[Misc] Address LingBot runner review feedback

Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>

benchmarks/lingbot_video/dense_pipeline_parity.py

@@ -170,6 +170,8 @@ def main() -> None:
         "examples/offline_inference/text_to_video/text_to_video.py",
         "--model",
         args.model,
+        "--model-class-name",
+        "LingBotVideoPipeline",
         "--prompt",
         args.prompt,
         "--output",

examples/offline_inference/text_to_video/text_to_video.py

@@ -16,7 +16,7 @@ from vllm_omni.entrypoints.omni import Omni
 from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 from vllm_omni.lora.request import LoRARequest
 from vllm_omni.lora.utils import stable_lora_int_id
-from vllm_omni.model_extras import get_extra_body_params, get_model_class_name
+from vllm_omni.model_extras import get_extra_body_params, get_model_class_name, get_output_tensor_range
 from vllm_omni.outputs import OmniRequestOutput
 from vllm_omni.platforms import current_omni_platform
 
@@ -155,13 +155,15 @@ def build_text_to_video_prompt(prompt: str, negative_prompt: str | None) -> dict
     return result
 
 
-def _normalize_float_tensor(tensor: torch.Tensor) -> torch.Tensor:
-    """Normalize decoded frames without remapping tensors already in [0, 1]."""
+def _normalize_float_tensor(tensor: torch.Tensor, source_range: str) -> torch.Tensor:
+    """Normalize decoded frames according to the pipeline's output contract."""
     if not tensor.is_floating_point():
         return tensor
-    if float(tensor.min()) < 0.0:
+    if source_range == "negative_one_to_one":
         return tensor.clamp(-1, 1) * 0.5 + 0.5
-    return tensor.clamp(0, 1)
+    if source_range == "zero_to_one":
+        return tensor.clamp(0, 1)
+    raise ValueError(f"Unsupported floating-point tensor range: {source_range!r}")
 
 
 def parse_profiler_config(value: str) -> dict[str, Any]:
@@ -514,6 +516,7 @@ def main():
     omni = Omni(**omni_kwargs)
     model_class_name = get_model_class_name(omni) or model_class_name
     declared_extra_body_params = get_extra_body_params(model_class_name)
+    output_tensor_range = get_output_tensor_range(model_class_name)
 
     if profiler_enabled:
         print("[Profiler] Starting profiling...")
@@ -663,7 +666,7 @@ def main():
                 frame_tensor = frame_tensor[0]
             if frame_tensor.dim() == 3 and frame_tensor.shape[0] in (3, 4):
                 frame_tensor = frame_tensor.permute(1, 2, 0)
-            frame_tensor = _normalize_float_tensor(frame_tensor)
+            frame_tensor = _normalize_float_tensor(frame_tensor, output_tensor_range)
             return frame_tensor.float().numpy()
         if isinstance(frame, np.ndarray):
             frame_array = frame
@@ -715,7 +718,7 @@ def main():
                 video_tensor = video_tensor[0]
         elif video_tensor.dim() == 4 and video_tensor.shape[0] in (3, 4):
             video_tensor = video_tensor.permute(1, 2, 3, 0)
-        video_tensor = _normalize_float_tensor(video_tensor)
+        video_tensor = _normalize_float_tensor(video_tensor, output_tensor_range)
         video_array = video_tensor.float().numpy()
     elif isinstance(frames, np.ndarray):
         video_array = frames

tests/examples/offline_inference/test_text_to_video_prompts.py

@@ -70,10 +70,22 @@ def test_lingbot_preset_matches_lingbot_defaults() -> None:
 def test_normalize_float_tensor_preserves_zero_to_one_frames() -> None:
     frames = torch.tensor([0.0, 0.25, 1.0, 1.5])
 
-    assert torch.equal(_normalize_float_tensor(frames), torch.tensor([0.0, 0.25, 1.0, 1.0]))
+    assert torch.equal(_normalize_float_tensor(frames, "zero_to_one"), torch.tensor([0.0, 0.25, 1.0, 1.0]))
 
 
 def test_normalize_float_tensor_maps_negative_one_to_one_frames() -> None:
     frames = torch.tensor([-1.5, -1.0, 0.0, 1.0, 1.5])
 
