[Feat] Support CFG-Parallel For Sensenova-U1-8B-MoT (#4188)

Signed-off-by: sphinxkkkbc <binchengkang8@gmail.com>
Signed-off-by: 汪志鹏 <wangzhipeng628@gmail.com>
Co-authored-by: sphinxkkkbc <binchengkang8@gmail.com>
Co-authored-by: 汪志鹏 <wangzhipeng628@gmail.com>

tests/diffusion/models/sensenova_u1/test_sensenova_u1_cfg.py

@@ -0,0 +1,62 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+import torch
+
+from vllm_omni.diffusion.models.sensenova_u1.pipeline_sensenova_u1 import SenseNovaU1Pipeline
+
+
+def _pipeline_without_init() -> SenseNovaU1Pipeline:
+    return object.__new__(SenseNovaU1Pipeline)
+
+
+@pytest.mark.parametrize("kwargs", [None, {"step_i": 0}])
+def test_combine_cfg_noise_requires_is_it2i(kwargs):
+    pipe = _pipeline_without_init()
+    out_cond = (torch.ones(1, 2, 3),)
+    out_uncond = (torch.zeros(1, 2, 3),)
+
+    with pytest.raises(ValueError, match="is_it2i"):
+        pipe.combine_cfg_noise(
+            out_cond,
+            out_uncond,
+            cfg_scale=4.0,
+            cfg_norm="cfg_zero_star",
+            kwargs=kwargs,
+        )
+
+
+@pytest.mark.parametrize(
+    "kwargs",
+    [{"is_it2i": False}, {"is_it2i": False, "step_i": None}],
+)
+def test_cfg_zero_star_requires_step_i(kwargs):
+    pipe = _pipeline_without_init()
+    out_cond = (torch.ones(1, 2, 3),)
+    out_uncond = (torch.zeros(1, 2, 3),)
+    with pytest.raises(ValueError, match="step_i"):
+        pipe.combine_cfg_noise(
+            out_cond,
+            out_uncond,
+            cfg_scale=4.0,
+            cfg_norm="cfg_zero_star",
+            kwargs=kwargs,
+        )
+
+
+def test_cfg_zero_star_accepts_step_i():
+    pipe = _pipeline_without_init()
+    out_cond = (torch.ones(1, 2, 3),)
+    out_uncond = (torch.zeros(1, 2, 3),)
+    result = pipe.combine_cfg_noise(
+        out_cond,
+        out_uncond,
+        cfg_scale=4.0,
+        cfg_norm="cfg_zero_star",
+        kwargs={"is_it2i": False, "step_i": 0},
+    )
+
+    assert result.shape == out_cond[0].shape
+    assert torch.equal(result, torch.zeros_like(out_cond[0]))
+    assert torch.isfinite(result).all()