180,6 @@ def _preprocess(
             processed_images_grouped[shape] = stacked_images
 
         processed_images = reorder_images(processed_images_grouped, grouped_images_index)
-        processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
 
         return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors)
 
diff --git a/src/transformers/models/kosmos2_5/image_processing_kosmos2_5_fast.py b/src/transformers/models/kosmos2_5/image_processing_kosmos2_5_fast.py
index d892436ea652..3e7b61c1c818 100644
--- a/src/transformers/models/kosmos2_5/image_processing_kosmos2_5_fast.py
+++ b/src/transformers/models/kosmos2_5/image_processing_kosmos2_5_fast.py
@@ -264,8 +264,8 @@ def _preprocess(
 
         encoded_outputs = BatchFeature(
             data={
-                "flattened_patches": torch.stack(flattened_patches, dim=0) if return_tensors else flattened_patches,
-                "attention_mask": torch.stack(attention_masks, dim=0) if return_tensors else attention_masks,
+                "flattened_patches": flattened_patches,
+                "attention_mask": attention_masks,
                 "width": width,
                 "height": height,
                 "rows": rows,
diff --git a/src/transformers/models/layoutlmv2/image_processing_layoutlmv2_fast.py b/src/transformers/models/layoutlmv2/image_processing_layoutlmv2_fast.py
index 2d6e6bc21cb3..a74b3f02c118 100644
--- a/src/transformers/models/layoutlmv2/image_processing_layoutlmv2_fast.py
+++ b/src/transformers/models/layoutlmv2/image_processing_layoutlmv2_fast.py
@@ -101,7 +101,6 @@ def _preprocess(
             processed_images_grouped[shape] = stacked_images
 
         processed_images = reorder_images(processed_images_grouped, grouped_images_index)
-        processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
 
         data = BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors)
 
diff --git a/src/transformers/models/layoutlmv3/image_processing_layoutlmv3_fast.py b/src/transformers/models/layoutlmv3/image_processing_layoutlmv3_fast.py
index b614c5ec9449..0553b2b3a1a6 100644
--- a/src/transformers/models/layoutlmv3/image_processing_layoutlmv3_fast.py
+++ b/src/transformers/models/layoutlmv3/image_processing_layoutlmv3_fast.py
@@ -115,7 +115,6 @@ def _preprocess(
             processed_images_grouped[shape] = stacked_images
 
         processed_images = reorder_images(processed_images_grouped, grouped_images_index)
-        processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
 
         data = BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors)
 
diff --git a/src/transformers/models/lightglue/image_processing_lightglue_fast.py b/src/transformers/models/lightglue/image_processing_lightglue_fast.py
index e99237cc104d..2785ac652730 100644
--- a/src/transformers/models/lightglue/image_processing_lightglue_fast.py
+++ b/src/transformers/models/lightglue/image_processing_lightglue_fast.py
@@ -174,9 +174,8 @@ def _preprocess(
         stacked_pairs = [torch.stack(pair, dim=0) for pair in image_pairs]
 
         # Return in same format as slow processor
-        image_pairs = torch.stack(stacked_pairs, dim=0) if return_tensors else stacked_pairs
 
-        return BatchFeature(data={"pixel_values": image_pairs})
+        return BatchFeature(data={"pixel_values": stacked_pairs}, tensor_type=return_tensors)
 
     def post_process_keypoint_matching(
         self,
diff --git a/src/transformers/models/llama4/image_processing_llama4_fast.py b/src/transformers/models/llama4/image_processing_llama4_fast.py
index ef44786f7c66..00c58cede8ff 100644
--- a/src/transformers/models/llama4/image_processing_llama4_fast.py
+++ b/src/transformers/models/llama4/image_processing_llama4_fast.py
@@ -419,10 +419,9 @@ def _preprocess(
                 )
                 grouped_processed_images[shape] = torch.cat([processed_images, global_tiles.unsqueeze(1)], dim=1)
         processed_images = reorder_images(grouped_processed_images, grouped_images_index)
-        aspect_ratios_list = reorder_images(grouped_aspect_ratios, grouped_images_index)
+        aspect_ratios = reorder_images(grouped_aspect_ratios, grouped_images_index)
 