-    assert torch.equal(_normalize_float_tensor(frames), torch.tensor([0.0, 0.0, 0.5, 1.0, 1.0]))
+    assert torch.equal(
+        _normalize_float_tensor(frames, "negative_one_to_one"),
+        torch.tensor([0.0, 0.0, 0.5, 1.0, 1.0]),
+    )
+
+
+def test_normalize_float_tensor_uses_one_contract_for_ambiguous_frames() -> None:
+    frames = [torch.tensor([0.0, 0.5, 1.0]), torch.tensor([-1.0, 0.0, 1.0])]
+
+    normalized = [_normalize_float_tensor(frame, "negative_one_to_one") for frame in frames]
+
+    assert torch.equal(normalized[0], torch.tensor([0.5, 0.75, 1.0]))
+    assert torch.equal(normalized[1], torch.tensor([0.0, 0.5, 1.0]))

tests/model_extras/test_model_extras.py

@@ -17,6 +17,7 @@ from vllm_omni.model_extras import (
     build_x_to_text_prompt,
     get_extra_body_params,
     get_extra_output_params,
+    get_output_tensor_range,
     get_x_to_text_model_family,
     should_init_extra_args_for_non_diffusion_stages,
 )
@@ -134,6 +135,8 @@ def test_lingbot_extra_registry_declares_request_params() -> None:
         }
     )
     assert get_extra_output_params("LingBotVideoPipeline") == frozenset()
+    assert get_output_tensor_range("LingBotVideoPipeline") == "zero_to_one"
+    assert get_output_tensor_range("WanPipeline") == "negative_one_to_one"
     assert should_init_extra_args_for_non_diffusion_stages("LingBotVideoPipeline") is False
 
 

vllm_omni/model_extras/__init__.py

@@ -9,6 +9,7 @@ from vllm_omni.model_extras.registry import (
     get_extra_body_params,
     get_extra_output_params,
     get_model_class_name,
+    get_output_tensor_range,
     get_x_to_text_model_family,
     should_init_extra_args_for_non_diffusion_stages,
 )
@@ -21,6 +22,7 @@ __all__ = [
     "get_extra_body_params",
     "get_extra_output_params",
     "get_model_class_name",
+    "get_output_tensor_range",
     "get_x_to_text_model_family",
     "should_init_extra_args_for_non_diffusion_stages",
 ]

vllm_omni/model_extras/registry.py

@@ -4,7 +4,7 @@
 from __future__ import annotations
 
 from collections.abc import Callable, Mapping
-from typing import Any
+from typing import Any, Literal
 
 from PIL import Image
 
@@ -93,6 +93,7 @@ ImageToVideoPromptBuilder = Callable[
     dict[str, Any],
 ]
 XToTextPromptBuilder = Callable[[str, str, bool], tuple[dict[str, Any], list[int] | None]]
+OutputTensorRange = Literal["negative_one_to_one", "zero_to_one"]
 
 
 def default_x_to_text_prompt(
@@ -193,6 +194,7 @@ _EXTRA_SPECS: dict[str, dict[str, Any]] = {
     },
     "LingBotVideoPipeline": {
         "extra_body_params": LINGBOT_VIDEO_EXTRA_BODY_PARAMS,
+        "output_tensor_range": "zero_to_one",
         # Shared T2I/I2V envelopes select the output modality. LingBot's
         # pipeline owns model-specific validation and normalization.
     },
@@ -262,6 +264,18 @@ def get_extra_output_params(model_class_name: str | None) -> frozenset[str]:
     return spec.get("extra_output_params", frozenset()) if spec is not None else frozenset()
 
 
+def get_output_tensor_range(model_class_name: str | None) -> OutputTensorRange:
+    """Return the declared range for floating-point tensor outputs.
+
+    The default preserves the shared examples' historical handling. Pipelines
+    that already return normalized tensors declare ``zero_to_one`` explicitly.
+    """
+    spec = _get_spec(model_class_name)
+    if spec is None:
+        return "negative_one_to_one"
+    return spec.get("output_tensor_range", "negative_one_to_one")
+
+
 def should_init_extra_args_for_non_diffusion_stages(model_class_name: str | None) -> bool:
     spec = _get_spec(model_class_name)
     return bool(spec and spec.get("init_extra_args_for_non_diffusion_stages", False))