diff --git a/optimum/exporters/onnx/model_configs.py b/optimum/exporters/onnx/model_configs.py
index fa39a47abf..adda0e6d8d 100644
--- a/optimum/exporters/onnx/model_configs.py
+++ b/optimum/exporters/onnx/model_configs.py
@@ -1675,6 +1675,7 @@ def inputs(self) -> Dict[str, Dict[int, str]]:
             inputs = {
                 "pixel_values": {0: "batch_size"},
                 "input_points": {0: "batch_size", 1: "point_batch_size", 2: "nb_points_per_image"},
+                "input_labels": {0: "batch_size", 1: "point_batch_size", 2: "nb_points_per_image"},
             }
         else:
             if self.vision_encoder:
@@ -1684,6 +1685,7 @@ def inputs(self) -> Dict[str, Dict[int, str]]:
                     "image_positional_embeddings": {0: "batch_size"},
                     "image_embeddings": {0: "batch_size"},
                     "input_points": {0: "batch_size", 1: "point_batch_size", 2: "nb_points_per_image"},
+                    "input_labels": {0: "batch_size", 1: "point_batch_size", 2: "nb_points_per_image"},
                 }
         return inputs
 
diff --git a/optimum/exporters/onnx/model_patcher.py b/optimum/exporters/onnx/model_patcher.py
index db52493b6f..cc448ee68b 100644
--- a/optimum/exporters/onnx/model_patcher.py
+++ b/optimum/exporters/onnx/model_patcher.py
@@ -510,6 +510,7 @@ def __init__(
         def patched_forward(
             pixel_values=None,
             input_points=None,
+            input_labels=None,
             image_embeddings=None,
             image_positional_embeddings=None,
             return_dict=True,
@@ -519,6 +520,7 @@ def patched_forward(
                 return self.orig_forward(
                     pixel_values=pixel_values,
                     input_points=input_points,
+                    input_labels=input_labels,
                     image_embeddings=image_embeddings,
                     return_dict=return_dict,
                     **kwargs,
@@ -549,11 +551,7 @@ def patched_forward(
                             "image_positional_embeddings": image_positional_embeddings,
                         }
                 else:
-                    if input_points is not None:
-                        input_labels = torch.ones_like(
-                            input_points[:, :, :, 0], dtype=torch.int, device=input_points.device
-                        )
-                    else:
+                    if input_points is None:
                         raise ValueError("input_points is required to export the prompt encoder / mask decoder.")
 
                     sparse_embeddings, dense_embeddings = model.prompt_encoder(
diff --git a/optimum/utils/input_generators.py b/optimum/utils/input_generators.py
index 1a9024db7a..0c82808131 100644
--- a/optimum/utils/input_generators.py
+++ b/optimum/utils/input_generators.py
@@ -836,7 +836,7 @@ class DummyPointsGenerator(DummyInputGenerator):
     Generates dummy time step inputs.
     """
 
-    SUPPORTED_INPUT_NAMES = ("input_points",)
+    SUPPORTED_INPUT_NAMES = ("input_points", "input_labels")
 
     def __init__(
         self,
@@ -854,8 +854,12 @@ def __init__(
         self.nb_points_per_image = nb_points_per_image
 
     def generate(self, input_name: str, framework: str = "pt", int_dtype: str = "int64", float_dtype: str = "fp32"):
-        shape = [self.batch_size, self.point_batch_size, self.nb_points_per_image, 2]
-        return self.random_float_tensor(shape, framework=framework, dtype=float_dtype)
+        if input_name == "input_points":
+            shape = [self.batch_size, self.point_batch_size, self.nb_points_per_image, 2]
+            return self.random_float_tensor(shape, framework=framework, dtype=float_dtype)
+        else:  # input_labels
+            shape = [self.batch_size, self.point_batch_size, self.nb_points_per_image]
+            return self.random_int_tensor(shape, min_value=0, max_value=1, framework=framework, dtype=int_dtype)
 
 
 class DummyVisionEmbeddingsGenerator(DummyInputGenerator):