diff --git a/src/transformers/pipelines/mask_generation.py b/src/transformers/pipelines/mask_generation.py
index 920ce9d0a39c..ffbe5868f7a7 100644
--- a/src/transformers/pipelines/mask_generation.py
+++ b/src/transformers/pipelines/mask_generation.py
@@ -231,7 +231,7 @@ def preprocess(
         for i in range(0, n_points, points_per_batch):
             batched_points = grid_points[:, i : i + points_per_batch, :, :]
             labels = input_labels[:, i : i + points_per_batch]
-            is_last = i == n_points - points_per_batch
+            is_last = i + points_per_batch >= n_points
             yield {
                 "input_points": batched_points,
                 "input_labels": labels,
diff --git a/tests/pipelines/test_pipelines_mask_generation.py b/tests/pipelines/test_pipelines_mask_generation.py
index 07ce9c5d91ca..4d25bb9c0cc1 100644
--- a/tests/pipelines/test_pipelines_mask_generation.py
+++ b/tests/pipelines/test_pipelines_mask_generation.py
@@ -93,6 +93,16 @@ def get_test_pipeline(
     def run_pipeline_test(self, mask_generator, examples):
         pass
 
+    def test_preprocess_is_last(self):
+        mask_generator = pipeline("mask-generation", model="hf-internal-testing/tiny-random-SamModel")
+        mask_generator.image_processor.pad_size = {"height": 24, "width": 24}
+        image = "./tests/fixtures/tests_samples/COCO/000000039769.png"
+        for points_per_batch in (100, 64):
+            with self.subTest(points_per_batch=points_per_batch):
+                batches = list(mask_generator.preprocess(image, points_per_batch=points_per_batch))
+                self.assertTrue(batches[-1]["is_last"])
+                self.assertFalse(any(b["is_last"] for b in batches[:-1]))
+
     @slow
     @require_torch
     def test_small_model_pt(self):