diff --git a/tests/models/decoder_only/vision_language/mm_processor_kwargs/test_internvl.py b/tests/models/decoder_only/vision_language/mm_processor_kwargs/test_internvl.py
new file mode 100644
index 00000000..af0c2aa2
--- /dev/null
+++ b/tests/models/decoder_only/vision_language/mm_processor_kwargs/test_internvl.py
@@ -0,0 +1,206 @@
+"""Tests for InternVL's multimodal preprocessing kwargs."""
+from typing import Callable, Optional
+
+import pytest
+from transformers import AutoTokenizer
+
+from vllm.inputs import InputContext, token_inputs
+from vllm.multimodal import MultiModalRegistry
+
+from .....conftest import _ImageAssets
+from ....utils import build_model_context
+
+models = ["OpenGVLab/InternVL2-2B"]
+
+
+# Wrap lazy imports to avoid initializing CUDA during test collection
+@pytest.fixture()
+def input_processor_for_internvl():
+    from vllm.model_executor.models.internvl import InternVLInputPipeline
+
+    pipeline = InternVLInputPipeline('<img>', '</img>', '<IMG_CONTEXT>')
+    return pipeline.input_processor
+
+
+@pytest.fixture()
+def dummy_data_for_internvl():
+    from vllm.model_executor.models.internvl import InternVLInputPipeline
+
+    pipeline = InternVLInputPipeline('<img>', '</img>', '<IMG_CONTEXT>')
+    return pipeline.dummy_data
+
+
+@pytest.fixture()
+def get_max_internvl_image_tokens():
+    from vllm.model_executor.models.internvl import (
+        get_max_internvl_image_tokens)
+    return get_max_internvl_image_tokens
+
+
+@pytest.mark.parametrize("model", models)
+@pytest.mark.parametrize("max_dynamic_patch", [1, 4])
+@pytest.mark.parametrize("dynamic_image_size", [True, False, None])
+def test_input_mapper_override(
+    model: str,
+    image_assets: _ImageAssets,
+    max_dynamic_patch: int,
+    dynamic_image_size: Optional[bool],
+):
+    mm_processor_kwargs = {
+        "max_dynamic_patch": max_dynamic_patch,
+    }
+    if dynamic_image_size is not None:
+        mm_processor_kwargs["dynamic_image_size"] = dynamic_image_size
+
+    expected_num_patches = max_dynamic_patch + 1 if max_dynamic_patch > 1 else 1
+    if dynamic_image_size is False:
+        expected_num_patches = 1
+
+    ctx = build_model_context(
+        model_name=model,
+        tokenizer_name=model,
+        trust_remote_code=True,
+        mm_processor_kwargs=mm_processor_kwargs,
+    )
+
+    mm_registry = MultiModalRegistry()
+    mm_registry.init_mm_limits_per_prompt(ctx.model_config)
+
+    image = image_assets[0].pil_image.resize((448 * 2, 448 * 2))
+    vllm_result = mm_registry.map_input(
+        ctx.model_config,
+        {"image": image},
+    )
+    assert vllm_result["pixel_values"].size(1) == expected_num_patches
+
+
+@pytest.mark.parametrize("model", models)
+@pytest.mark.parametrize("max_dynamic_patch", [1, 4, None])
+@pytest.mark.parametrize("dynamic_image_size", [True, False, None])
+def test_max_tokens_override(
+    get_max_internvl_image_tokens: Callable,
+    model: str,
+    max_dynamic_patch: Optional[int],
+    dynamic_image_size: Optional[bool],
+):
+    """Ensure get_max_internvl_image_tokens handles mm_processor_kwargs."""
+    ctx = build_model_context(
+        model_name=model,
+        tokenizer_name=model,
+        trust_remote_code=True,
+        mm_processor_kwargs=None,
+    )
+
+    if max_dynamic_patch is None:
+        max_dynamic_patch = ctx.get_hf_config().max_dynamic_patch
+    expected_num_patches = max_dynamic_patch + 1 if max_dynamic_patch > 1 else 1
+    if dynamic_image_size is False:
+        expected_num_patches = 1
+    expected_max_tokens = 256 * expected_num_patches
+
+    actual_max_tokens = get_max_internvl_image_tokens(
+        ctx=InputContext(ctx.model_config),
+        max_dynamic_patch=max_dynamic_patch,
+        dynamic_image_size=dynamic_image_size,
+    )
+    assert expected_max_tokens == actual_max_tokens
+
+
+@pytest.mark.parametrize("model", models)
+@pytest.mark.parametrize("num_imgs", [1, 2])
+@pytest.mark.parametrize("max_dynamic_patch", [1, 4, None])
+@pytest.mark.parametrize("dynamic_image_size", [True, False, None])
+def test_dummy_data_override(
+    dummy_data_for_internvl: Callable,
