[Bugfix] Fix washed-out LingBot Video outputs (#7037)

Signed-off-by: wtz2333 <2955110911@qq.com>
Co-authored-by: Yuanheng Zhao <54058983+yuanheng-zhao@users.noreply.github.com>

tests/diffusion/models/lingbot_video/test_pipeline_lingbot_video.py

@@ -148,7 +148,8 @@ def test_postprocess_keeps_torch_by_default_and_converts_np_when_requested():
     assert array.shape == (2, 4, 4, 3)
     video = post({"video": frames}, SimpleNamespace(output_type="pt"))
     assert set(video) == {"video"}
-    assert video["video"] is frames
+    assert isinstance(video["video"], np.ndarray)
+    np.testing.assert_array_equal(video["video"], frames.numpy())
     image = post(
         {"image": torch.ones(4, 4, 3)},
         SimpleNamespace(output_type="pt"),
@@ -162,6 +163,40 @@ def test_postprocess_keeps_torch_by_default_and_converts_np_when_requested():
     assert latent_output["image"] is latent
 
 
+@pytest.mark.parametrize("output_type", [None, "pt", "np"])
+@pytest.mark.parametrize("dtype", [torch.float32, torch.bfloat16])
+def test_postprocess_preserves_video_range_for_serving(output_type: str | None, dtype: torch.dtype) -> None:
+    from vllm_omni.diffusion.models.lingbot_video import get_lingbot_video_post_process_func
+    from vllm_omni.entrypoints.openai.video_api_utils import _coerce_video_to_frames
+
+    # Include black and midtones: all-white frames cannot expose double normalization.
+    frames = torch.tensor([0.0, 0.25, 0.5, 0.75, 1.0], dtype=dtype)
+    frames = frames.reshape(5, 1, 1, 1).expand(5, 2, 4, 3).clone()
+    expected = frames.float().numpy().copy()
+    sampling = None if output_type is None else OmniDiffusionSamplingParams(output_type=output_type)
+    post = get_lingbot_video_post_process_func(None)
+
+    processed = post({"video": frames}, sampling)
+    encoded_frames = np.stack(_coerce_video_to_frames(processed["video"]))
+
+    assert set(processed) == {"video"}
+    assert encoded_frames.dtype == np.float32
+    np.testing.assert_array_equal(encoded_frames, expected)
+    np.testing.assert_array_equal(frames.float().numpy(), expected)
+
+
+@pytest.mark.parametrize("output_key", ["image", "video"])
+def test_postprocess_preserves_latent_payload(output_key: str) -> None:
+    from vllm_omni.diffusion.models.lingbot_video import get_lingbot_video_post_process_func
+
+    latents = torch.tensor([-2.0, 0.0, 2.0]).reshape(1, 3, 1, 1, 1)
+    sampling = OmniDiffusionSamplingParams(output_type="latent")
+    processed = get_lingbot_video_post_process_func(None)({output_key: latents}, sampling)
+
+    assert set(processed) == {output_key}
+    assert processed[output_key] is latents
+
+
 def test_postprocess_preserves_image_contract_for_serving():
     from vllm_omni.diffusion.data import DiffusionOutput
     from vllm_omni.diffusion.models.lingbot_video import get_lingbot_video_post_process_func
@@ -181,7 +216,9 @@ def test_postprocess_preserves_image_contract_for_serving():
         sampling_params=sampling,
         request_id="lingbot-image-contract-test",
     )
-    raw_output = {"image": torch.ones(8, 12, 3)}
+    image = torch.zeros(8, 12, 3)
+    image[4:] = 1.0
+    raw_output = {"image": image}
     processed = get_lingbot_video_post_process_func(SimpleNamespace())(raw_output, sampling)
     normalized = normalize_diffusion_postprocess_output(processed)
 
@@ -199,6 +236,8 @@ def test_postprocess_preserves_image_contract_for_serving():
     endpoint_images = _extract_images_from_result(result)
     assert len(endpoint_images) == 1
     assert endpoint_images[0].size == (12, 8)
+    assert endpoint_images[0].getpixel((0, 0)) == (0, 0, 0)
+    assert endpoint_images[0].getpixel((0, 7)) == (255, 255, 255)
 
 
 def test_forward_resolves_t2v_sampling_and_flow_shift_alias():

vllm_omni/diffusion/models/lingbot_video/pipeline_lingbot_video.py

@@ -288,13 +288,13 @@ def get_lingbot_video_post_process_func(od_config: OmniDiffusionConfig):
             output_key = output_keys[0]
             frames = frames[output_key]
         output_type = getattr(sampling_params, "output_type", None) or "pt"
-        # The image serving path currently accepts PIL images or NumPy arrays,
-        # while LingBot decodes T2I outputs to tensors. Keep that compatibility
-        # conversion without discarding the model's image/video payload key.
+        # Decoded LingBot pixels are already in [0, 1]. The video API treats
+        # floating tensors as [-1, 1], so use its NumPy path to avoid normalizing
+        # twice. Images also need NumPy for serving; keep latent tensors intact.
         if (
             isinstance(frames, torch.Tensor)
             and output_type != "latent"
-            and (output_type == "np" or output_key == "image")
+            and (output_type == "np" or output_key in {"image", "video"})
         ):
             frames = frames.float().cpu().numpy()
         return {output_key: frames} if output_key is not None else frames