diff --git a/tests/diffusion/models/bagel/test_trajectory_recording.py b/tests/diffusion/models/bagel/test_trajectory_recording.py
index aa86f033a..f3dbc0df2 100644
--- a/tests/diffusion/models/bagel/test_trajectory_recording.py
+++ b/tests/diffusion/models/bagel/test_trajectory_recording.py
@@ -19,15 +19,18 @@ pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
 NUM_TOKENS = 8
 HIDDEN_DIM = 16
 NUM_TIMESTEPS = 5
-# generate_image samples (num_timesteps + 1) points then drops the last, matching
-# upstream lance: see Bagel.generate_image in bagel_transformer.py.
-EXPECTED_STEPS = NUM_TIMESTEPS
+# Official BAGEL samples num_timesteps points then drops the terminal t=0,
+# yielding num_timesteps - 1 denoise steps.
+EXPECTED_STEPS = NUM_TIMESTEPS - 1
 
 
 def _make_mock_bagel(mocker: MockerFixture):
     """Create a mock Bagel with forward returning constant velocity."""
     mock = mocker.MagicMock(spec=Bagel)
     mock._sp_size = 1
+    # MagicMock would otherwise auto-return a truthy stub for this flag; pin it
+    # to the official BAGEL schedule convention (num_timesteps points).
+    mock._denoise_schedule_extra_step = False
 
     # forward returns a small constant velocity so x_t changes each step
     def fake_forward(self, x_t, **kwargs):
@@ -179,7 +182,7 @@ class TestTrajectoryRecording:
         bagel, args = bagel_and_args
 
         # Recompute the expected timestep schedule (mirrors generate_image logic)
-        ts = torch.linspace(1, 0, args["num_timesteps"] + 1)
+        ts = torch.linspace(1, 0, args["num_timesteps"])
         shift = args.get("timestep_shift", 1.0)
         ts = shift * ts / (1 + (shift - 1) * ts)
         expected_timesteps = ts[:-1]  # last element is dropped
@@ -197,7 +200,7 @@ class TestTrajectoryRecording:
         bagel, args = bagel_and_args
 
         # Compute the number of denoising steps the same way generate_image does
-        ts = torch.linspace(1, 0, args["num_timesteps"] + 1)
+        ts = torch.linspace(1, 0, args["num_timesteps"])
         shift = args.get("timestep_shift", 1.0)
         ts = shift * ts / (1 + (shift - 1) * ts)
         num_steps = len(ts) - 1  # timesteps = ts[:-1]
diff --git a/tests/distributed/omni_connectors/test_bagel_mooncake_connector.py b/tests/distributed/omni_connectors/test_bagel_mooncake_connector.py
index 106507114..cac7d7ed0 100644
--- a/tests/distributed/omni_connectors/test_bagel_mooncake_connector.py
+++ b/tests/distributed/omni_connectors/test_bagel_mooncake_connector.py
@@ -32,7 +32,7 @@ BAGEL_MOONCAKE_CI_DEPLOY = get_deploy_config_path("ci/bagel_mooncake.yaml")
 
 # Reference pixel data extracted from the known-good output image
 # Each entry contains (x, y) position and expected (R, G, B) values
-# "Generated with seed=52, num_inference_steps=14,
+# "Generated with seed=52, num_inference_steps=15,
 # prompt='A cute cat'"
 REFERENCE_PIXELS = [
     {"position": (100, 100), "rgb": (115, 113, 94)},
@@ -63,7 +63,7 @@ def _find_free_port() -> int:
     return port
 
 
-def _configure_sampling_params(omni: Omni, num_inference_steps: int = 14) -> list:
+def _configure_sampling_params(omni: Omni, num_inference_steps: int = 15) -> list:
     """Configure sampling parameters for Bagel text2img generation.
 