         processed_images = torch.cat(processed_images, dim=0) if return_tensors else processed_images
-        aspect_ratios = torch.stack(aspect_ratios_list, dim=0) if return_tensors else aspect_ratios_list
         return BatchFeature(
             data={"pixel_values": processed_images, "aspect_ratios": aspect_ratios}, tensor_type=return_tensors
         )
diff --git a/src/transformers/models/llava/image_processing_llava_fast.py b/src/transformers/models/llava/image_processing_llava_fast.py
index 66ccb49c3671..e2f941d7ac49 100644
--- a/src/transformers/models/llava/image_processing_llava_fast.py
+++ b/src/transformers/models/llava/image_processing_llava_fast.py
@@ -149,7 +149,6 @@ def _preprocess(
             processed_images_grouped[shape] = stacked_images
 
         processed_images = reorder_images(processed_images_grouped, grouped_images_index)
-        processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
 
         return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors)
 
diff --git a/src/transformers/models/llava_next/image_processing_llava_next_fast.py b/src/transformers/models/llava_next/image_processing_llava_next_fast.py
index cc5dc756b237..936d88fba086 100644
--- a/src/transformers/models/llava_next/image_processing_llava_next_fast.py
+++ b/src/transformers/models/llava_next/image_processing_llava_next_fast.py
@@ -260,7 +260,6 @@ def _preprocess(
 
         if do_pad:
             processed_images = self._pad_for_batching(processed_images)
-        processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
         return BatchFeature(
             data={"pixel_values": processed_images, "image_sizes": image_sizes}, tensor_type=return_tensors
         )
diff --git a/src/transformers/models/llava_onevision/image_processing_llava_onevision_fast.py b/src/transformers/models/llava_onevision/image_processing_llava_onevision_fast.py
index beb1c1b982e0..02f108105d00 100644
--- a/src/transformers/models/llava_onevision/image_processing_llava_onevision_fast.py
+++ b/src/transformers/models/llava_onevision/image_processing_llava_onevision_fast.py
@@ -279,7 +279,6 @@ def _preprocess(
 
         if do_pad:
             processed_images = self._pad_for_batching(processed_images)
-        processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
         return BatchFeature(
             data={"pixel_values": processed_images, "image_sizes": image_sizes, "batch_num_images": batch_num_images},
             tensor_type=return_tensors,
diff --git a/src/transformers/models/llava_onevision/modular_llava_onevision.py b/src/transformers/models/llava_onevision/modular_llava_onevision.py
index dd714def07c2..cb837861f9a1 100644
--- a/src/transformers/models/llava_onevision/modular_llava_onevision.py
+++ b/src/transformers/models/llava_onevision/modular_llava_onevision.py
@@ -211,7 +211,6 @@ def _preprocess(
 
         if do_pad:
             processed_images = self._pad_for_batching(processed_images)
-        processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
         return BatchFeature(
             data={"pixel_values": processed_images, "image_sizes": image_sizes, "batch_num_images": batch_num_images},
             tensor_type=return_tensors,
diff --git a/src/transformers/models/mask2former/image_processing_mask2former_fast.py b/src/transformers/models/mask2former/image_processing_mask2former_fast.py
index f8d176dcf042..dfd91ffca490 100644
--- a/src/transformers/models/mask2former/image_processing_mask2former_fast.py
+++ b/src/transformers/models/mask2former/image_processing_mask2former_fast.py
@@ -387,10 +387,7 @@ def _preprocess(
         processed_images = reorder_images(processed_images_grouped, grouped_images_index)
         processed_pixel_masks = reorder_images(processed_pixel_masks_grouped, grouped_images_index)
         encoded_inputs = BatchFeature(
-            data={
-                "pixel_values": torch.stack(processed_images, dim=0) if return_tensors else processed_images,
-                "pixel_mask": torch.stack(processed_pixel_masks, dim=0) if return_tensors else processed_pixel_masks,
-            },
+            data={"pixel_values": processed_images, "pixel_mask": processed_pixel_masks},
             tensor_type=return_tensors,
         )
         if segmentation_maps is not None:
diff --git a/src/transformers/models/maskformer/image_processing_maskformer_fast.py b/src/transformers/models/maskformer/image_processing_maskformer_fast.py
index 2be8ca8f16a9..bbe1dd39857f 100644
--- a/src/transformers/models/maskformer/image_processing_maskformer_fast.py
+++ b/src/transformers/models/maskformer/image_processing_maskformer_fast.py
@@ -391,10 +391,7 @@ def _preprocess(
         processed_images = reorder_images(processed_images_grouped, grouped_images_index)
         processed_pixel_masks = reorder_images(processed_pixel_masks_grouped, grouped_images_index)
         encoded_inputs = BatchFeature(
-            data={
-                "pixel_values": torch.stack(processed_images, dim=0) if return_tensors else processed_images,
-                "pixel_mask": torch.stack(processed_pixel_masks, dim=0) if return_tensors else processed_pixel_masks,
-            },
+            data={"pixel_values": processed_images, "pixel_mask": processed_pixel_masks},
             tensor_type=return_tensors,
         )
         if segmentation_maps is not None:
diff --git a/src/transformers/models/mobilenet_v2/image_processing_mobilenet_v2_fast.py b/src/transformers/models/mobilenet_v2/image_processing_mobilenet_v2_fast.py
index 2c8329a034c1..f29035e44422 100644
--- a/src/transformers/models/mobilenet_v2/image_processing_mobilenet_v2_fast.py
+++ b/src/transformers/models/mobilenet_v2/image_processing_mobilenet_v2_fast.py
@@ -180,7 +180,6 @@ def _preprocess(
         processed_images = reorder_images(processed_images_grouped, grouped_images_index)
 
