diff --git a/recipes/dev/generate_v2.py b/recipes/dev/generate_v2.py
index e63ea2dcb0..3ce95a9fdf 100644
--- a/recipes/dev/generate_v2.py
+++ b/recipes/dev/generate_v2.py
@@ -152,7 +152,10 @@ def generate(self, cfg: DictConfig):
         batch = {}
         if is_multimodal_input:
             batch = padded_collate_tiled_images_and_mask(
-                [model_inputs], pad_direction="left", pad_max_images=1
+                [model_inputs],
+                pad_direction="left",
+                pad_max_images=1,
+                pad_max_tiles=self.model_transform.max_num_tiles,
             )
             batch["encoder_mask"] = batch["encoder_mask"][:, :seq_len]
             prompt = batch.pop("tokens").to(self._device)
diff --git a/tests/torchtune/data/test_collate.py b/tests/torchtune/data/test_collate.py
index d17ce2b0ae..02477ad7e5 100644
--- a/tests/torchtune/data/test_collate.py
+++ b/tests/torchtune/data/test_collate.py
@@ -138,17 +138,14 @@ def test_left_pad_sequence(self, batch):
         )
         imgs, tiles = actual["encoder_input"]["images"].shape[1:3]
         seq_len = actual["encoder_mask"].shape[-1]
-        assert imgs * tiles * self.tokens_per_tile == seq_len
+        assert 5 * 4 * self.tokens_per_tile == seq_len
 
-        mask_1 = torch.concat(
-            [torch.ones(4, 5 * 2), torch.zeros(4, 5 * 3)], dim=1
-        )  # pad 3 extra tiles
-        mask_2 = torch.concat(
-            [torch.ones(4, 5 * 3), torch.zeros(4, 5 * 2)], dim=1
-        )  # pad 2 extra tiles
-        mask_3 = torch.concat(
-            [torch.zeros(2, 20), torch.ones(2, 5 * 4)], dim=0
-        )  # Left pad text tokens
+        # pad 3 extra tiles
+        mask_1 = torch.concat([torch.ones(4, 5 * 2), torch.zeros(4, 5 * 3)], dim=1)
+        # pad 2 extra tiles
+        mask_2 = torch.concat([torch.ones(4, 5 * 3), torch.zeros(4, 5 * 2)], dim=1)
+        # Left pad text tokens
+        mask_3 = torch.concat([torch.zeros(2, 20), torch.ones(2, 5 * 4)], dim=0)
         mask_3 = F.pad(mask_3, (0, 5), value=0)  # pad 5th tile
         sample_1 = torch.stack([mask_1, mask_2])
         sample_2 = torch.stack([mask_3, torch.zeros(4, 25)])
@@ -163,14 +160,10 @@ def test_left_pad_sequence(self, batch):
                         [
                             [[[[1.0]]], [[[1.0]]], [[[0.0]]], [[[0.0]]], [[[0.0]]]],
                             [[[[1.0]]], [[[1.0]]], [[[1.0]]], [[[0.0]]], [[[0.0]]]],
-                            [[[[0.0]]], [[[0.0]]], [[[0.0]]], [[[0.0]]], [[[0.0]]]],
-                            [[[[0.0]]], [[[0.0]]], [[[0.0]]], [[[0.0]]], [[[0.0]]]],
                         ],
                         [
                             [[[[1.0]]], [[[1.0]]], [[[1.0]]], [[[1.0]]], [[[0.0]]]],
                             [[[[0.0]]], [[[0.0]]], [[[0.0]]], [[[0.0]]], [[[0.0]]]],
-                            [[[[0.0]]], [[[0.0]]], [[[0.0]]], [[[0.0]]], [[[0.0]]]],
-                            [[[[0.0]]], [[[0.0]]], [[[0.0]]], [[[0.0]]], [[[0.0]]]],
                         ],
                     ]
                 ),