[Doc] Clarify private security contact guidance

Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>
[Doc] Move meeting details to dedicated page
2026-08-13 16:33:09 +08:00 · 2026-08-13 12:26:00 +08:00 · 2026-08-13 11:18:39 +08:00 · 2026-08-13 09:36:17 +08:00 · 2026-08-13 08:42:37 +08:00 · 2026-08-13 08:41:59 +08:00
1141 changed files with 121994 additions and 19842 deletions
@@ -89,7 +89,7 @@ steps:
      mirror_hardwares: [amdproduction]
      grade: Blocking
      commands:
-        - pytest -s -v tests/diffusion/distributed/test_zimage_parallelism.py
+        - pytest -s -v tests/diffusion/distributed/test_tensor_parallel.py

    - label: "Diffusion Model Test"
      agent_pool: mi325_1
@@ -35,7 +35,7 @@ steps:
  mirror_hardwares: [amdproduction]
  grade: Blocking
  commands:
-    - timeout 20m pytest -s -v tests/e2e/offline_inference/custom_pipeline/ -m "core_model"
+    - timeout 20m pytest -s -v tests/e2e/features/custom_pipeline/ -m "core_model"


 - label: "Voxtral TTS CUDA Unit Test"
@@ -8,7 +8,7 @@
 # NPU naming: {queue}_{resource_class}
 #   a2b3_npu_1  — ascend-a2b3, resource_class npu-1 (A2/B3 CI image)
 #   a2b3_npu_4  — ascend-a2b3, resource_class npu-4
-#   a3_npu_2    — ascend-a3, resource_class npu-2 (A3 CI image tag …:$BUILDKITE_COMMIT-a3)
+#   a3_npu_2    — ascend-a3, resource_class npu-2

 x-npu-k8s-plugins: &npu_k8s_plugins
  - kubernetes:
@@ -8,14 +8,24 @@ steps:
  - group: ":card_index_dividers: Simple Test"
    depends_on: upload-merge-pipeline
    steps:
-      - label: "Simple · Diffusion & Model Executor Test"
+      - label: "Simple · Model Executor Test"
        commands:
-          - "timeout 20m pytest -sv tests/diffusion tests/model_executor -m 'core_model and cpu' --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
+          - "timeout 20m pytest -sv tests/model_executor -m 'core_model and cpu' --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
+        mirror_hardwares: l4_1
+
+      - label: "Simple · Diffusion Test"
+        commands:
+          - "timeout 20m pytest -sv tests/diffusion -m 'core_model and cpu' --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
+        mirror_hardwares: l4_1
+
+      - label: "Simple · Engine&Entrypoints Test"
+        commands:
+          - "timeout 20m pytest -sv tests/entrypoints tests/engine -m 'core_model and cpu' --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
        mirror_hardwares: l4_1

      - label: "Simple · Other Test"
        commands:
-          - "timeout 20m pytest -sv -m 'core_model and cpu' --ignore=tests/diffusion --ignore=tests/model_executor --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
+          - "timeout 20m pytest -sv tests/ -m 'core_model and cpu' --ignore=tests/diffusion --ignore=tests/model_executor --ignore=tests/entrypoints --ignore=tests/engine --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
        mirror_hardwares: l4_1

  - group: ":card_index_dividers: Diffusion Test"
@@ -27,12 +37,6 @@ steps:
          - pytest -sv tests/diffusion/lora/ -m 'advanced_model and cuda' --run-level "advanced_model"
        mirror_hardwares: l4_1

-      - label: "Diffusion · Offloader Test"
-        timeout_in_minutes: 40
-        commands:
-          - timeout 40m pytest -s -v tests/diffusion/offloader -m "advanced_model and cuda"
-        mirror_hardwares: l4_1
-
  - label: "Distributed Test"
    depends_on: upload-merge-pipeline
    timeout_in_minutes: 60
@@ -135,39 +139,37 @@ steps:
        timeout_in_minutes: 50
        commands:
          - pytest -s -v tests/e2e/offline_inference/test_minicpmo_4_5.py tests/e2e/online_serving/test_minicpmo_4_5.py -m 'advanced_model and cuda' --run-level 'advanced_model'
-        agents:
-          queue: "mithril-h100-pool"
-        plugins:
-          - kubernetes:
-              podSpec:
-                containers:
-                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                    resources:
-                      limits:
-                        nvidia.com/gpu: 2
-                    volumeMounts:
-                      - name: devshm
-                        mountPath: /dev/shm
-                      - name: hf-cache
-                        mountPath: /root/.cache/huggingface
-                    env:
-                      - name: HF_HOME
-                        value: /root/.cache/huggingface
-                      - name: HF_TOKEN
-                        valueFrom:
-                          secretKeyRef:
-                            name: hf-token-secret
-                            key: token
-                nodeSelector:
-                  node.kubernetes.io/instance-type: gpu-h100-sxm
-                volumes:
-                  - name: devshm
-                    emptyDir:
-                      medium: Memory
-                  - name: hf-cache
-                    hostPath:
-                      path: /mnt/hf-cache
-                      type: DirectoryOrCreate
+        # h100_2, not h100_1: advanced_model asserts transcribe every audio
+        # response with Whisper, and tests/helpers/media.py only uses an
+        # accelerator for Whisper when a spare GPU exists. On a single card it
+        # grades on CPU: ~8-10 min per clip vs ~30 s, which alone overruns the
+        # 50-minute job timeout (build 2956).
+        mirror_hardwares: h100_2
+
+      - label: "Omni · MiniCPM-o 4.5 Duplex Test"
+        source_file_dependencies:
+          - tests/assets/minicpmo_4_5/response_required_16k.wav
+          - tests/assets/minicpmo_4_5/soft_interrupt_16k.wav
+          - tests/e2e/online_serving/test_minicpmo_4_5_duplex.py
+          - tests/e2e/online_serving/test_minicpmo_4_5_duplex_expansion.py
+          - tests/e2e/online_serving/test_minicpmo_realtime_duplex_drivers.py
+          - tests/e2e/online_serving/helpers/minicpmo_realtime_duplex_scenarios.py
+          - tests/e2e/online_serving/run_minicpmo_realtime_duplex_multi_session.py
+          - tests/e2e/online_serving/run_minicpmo_realtime_duplex_soft_interrupt.py
+          - tests/e2e/online_serving/helpers/minicpmo_4_5_duplex.py
+          - vllm_omni/deploy/minicpmo_4_5_duplex.yaml
+          - vllm_omni/experimental/fullduplex/
+          - vllm_omni/model_executor/models/minicpmo_4_5/
+          - vllm_omni/model_executor/stage_input_processors/minicpmo_4_5_omni.py
+          - vllm_omni/core/sched/
+          - vllm_omni/outputs/multimodal_accumulation.py
+          - vllm_omni/engine/stage_init_utils.py
+          - vllm_omni/engine/async_omni_engine.py
+          - vllm_omni/entrypoints/cli/serve.py
+        timeout_in_minutes: 50
+        commands:
+          - pytest -s -v tests/e2e/online_serving/test_minicpmo_4_5_duplex.py -m 'advanced_model and cuda' --run-level 'advanced_model'
+        mirror_hardwares: h100_1