         # Stack all processed images if return_tensors is specified
-        processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
 
         return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors)
 
diff --git a/src/transformers/models/mobilevit/image_processing_mobilevit_fast.py b/src/transformers/models/mobilevit/image_processing_mobilevit_fast.py
index 81d745c2b54d..5edb9a6dd015 100644
--- a/src/transformers/models/mobilevit/image_processing_mobilevit_fast.py
+++ b/src/transformers/models/mobilevit/image_processing_mobilevit_fast.py
@@ -182,7 +182,6 @@ def _preprocess(
         processed_images = reorder_images(processed_images_grouped, grouped_images_index)
 
         # Stack all processed images if return_tensors is specified
-        processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
 
         return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors)
 
diff --git a/src/transformers/models/nougat/image_processing_nougat_fast.py b/src/transformers/models/nougat/image_processing_nougat_fast.py
index b059688d0046..e5b60b5ffe8e 100644
--- a/src/transformers/models/nougat/image_processing_nougat_fast.py
+++ b/src/transformers/models/nougat/image_processing_nougat_fast.py
@@ -290,7 +290,6 @@ def _preprocess(
             processed_images_grouped[shape] = stacked_images
 
         processed_images = reorder_images(processed_images_grouped, grouped_images_index)
-        processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
 
         return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors)
 
diff --git a/src/transformers/models/ovis2/image_processing_ovis2_fast.py b/src/transformers/models/ovis2/image_processing_ovis2_fast.py
index ea618e073526..a8ec773ddb59 100644
--- a/src/transformers/models/ovis2/image_processing_ovis2_fast.py
+++ b/src/transformers/models/ovis2/image_processing_ovis2_fast.py
@@ -213,7 +213,6 @@ def _preprocess(
             processed_images_grouped[shape] = stacked_images
 
         processed_images = reorder_images(processed_images_grouped, grouped_images_index)
-        processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
         return BatchFeature(data={"pixel_values": processed_images, "grids": grids}, tensor_type=return_tensors)
 
 
diff --git a/src/transformers/models/owlv2/image_processing_owlv2_fast.py b/src/transformers/models/owlv2/image_processing_owlv2_fast.py
index d31173c997c4..2fda6f16cbf9 100644
--- a/src/transformers/models/owlv2/image_processing_owlv2_fast.py
+++ b/src/transformers/models/owlv2/image_processing_owlv2_fast.py
@@ -336,8 +336,6 @@ def _preprocess(
 
         processed_images = reorder_images(processed_images_grouped, grouped_images_index)
 
-        processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
-
         return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors)
 
 
diff --git a/src/transformers/models/owlv2/modular_owlv2.py b/src/transformers/models/owlv2/modular_owlv2.py
index 590fa5b4b31c..3d2012e71a6f 100644
--- a/src/transformers/models/owlv2/modular_owlv2.py
+++ b/src/transformers/models/owlv2/modular_owlv2.py
@@ -205,8 +205,6 @@ def _preprocess(
 
         processed_images = reorder_images(processed_images_grouped, grouped_images_index)
 