+    model: str,
+    num_imgs: int,
+    max_dynamic_patch: Optional[int],
+    dynamic_image_size: Optional[bool],
+):
+    """Ensure dummy_data_for_internvl handles kwargs properly."""
+    # Same as the previous test - don't initialize mm_processor_kwargs
+    # in this test and assume that the kwargs will be correctly expanded by
+    # the partial when calling the dummy data func.
+    ctx = build_model_context(
+        model_name=model,
+        tokenizer_name=model,
+        trust_remote_code=True,
+        mm_processor_kwargs=None,
+    )
+
+    if max_dynamic_patch is None:
+        max_dynamic_patch = ctx.get_hf_config().max_dynamic_patch
+    expected_num_patches = max_dynamic_patch + 1 if max_dynamic_patch > 1 else 1
+    if dynamic_image_size is False:
+        expected_num_patches = 1
+    expected_max_tokens = 256 * expected_num_patches
+
+    dummy_data = dummy_data_for_internvl(
+        ctx=ctx,
+        seq_len=8192,  # Should be bigger than num_imgs * toks_per_img
+        mm_counts={"image": num_imgs},
+        max_dynamic_patch=max_dynamic_patch,
+        dynamic_image_size=dynamic_image_size,
+    )
+    sequence_data = dummy_data.seq_data
+
+    tokenizer = AutoTokenizer.from_pretrained(model, trust_remote_code=True)
+    image_token_id = tokenizer.encode('<IMG_CONTEXT>',
+                                      add_special_tokens=False)[0]
+
+    # Ensure we have the right number of placeholders per size
+    img_tok_count = sequence_data.get_token_ids().count(image_token_id)
+    assert img_tok_count == expected_max_tokens * num_imgs
+
+
+@pytest.mark.parametrize("model", models)
+@pytest.mark.parametrize("max_dynamic_patch", [1, 4])
+@pytest.mark.parametrize("dynamic_image_size", [True, False, None])
+@pytest.mark.parametrize("num_imgs", [1, 2])
+def test_input_processor_override(
+    input_processor_for_internvl: Callable,
+    image_assets: _ImageAssets,
+    model: str,
+    num_imgs: int,
+    max_dynamic_patch: int,
+    dynamic_image_size: Optional[bool],
+):
+    """Ensure input_processor_for_internvl handles kwargs properly."""
+    # Same as the previous test - don't initialize mm_processor_kwargs
+    # in this test and assume that the kwargs will be correctly expanded by
+    # the partial when calling the custom input processor.
+    expected_num_patches = max_dynamic_patch + 1 if max_dynamic_patch > 1 else 1
+    if dynamic_image_size is False:
+        expected_num_patches = 1
+
+    ctx = build_model_context(
+        model_name=model,
+        tokenizer_name=model,
+        trust_remote_code=True,
+        mm_processor_kwargs=None,
+    )
+    expected_toks_per_img = 256 * expected_num_patches
+
+    # Build the image str / prompt based on the number of images we pass
+    tokenizer = AutoTokenizer.from_pretrained(model, trust_remote_code=True)
+    placeholders = "<image>" if num_imgs == 1 else "\n".join(
+        f"Image-{i}: <image>\n" for i in range(1, num_imgs + 1))
+    prompt = placeholders
+    images = [image_assets[0].pil_image.resize((448 * 2, 448 * 2))] * num_imgs
+
+    inputs = token_inputs(prompt_token_ids=tokenizer.encode(prompt),
+                          prompt=prompt,
+                          multi_modal_data={"image": images})
+
+    processed_inputs = input_processor_for_internvl(
+        ctx,
+        inputs,
+        max_dynamic_patch=max_dynamic_patch,
+        dynamic_image_size=dynamic_image_size,
+    )
+
+    # Ensure we have the right number of placeholders per num_crops size
+    image_token_id = tokenizer.encode('<IMG_CONTEXT>',
+                                      add_special_tokens=False)[0]
+    img_tok_count = processed_inputs["prompt_token_ids"].count(image_token_id)
+    assert img_tok_count == expected_toks_per_img * num_imgs
diff --git a/vllm/model_executor/models/internvl.py b/vllm/model_executor/models/internvl.py
index 5d38b4b1..47ac00b6 100644
--- a/vllm/model_executor/models/internvl.py
+++ b/vllm/model_executor/models/internvl.py
@@ -123,8 +123,15 @@ def calculate_num_blocks(orig_width: int, orig_height: int, min_num: int,
     return blocks, target_width, target_height
 