      - label: "TTS · VoxCPM2 Test"
        source_file_dependencies:
@@ -185,22 +187,6 @@ steps:
            "
        mirror_hardwares: l4_1

-      - label: "TTS · SoulX-Singer Test"
-        source_file_dependencies:
-          - tests/e2e/offline_inference/test_soulxsinger.py
-          - vllm_omni/diffusion/models/soulx_singer/
-          - vllm_omni/deploy/soulxsinger_svc.yaml
-          - vllm_omni/deploy/soulxsinger_svs.yaml
-        timeout_in_minutes: 30
-        commands:
-          - |
-            timeout 30m bash -c "
-              export VLLM_LOGGING_LEVEL=DEBUG
-              export VLLM_WORKER_MULTIPROC_METHOD=spawn
-              pytest -s -v tests/e2e/offline_inference/test_soulxsinger.py -k 'svc or precomputed' -m 'advanced_model and cuda' --run-level 'advanced_model'
-            "
-        mirror_hardwares: l4_1
-
      - label: "Diffusion · Qwen Image Edit Test"
        source_file_dependencies:
          - tests/e2e/online_serving/test_qwen_image_edit.py
@@ -15,9 +15,15 @@ steps:
      - label: ":full_moon: Omni · Function Test with H100"
        timeout_in_minutes: 120
        commands:
-          - pytest -sv tests/e2e --run-level "full_model" -m "full_model and H100 and omni" --ignore=tests/e2e/accuracy --ignore=tests/e2e/online_serving/test_qwen3_omni_multi_replicas.py
+          - pytest -sv tests/e2e --run-level "full_model" -m "full_model and H100 and omni" --ignore=tests/e2e/accuracy --ignore=tests/e2e/online_serving/test_qwen3_omni_multi_replicas.py --ignore=tests/e2e/online_serving/test_minicpmo_4_5_duplex_expansion.py
        mirror_hardwares: h100_2

+      - label: ":full_moon: Omni · MiniCPM-o 4.5 Duplex Test"
+        timeout_in_minutes: 50
+        commands:
+          - pytest -s -v tests/e2e/online_serving/test_minicpmo_4_5_duplex_expansion.py -m "full_model and cuda and H100 and omni" --run-level "full_model"
+        mirror_hardwares: h100_1
+
      - label: ":full_moon: Omni · Doc Test with L4"
        timeout_in_minutes: 90
        commands:
@@ -44,6 +50,19 @@ steps:
            exit $$EXIT
        mirror_hardwares: h100_2

+      - label: ":full_moon: Omni · MiniCPM-o 4.5 · Accuracy Test"
+        timeout_in_minutes: 180
+        commands:
+          - export SEED_TTS_WER_EVAL=1
+          - export SEED_TTS_EVAL_DEVICE=cuda:1
+          - |
+            set +e
+            pytest -s -v tests/e2e/accuracy/minicpmo_4_5/test_minicpmo_4_5.py -m "full_model and cuda" --run-level full_model
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/e2e/accuracy/minicpmo_4_5/results/*.json"
+            exit $$EXIT
+        mirror_hardwares: h100_2
+
      - label: ":full_moon: Omni · Perf Test · No Async Chunk"
        key: nightly-omni-performance-no-async-chunk
        timeout_in_minutes: 300
@@ -96,6 +115,32 @@ steps:
            exit $$EXIT
        mirror_hardwares: h100_3

+      - label: ":full_moon: Omni · MiniCPM-o 4.5 · Perf Test"
+        key: nightly-omni-performance-minicpmo-4-5
+        timeout_in_minutes: 180
+        commands:
+          - export BENCHMARK_DIR=tests/dfx/perf/results
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_minicpmo_4_5.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
+            exit $$EXIT
+        mirror_hardwares: h100_1
+
+      - label: ":full_moon: Omni · MiniCPM-o 4.5 · Duplex Seed-TTS Perf Test"
+        key: nightly-omni-performance-minicpmo-4-5-duplex-seed-tts
+        timeout_in_minutes: 180
+        commands:
+          - export BENCHMARK_DIR=tests/dfx/perf/results
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_minicpmo_4_5_duplex_seed_tts.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
+            exit $$EXIT
+        mirror_hardwares: h100_1
+
      - label: ":full_moon: Omni · Multi-Replica Startup Test with 4x H100"
        timeout_in_minutes: 45
        commands:
@@ -300,7 +345,7 @@ steps:
        commands:
          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
          - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
-          - export CACHE_DIT_VERSION=1.3.0
+          - export CACHE_DIT_VERSION=1.5.0
          - |
            set +e
            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_vllm_omni.json
@@ -316,7 +361,7 @@ steps:
        commands:
          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
          - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
-          - export CACHE_DIT_VERSION=1.3.0
+          - export CACHE_DIT_VERSION=1.5.0
          - |
            set +e
            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_edit_vllm_omni.json
@@ -332,7 +377,7 @@ steps:
        commands:
          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
          - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
-          - export CACHE_DIT_VERSION=1.3.0
+          - export CACHE_DIT_VERSION=1.5.0
          - |
            set +e
            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_edit_2511_vllm_omni.json
@@ -348,7 +393,7 @@ steps:
        commands:
          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
          - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
-          - export CACHE_DIT_VERSION=1.3.0
+          - export CACHE_DIT_VERSION=1.5.0
          - |
            set +e
            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_layered_vllm_omni.json
@@ -364,7 +409,7 @@ steps:
        commands:
          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
          - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
-          - export CACHE_DIT_VERSION=1.3.0
+          - export CACHE_DIT_VERSION=1.5.0
          - |
            set +e
            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_bagel_vllm_omni.json
@@ -398,7 +443,7 @@ steps:
      - label: ":full_moon: Diffusion X2V · LingBot Video MoE Function Test"
        timeout_in_minutes: 90
        commands:
-          - pytest -s -v tests/e2e/online_serving/test_lingbot_video_moe.py -m "full_model and cuda" --run-level "full_model"
+          - pytest -s -v tests/e2e/online_serving/test_lingbot_video_moe.py -k "video_generation" -m "full_model and cuda" --run-level "full_model"
        mirror_hardwares: h100_1