-        processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
-
         return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors)
 
 
diff --git a/src/transformers/models/perceiver/image_processing_perceiver_fast.py b/src/transformers/models/perceiver/image_processing_perceiver_fast.py
index 72cb17cd40cd..5f103bf03233 100644
--- a/src/transformers/models/perceiver/image_processing_perceiver_fast.py
+++ b/src/transformers/models/perceiver/image_processing_perceiver_fast.py
@@ -113,7 +113,6 @@ def _preprocess(
             processed_images_grouped[shape] = stacked_images
 
         processed_images = reorder_images(processed_images_grouped, grouped_images_index)
-        processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
 
         return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors)
 
diff --git a/src/transformers/models/perception_lm/image_processing_perception_lm_fast.py b/src/transformers/models/perception_lm/image_processing_perception_lm_fast.py
index 03ff515e63af..8c169a0b6804 100644
--- a/src/transformers/models/perception_lm/image_processing_perception_lm_fast.py
+++ b/src/transformers/models/perception_lm/image_processing_perception_lm_fast.py
@@ -307,7 +307,6 @@ def _preprocess(
             processed_images_grouped[shape] = stacked_images
         processed_images = reorder_images(processed_images_grouped, grouped_images_index)
         processed_images = [p[None] if p.ndim == 3 else p for p in processed_images]  # add tiles dimension if needed
-        processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
         return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors)
 
 
diff --git a/src/transformers/models/poolformer/image_processing_poolformer_fast.py b/src/transformers/models/poolformer/image_processing_poolformer_fast.py
index 594d076a924c..31c73fadb628 100644
--- a/src/transformers/models/poolformer/image_processing_poolformer_fast.py
+++ b/src/transformers/models/poolformer/image_processing_poolformer_fast.py
@@ -231,7 +231,6 @@ def _preprocess(
             processed_images_grouped[shape] = stacked_images
 
         processed_images = reorder_images(processed_images_grouped, grouped_images_index)
-        processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
 
         return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors)
 
diff --git a/src/transformers/models/sam/image_processing_sam_fast.py b/src/transformers/models/sam/image_processing_sam_fast.py
index fa824daee4be..efdee9e232e6 100644
--- a/src/transformers/models/sam/image_processing_sam_fast.py
+++ b/src/transformers/models/sam/image_processing_sam_fast.py
@@ -267,7 +267,6 @@ def _preprocess(
         if do_pad:
             processed_images = self.pad(processed_images, pad_size=pad_size, disable_grouping=disable_grouping)
 
-        processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
         return BatchFeature(
             data={"pixel_values": processed_images, "reshaped_input_sizes": reshaped_input_sizes},
             tensor_type=return_tensors,
diff --git a/src/transformers/models/segformer/image_processing_segformer_fast.py b/src/transformers/models/segformer/image_processing_segformer_fast.py
index d3dc35e609de..ec9a070f23b9 100644
--- a/src/transformers/models/segformer/image_processing_segformer_fast.py
+++ b/src/transformers/models/segformer/image_processing_segformer_fast.py
@@ -168,7 +168,6 @@ def _preprocess(
         processed_images = reorder_images(processed_images_grouped, grouped_images_index)
 
         # Stack images into a single tensor if return_tensors is set
-        processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
 
         return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors)
 
diff --git a/src/transformers/models/segformer/modular_segformer.py b/src/transformers/models/segformer/modular_segformer.py
index 6bbbe9ecd4fd..1fcb7d840f71 100644
--- a/src/transformers/models/segformer/modular_segformer.py
+++ b/src/transformers/models/segformer/modular_segformer.py
@@ -140,7 +140,6 @@ def _preprocess(
         processed_images = reorder_images(processed_images_grouped, grouped_images_index)
 
         # Stack images into a single tensor if return_tensors is set
-        processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
 
         return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors)
 
diff --git a/src/transformers/models/smolvlm/video_processing_smolvlm.py b/src/transformers/models/smolvlm/video_processing_smolvlm.py
index 09751486f0ae..9e08c6a85c41 100644
--- a/src/transformers/models/smolvlm/video_processing_smolvlm.py
+++ b/src/transformers/models/smolvlm/video_processing_smolvlm.py
@@ -331,7 +331,6 @@ def _preprocess(
             processed_videos = reorder_videos(processed_videos_grouped, grouped_videos_index)
             pixel_attention_mask = reorder_videos(processed_padded_mask_grouped, grouped_videos_index)
 