 
-def calculate_num_blocks_wrapper(hf_config: PretrainedConfig,
-                                 max_dynamic_patch: Optional[int] = None):
+def calculate_num_blocks_wrapper(
+    hf_config: PretrainedConfig,
+    max_dynamic_patch: Optional[int] = None,
+    dynamic_image_size: Optional[bool] = None,
+):
+    if dynamic_image_size is None:
+        dynamic_image_size = hf_config.dynamic_image_size
+
+    max_dynamic_patch = max_dynamic_patch if dynamic_image_size else 1
     if max_dynamic_patch is None:
         max_dynamic_patch = hf_config.max_dynamic_patch
     min_num = hf_config.min_dynamic_patch
@@ -183,10 +190,17 @@ def image_to_pixel_values(image: Image.Image, input_size: int, min_num: int,
     return pixel_values
 
 
-def image_to_pixel_values_wrapper(hf_config: PretrainedConfig,
-                                  max_dynamic_patch: Optional[int] = None):
+def image_to_pixel_values_wrapper(
+    hf_config: PretrainedConfig,
+    max_dynamic_patch: Optional[int] = None,
+    dynamic_image_size: Optional[bool] = None,
+):
     image_size = hf_config.vision_config.image_size
     min_num = hf_config.min_dynamic_patch
+    if dynamic_image_size is None:
+        dynamic_image_size = hf_config.dynamic_image_size
+
+    max_dynamic_patch = max_dynamic_patch if dynamic_image_size else 1
     if max_dynamic_patch is None:
         max_dynamic_patch = hf_config.max_dynamic_patch
     use_thumbnail = hf_config.use_thumbnail
@@ -207,11 +221,17 @@ def get_internvl_num_patches(hf_config: PretrainedConfig):
         (downsample_ratio**2))
 
 
-def get_max_internvl_image_tokens(ctx: InputContext,
-                                  *,
-                                  max_dynamic_patch: Optional[int] = None):
+def get_max_internvl_image_tokens(
+    ctx: InputContext,
+    *,
+    max_dynamic_patch: Optional[int] = None,
+    dynamic_image_size: Optional[bool] = None,
+):
     hf_config = ctx.get_hf_config()
+    if dynamic_image_size is None:
+        dynamic_image_size = hf_config.dynamic_image_size
 
+    max_dynamic_patch = max_dynamic_patch if dynamic_image_size else 1
     if max_dynamic_patch is None:
         max_dynamic_patch = hf_config.max_dynamic_patch
     use_thumbnail = hf_config.use_thumbnail
@@ -222,12 +242,18 @@ def get_max_internvl_image_tokens(ctx: InputContext,
     return num_patches * max_dynamic_patch
 
 
-def get_max_internvl_image_size(ctx: InputContext,
-                                *,
-                                max_dynamic_patch: Optional[int] = None):
+def get_max_internvl_image_size(
+    ctx: InputContext,
+    *,
+    max_dynamic_patch: Optional[int] = None,
+    dynamic_image_size: Optional[bool] = None,
+):
     hf_config = ctx.get_hf_config()
     image_size = hf_config.vision_config.image_size
+    if dynamic_image_size is None:
+        dynamic_image_size = hf_config.dynamic_image_size
 
+    max_dynamic_patch = max_dynamic_patch if dynamic_image_size else 1
     if max_dynamic_patch is None:
         max_dynamic_patch = hf_config.max_dynamic_patch
     use_thumbnail = hf_config.use_thumbnail
@@ -281,6 +307,7 @@ class InternVLInputPipeline:
         inputs: DecoderOnlyInputs,
         *,
         max_dynamic_patch: Optional[int] = None,
+        dynamic_image_size: Optional[bool] = None,
     ) -> DecoderOnlyInputs:
         multi_modal_data = inputs.get("multi_modal_data")
         if multi_modal_data is None or "image" not in multi_modal_data:
@@ -292,7 +319,7 @@ class InternVLInputPipeline:
         image_data = multi_modal_data["image"]
         num_patches = get_internvl_num_patches(hf_config)
         num_blocks_calculator = calculate_num_blocks_wrapper(
-            hf_config, max_dynamic_patch)
+            hf_config, max_dynamic_patch, dynamic_image_size)
         if isinstance(image_data, Image.Image):
             width, height = image_data.size
             num_blocks, _, _ = num_blocks_calculator(width, height)
@@ -332,11 +359,12 @@ class InternVLInputPipeline:
         data: object,
         *,
         max_dynamic_patch: Optional[int] = None,
+        dynamic_image_size: Optional[bool] = None,
     ):
         hf_config = ctx.get_hf_config()
 
         image_pixel_values_mapper = image_to_pixel_values_wrapper(
-            hf_config, max_dynamic_patch)
+            hf_config, max_dynamic_patch, dynamic_image_size)
         if isinstance(data, Image.Image):
             data = image_pixel_values_mapper(data)
             # Add an N dimension for number of images per prompt (currently 1).
@@ -366,13 +394,17 @@ class InternVLInputPipeline:
         mm_counts: Mapping[str, int],
         *,
         max_dynamic_patch: Optional[int] = None,
+        dynamic_image_size: Optional[bool] = None,
     ):
         num_images = mm_counts["image"]
 
         hf_config = ctx.get_hf_config()
 
         image_feature_size = get_max_internvl_image_tokens(
-            ctx, max_dynamic_patch=max_dynamic_patch)
+            ctx,
+            max_dynamic_patch=max_dynamic_patch,
+            dynamic_image_size=dynamic_image_size,
+        )
         model_config = ctx.model_config
         tokenizer = cached_get_tokenizer(
             model_config.tokenizer,
@@ -388,7 +420,10 @@ class InternVLInputPipeline:
         )
 
         max_image_width, max_image_height = get_max_internvl_image_size(
-            ctx, max_dynamic_patch=max_dynamic_patch)
+            ctx,
+            max_dynamic_patch=max_dynamic_patch,
+            dynamic_image_size=dynamic_image_size,
+        )
 
         mm_data = dummy_image_for_clip(
             hf_config.vision_config,