      - label: ":full_moon: Diffusion X2V · Doc Test"
@@ -421,12 +466,18 @@ steps:
          - pytest -s -v tests/e2e/accuracy/test_diffusers_backend_similarity.py -k '2v_matches_diffusers' -m full_model --run-level full_model
        mirror_hardwares: h100_2

+      - label: ":full_moon: Diffusion X2V · MiniMax H3 I2VA/Ref2VA Accuracy Test"
+        timeout_in_minutes: 180
+        commands:
+          - pytest -s -v tests/e2e/accuracy/minimax_h3/test_minimax_h3_i2va_ref2va_similarity.py -m full_model --run-level full_model
+        mirror_hardwares: h100_4
+
      - label: ":full_moon: Diffusion X2V · Other Accuracy Test"
        timeout_in_minutes: 180
        commands:
          - pytest -s -v tests/e2e/accuracy/hunyuanvideo15_t2v/test_hunyuanvideo15_t2v_video_similarity.py -m full_model --run-level full_model
          - pytest -s -v tests/e2e/accuracy/hunyuanvideo15_i2v/test_hunyuanvideo15_i2v_video_similarity.py -m full_model --run-level full_model
-          - pytest -s -v tests/e2e/accuracy/test_ltx2_3_video_similarity.py -m full_model --run-level full_model
+          - pytest -s -v 'tests/e2e/accuracy/ltx/test_ltx_official_similarity.py::test_ltx_one_stage_matches_official[ltx2_3_i2v]' -m full_model --run-level full_model
          - pytest -s -v tests/e2e/accuracy/test_video_streaming_output_similarity.py -m full_model --run-level full_model
        mirror_hardwares: h100_2

@@ -448,37 +499,63 @@ steps:
            exit $$EXIT2
        mirror_hardwares: h100_2

+      - label: ":full_moon: Diffusion X2V · Perf Test · MiniMax-H3"
+        key: nightly-diffusion-x2v-performance-minimax-h3
+        timeout_in_minutes: 180
+        commands:
+          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
+          - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_minimax_h3_vllm_omni.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
+            buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
+            exit $$EXIT
+        mirror_hardwares: h100_4
+
  - group: ":card_index_dividers: Diffusion Test"
+    key: nightly-diffusion-test-group
    depends_on: upload-nightly-pipeline
-    if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test" || build.pull_request.labels includes "diffusion-x2iat-test"
+    if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test"
    steps:
+      - label: ":full_moon: Diffusion · Distributed Test with L4"
+        timeout_in_minutes: 60
+        commands:
+          - pytest -sv tests/diffusion/distributed/ -m 'full_model and cuda and L4' --run-level "full_model"
+        mirror_hardwares: l4_4
+
+      - label: ":full_moon: Diffusion · Distributed Test with H100"
+        timeout_in_minutes: 60
+        commands:
+          - pytest -sv tests/diffusion/distributed/ -m 'full_model and cuda and H100' --run-level "full_model"
+        mirror_hardwares: h100_4

      - label: ":full_moon: Diffusion · Offloader Test"
-        timeout_in_minutes: 40
+        timeout_in_minutes: 60
        commands:
-          - timeout 40m pytest -s -v tests/diffusion/offloader -m "full_model and cuda"
+          - pytest -sv tests/diffusion/offloader -m "full_model and cuda"
        mirror_hardwares: l4_1

-      - label: ":full_moon: Diffusion · Quantization Test with H100"
+      - label: ":full_moon: Diffusion · Quantization Test with H100 · Single-GPU"
+        timeout_in_minutes: 60
        commands:
-          - timeout 60m pytest -sv tests/diffusion/quantization -m 'full_model and cuda and H100' --run-level "full_model"
+          - pytest -sv tests/diffusion/quantization -m 'full_model and cuda and H100' --run-level "full_model"
        mirror_hardwares: h100_1

+      - label: ":full_moon: Diffusion · MiniMax H3 FP8 Quality Test with H100 · 2-GPU"
+        timeout_in_minutes: 60
+        commands:
+          - pytest -sv tests/diffusion/models/minimax_h3/test_minimax_h3_quantization_quality.py -m 'full_model and cuda and H100' --run-level "full_model"
+        mirror_hardwares: h100_2
+
      - label: ":full_moon: Diffusion · Quantization Test with L4"
        timeout_in_minutes: 60
        commands:
+          - pip install "vllm-gguf-plugin>=0.0.3"
          - pytest -sv tests/diffusion/quantization -m 'full_model and cuda and L4' --run-level "full_model"
        mirror_hardwares: l4_1

-      - label: ":full_moon: Diffusion · Wan VAE Spatial Shard Correctness Test"
-        timeout_in_minutes: 60
-        commands:
-          - >-
-            pytest -sv tests/diffusion/distributed/test_wan_spatial_shard.py
-            -m "full_model and diffusion and H100 and distributed_cuda"
-            --run-level "full_model"
-        mirror_hardwares: h100_2
-
  - label: ":bar_chart: Testcase Statistics"
    key: nightly-testcase-statistics
    timeout_in_minutes: 120
@@ -8,22 +8,26 @@ steps:
  - group: ":card_index_dividers: Simple Test"
    depends_on: upload-ready-pipeline
    steps:
-      - label: "Simple · Diffusion & Model Executor Test"
+      - label: "Simple · Model Executor Test"
        commands:
-          - "timeout 20m pytest -sv tests/diffusion tests/model_executor -m 'core_model and cpu' --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
+          - "timeout 20m pytest -sv tests/model_executor -m 'core_model and cpu' --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
+        mirror_hardwares: l4_1
+
+      - label: "Simple · Diffusion Test"
+        commands:
+          - "timeout 20m pytest -sv tests/diffusion -m 'core_model and cpu' --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
+        mirror_hardwares: l4_1
+
+      - label: "Simple · Engine&Entrypoints Test"
+        commands:
+          - "timeout 20m pytest -sv tests/entrypoints tests/engine -m 'core_model and cpu' --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
        mirror_hardwares: l4_1

      - label: "Simple · Other Test"
        commands:
-          - "timeout 20m pytest -sv -m 'core_model and cpu' --ignore=tests/diffusion --ignore=tests/model_executor --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
+          - "timeout 20m pytest -sv tests/ -m 'core_model and cpu' --ignore=tests/diffusion --ignore=tests/model_executor --ignore=tests/entrypoints --ignore=tests/engine --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
        mirror_hardwares: l4_1