-        processed_videos = torch.stack(processed_videos, dim=0) if return_tensors else processed_videos
         data = {"pixel_values": processed_videos}
 
         if do_pad:
diff --git a/src/transformers/models/superglue/image_processing_superglue_fast.py b/src/transformers/models/superglue/image_processing_superglue_fast.py
index ffbcc7ce9508..f751928471ca 100644
--- a/src/transformers/models/superglue/image_processing_superglue_fast.py
+++ b/src/transformers/models/superglue/image_processing_superglue_fast.py
@@ -161,9 +161,8 @@ def _preprocess(
         stacked_pairs = [torch.stack(pair, dim=0) for pair in image_pairs]
 
         # Return in same format as slow processor
-        image_pairs = torch.stack(stacked_pairs, dim=0) if return_tensors else stacked_pairs
 
-        return BatchFeature(data={"pixel_values": image_pairs})
+        return BatchFeature(data={"pixel_values": stacked_pairs}, tensor_type=return_tensors)
 
     def post_process_keypoint_matching(
         self,
diff --git a/src/transformers/models/superpoint/image_processing_superpoint_fast.py b/src/transformers/models/superpoint/image_processing_superpoint_fast.py
index 3750441fc9f0..24638c1892ea 100644
--- a/src/transformers/models/superpoint/image_processing_superpoint_fast.py
+++ b/src/transformers/models/superpoint/image_processing_superpoint_fast.py
@@ -110,8 +110,7 @@ def _preprocess(
                 stacked_images = self.rescale(stacked_images, rescale_factor)
             processed_images_grouped[shape] = stacked_images
         processed_images = reorder_images(processed_images_grouped, grouped_images_index)
-        processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
-        return BatchFeature(data={"pixel_values": processed_images})
+        return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors)
 
     def post_process_keypoint_detection(
         self, outputs: "SuperPointKeypointDescriptionOutput", target_sizes: Union[TensorType, list[tuple]]
diff --git a/src/transformers/models/swin2sr/image_processing_swin2sr_fast.py b/src/transformers/models/swin2sr/image_processing_swin2sr_fast.py
index f85c124041bb..82fe6b71ee38 100644
--- a/src/transformers/models/swin2sr/image_processing_swin2sr_fast.py
+++ b/src/transformers/models/swin2sr/image_processing_swin2sr_fast.py
@@ -97,7 +97,6 @@ def _preprocess(
                 stacked_images = self.pad(stacked_images, size_divisor=size_divisor)
             processed_image_grouped[shape] = stacked_images
         processed_images = reorder_images(processed_image_grouped, grouped_images_index)
-        processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
 
         return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors)
 
diff --git a/src/transformers/models/textnet/image_processing_textnet_fast.py b/src/transformers/models/textnet/image_processing_textnet_fast.py
index eba6e14e64bc..574aafba4d3e 100644
--- a/src/transformers/models/textnet/image_processing_textnet_fast.py
+++ b/src/transformers/models/textnet/image_processing_textnet_fast.py
@@ -137,7 +137,6 @@ def _preprocess(
             processed_images_grouped[shape] = stacked_images
 
         processed_images = reorder_images(processed_images_grouped, grouped_images_index)
-        processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
 
         return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors)
 
diff --git a/src/transformers/models/vitmatte/image_processing_vitmatte_fast.py b/src/transformers/models/vitmatte/image_processing_vitmatte_fast.py
index 54f54d18cc89..b8719d9e358d 100644
--- a/src/transformers/models/vitmatte/image_processing_vitmatte_fast.py
+++ b/src/transformers/models/vitmatte/image_processing_vitmatte_fast.py
@@ -152,7 +152,6 @@ def _preprocess(
             processed_images_grouped[shape] = stacked_images
 
         processed_images = reorder_images(processed_images_grouped, grouped_images_index)
-        processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
 
         return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors)
 
diff --git a/src/transformers/models/vitpose/image_processing_vitpose_fast.py b/src/transformers/models/vitpose/image_processing_vitpose_fast.py
index ec5fadbfd6c1..c9dcb959431a 100644
--- a/src/transformers/models/vitpose/image_processing_vitpose_fast.py
+++ b/src/transformers/models/vitpose/image_processing_vitpose_fast.py
@@ -156,7 +156,6 @@ def _preprocess(
         processed_images = reorder_images(processed_images_grouped, grouped_images_index)
 