     Args:
diff --git a/tests/distributed/omni_connectors/test_bagel_shared_memory_connector.py b/tests/distributed/omni_connectors/test_bagel_shared_memory_connector.py
index eb5a79d6a..19457f0f2 100644
--- a/tests/distributed/omni_connectors/test_bagel_shared_memory_connector.py
+++ b/tests/distributed/omni_connectors/test_bagel_shared_memory_connector.py
@@ -30,7 +30,7 @@ pytestmark = [pytest.mark.usefixtures("clean_gpu_memory_between_tests")]
 BAGEL_CI_DEPLOY = get_deploy_config_path("ci/bagel.yaml")
 
 # Reference pixel data extracted from the known-good output image
-# Generated with seed=52, num_inference_steps=14,
+# Generated with seed=52, num_inference_steps=15,
 # prompt='Change the grass color to red',
 # input image: 2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg
 REFERENCE_PIXELS = [
@@ -48,7 +48,7 @@ REFERENCE_PIXELS = [
 
 
 # text2img reference pixels (aligned with offline `bagel/end2end.py` text2img, 15 steps)
-# "Generated with seed=52, num_inference_steps=14,
+# "Generated with seed=52, num_inference_steps=15,
 # prompt='A cute cat'"
 TEXT2IMG_REFERENCE_PIXELS = [
     {"position": (100, 100), "rgb": (115, 113, 94)},
@@ -88,7 +88,7 @@ def _find_free_port() -> int:
     return port
 
 
-def _configure_sampling_params(omni: Omni, num_inference_steps: int = 14) -> list:
+def _configure_sampling_params(omni: Omni, num_inference_steps: int = 15) -> list:
     """Configure sampling parameters for Bagel img2img generation.
 
     Args:
diff --git a/vllm_omni/diffusion/models/bagel/bagel_transformer.py b/vllm_omni/diffusion/models/bagel/bagel_transformer.py
index ba3a692bc..bc3eef49d 100644
--- a/vllm_omni/diffusion/models/bagel/bagel_transformer.py
+++ b/vllm_omni/diffusion/models/bagel/bagel_transformer.py
@@ -1129,6 +1129,13 @@ class Bagel(nn.Module):
     config_class = BagelConfig
     base_model_prefix = "bagel"
 
+    # Flow-matching denoise schedule convention. Official BAGEL samples
+    # ``num_timesteps`` points over [1, 0] and drops the terminal t=0, yielding
+    # ``num_timesteps - 1`` Euler steps. Lance samples one extra point
+    # (``num_timesteps + 1``) for ``num_timesteps`` steps; ``LanceBagel`` flips
+    # this on. See https://github.com/vllm-project/vllm-omni/issues/4470.
+    _denoise_schedule_extra_step: bool = False
+
     def __init__(
         self,
         language_model,
@@ -1690,11 +1697,11 @@ class Bagel(nn.Module):
             frame_condition_token_indexes = frame_condition_token_indexes.to(x_t.device).long()
             pinned_x_t = x_t[frame_condition_token_indexes].clone()
 
-        # Use num_timesteps + 1 sample points so we get `num_timesteps` denoise
-        # steps after dropping the terminal t=0 (which has no dt).  Upstream
-        # Lance / BAGEL both use this convention; without the +1 we silently
-        # run one fewer denoise iteration than the user asked for.
-        timesteps = torch.linspace(1, 0, num_timesteps + 1, device=x_t.device)
+        # Build the flow-matching schedule. BAGEL drops the terminal t=0 for
+        # ``num_timesteps - 1`` Euler steps; Lance keeps it for ``num_timesteps``.
+        # ``_denoise_schedule_extra_step`` (overridden by ``LanceBagel``) selects which.
+        num_sample_points = num_timesteps + 1 if self._denoise_schedule_extra_step else num_timesteps
+        timesteps = torch.linspace(1, 0, num_sample_points, device=x_t.device)
         timesteps = timestep_shift * timesteps / (1 + (timestep_shift - 1) * timesteps)
         dts = timesteps[:-1] - timesteps[1:]
         timesteps = timesteps[:-1]
diff --git a/vllm_omni/diffusion/models/lance/lance_transformer.py b/vllm_omni/diffusion/models/lance/lance_transformer.py
index 012ba3f42..12959eb64 100644
--- a/vllm_omni/diffusion/models/lance/lance_transformer.py
+++ b/vllm_omni/diffusion/models/lance/lance_transformer.py
@@ -278,6 +278,11 @@ class LanceBagel(Bagel):
     ``forward_cache_update_vae`` — is reused unchanged.
     """
 
+    # Upstream Lance samples ``num_timesteps + 1`` schedule points (one extra
+    # over official BAGEL) so the denoise loop runs exactly ``num_timesteps``
+    # Euler steps. See Bagel.generate_image and issue #4470.
+    _denoise_schedule_extra_step: bool = True
+
     @staticmethod
     def _lance_compute_vit_buckets():
         """Replicate upstream Lance's ``BucketResize.init_buckets`` for the ViT path.