-  - label: "Custom Pipeline Test"
-    depends_on: upload-ready-pipeline
-    commands:
-      - timeout 20m pytest -s -v tests/e2e/offline_inference/custom_pipeline/ -m "core_model"
-    mirror_hardwares: l4_1
-
  - group: ":card_index_dividers: Diffusion Test"
    depends_on: upload-ready-pipeline
    steps:
@@ -43,15 +47,17 @@ steps:
          - timeout 15m pytest -sv tests/diffusion/batching -m 'core_model and cuda' --run-level "core_model"
        mirror_hardwares: l4_1

-      - label: "Diffusion · Distributed Test"
+      - label: "Diffusion · Distributed & Attention Test"
        source_file_dependencies:
          - tests/diffusion/distributed/
+          - tests/diffusion/attention/test_attention_sp.py
          - vllm_omni/diffusion/distributed/
-          - vllm_omni/diffusion/attention/parallel/
+          - vllm_omni/diffusion/attention/
          - vllm_omni/diffusion/data.py
          - vllm_omni/diffusion/models/
        commands:
          - timeout 20m pytest -sv tests/diffusion/distributed -m 'core_model and cuda' --run-level "core_model"
+          - timeout 20m pytest -sv tests/diffusion/attention/test_attention_sp.py -m 'core_model and cuda' --run-level "core_model"
        mirror_hardwares: l4_4

      - label: "Diffusion · Model Test"
@@ -104,11 +110,49 @@ steps:
  - group: ":card_index_dividers: E2E Test"
    depends_on: upload-ready-pipeline
    steps:
+      - label: "Custom Pipeline Test"
+        source_file_dependencies:
+          - tests/e2e/features/custom_pipeline/
+          - tests/e2e/features/helpers/custom_pipeline.py
+          - vllm_omni/diffusion/
+          - vllm_omni/engine/
+          - vllm_omni/entrypoints/
+          - vllm_omni/inputs/
+          - vllm_omni/outputs/
+        commands:
+          - timeout 20m pytest -s -v tests/e2e/features/custom_pipeline/ -m "core_model"
+        agents:
+          queue: "gpu_1_queue"
+        plugins:
+          - docker#v5.2.0:
+              image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+              always-pull: true
+              propagate-environment: true
+              # vLLM 0.27 added a pre-flight capacity check in
+              # distributed/device_communicators/shm_broadcast.py. Without this
+              # the container gets Docker's 64 MiB default and every test in
+              # this step dies at engine start with
+              #   RuntimeError: Insufficient space in /dev/shm:
+              #                 240 MiB required, 64 MiB available
+              # (9 failed in 8.7s on build #2949). vLLM 0.26 had no such check
+              # and allocated regardless, so this step passed on main while
+              # being under-provisioned the whole time — 0.27 turned a silent
+              # risk into an explicit error.
+              #
+              # This is the only inline docker plugin block in .buildkite/cuda/;
+              # every other step inherits common/ci_mirror_hardwares.yml, which
+              # already sets 8gb. Matching that rather than inventing a value.
+              shm-size: "8gb"
+              environment:
+                - "HF_HOME=/fsx/hf_cache"
+              volumes:
+                - "/fsx/hf_cache:/fsx/hf_cache"

      - label: "RLHF · VeRL-Omni E2E Test"
        source_file_dependencies:
-          - tests/e2e/offline_inference/rlhf_test/test_verl_omni_e2e.py
-          - tests/e2e/offline_inference/custom_pipeline/
+          - tests/e2e/features/rlhf_test/test_verl_omni_e2e.py
+          - tests/e2e/features/helpers/verl_omni_server.py
+          - tests/e2e/features/helpers/custom_pipeline.py
          - vllm_omni/diffusion/
          - vllm_omni/engine/
          - vllm_omni/entrypoints/
@@ -117,7 +161,7 @@ steps:
        commands:
          - |
            timeout 20m bash -c "
-              pytest -s -v tests/e2e/offline_inference/rlhf_test/test_verl_omni_e2e.py
+              pytest -s -v tests/e2e/features/rlhf_test/test_verl_omni_e2e.py
            "
        mirror_hardwares: l4_1

@@ -143,34 +187,7 @@ steps:
            timeout 20m bash -c "
              pytest -s -v tests/e2e/offline_inference/test_minicpmo_4_5.py -m 'core_model' --run-level 'core_model'
            "
-        agents:
-          queue: "mithril-h100-pool"
-        plugins:
-          - kubernetes:
-              podSpec:
-                containers:
-                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                    resources:
-                      limits:
-                        nvidia.com/gpu: 2
-                    volumeMounts:
-                      - name: devshm
-                        mountPath: /dev/shm
-                      - name: hf-cache
-                        mountPath: /root/.cache/huggingface
-                    env:
-                      - name: HF_HOME
-                        value: /root/.cache/huggingface
-                nodeSelector:
-                  node.kubernetes.io/instance-type: gpu-h100-sxm
-                volumes:
-                  - name: devshm
-                    emptyDir:
-                      medium: Memory
-                  - name: hf-cache
-                    hostPath:
-                      path: /mnt/hf-cache
-                      type: DirectoryOrCreate
+        mirror_hardwares: h100_1