         # Stack into batch tensor
-        processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
 
         return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors)
 
diff --git a/src/transformers/models/zoedepth/image_processing_zoedepth_fast.py b/src/transformers/models/zoedepth/image_processing_zoedepth_fast.py
index 852ee161aff1..facc7d744d39 100644
--- a/src/transformers/models/zoedepth/image_processing_zoedepth_fast.py
+++ b/src/transformers/models/zoedepth/image_processing_zoedepth_fast.py
@@ -171,9 +171,7 @@ def _preprocess(
             if do_normalize:
                 stacked_images = self.normalize(stacked_images, image_mean, image_std)
             resized_images_grouped[shape] = stacked_images
-        resized_images = reorder_images(resized_images_grouped, grouped_images_index)
-
-        processed_images = torch.stack(resized_images, dim=0) if return_tensors else resized_images
+        processed_images = reorder_images(resized_images_grouped, grouped_images_index)
 
         return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors)
 
diff --git a/src/transformers/video_processing_utils.py b/src/transformers/video_processing_utils.py
index d73bbce889f1..094e526d5d8b 100644
--- a/src/transformers/video_processing_utils.py
+++ b/src/transformers/video_processing_utils.py
@@ -442,7 +442,6 @@ def _preprocess(
             processed_videos_grouped[shape] = stacked_videos
 
         processed_videos = reorder_videos(processed_videos_grouped, grouped_videos_index)
-        processed_videos = torch.stack(processed_videos, dim=0) if return_tensors else processed_videos
 
         return BatchFeature(data={"pixel_values_videos": processed_videos}, tensor_type=return_tensors)
 
diff --git a/tests/models/dac/test_feature_extraction_dac.py b/tests/models/dac/test_feature_extraction_dac.py
index c995485d3311..d71cb0370895 100644
--- a/tests/models/dac/test_feature_extraction_dac.py
+++ b/tests/models/dac/test_feature_extraction_dac.py
@@ -207,7 +207,7 @@ def test_truncation_and_padding(self):
         # force no pad
         with self.assertRaisesRegex(
             ValueError,
-            "^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$",
+            r"Unable to convert output[\s\S]*padding=True",
         ):
             truncated_outputs = feature_extractor(input_audio, padding=False, return_tensors="pt").input_values
 
diff --git a/tests/models/dia/test_feature_extraction_dia.py b/tests/models/dia/test_feature_extraction_dia.py
index 9a6f797d5346..990e6ead9c59 100644
--- a/tests/models/dia/test_feature_extraction_dia.py
+++ b/tests/models/dia/test_feature_extraction_dia.py
@@ -223,7 +223,7 @@ def test_truncation_and_padding(self):
         # force no pad
         with self.assertRaisesRegex(
             ValueError,
-            "^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$",
+            r"Unable to convert output[\s\S]*padding=True",
         ):
             truncated_outputs = feature_extractor(input_audio, padding=False, return_tensors="pt").input_values
 
diff --git a/tests/models/encodec/test_feature_extraction_encodec.py b/tests/models/encodec/test_feature_extraction_encodec.py
index 2823b0099372..c3850dd71358 100644
--- a/tests/models/encodec/test_feature_extraction_encodec.py
+++ b/tests/models/encodec/test_feature_extraction_encodec.py
@@ -221,7 +221,7 @@ def test_truncation_and_padding(self):
         # force no pad
         with self.assertRaisesRegex(
             ValueError,
-            "^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$",
+            r"Unable to convert output[\s\S]*padding=True",
         ):
             truncated_outputs = feature_extractor(input_audio, padding=False, return_tensors="pt").input_values
 
@@ -232,7 +232,7 @@ def test_truncation_and_padding(self):
         feature_extractor.chunk_length_s = None
         with self.assertRaisesRegex(
             ValueError,
-            "^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$",
+            r"Unable to convert output[\s\S]*padding=True",
         ):
             truncated_outputs = feature_extractor(input_audio, padding=False, return_tensors="pt").input_values
 
