[Diffusion] Refactor LTX2 to use unified CFG parallel framework (#2160)

Signed-off-by: Yangshen Deng <yangshen.d@outlook.com>
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

tests/diffusion/models/ltx2/test_ltx2_cfg_parallel_adaptation.py

@@ -0,0 +1,58 @@
+from types import SimpleNamespace
+
+import pytest
+import torch
+
+from vllm_omni.diffusion.models.ltx2.pipeline_ltx2 import LTX2Pipeline
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
+
+
+def _make_pipeline(sequence_parallel_size: int = 1) -> LTX2Pipeline:
+    pipeline = object.__new__(LTX2Pipeline)
+    torch.nn.Module.__init__(pipeline)
+    pipeline.audio_vae_temporal_compression_ratio = 4
+    pipeline.audio_vae_mel_compression_ratio = 4
+    pipeline.od_config = SimpleNamespace(parallel_config=SimpleNamespace(sequence_parallel_size=sequence_parallel_size))
+    # Mock audio_vae with identity normalization (mean=0, std=1) so
+    # _normalize_audio_latents is a no-op and test values are preserved.
+    pipeline.audio_vae = SimpleNamespace(
+        latents_mean=torch.tensor(0.0),
+        latents_std=torch.tensor(1.0),
+    )
+    return pipeline
+
+
+def test_prepare_audio_latents_pads_packed_sequence_dim_for_provided_latents():
+    pipeline = _make_pipeline(sequence_parallel_size=4)
+    latents = torch.arange(40, dtype=torch.float32).view(1, 10, 4)
+
+    padded, original_num_frames, padded_num_frames = pipeline.prepare_audio_latents(
+        batch_size=1,
+        num_channels_latents=2,
+        num_mel_bins=8,
+        audio_latent_length=10,
+        dtype=torch.float32,
+        device=torch.device("cpu"),
+        latents=latents,
+    )
+
+    assert original_num_frames == 10
+    assert padded_num_frames == 12
+    assert padded.shape == (1, 12, 4)
+    torch.testing.assert_close(padded[:, :10], latents)
+    torch.testing.assert_close(padded[:, 10:], torch.zeros(1, 2, 4))
+
+
+def test_unpad_audio_latents_restores_original_frames_before_unpack():
+    pipeline = _make_pipeline()
+    original = torch.arange(40, dtype=torch.float32).view(1, 10, 4)
+    padded = torch.cat([original, torch.full((1, 2, 4), 999.0)], dim=1)
+
+    unpadded = pipeline._unpad_audio_latents(padded, 10)
+    unpacked = pipeline._unpack_audio_latents(unpadded, latent_length=10, num_mel_bins=2)
+    expected = pipeline._unpack_audio_latents(original, latent_length=10, num_mel_bins=2)
+
+    assert unpacked.shape == (1, 2, 10, 2)
+    assert not (unpacked == 999.0).any()
+    torch.testing.assert_close(unpacked, expected)