diff --git a/tests/models/test_internvl.py b/tests/models/test_internvl.py
index fa3369dc53345..881068b3afe41 100644
--- a/tests/models/test_internvl.py
+++ b/tests/models/test_internvl.py
@@ -331,6 +331,41 @@ def test_multi_images_models(hf_runner, vllm_runner, image_assets, model,
     )
 
 
+@pytest.mark.parametrize("model", ["OpenGVLab/InternVL2-2B"])
+@pytest.mark.parametrize("size_factors", [[0.5, 1.0]])
+@pytest.mark.parametrize("dtype", [target_dtype])
+@pytest.mark.parametrize("max_tokens", [128])
+@pytest.mark.parametrize("num_logprobs", [5])
+@torch.inference_mode()
+def test_different_num_patches(hf_runner, vllm_runner, image_assets, model,
+                               size_factors, dtype: str, max_tokens: int,
+                               num_logprobs: int) -> None:
+    images = [asset.pil_image.resize((896, 896)) for asset in image_assets]
+
+    inputs_batching = [(
+        [prompt for _ in size_factors],
+        [rescale_image_size(image, factor) for factor in size_factors],
+    ) for image, prompt in zip(images, HF_IMAGE_PROMPTS)]
+
+    inputs_multi_images = [
+        ([HF_MULTIIMAGE_IMAGE_PROMPT for _ in size_factors],
+         [[rescale_image_size(image, factor) for image in images]
+          for factor in size_factors])
+    ]
+    for inputs in [inputs_batching, inputs_multi_images]:
+        run_test(
+            hf_runner,
+            vllm_runner,
+            inputs,
+            model,
+            dtype=dtype,
+            max_tokens=max_tokens,
+            num_logprobs=num_logprobs,
+            mm_limit=2,
+            tensor_parallel_size=1,
+        )
+
+
 @pytest.mark.parametrize(
     "models", [("OpenGVLab/InternVL2-2B", "OpenGVLab/InternVL2-2B-AWQ")])
 @pytest.mark.parametrize(
diff --git a/vllm/model_executor/models/internvl.py b/vllm/model_executor/models/internvl.py
index 81819578a4d8c..507d7014714a2 100644
--- a/vllm/model_executor/models/internvl.py
+++ b/vllm/model_executor/models/internvl.py
@@ -270,6 +270,7 @@ def input_mapper_for_internvl(ctx: InputContext, data: object):
         # Add an N dimension for number of images per prompt (currently 1).
         data = data.unsqueeze(0)
     elif is_list_of(data, Image.Image):
+        # we can't stack here because the images may have different num_patches
         data = [
             image_to_pixel_values(img,
                                   image_size,
@@ -277,7 +278,6 @@ def input_mapper_for_internvl(ctx: InputContext, data: object):
                                   max_num,
                                   use_thumbnail=use_thumbnail) for img in data
         ]
-        data = torch.stack(data)
     model_config = ctx.model_config
     tokenizer = cached_get_tokenizer(model_config.tokenizer,
                                      trust_remote_code=True)
@@ -449,11 +449,12 @@ def _parse_and_validate_image_input(
             if not isinstance(pixel_values, (torch.Tensor, list)):
                 raise ValueError("Incorrect type of pixel values. "
                                  f"Got type: {type(pixel_values)}")
-
+            # We need to flatten (B, N, P) to (B*N*P),
+            # so we call flatten_bn twice.
             return InternVLImagePixelInputs(
                 type="pixel_values",
                 data=self._validate_pixel_values(
-                    flatten_bn(pixel_values, concat=True).flatten(0, 1)),
+                    flatten_bn(flatten_bn(pixel_values), concat=True)),
             )
 
         raise AssertionError("This line should be unreachable.")