diff --git a/vllm/model_executor/models/qwen2_vl.py b/vllm/model_executor/models/qwen2_vl.py
index af263262bd239..0e820cf123139 100644
--- a/vllm/model_executor/models/qwen2_vl.py
+++ b/vllm/model_executor/models/qwen2_vl.py
@@ -22,8 +22,8 @@
 # limitations under the License.
 """Inference-only Qwen2-VL model compatible with HuggingFace weights."""
 from functools import partial
-from typing import (Any, Callable, Iterable, List, Literal, Mapping, Optional,
-                    Tuple, Type, TypedDict, Union)
+from typing import (Any, Callable, Dict, Iterable, List, Literal, Mapping,
+                    Optional, Tuple, Type, TypedDict, Union)
 
 import torch
 import torch.nn as nn
@@ -558,6 +558,17 @@ def forward(
 # === Vision input helpers === #
 
 
+def get_mm_processor_kwargs(
+        min_pixels: Optional[int] = None,
+        max_pixels: Optional[int] = None) -> Dict[str, int]:
+    mm_processor_kwargs = {}
+    if min_pixels:
+        mm_processor_kwargs["min_pixels"] = min_pixels
+    if max_pixels:
+        mm_processor_kwargs["max_pixels"] = max_pixels
+    return mm_processor_kwargs
+
+
 def mm_input_mapper_for_qwen2_vl(
     ctx: InputContext,
     data: MultiModalData[object],
@@ -575,12 +586,8 @@ def mm_input_mapper_for_qwen2_vl(
     model_config = ctx.model_config
     # Handle mm processor kwargs; we pass these at creation time
     # because preprocess() in transformers doesn't expose them
-    mm_processor_kwargs = {}
-    if min_pixels:
-        mm_processor_kwargs["min_pixels"] = min_pixels
-    if max_pixels:
-        mm_processor_kwargs["max_pixels"] = max_pixels
-
+    mm_processor_kwargs = get_mm_processor_kwargs(min_pixels=min_pixels,
+                                                  max_pixels=max_pixels)
     image_processor = cached_get_image_processor(
         model_config.model,
         trust_remote_code=model_config.trust_remote_code,
@@ -683,7 +690,10 @@ def get_max_qwen2_vl_mm_tokens(ctx: InputContext,
                                *,
                                min_pixels=None,
                                max_pixels=None) -> int:
-    image_processor = cached_get_image_processor(ctx.model_config.model)
+    mm_processor_kwargs = get_mm_processor_kwargs(min_pixels=min_pixels,
+                                                  max_pixels=max_pixels)
+    image_processor = cached_get_image_processor(ctx.model_config.model,
+                                                 **mm_processor_kwargs)
     max_resized_height, max_resized_width, max_llm_image_tokens = \
         _get_max_image_info(image_processor, data_type_key=data_type_key,
                             mm_count=1, min_pixels=min_pixels,
@@ -705,7 +715,10 @@ def dummy_data_for_qwen2_vl(
     min_pixels: Optional[int] = None,
     max_pixels: Optional[int] = None
 ) -> Tuple[SequenceData, Optional[MultiModalDataDict]]:
-    image_processor = cached_get_image_processor(ctx.model_config.model)
+    mm_processor_kwargs = get_mm_processor_kwargs(min_pixels=min_pixels,
+                                                  max_pixels=max_pixels)
+    image_processor = cached_get_image_processor(ctx.model_config.model,
+                                                 **mm_processor_kwargs)
 
     num_images = mm_counts["image"]
     max_resized_height, max_resized_width, max_llm_image_tokens = \