@@ -244,7 +244,7 @@ def test_truncation_and_padding(self):
         feature_extractor.overlap = None
         with self.assertRaisesRegex(
             ValueError,
-            "^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$",
+            r"Unable to convert output[\s\S]*padding=True",
         ):
             truncated_outputs = feature_extractor(input_audio, padding=False, return_tensors="pt").input_values
 
diff --git a/tests/models/owlv2/test_image_processing_owlv2.py b/tests/models/owlv2/test_image_processing_owlv2.py
index 0299404a75b7..eef7d5771522 100644
--- a/tests/models/owlv2/test_image_processing_owlv2.py
+++ b/tests/models/owlv2/test_image_processing_owlv2.py
@@ -127,7 +127,7 @@ def test_image_processor_integration_test(self):
             pixel_values = processor(image, return_tensors="pt").pixel_values
 
             mean_value = round(pixel_values.mean().item(), 4)
-            self.assertEqual(mean_value, 0.2353)
+            self.assertEqual(mean_value, -0.2303)
 
     @slow
     def test_image_processor_integration_test_resize(self):
diff --git a/tests/utils/test_feature_extraction_utils.py b/tests/utils/test_feature_extraction_utils.py
index b0a6a193d10d..01e511bc289a 100644
--- a/tests/utils/test_feature_extraction_utils.py
+++ b/tests/utils/test_feature_extraction_utils.py
@@ -20,9 +20,12 @@
 from pathlib import Path
 
 import httpx
+import numpy as np
 
 from transformers import AutoFeatureExtractor, Wav2Vec2FeatureExtractor
-from transformers.testing_utils import TOKEN, TemporaryHubRepo, get_tests_dir, is_staging_test
+from transformers.feature_extraction_utils import BatchFeature
+from transformers.testing_utils import TOKEN, TemporaryHubRepo, get_tests_dir, is_staging_test, require_torch
+from transformers.utils import is_torch_available
 
 
 sys.path.append(str(Path(__file__).parent.parent.parent / "utils"))
@@ -30,9 +33,143 @@
 from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402
 
 
+if is_torch_available():
+    import torch
+
+
 SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR = get_tests_dir("fixtures")
 