      - label: "Omni · MiniCPM-o 4.5 Online Test"
        source_file_dependencies:
@@ -181,34 +198,46 @@ steps:
            timeout 20m bash -c "
              pytest -s -v tests/e2e/online_serving/test_minicpmo_4_5.py -m 'core_model' --run-level 'core_model'
            "
-        agents:
-          queue: "mithril-h100-pool"
-        plugins:
-          - kubernetes:
-              podSpec:
-                containers:
-                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                    resources:
-                      limits:
-                        nvidia.com/gpu: 2
-                    volumeMounts:
-                      - name: devshm
-                        mountPath: /dev/shm
-                      - name: hf-cache
-                        mountPath: /root/.cache/huggingface
-                    env:
-                      - name: HF_HOME
-                        value: /root/.cache/huggingface
-                nodeSelector:
-                  node.kubernetes.io/instance-type: gpu-h100-sxm
-                volumes:
-                  - name: devshm
-                    emptyDir:
-                      medium: Memory
-                  - name: hf-cache
-                    hostPath:
-                      path: /mnt/hf-cache
-                      type: DirectoryOrCreate
+        mirror_hardwares: h100_1
+
+      - label: "Omni · MiniCPM-o 4.5 Duplex Test"
+        source_file_dependencies:
+          - tests/assets/minicpmo_4_5/response_required_16k.wav
+          - tests/assets/minicpmo_4_5/soft_interrupt_16k.wav
+          - tests/e2e/online_serving/test_minicpmo_4_5_duplex.py
+          - tests/e2e/online_serving/test_minicpmo_4_5_duplex_expansion.py
+          - tests/e2e/online_serving/test_minicpmo_realtime_duplex_drivers.py
+          - tests/e2e/online_serving/helpers/minicpmo_realtime_duplex_scenarios.py
+          - tests/e2e/online_serving/run_minicpmo_realtime_duplex_multi_session.py
+          - tests/e2e/online_serving/run_minicpmo_realtime_duplex_soft_interrupt.py
+          - tests/e2e/online_serving/helpers/minicpmo_4_5_duplex.py
+          - vllm_omni/deploy/minicpmo_4_5_duplex.yaml
+          - vllm_omni/experimental/fullduplex/
+          - vllm_omni/model_executor/models/minicpmo_4_5/
+          - vllm_omni/model_executor/stage_input_processors/minicpmo_4_5_omni.py
+          - vllm_omni/core/sched/
+          - vllm_omni/outputs/multimodal_accumulation.py
+          - vllm_omni/engine/stage_init_utils.py
+          - vllm_omni/engine/async_omni_engine.py
+          - vllm_omni/entrypoints/cli/serve.py
+        commands:
+          - |
+            timeout 20m bash -c "
+              pytest -s -v tests/e2e/online_serving/test_minicpmo_4_5_duplex.py -m 'core_model and cuda' --run-level 'core_model'
+            "
+        mirror_hardwares: h100_1
+
+      - label: "Omni · MiniCPM-o 4.5 Perf Test"
+        timeout_in_minutes: 60
+        source_file_dependencies:
+          - tests/dfx/perf/tests/test_minicpmo_4_5_ready.json
+          - vllm_omni/deploy/minicpmo_4_5.yaml
+          - vllm_omni/model_executor/models/minicpmo_4_5/
+          - vllm_omni/model_executor/stage_input_processors/minicpmo_4_5_omni.py
+        commands:
+          - export BENCHMARK_DIR=tests/dfx/perf/results
+          - pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_minicpmo_4_5_ready.json -m 'core_model and cuda'
+        mirror_hardwares: h100_1

      - label: "TTS · VoxCPM2 Test"
        source_file_dependencies:
@@ -344,13 +373,3 @@ steps:
              pytest -s -v tests/e2e/online_serving/test_qwen_image_layered.py -m 'core_model' --run-level 'core_model'
            "
        mirror_hardwares: h100_1
-
-      - label: "Diffusion · GGUF Plugin Test"
-        soft_fail: true
-        source_file_dependencies:
-          - tests/plugin/test_gguf_diffusion.py
-          - vllm_omni/diffusion/model_loader
-        commands:
-          - pip install "vllm-gguf-plugin>=0.0.3"
-          - timeout 30m pytest -sv tests/plugin/test_gguf_diffusion.py
-        mirror_hardwares: l4_1
@@ -10,7 +10,7 @@ steps:
          DOCKER_BUILDKIT: "1"
          # Buildkite will automatically replace this with the actual commit hash
          VLLM_IMAGE_TAG: "${BUILDKITE_COMMIT}"
-          VLLM_VERSION: "v0.25.0"
+          VLLM_VERSION: "v0.27.0"
        priority: 100
        timeout_in_minutes: 60
        soft_fail: false
@@ -34,8 +34,23 @@ docker_build() {
    docker build "${out[@]}" "$@" -f docker/Dockerfile.xpu .
 }

+build_vllm_base() (
+    local vllm_source_dir
+    vllm_source_dir=$(mktemp -d)
+    trap 'rm -rf "${vllm_source_dir}"' EXIT
+
+    git clone --depth 1 --branch "${VLLM_VERSION}" \
+        https://github.com/vllm-project/vllm "${vllm_source_dir}"
+
+    local out=("${export_args[@]/'{{IMAGE}}'/${base_image_name}}")
+    docker build "${out[@]}" \
+        --target vllm-openai \
+        -f "${vllm_source_dir}/docker/Dockerfile.xpu" \
+        "${vllm_source_dir}"
+)
+
 if [ -z "$(docker images -q "${base_image_name}")" ]; then
-    docker_build "${base_image_name}" --target vllm-base --build-arg "VLLM_VERSION=${VLLM_VERSION}"
+    build_vllm_base
 fi

