diff --git a/benchmarks/diffusion/quantization_quality.py b/benchmarks/diffusion/quantization_quality.py
index 81132e5aa..09a9fa74e 100644
--- a/benchmarks/diffusion/quantization_quality.py
+++ b/benchmarks/diffusion/quantization_quality.py
@@ -148,6 +148,7 @@ def _build_omni_kwargs(args, quantization=None):
 def _generate_image(omni, args, prompt, seed):
     """Generate a single image and return (PIL.Image, time_seconds, memory_gib)."""
     from vllm_omni.inputs.data import OmniDiffusionSamplingParams
+    from vllm_omni.outputs import OmniRequestOutput
     from vllm_omni.platforms import current_omni_platform
 
     generator = torch.Generator(device=current_omni_platform.device_type).manual_seed(seed)
@@ -165,8 +166,9 @@ def _generate_image(omni, args, prompt, seed):
     elapsed = time.perf_counter() - start
     peak_mem = torch.accelerator.max_memory_allocated() / (1024**3)
 
-    first = outputs[0]
-    req_out = first.request_output[0] if hasattr(first, "request_output") else first
+    req_out = OmniRequestOutput.unwrap_result(outputs)
+    if not req_out.images:
+        raise ValueError("Could not extract image output from result.")
     img = req_out.images[0]
     return img, elapsed, peak_mem
 
diff --git a/tests/diffusion/quantization/test_quantization_quality.py b/tests/diffusion/quantization/test_quantization_quality.py
index b94df986a..02ef0400f 100644
--- a/tests/diffusion/quantization/test_quantization_quality.py
+++ b/tests/diffusion/quantization/test_quantization_quality.py
@@ -32,17 +32,31 @@ Optional artifact dump:
 from __future__ import annotations
 
 import gc
+import importlib.util
 import json
 import os
+import sys
 from dataclasses import dataclass
 from pathlib import Path
+from types import SimpleNamespace
 
 import numpy as np
 import pytest
 import torch
+from PIL import Image
 
 from tests.helpers.mark import hardware_marks
 
+_REPO_ROOT = Path(__file__).resolve().parents[3]
+_BENCH_MODULE_PATH = _REPO_ROOT / "benchmarks" / "diffusion" / "quantization_quality.py"
+_BENCH_MODULE_NAME = "benchmarks.diffusion.quantization_quality"
+
+if _BENCH_MODULE_NAME not in sys.modules:
+    _spec = importlib.util.spec_from_file_location(_BENCH_MODULE_NAME, _BENCH_MODULE_PATH)
+    _mod = importlib.util.module_from_spec(_spec)
+    sys.modules[_BENCH_MODULE_NAME] = _mod
+    _spec.loader.exec_module(_mod)
+
 # ---------------------------------------------------------------------------
 # Configuration — add new quantization methods / models here
 # ---------------------------------------------------------------------------
@@ -316,6 +330,37 @@ def _unload(omni):
 # Tests
 # ---------------------------------------------------------------------------
 
+
+def test_benchmark_generate_image_unwraps_nested_omni_request_output(monkeypatch):
+    from benchmarks.diffusion.quantization_quality import _generate_image as benchmark_generate_image
+    from vllm_omni.outputs import OmniRequestOutput
+    from vllm_omni.platforms import current_omni_platform
+
+    monkeypatch.setattr(current_omni_platform, "device_type", "cpu", raising=False)
+    monkeypatch.setattr(torch.accelerator, "reset_peak_memory_stats", lambda: None, raising=False)
+    monkeypatch.setattr(torch.accelerator, "max_memory_allocated", lambda: 0, raising=False)
+
+    image = Image.new("RGB", (2, 2))
+    inner = OmniRequestOutput.from_diffusion(request_id="req", images=[image])
+    outer = OmniRequestOutput(
+        request_id="req",
+        stage_id=0,
+        final_output_type="image",
+        request_output=inner,
+        finished=True,
+    )
+
+    class DummyOmni:
+        def generate(self, *_args, **_kwargs):
+            return [outer]
+
+    args = SimpleNamespace(height=2, width=2, num_inference_steps=1)
+    output, _elapsed, peak_mem = benchmark_generate_image(DummyOmni(), args, "prompt", 42)
+
+    assert output is image
+    assert peak_mem == 0.0
+
+
 _marks = hardware_marks(res={"cuda": "H100"})
 _OUTPUT_DIR = Path(os.environ["VLLM_OMNI_QUALITY_OUTPUT_DIR"]) if "VLLM_OMNI_QUALITY_OUTPUT_DIR" in os.environ else None