Reject non-positive Flux2 Klein inference steps (#3717)

Signed-off-by: MmMaiIIi <2360655509@qq.com>

tests/diffusion/models/flux2/test_flux2_klein_num_inference_steps.py

@@ -0,0 +1,119 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from unittest.mock import MagicMock
+
+import pytest
+
+from vllm_omni.diffusion.models.flux2_klein.pipeline_flux2_klein import (
+    Flux2KleinPipeline,
+)
+from vllm_omni.inputs.data import OmniDiffusionSamplingParams
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu, pytest.mark.diffusion]
+
+
+class _StopAfterCheckInputsError(Exception):
+    pass
+
+
+def _make_pipeline():
+    pipeline = object.__new__(Flux2KleinPipeline)
+    pipeline.vae_scale_factor = 8
+    pipeline.is_distilled = True
+    pipeline._guidance_scale = 0.0
+    return pipeline
+
+
+def _make_minimal_request(
+    prompt="valid prompt",
+    *,
+    num_inference_steps=None,
+):
+    """Build a minimal OmniDiffusionRequest-like object that forward() reads from."""
+    params = OmniDiffusionSamplingParams(
+        height=512,
+        width=512,
+        num_inference_steps=num_inference_steps,
+        seed=42,
+    )
+    req = MagicMock()
+    req.sampling_params = params
+    req.prompts = [prompt]
+    req.multi_modal_data = {}
+    return req
+
+
+def _capture_num_inference_steps(pipe, steps):
+    """Run pipe.forward() and capture the num_inference_steps passed to check_inputs."""
+    captured = {}
+
+    def fake_check_inputs(**kwargs):
+        captured["num_inference_steps"] = kwargs.get("num_inference_steps")
+        raise _StopAfterCheckInputsError
+
+    original = pipe.check_inputs
+    pipe.check_inputs = fake_check_inputs
+    try:
+        req = _make_minimal_request(num_inference_steps=steps)
+        pipe.forward(req)
+    except _StopAfterCheckInputsError:
+        pass
+    finally:
+        pipe.check_inputs = original
+    return captured.get("num_inference_steps")
+
+
+# --- Forward resolution tests (the actual regression) ---
+
+
+def test_forward_preserves_zero_not_default():
+    """#3703: num_inference_steps=0 must reach validation as 0, not as default 50."""
+    pipe = _make_pipeline()
+    captured = _capture_num_inference_steps(pipe, 0)
+    assert captured == 0, f"Expected 0, got {captured}"
+
+
+def test_forward_preserves_negative():
+    pipe = _make_pipeline()
+    captured = _capture_num_inference_steps(pipe, -1)
+    assert captured == -1
+
+
+def test_forward_none_uses_default():
+    """None means unset — forward() should substitute the pipeline default."""
+    pipe = _make_pipeline()
+    captured = _capture_num_inference_steps(pipe, None)
+    assert captured == 50
+
+
+def test_forward_preserves_positive():
+    pipe = _make_pipeline()
+    captured = _capture_num_inference_steps(pipe, 9)
+    assert captured == 9
+
+
+# --- check_inputs validation tests ---
+
+
+@pytest.mark.parametrize("steps", [0, -1])
+def test_check_inputs_rejects_non_positive(steps):
+    pipe = _make_pipeline()
+    with pytest.raises(ValueError):
+        pipe.check_inputs(
+            prompt="valid prompt",
+            height=512,
+            width=512,
+            num_inference_steps=steps,
+        )
+
+
+def test_check_inputs_accepts_none():
+    pipe = _make_pipeline()
+    pipe.check_inputs(prompt="valid prompt", height=512, width=512, num_inference_steps=None)
+
+
+@pytest.mark.parametrize("steps", [1, 2])
+def test_check_inputs_accepts_positive(steps):
+    pipe = _make_pipeline()
+    pipe.check_inputs(prompt="valid prompt", height=512, width=512, num_inference_steps=steps)

vllm_omni/diffusion/models/flux2_klein/pipeline_flux2_klein.py

@@ -865,7 +865,11 @@ class Flux2KleinPipeline(nn.Module, CFGParallelMixin, SupportImageInput, Diffusi
 
         height = req.sampling_params.height or height
         width = req.sampling_params.width or width
-        num_inference_steps = req.sampling_params.num_inference_steps or num_inference_steps
+        num_inference_steps = (
+            req.sampling_params.num_inference_steps
+            if req.sampling_params.num_inference_steps is not None
+            else num_inference_steps
+        )
         sigmas = req.sampling_params.sigmas or sigmas
         guidance_scale = (
             req.sampling_params.guidance_scale if req.sampling_params.guidance_scale is not None else guidance_scale