diff --git a/vllm/model_executor/models/aria.py b/vllm/model_executor/models/aria.py
index 8ae1680a71f3..e1368a3f6478 100644
--- a/vllm/model_executor/models/aria.py
+++ b/vllm/model_executor/models/aria.py
@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 from collections.abc import Iterable, Mapping, Sequence
-from typing import Optional, TypedDict, Union
+from typing import Annotated, Optional, Union
 
 import torch
 import torch.nn as nn
@@ -29,6 +29,7 @@ from vllm.multimodal.processing import (BaseMultiModalProcessor,
                                         PromptUpdate)
 from vllm.multimodal.profiling import BaseDummyInputsBuilder
 from vllm.sequence import IntermediateTensors
+from vllm.utils.tensor_schema import TensorSchema, TensorShape
 
 # yapf: disable
 from .idefics2_vision_model import Idefics2VisionConfig
@@ -42,15 +43,26 @@ from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn,
                     merge_multimodal_embeddings)
 
 
-class AriaImagePixelInputs(TypedDict):
-    pixel_values: torch.Tensor
-    pixel_mask: Optional[torch.Tensor]
+class AriaImagePixelInputs(TensorSchema):
     """
-    Shape:
-        pixel_values: `(batch_size * num_images, num_channels, height, width)`
-        pixel_mask: `(batch_size * num_images, height, width)`
+    Dimensions:
+        - b: Batch size
+        - n: Number of images
+        - c: Number of channels
+        - h: Height of each image
+        - w: Width of each image
     """
 
+    pixel_values: Annotated[
+        torch.Tensor,
+        TensorShape("bn", 3, "h", "w"),
+    ]
+
+    pixel_mask: Annotated[
+        Optional[torch.Tensor],
+        TensorShape("bn", "h", "w"),
+    ]
+
 
 class AriaVisionTransformer(Idefics3VisionTransformer, SupportsQuant):
     packed_modules_mapping = {"qkv_proj": ["q_proj", "k_proj", "v_proj"]}
@@ -540,12 +552,6 @@ class AriaForConditionalGeneration(nn.Module, SupportsMultiModal):
         self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
                                                 self.vocab_size, logit_scale)
 
-    def _validate_image_sizes(
-            self, images: list[torch.Tensor]) -> list[torch.Tensor]:
-        if not all(img.shape == images[0].shape for img in images):
-            raise ValueError("All images must be the same size")
-        return images
-
     def _parse_and_validate_image_input(
             self, **kwargs: object) -> Optional[AriaImagePixelInputs]:
         pixel_values = kwargs.pop("pixel_values", None)
@@ -554,23 +560,9 @@ class AriaForConditionalGeneration(nn.Module, SupportsMultiModal):
         if pixel_values is None:
             return None
 
-        if not isinstance(pixel_values, (torch.Tensor, list)):
-            raise ValueError("Incorrect type of pixel values. "
-                             f"Got type: {type(pixel_values)}")
-
-        pixel_values = self._validate_image_sizes(pixel_values)
-        pixel_values = flatten_bn(pixel_values, concat=True)
-
-        if pixel_mask is not None:
-            if not isinstance(pixel_mask, (torch.Tensor, list)):
-                raise ValueError("Incorrect type of pixel mask. "
-                                 f"Got type: {type(pixel_mask)}")
-
-            pixel_mask = flatten_bn(pixel_mask, concat=True)
-
         return AriaImagePixelInputs(
-            pixel_values=pixel_values,
-            pixel_mask=pixel_mask,
+            pixel_values=flatten_bn(pixel_values, concat=True),
+            pixel_mask=flatten_bn(pixel_mask, concat=True),
         )
 
     def _create_patch_attention_mask(