 # Try building the docker image
@@ -1,25 +1,87 @@
 steps:
-  - group: ":card_index_dividers: Diffusion X2V Model Test"
-    key: nightly-diffusion-x2v-group
+  - group: ":card_index_dividers: Omni Model Test"
+    key: nightly-omni-test-group
    depends_on: upload-nightly-pipeline
    if: >-
      build.env("NIGHTLY") == "1" ||
      build.pull_request.labels includes "nightly-test" ||
-      build.pull_request.labels includes "diffusion-x2v-test"
+      build.pull_request.labels includes "omni-test"
    steps:
-      - label: ":full_moon: Diffusion X2V · Function Test"
-        mirror_hardwares: a2b3_npu_4
+      - label: ":full_moon: Omni · Perf Test · No Async Chunk"
+        key: nightly-omni-performance-no-async-chunk
+        timeout_in_minutes: 180
+        mirror_hardwares: a3_npu_4
        env:
          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
          HF_TOKEN: "${HF_TOKEN}"
        commands:
-          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k npu_i2v --run-level "full_model"
+          - export BENCHMARK_DIR=tests/dfx/perf/results
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py -m npu --test-config-file tests/dfx/perf/tests/test_qwen3_omni_no_async_chunk.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
+            exit $$EXIT
+      - label: ":full_moon: Omni · Perf Test · Async Chunk"
+        key: nightly-omni-performance-async-chunk
+        timeout_in_minutes: 180
+        mirror_hardwares: a3_npu_4
+        env:
+          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
+          HF_TOKEN: "${HF_TOKEN}"
+        commands:
+          - export BENCHMARK_DIR=tests/dfx/perf/results
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py -m npu --test-config-file tests/dfx/perf/tests/test_qwen3_omni_async_chunk.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
+            exit $$EXIT
+      - label: ":full_moon: Omni · MiniCPM-o 4.5 Test"
+        mirror_hardwares: a3_npu_2
+        env:
+          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
+          HF_TOKEN: "${HF_TOKEN}"
+        commands:
+          - pytest -s -v tests/e2e/offline_inference/test_minicpmo_4_5.py tests/e2e/online_serving/test_minicpmo_4_5.py tests/e2e/online_serving/test_minicpmo_4_5_expansion.py -m 'full_model' --run-level 'full_model'
+      - label: ":full_moon: Omni · MiniCPM-o 4.5 · Duplex Test"
+        timeout_in_minutes: 180
+        mirror_hardwares: a3_npu_2
+        env:
+          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
+          HF_TOKEN: "${HF_TOKEN}"
+        commands:
+          - >-
+            pytest -s -v
+            tests/e2e/online_serving/test_minicpmo_4_5_duplex.py
+            tests/e2e/online_serving/test_minicpmo_4_5_duplex_expansion.py
+            -m "npu and A3 and omni"
+            --run-level full_model
+      - label: ":full_moon: Omni · MiniCPM-o 4.5 · Accuracy Test"
+        timeout_in_minutes: 180
+        mirror_hardwares: a3_npu_2
+        env:
+          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
+          HF_TOKEN: "${HF_TOKEN}"
+        commands:
+          - |
+            set +e
+            pytest -s -v tests/e2e/accuracy/minicpmo_4_5/test_minicpmo_4_5.py -m "full_model and npu and A3" --run-level full_model
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/e2e/accuracy/minicpmo_4_5/results/*.json"
+            exit $$EXIT
  - group: ":card_index_dividers: TTS Model Test"
    key: nightly-tts-test-group
    depends_on: upload-nightly-pipeline
    if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test" || build.pull_request.labels includes "tts-test"
    steps:
      - label: ":full_moon: TTS · Perf Test"
+        timeout_in_minutes: 180
        mirror_hardwares: a3_npu_2
        env:
          VLLM_WORKER_MULTIPROC_METHOD: spawn
@@ -34,25 +96,138 @@ steps:
            EXIT=$$?
            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
            exit $$EXIT
-
-  - group: ":card_index_dividers: Omni Model Test"
-    key: nightly-omni-group
+  - group: ":card_index_dividers: Diffusion X2I(&A&T) Model Test"
+    key: nightly-diffusion-x2iat-group
    depends_on: upload-nightly-pipeline
-    if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test" || build.pull_request.labels includes "omni-test"
+    if: >-
+      build.env("NIGHTLY") == "1" ||
+      build.pull_request.labels includes "nightly-test" ||
+      build.pull_request.labels includes "diffusion-x2iat-test"
    steps:
-      - label: ":robot: Omni · MiniCPM-o 4.5 Test"
-        agents:
-          queue: "ascend-a3"
-          resource_class: "npu-2"
-        image: "swr.cn-southwest-2.myhuaweicloud.com/modelfoundry/vllm-omni-ci-npu:${BUILDKITE_COMMIT}-a3"
-        plugins:
-          - kubernetes:
-              podSpecPatch:
-                imagePullSecrets:
-                  - name: swr-secret
+      - label: ":full_moon: Diffusion X2I(&A&T) · Perf Test · Qwen-Image-Edit-2511"
+        key: nightly-diffusion-x2iat-performance-qwen-image-edit-2511
+        timeout_in_minutes: 180
+        mirror_hardwares: a3_npu_8
+        env:
+          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
+          HF_TOKEN: "${HF_TOKEN}"
+          DIFFUSION_ATTENTION_BACKEND: TORCH_SDPA
+        commands:
+          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
+          - export CACHE_DIT_VERSION=1.5.0
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py -m npu --test-config-file tests/dfx/perf/tests/test_qwen_image_edit_2511_vllm_omni.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
+            buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
+            exit $$EXIT
+  - group: ":card_index_dividers: Diffusion I2I Model Test"
+    key: nightly-HunyuanImage3-x2i-test-group
+    depends_on: upload-nightly-pipeline
+    if: >-
+      build.env("NIGHTLY") == "1" ||
+      build.pull_request.labels includes "nightly-test" ||
+      build.pull_request.labels includes "diffusion-x2iat-test"
+    steps:
+      - label: ":full_moon: HunyuanImage3-DIT · Accuracy Test"
+        timeout_in_minutes: 180
+        mirror_hardwares: a3_npu_4
+        env:
+          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          HUNYUAN_IMAGE3_MODEL: "tencent/HunyuanImage-3.0-Instruct"
+          HUNYUAN_IMAGE3_DEVICES: "0,1,2,3"
+          DIFFUSION_ATTENTION_BACKEND: TORCH_SDPA
+        commands:
+          - pytest -s -v tests/e2e/accuracy/test_hunyuan_image3_pixel_accuracy.py -m "full_model and A3" --run-level full_model
+  - group: ":card_index_dividers: Diffusion Benchmark Perf Test"
+    key: nightly-diffusion-benchmark-group
+    depends_on: upload-nightly-pipeline
+    if: >-
+      build.env("NIGHTLY") == "1" ||
+      build.pull_request.labels includes "nightly-test" ||
+      build.pull_request.labels includes "diffusion-x2iat-test"
+    steps:
+      - label: ":full_moon: Diffusion Perf · HunyuanImage3 TP=2 CFGP=2"
+        timeout_in_minutes: 180
+        mirror_hardwares: a3_npu_4
+        env:
+          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          HF_TOKEN: "${HF_TOKEN}"
+          DIFFUSION_ATTENTION_BACKEND: TORCH_SDPA
+        commands:
+          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py -m A3 --test-config-file tests/dfx/perf/tests/test_hunyuan_image_tp2_cfgp2.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
+            buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
+            exit $$EXIT
+      - label: ":full_moon: Diffusion Perf · HunyuanImage3 TP=2 SP=2"
+        timeout_in_minutes: 180
+        mirror_hardwares: a3_npu_4
+        env:
+          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          HF_TOKEN: "${HF_TOKEN}"
+          DIFFUSION_ATTENTION_BACKEND: TORCH_SDPA
+        commands:
+          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py -m A3 --test-config-file tests/dfx/perf/tests/test_hunyuan_image_tp2_sp2.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
+            buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
+            exit $$EXIT
+      - label: ":full_moon: Diffusion Perf · HunyuanImage3 TP=4"
+        timeout_in_minutes: 180
+        mirror_hardwares: a3_npu_4
+        env:
+          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          HF_TOKEN: "${HF_TOKEN}"
+          DIFFUSION_ATTENTION_BACKEND: TORCH_SDPA
+        commands:
+          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py -m A3 --test-config-file tests/dfx/perf/tests/test_hunyuan_image_tp4.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
+            buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
+            exit $$EXIT
+  - group: ":card_index_dividers: Diffusion X2V Model Test"
+    key: nightly-diffusion-x2v-group
+    depends_on: upload-nightly-pipeline
+    if: >-
+      build.env("NIGHTLY") == "1" ||
+      build.pull_request.labels includes "nightly-test" ||
+      build.pull_request.labels includes "diffusion-x2v-test"
+    steps:
+      - label: ":full_moon: Diffusion X2V · Function Test"
+        mirror_hardwares: a2b3_npu_4
        env:
          VLLM_WORKER_MULTIPROC_METHOD: spawn
          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
          HF_TOKEN: "${HF_TOKEN}"
        commands:
-          - pytest -s -v tests/e2e/offline_inference/test_minicpmo_4_5.py tests/e2e/online_serving/test_minicpmo_4_5.py tests/e2e/online_serving/test_minicpmo_4_5_expansion.py -m 'full_model' --run-level 'full_model'
+          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k npu_i2v --run-level "full_model"
+      - label: ":full_moon: Diffusion X2V · Perf Test"
+        key: nightly-diffusion-x2v-performance
+        timeout_in_minutes: 180
+        mirror_hardwares: a3_npu_4
+        env:
+          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
+          HF_TOKEN: "${HF_TOKEN}"
+          DIFFUSION_ATTENTION_BACKEND: TORCH_SDPA
+        commands:
+          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py -m npu --test-config-file tests/dfx/perf/tests/test_wan22_i2v_vllm_omni.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
+            buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
+            exit $$EXIT
@@ -3,22 +3,29 @@ steps:
    key: ready-omni-group
    depends_on: upload-ready-pipeline
    steps:
-      - label: ":robot: Omni · MiniCPM-o 4.5 Test"
-        agents:
-          queue: "ascend-a3"
-          resource_class: "npu-2"
-        image: "swr.cn-southwest-2.myhuaweicloud.com/modelfoundry/vllm-omni-ci-npu:${BUILDKITE_COMMIT}-a3"
-        plugins:
-          - kubernetes:
-              podSpecPatch:
-                imagePullSecrets:
-                  - name: swr-secret
+      - label: "Omni · MiniCPM-o 4.5 Test"
+        mirror_hardwares: a3_npu_2
        env:
          VLLM_WORKER_MULTIPROC_METHOD: spawn
          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
-          HF_TOKEN: "${HF_TOKEN}"
        commands:
-          - pytest -s -v tests/e2e/offline_inference/test_minicpmo_4_5.py tests/e2e/online_serving/test_minicpmo_4_5.py -m 'core_model' --run-level 'core_model'
+          - pytest -s -v tests/e2e/offline_inference/test_minicpmo_4_5.py tests/e2e/online_serving/test_minicpmo_4_5.py -m 'core_model and npu and A3 and omni' --run-level 'core_model'
+      - label: "Omni · MiniCPM-o 4.5 Duplex Test"
+        mirror_hardwares: a3_npu_2
+        env:
+          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
+        commands:
+          - pytest -s -v tests/e2e/online_serving/test_minicpmo_4_5_duplex.py -m 'core_model and npu and A3 and omni' --run-level 'core_model'
+      - label: "Omni · MiniCPM-o 4.5 Perf Test"
+        timeout_in_minutes: 60
+        mirror_hardwares: a3_npu_2
+        env:
+          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
+        commands:
+          - export BENCHMARK_DIR=tests/dfx/perf/results
+          - pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_minicpmo_4_5_ready.json -m 'core_model and npu and A3 and omni'

