diff --git a/vllm/model_executor/models/qwen_vl.py b/vllm/model_executor/models/qwen_vl.py
index 2950ca664a98f..90200f319464b 100644
--- a/vllm/model_executor/models/qwen_vl.py
+++ b/vllm/model_executor/models/qwen_vl.py
@@ -11,7 +11,7 @@ import math
 import unicodedata
 from collections.abc import Collection, Mapping, Sequence, Set
 from functools import lru_cache, partial
-from typing import Callable, Literal, Optional, TypedDict, Union
+from typing import Annotated, Callable, Literal, Optional, Union
 
 import regex as re
 import torch
@@ -40,6 +40,7 @@ from vllm.multimodal.processing import (BaseMultiModalProcessor,
                                         PromptUpdate, PromptUpdateDetails)
 from vllm.multimodal.profiling import BaseDummyInputsBuilder
 from vllm.sequence import IntermediateTensors
+from vllm.utils.tensor_schema import TensorSchema, TensorShape
 
 from .interfaces import (MultiModalEmbeddings, SupportsLoRA,
                          SupportsMultiModal, SupportsPP)
@@ -47,26 +48,34 @@ from .qwen import QWenBaseModel, QWenModel
 from .utils import flatten_bn, merge_multimodal_embeddings
 
 
-class QwenImagePixelInputs(TypedDict):
-    type: Literal["pixel_values"]
-    data: torch.Tensor
+class QwenImagePixelInputs(TensorSchema):
     """
-    Shape: `(batch_size * num_images, 3, image_size, image_size)`
-
+    Dimensions:
+        - bn: Batch size * number of images
+        - c: Number of channels (3)
+        - h: Height
+        - w: Width
+    
     Note that image_size is the value in the vision config to which we resize
     the image to in the normalization transform. Currently multi-image support
     can only be leveraged by passing image embeddings directly.
     """
+    type: Literal["pixel_values"] = "pixel_values"
+    data: Annotated[torch.Tensor, TensorShape("bn", 3, "h", "w")]
 
 
-class QwenImageEmbeddingInputs(TypedDict):
-    type: Literal["image_embeds"]
-    data: torch.Tensor
-    """Shape: `(batch_size * num_images, 256, hidden_size)`
-
+class QwenImageEmbeddingInputs(TensorSchema):
+    """
+    Dimensions:
+        - bn: Batch size * number of images
+        - ifs: Image feature size (256)
+        - hs: Hidden size
+    
     `hidden_size` must match the hidden size of the language model backbone
     and is stored in the visual config of the model if we have one.
     """
+    type: Literal["image_embeds"] = "image_embeds"
+    data: Annotated[torch.Tensor, TensorShape("bn", 256, "hs")]
 
 
 QwenImageInputs = Union[QwenImagePixelInputs, QwenImageEmbeddingInputs]
@@ -697,19 +706,6 @@ class QwenVLForConditionalGeneration(QWenBaseModel, SupportsPP, SupportsLoRA,
 
         self.transformer: QwenVLModel
 
-    def _validate_pixel_values(self, data: torch.Tensor) -> torch.Tensor:
-        h = w = self.config.visual["image_size"]
-        expected_dims = (3, h, w)
-        actual_dims = tuple(data.shape[1:])
-
-        if actual_dims != expected_dims:
-            expected_expr = ("batch_size", *map(str, expected_dims))
-            raise ValueError(
-                f"The expected shape of pixel values is {expected_expr}. "
-                f"You supplied {tuple(data.shape)}.")
-
-        return data
-
     def _parse_and_validate_image_input(
             self, **kwargs: object) -> Optional[QwenImageInputs]:
         pixel_values = kwargs.pop("pixel_values", None)
@@ -720,10 +716,13 @@ class QwenVLForConditionalGeneration(QWenBaseModel, SupportsPP, SupportsLoRA,
                 raise ValueError("Incorrect type of pixel values. "
                                  f"Got type: {type(pixel_values)}")
 
+            expected_h = expected_w = self.config.visual["image_size"]
+            resolve_bindings = {"h": expected_h, "w": expected_w}
+
             return QwenImagePixelInputs(
                 type="pixel_values",
-                data=self._validate_pixel_values(
-                    flatten_bn(pixel_values, concat=True)),
+                data=flatten_bn(pixel_values, concat=True),
+                resolve_bindings=resolve_bindings,
             )
 
         if image_embeds is not None: