Nextstep online e2e

tests/e2e/online_serving/test_nextstep_expansion.py

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+"""
+Online serving E2E for NextStep-1.1 text-to-image (tensor parallel).
+"""
+
+import os
+
+import pytest
+
+from tests.conftest import (
+    OmniServer,
+    OmniServerParams,
+    OpenAIClientHandler,
+    dummy_messages_from_mix_data,
+)
+from tests.utils import hardware_marks
+
+# L4: 4 GPUs + TP=4; XPU B60: 2 cards (use num_cards={"cuda": 4, "xpu": 4} if needed)
+FOUR_CARD_MARKS = hardware_marks(
+    res={"cuda": "L4", "xpu": "B60"},
+    num_cards={"cuda": 2, "xpu": 2},
+)
+
+POSITIVE_PROMPT = "A small red barn in a snowy field, simple illustration."
+NEGATIVE_PROMPT = "blurry, low quality"
+
+_DEFAULT_MODEL = "stepfun-ai/NextStep-1.1"
+
+
+def _get_diffusion_feature_cases(model: str):
+    """Single online config: TP=4, explicit pipeline class."""
+    return [
+        pytest.param(
+            OmniServerParams(
+                model=model,
+                server_args=[
+                    "--tensor-parallel-size",
+                    "2",
+                    "--model-class-name",
+                    "NextStep11Pipeline",
+                ],
+            ),
+            id="nextstep_tp4_pipeline",
+            marks=FOUR_CARD_MARKS,
+        ),
+    ]
+
+
+@pytest.mark.advanced_model
+@pytest.mark.diffusion
+@pytest.mark.parametrize(
+    "omni_server",
+    _get_diffusion_feature_cases(model=os.environ.get("VLLM_TEST_NEXTSTEP_MODEL", _DEFAULT_MODEL)),
+    indirect=True,
+)
+def test_nextstep_11(omni_server: OmniServer, openai_client: OpenAIClientHandler):
+    messages = dummy_messages_from_mix_data(content_text=POSITIVE_PROMPT)
+    request_config = {
+        "model": omni_server.model,
+        "messages": messages,
+        "extra_body": {
+            "height": 512,
+            "width": 512,
+            "num_inference_steps": 2,
+            "guidance_scale": 5.0,
+            "guidance_scale_2": 1.0,
+            "negative_prompt": NEGATIVE_PROMPT,
+            "seed": 42,
+        },
+    }
+
+    openai_client.send_diffusion_request(request_config)