  - group: ":card_index_dividers: TTS Model Test"
    key: ready-tts-group
@@ -31,4 +38,4 @@ steps:
          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
          HF_TOKEN: "${HF_TOKEN}"
        commands:
-          - pytest -s -v tests/e2e/online_serving/test_qwen3_tts_customvoice.py -m 'core_model' --run-level 'core_model'
+          - pytest -s -v tests/e2e/online_serving/test_qwen3_tts_customvoice.py -m 'core_model and npu' --run-level 'core_model'
@@ -40,9 +40,11 @@ steps:
    commands:
      - "bash .buildkite/release/scripts/publish-release-wheels-pypi.sh"

+  # Auto-skip when NIGHTLY=1 (scheduled); otherwise require manual unblock.
  - block: "Unblock to build release Docker images"
    depends_on: ~
    key: block-build-release-images
+    if: build.env("NIGHTLY") != "1"

  - group: "Build release Docker images"
    key: "build-release-images"
@@ -63,7 +65,7 @@ steps:
              --progress plain \
              -f docker/Dockerfile.cuda .
          - "docker push public.ecr.aws/q9t5s3a7/vllm-omni-release-repo:$BUILDKITE_COMMIT-$(uname -m)"
-          # re-tag to default image tag and push, just in case arm64 build fails
+          # Fallback default tag (no arch suffix) if arm64 build fails.
          - "docker tag public.ecr.aws/q9t5s3a7/vllm-omni-release-repo:$BUILDKITE_COMMIT-$(uname -m) public.ecr.aws/q9t5s3a7/vllm-omni-release-repo:$BUILDKITE_COMMIT"
          - "docker push public.ecr.aws/q9t5s3a7/vllm-omni-release-repo:$BUILDKITE_COMMIT"
        env:
@@ -86,6 +88,26 @@ steps:
        env:
          DOCKER_BUILDKIT: "1"

