[Misc] Consolidate LingBot text-to-video runner

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

tests/examples/offline_inference/test_text_to_video_prompts.py

@@ -0,0 +1,79 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from __future__ import annotations
+
+import pytest
+import torch
+
+from examples.offline_inference.text_to_video.text_to_video import (
+    _MODEL_PRESETS,
+    _detect_preset,
+    _normalize_float_tensor,
+    build_text_to_video_prompt,
+)
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
+
+
+@pytest.mark.parametrize(
+    ("negative_prompt", "expected_negative_prompt"),
+    [(None, None), ("", ""), ("blurry", "blurry")],
+)
+def test_text_to_video_builds_canonical_prompt(
+    negative_prompt: str | None,
+    expected_negative_prompt: str | None,
+) -> None:
+    result = build_text_to_video_prompt("a robot waves", negative_prompt)
+
+    assert result["prompt"] == "a robot waves"
+    assert result["modalities"] == ["video"]
+    if expected_negative_prompt is None:
+        assert "negative_prompt" not in result
+    else:
+        assert result["negative_prompt"] == expected_negative_prompt
+
+
+@pytest.mark.parametrize(
+    ("model", "model_class_name", "preset_name"),
+    [
+        ("robbyant/lingbot-video-dense-1.3b", None, "lingbot"),
+        ("/models/custom-checkpoint", "LingBotVideoPipeline", "lingbot"),
+        ("Lightricks/LTX-2", "LTX2DistilledPipeline", "ltx2_distilled"),
+        ("BestWishYsh/Helios-Distilled", None, "helios"),
+        ("/models/cosmos-edge", "Cosmos3EdgeVFMTransformer", "cosmos3_edge"),
+        ("/models/vace", "WanVACEPipeline", "vace"),
+    ],
+)
+def test_detect_preset_is_scoped_to_model_family(
+    model: str,
+    model_class_name: str | None,
+    preset_name: str,
+) -> None:
+    assert _detect_preset(model, model_class_name) is _MODEL_PRESETS[preset_name]
+
+
+def test_lingbot_preset_matches_lingbot_defaults() -> None:
+    assert _MODEL_PRESETS["lingbot"] == {
+        "model_class_name": "LingBotVideoPipeline",
+        "height": 192,
+        "width": 320,
+        "num_frames": 9,
+        "num_inference_steps": 2,
+        "guidance_scale": 3.0,
+        "fps": 24,
+        "flow_shift": 3.0,
+        "output": "lingbot_video_output.mp4",
+    }
+
+
+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]))
+
+
+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]))