diff --git a/optimum/intel/openvino/modeling_visual_language.py b/optimum/intel/openvino/modeling_visual_language.py
index 1c0e35cca..eb94afadc 100644
--- a/optimum/intel/openvino/modeling_visual_language.py
+++ b/optimum/intel/openvino/modeling_visual_language.py
@@ -695,8 +695,10 @@ def forward(
         image_grid_thw=None,
         video_grid_thw=None,
         rope_deltas=None,
+        images=None,
         **kwargs,
     ):
+        pixel_values = pixel_values if pixel_values is not None else images
         inputs_embeds, attention_mask, position_ids = self.get_multimodal_embeddings(
             input_ids,
             pixel_values,
@@ -794,6 +796,9 @@ def prepare_inputs_for_generation(
         else:
             model_inputs = {"input_ids": input_ids}
 
+        if pixel_values is None:
+            pixel_values = kwargs.get("images")
+
         model_inputs.update(
             {
                 "position_ids": position_ids,
@@ -1733,6 +1738,8 @@ def get_multimodal_embeddings(
         vision_embeds = None
         IGNORE_INDEX = -100
         IMAGE_TOKEN_INDEX = -200
+        if pixel_values is None and "images" in kwargs:
+            pixel_values = kwargs["images"]
         if pixel_values is not None:
             vision_embeds = self.get_vision_embeddings(pixel_values, input_ids=input_ids, **kwargs)
         if vision_embeds is None:
@@ -1907,7 +1914,7 @@ def preprocess_inputs(
         attention_mask = torch.ones_like(input_ids, dtype=torch.int64)
         result = {"input_ids": input_ids, "attention_mask": attention_mask}
         if image is not None:
-            result["pixel_values"] = processor(images=[image], return_tensors="pt")["pixel_values"]
+            result["images"] = processor(images=[image], return_tensors="pt")["pixel_values"]
         return result
 
 
diff --git a/tests/openvino/test_modeling.py b/tests/openvino/test_modeling.py
index 57d4b6476..abe3d7d36 100644
--- a/tests/openvino/test_modeling.py
+++ b/tests/openvino/test_modeling.py
@@ -2182,11 +2182,7 @@ def test_compare_to_transformers(self, model_arch):
         ov_model.clear_requests()
         self._check_device_and_request(ov_model, test_device, False)
 
-        # nanollava pixel_values input named as images
-        if model_arch == "nanollava":
-            pixel_values = transformers_inputs.pop("pixel_values", None)
-            transformers_inputs["images"] = pixel_values
-        # pytorch minicpmv is not designed to be used via forward
+        # pytorch minicpmv and internvl2 are not designed to be used via forward
         if model_arch not in ["minicpmv", "internvl2"]:
             set_seed(SEED)
             ov_outputs = ov_model(**inputs)