diff --git a/vllm/model_executor/models/blip2.py b/vllm/model_executor/models/blip2.py
index 27a9208107871..8e3505f872eb2 100644
--- a/vllm/model_executor/models/blip2.py
+++ b/vllm/model_executor/models/blip2.py
@@ -2,7 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 from collections.abc import Iterable, Mapping, Sequence
-from typing import Literal, Optional, TypedDict, Union
+from typing import Annotated, Literal, Optional, Union
 
 import torch
 import torch.nn as nn
@@ -22,6 +22,7 @@ from vllm.multimodal.processing import (BaseMultiModalProcessor,
                                         PromptInsertion, PromptUpdate)
 from vllm.multimodal.profiling import BaseDummyInputsBuilder
 from vllm.sequence import IntermediateTensors
+from vllm.utils.tensor_schema import TensorSchema, TensorShape
 
 from .blip import BlipVisionModel
 from .interfaces import (MultiModalEmbeddings, SupportsMultiModal, SupportsPP,
@@ -34,19 +35,27 @@ from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
 _IMAGE_TOKEN_ID = 50265
 
 
-class Blip2ImagePixelInputs(TypedDict):
-    type: Literal["pixel_values"]
-    data: torch.Tensor
-    """Shape: `(batch_size * num_images, num_channels, height, width)`"""
-
-
-class Blip2ImageEmbeddingInputs(TypedDict):
-    type: Literal["image_embeds"]
-    data: torch.Tensor
-    """Shape: `(batch_size * num_images, image_feature_size, hidden_size)`
-
-    `hidden_size` must match the hidden size of language model backbone.
+class Blip2ImagePixelInputs(TensorSchema):
     """
+    Dimensions:
+        - bn: Batch size * number of images
+        - c: Number of channels (3)
+        - h: Height of each image
+        - w: Width of each image
+    """
+    type: Literal["pixel_values"]
+    data: Annotated[torch.Tensor, TensorShape("bn", 3, "h", "w")]
+
+
+class Blip2ImageEmbeddingInputs(TensorSchema):
+    """
+    Dimensions:
+        - bn: Batch size * number of images
+        - f: Image feature size
+        - h: Hidden size (must match the hidden size of language model backbone)
+    """
+    type: Literal["image_embeds"]
+    data: Annotated[torch.Tensor, TensorShape("bn", "f", "h")]
 
 
 Blip2ImageInputs = Union[Blip2ImagePixelInputs, Blip2ImageEmbeddingInputs]
@@ -551,21 +560,8 @@ class Blip2ForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP,
         self.make_empty_intermediate_tensors = (
             self.language_model.make_empty_intermediate_tensors)
 
-    def _validate_pixel_values(self, data: torch.Tensor) -> torch.Tensor:
-        h = w = self.config.vision_config.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[Blip2ImageInputs]:
+    def _create_image_input(self,
+                            **kwargs: object) -> Optional[Blip2ImageInputs]:
         pixel_values = kwargs.pop("pixel_values", None)
         image_embeds = kwargs.pop("image_embeds", None)
 
@@ -573,27 +569,19 @@ class Blip2ForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP,
             return None
 
         if pixel_values is not None:
-            if not isinstance(pixel_values, (torch.Tensor, list)):
-                raise ValueError("Incorrect type of pixel values. "
-                                 f"Got type: {type(pixel_values)}")
-
-            pixel_values = flatten_bn(pixel_values, concat=True)
-
-            return Blip2ImagePixelInputs(
-                type="pixel_values",
-                data=self._validate_pixel_values(pixel_values),
-            )
+            expected_h = expected_w = self.config.vision_config.image_size
+            return Blip2ImagePixelInputs(type="pixel_values",
+                                         data=flatten_bn(pixel_values,
+                                                         concat=True),
+                                         resolve_bindings={
+                                             "h": expected_h,
+                                             "w": expected_w
+                                         })
 
         if image_embeds is not None:
-            if not isinstance(image_embeds, (torch.Tensor, list)):
-                raise ValueError("Incorrect type of image embeddings. "
-                                 f"Got type: {type(image_embeds)}")
-
-            image_embeds = flatten_bn(image_embeds, concat=True)
-
             return Blip2ImageEmbeddingInputs(
                 type="image_embeds",
-                data=image_embeds,
+                data=flatten_bn(image_embeds, concat=True),
             )
 
         raise AssertionError("This line should be unreachable.")