 
+class BatchFeatureTester(unittest.TestCase):
+    """Tests for the BatchFeature class and tensor conversion."""
+
+    def test_batch_feature_basic_access_and_no_conversion(self):
+        """Test basic dict/attribute access and no conversion when tensor_type=None."""
+        data = {"input_values": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}
+        batch = BatchFeature(data)
+
+        # Dict-style and attribute-style access
+        self.assertEqual(batch["input_values"], [[1, 2, 3], [4, 5, 6]])
+        self.assertEqual(batch.labels, [0, 1])
+
+        # No conversion without tensor_type
+        self.assertIsInstance(batch["input_values"], list)
+
+    @require_torch
+    def test_batch_feature_numpy_conversion(self):
+        """Test conversion to numpy arrays from lists and existing numpy arrays."""
+        # From lists
+        batch = BatchFeature({"input_values": [[1, 2, 3], [4, 5, 6]]}, tensor_type="np")
+        self.assertIsInstance(batch["input_values"], np.ndarray)
+        self.assertEqual(batch["input_values"].shape, (2, 3))
+
+        # From numpy arrays (should remain numpy)
+        numpy_data = np.array([[1, 2, 3], [4, 5, 6]])
+        batch_arrays = BatchFeature({"input_values": numpy_data}, tensor_type="np")
+        np.testing.assert_array_equal(batch_arrays["input_values"], numpy_data)
+
+        # From list of numpy arrays with same shape should stack
+        numpy_data = [np.array([[1, 2, 3], [4, 5, 6]]), np.array([[7, 8, 9], [10, 11, 12]])]
+        batch_stacked = BatchFeature({"input_values": numpy_data}, tensor_type="np")
+        np.testing.assert_array_equal(
+            batch_stacked["input_values"], np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])
+        )
+
+        # from tensor
+        tensor = torch.tensor([[1, 2, 3], [4, 5, 6]])
+        batch_tensor = BatchFeature({"input_values": tensor}, tensor_type="np")
+        np.testing.assert_array_equal(batch_tensor["input_values"], tensor.numpy())
+
+        # from list of tensors with same shape should stack
+        tensors = [torch.tensor([[1, 2, 3], [4, 5, 6]]), torch.tensor([[7, 8, 9], [10, 11, 12]])]
+        batch_stacked = BatchFeature({"input_values": tensors}, tensor_type="np")
+        self.assertIsInstance(batch_stacked["input_values"], np.ndarray)
+        np.testing.assert_array_equal(
+            batch_stacked["input_values"], np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])
+        )
+
+    @require_torch
+    def test_batch_feature_pytorch_conversion(self):
+        """Test conversion to PyTorch tensors from various input types."""
+        # From lists
+        batch = BatchFeature({"input_values": [[1, 2, 3], [4, 5, 6]]}, tensor_type="pt")
+        self.assertIsInstance(batch["input_values"], torch.Tensor)
+        self.assertEqual(batch["input_values"].shape, (2, 3))
+
+        # from tensor (should be returned as-is)
+        tensor = torch.tensor([[1, 2, 3], [4, 5, 6]])
+        batch_tensor = BatchFeature({"input_values": tensor}, tensor_type="pt")
+        torch.testing.assert_close(batch_tensor["input_values"], tensor)
+
+        # From numpy arrays
+        batch_numpy = BatchFeature({"input_values": np.array([[1, 2]])}, tensor_type="pt")
+        self.assertIsInstance(batch_numpy["input_values"], torch.Tensor)
+
+        # List of same-shape tensors should stack
+        tensors = [torch.randn(3, 10, 10) for _ in range(3)]
+        batch_stacked = BatchFeature({"pixel_values": tensors}, tensor_type="pt")
+        self.assertEqual(batch_stacked["pixel_values"].shape, (3, 3, 10, 10))
+
+        # List of same-shape numpy arrays should stack
+        numpy_arrays = [np.random.randn(3, 10, 10) for _ in range(3)]
+        batch_stacked = BatchFeature({"pixel_values": numpy_arrays}, tensor_type="pt")
+        self.assertIsInstance(batch_stacked["pixel_values"], torch.Tensor)
+        self.assertEqual(batch_stacked["pixel_values"].shape, (3, 3, 10, 10))
+
+    @require_torch
+    def test_batch_feature_error_handling(self):
+        """Test clear error messages for common conversion failures."""
+        # Ragged tensors (different shapes)
+        data_ragged = {"values": [torch.randn(3, 224, 224), torch.randn(3, 448, 448)]}
+        with self.assertRaises(ValueError) as context:
+            BatchFeature(data_ragged, tensor_type="pt")
+        error_msg = str(context.exception)
+        self.assertIn("stack expects each tensor to be equal size", error_msg.lower())
+        self.assertIn("return_tensors=None", error_msg)
+
+        # Ragged numpy arrays (different shapes)
+        data_ragged = {"values": [np.random.randn(3, 224, 224), np.random.randn(3, 448, 448)]}
+        with self.assertRaises(ValueError) as context:
+            BatchFeature(data_ragged, tensor_type="np")
+        error_msg = str(context.exception)
+        self.assertIn("inhomogeneous", error_msg.lower())
+        self.assertIn("return_tensors=None", error_msg)
+
+        # Unconvertible type (dict)
+        data_dict = {"values": [[1, 2]], "metadata": {"key": "val"}}
+        with self.assertRaises(ValueError) as context:
+            BatchFeature(data_dict, tensor_type="pt")
+        self.assertIn("metadata", str(context.exception))
+
+    @require_torch
+    def test_batch_feature_skip_tensor_conversion(self):
+        """Test skip_tensor_conversion parameter for metadata fields."""
+        import torch
+
+        data = {"pixel_values": [[1, 2, 3]], "num_crops": [1, 2], "sizes": [(224, 224)]}
+        batch = BatchFeature(data, tensor_type="pt", skip_tensor_conversion=["num_crops", "sizes"])
+
+        # pixel_values should be converted
+        self.assertIsInstance(batch["pixel_values"], torch.Tensor)
+        # num_crops and sizes should remain as lists
+        self.assertIsInstance(batch["num_crops"], list)
+        self.assertIsInstance(batch["sizes"], list)
+
+    @require_torch
+    def test_batch_feature_convert_to_tensors_method(self):
+        """Test convert_to_tensors method can be called after initialization."""
+        import torch
+
+        data = {"input_values": [[1, 2, 3]], "metadata": [1, 2]}
+        batch = BatchFeature(data)  # No conversion initially
+        self.assertIsInstance(batch["input_values"], list)
+
+        # Convert with skip parameter
+        batch.convert_to_tensors(tensor_type="pt", skip_tensor_conversion=["metadata"])
+        self.assertIsInstance(batch["input_values"], torch.Tensor)
+        self.assertIsInstance(batch["metadata"], list)
+
+
 class FeatureExtractorUtilTester(unittest.TestCase):
     def test_cached_files_are_used_when_internet_is_down(self):
         # A mock response for an HTTP head request to emulate server down