Increase timeout for Diffusion Model Test and Diffusion Images API LoRA E2E in Buildkite configurations to 30 minutes. Also, introduce a new timeout constant for diffusion initialization in relevant test files to enhance stability during testing. Update OmniServer initialization to utilize the new timeout settings.

.buildkite/test-amd.yaml

@@ -1,7 +1,7 @@
 steps:
 
 - label: "Diffusion Model Test"
-  timeout_in_minutes: 20
+  timeout_in_minutes: 30
   agent_pool: mi325_2
   depends_on: amd-build
   mirror_hardwares: [amdproduction]
@@ -11,7 +11,7 @@ steps:
     - pytest -s -v tests/e2e/offline_inference/test_t2i_model.py
 
 - label: "Diffusion Images API LoRA E2E"
-  timeout_in_minutes: 20
+  timeout_in_minutes: 30
   agent_pool: mi325_1
   depends_on: amd-build
   mirror_hardwares: [amdproduction]

.buildkite/test-merge.yml

@@ -16,7 +16,7 @@ steps:
             - "/fsx/hf_cache:/fsx/hf_cache"
 
   - label: "Diffusion Model Test"
-    timeout_in_minutes: 20
+    timeout_in_minutes: 30
     depends_on: upload-merge-pipeline
     commands:
       - pytest -s -v tests/e2e/offline_inference/test_t2i_model.py -m "advanced_model and diffusion" --run-level "advanced_model"
@@ -33,7 +33,7 @@ steps:
             - "/fsx/hf_cache:/fsx/hf_cache"
 
   - label: "Diffusion Images API LoRA E2E"
-    timeout_in_minutes: 20
+    timeout_in_minutes: 30
     depends_on: upload-merge-pipeline
     commands:
       - pytest -s -v tests/e2e/online_serving/test_images_generations_lora.py

.buildkite/test-ready.yml

@@ -18,7 +18,7 @@ steps:
   - label: "Diffusion Model Test"
     depends_on: upload-ready-pipeline
     commands:
-      - timeout 20m pytest -s -v tests/e2e/offline_inference/test_t2i_model.py -m "core_model and diffusion" --run-level "core_model"
+      - timeout 30m pytest -s -v tests/e2e/offline_inference/test_t2i_model.py -m "core_model and diffusion" --run-level "core_model"
     agents:
       queue: "gpu_1_queue"
     plugins:

tests/e2e/offline_inference/test_diffusion_lora.py

@@ -24,6 +24,7 @@ os.environ["VLLM_TEST_CLEAN_GPU_MEMORY"] = "1"
 # This test is specific to Z-Image LoRA behavior. Keep it focused on a single
 # model to reduce runtime and avoid extra downloads.
 models = ["Tongyi-MAI/Z-Image-Turbo"]
+DIFFUSION_INIT_TIMEOUT_S = 600
 
 
 @pytest.mark.parametrize("model_name", models)
@@ -76,7 +77,11 @@ def test_diffusion_model(model_name: str, tmp_path: Path):
         )
         return str(adapter_dir)
 
-    m = Omni(model=model_name)
+    m = Omni(
+        model=model_name,
+        stage_init_timeout=DIFFUSION_INIT_TIMEOUT_S,
+        init_timeout=DIFFUSION_INIT_TIMEOUT_S,
+    )
     try:
         # high resolution may cause OOM on L4
         height = 256

tests/e2e/online_serving/test_images_generations_lora.py

@@ -28,6 +28,7 @@ from tests.utils import hardware_test
 os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
 
 MODEL = "Tongyi-MAI/Z-Image-Turbo"
+DIFFUSION_INIT_TIMEOUT_S = 600
 
 
 PROMPT = "a photo of a cat sitting on a laptop keyboard"
@@ -37,7 +38,17 @@ SEED = 42
 
 @pytest.fixture(scope="module")
 def omni_server():
-    with OmniServer(MODEL, ["--num-gpus", "1"]) as server:
+    with OmniServer(
+        MODEL,
+        [
+            "--num-gpus",
+            "1",
+            "--stage-init-timeout",
+            str(DIFFUSION_INIT_TIMEOUT_S),
+            "--init-timeout",
+            str(DIFFUSION_INIT_TIMEOUT_S),
+        ],
+    ) as server:
         yield server
 
 

vllm_omni/model_executor/models/qwen3_omni/qwen3_omni_moe_thinker.py

@@ -161,6 +161,7 @@ class Qwen3Omni_VisionTransformer(_Qwen3Omni_VisionTransformer):
             cu_seqlens,
             self.hidden_size,
             self.tp_size,
+            self.device,
         )
         cu_seqlens = torch.from_numpy(cu_seqlens).to(self.device, non_blocking=True)