+  # NIGHTLY=1 only: promote ECR images to DockerHub nightly tags, then prune.
+  - label: "Publish nightly multi-arch image to DockerHub"
+    depends_on:
+      - build-release-image-x86
+      - build-release-image-arm64
+    if: build.env("NIGHTLY") == "1"
+    key: publish-nightly-images-dockerhub
+    agents:
+      queue: small_cpu_queue_release
+    commands:
+      - "bash .buildkite/release/scripts/publish-release-images.sh nightly"
+      - "bash .buildkite/release/scripts/cleanup-nightly-builds.sh"
+    plugins:
+      - docker-login#v3.0.0:
+          username: vllmbot
+          password-env: DOCKERHUB_TOKEN
+    env:
+      DOCKER_BUILDKIT: "1"
+      DOCKERHUB_USERNAME: "vllmbot"
+
  - block: "Publish release images to DockerHub"
    key: block-publish-release-images
    depends_on:
@@ -0,0 +1,147 @@
+#!/bin/bash
+# SPDX-License-Identifier: Apache-2.0
+#
+# Clean up old nightly builds from DockerHub (vllm/vllm-omni), keeping the
+# newest 14 tags with the nightly- prefix.
+
+set -ex
+
+TAG_PREFIX="nightly-"
+REPO="vllm/vllm-omni"
+REPO_API_URL="https://hub.docker.com/v2/repositories/${REPO}/tags"
+
+echo "Cleaning up tags with prefix: $TAG_PREFIX in repository: $REPO"
+
+if [ -z "$DOCKERHUB_TOKEN" ]; then
+  echo "Error: DOCKERHUB_TOKEN environment variable is not set"
+  exit 1
+fi
+
+if [ -z "$DOCKERHUB_USERNAME" ]; then
+  echo "Error: DOCKERHUB_USERNAME environment variable is not set"
+  exit 1
+fi
+
+echo "Getting DockerHub bearer token..."
+set +x
+BEARER_TOKEN=$(curl -s -X POST \
+  -H "Content-Type: application/json" \
+  -d "{\"username\": \"$DOCKERHUB_USERNAME\", \"password\": \"$DOCKERHUB_TOKEN\"}" \
+  "https://hub.docker.com/v2/users/login" | jq -r '.token')
+set -x
+
+if [ -z "$BEARER_TOKEN" ] || [ "$BEARER_TOKEN" = "null" ]; then
+  echo "Error: Failed to get DockerHub bearer token"
+  exit 1
+fi
+
+get_all_tags() {
+  local page=1
+  local all_tags=""
+  # Hub `name` is a partial match (not a strict prefix). Use it to narrow the
+  # list, then enforce startswith() client-side. Exhaust API pages; do not
+  # stop merely because a page has no matching tags after filtering.
+  local name_query
+  name_query=$(printf '%s' "$TAG_PREFIX" | jq -sRr @uri)
+
+  while true; do
+    set +x
+    local response
+    response=$(curl -s -H "Authorization: Bearer $BEARER_TOKEN" \
+      "$REPO_API_URL?page=$page&page_size=100&name=${name_query}")
+    set -x
+
+    local page_count
+    page_count=$(echo "$response" | jq -r '.results | length')
+    if [ -z "$page_count" ] || [ "$page_count" = "0" ] || [ "$page_count" = "null" ]; then
+      break
+    fi
+
+    local tags
+    tags=$(echo "$response" | jq -r --arg prefix "$TAG_PREFIX" \
+      '.results[] | select(.name | startswith($prefix)) | "\(.last_updated)|\(.name)"')
+
+    if [ -n "$tags" ]; then
+      all_tags="$all_tags$tags"$'\n'
+    fi
+    page=$((page + 1))
+  done
+
+  echo "$all_tags" | sort -r | cut -d'|' -f2
+}
+
+delete_tag() {
+  local tag_name="$1"
+  echo "Deleting tag: $tag_name"
+
+  local delete_url="https://hub.docker.com/v2/repositories/${REPO}/tags/$tag_name"
+  set +x
+  local response http_code body
+  response=$(curl -s -w "\n%{http_code}" -X DELETE \
+    -H "Authorization: Bearer $BEARER_TOKEN" "$delete_url")
+  set -x
+  http_code=$(printf '%s\n' "$response" | tail -n1)
+  body=$(printf '%s\n' "$response" | sed '$d')
+
+  if [ "$http_code" -ge 200 ] && [ "$http_code" -lt 300 ]; then
+    echo "Successfully deleted tag: $tag_name (HTTP ${http_code})"
+    return 0
+  fi
+
+  local detail=""
+  if echo "$body" | jq -e '.detail' > /dev/null 2>&1; then
+    detail=$(echo "$body" | jq -r '.detail')
+  elif [ -n "$body" ]; then
+    detail="$body"
+  else
+    detail="(empty response body)"
+  fi
+  echo "Error: Failed to delete tag $tag_name (HTTP ${http_code}): ${detail}"
+  return 1
+}
+
+echo "Fetching all tags from DockerHub..."
+all_tags=$(get_all_tags)
+
+if [ -z "$all_tags" ]; then
+  echo "No tags found to clean up"
+  exit 0
+fi
+
+total_tags=$(echo "$all_tags" | wc -l)
+echo "Found $total_tags tags"
+
+tags_to_keep=14
+tags_to_delete=$((total_tags - tags_to_keep))
+
+if [ $tags_to_delete -le 0 ]; then
+  echo "No tags need to be deleted (only $total_tags tags found, keeping $tags_to_keep)"
+  exit 0
+fi
+
+echo "Will delete $tags_to_delete old tags, keeping the newest $tags_to_keep"
+
+tags_to_delete_list=$(echo "$all_tags" | tail -n +$((tags_to_keep + 1)))
+
+if [ -z "$tags_to_delete_list" ]; then
+  echo "No tags to delete"
+  exit 0
+fi
+
+echo "Deleting old tags..."
+delete_failures=0
+while IFS= read -r tag; do
+  if [ -n "$tag" ]; then
+    if ! delete_tag "$tag"; then
+      delete_failures=$((delete_failures + 1))
+    fi
+    sleep 1
+  fi
+done <<< "$tags_to_delete_list"
+
+if [ "$delete_failures" -gt 0 ]; then
+  echo "Cleanup finished with ${delete_failures} deletion failure(s)"
+  exit 1
+fi
+
+echo "Cleanup completed successfully"
@@ -1,50 +1,87 @@
 #!/bin/bash
 # SPDX-License-Identifier: Apache-2.0
 #
-# Publish release Docker images from ECR to DockerHub.
-# Pulls per-arch images, tags with `latest` and versioned tags, pushes them,
-# then creates and pushes a multi-arch manifest.
+# Publish Docker images from ECR staging to DockerHub.
+#
+# Usage:
+#   publish-release-images.sh            # release mode (default)
+#   publish-release-images.sh release
+#   publish-release-images.sh nightly
+#
+# Release tags: latest, v<version>
+# Nightly tags: nightly, nightly-<commit>

 set -euo pipefail

-RELEASE_VERSION=$(buildkite-agent meta-data get release-version --default "" | sed 's/^v//')
-if [ -z "${RELEASE_VERSION}" ]; then
-  echo "ERROR: release-version metadata not set"
-  exit 1
-fi
+MODE="${1:-release}"

-COMMIT="$BUILDKITE_COMMIT"
 DOCKERHUB_REPO="vllm/vllm-omni"
 ECR_REPO="public.ecr.aws/q9t5s3a7/vllm-omni-release-repo"
+COMMIT="$BUILDKITE_COMMIT"
+PRIMARY_TAGS=()
+EXTRA_MANIFEST_TAGS=()
+
+case "${MODE}" in
+  release)
+    RELEASE_VERSION=$(buildkite-agent meta-data get release-version --default "" | sed 's/^v//')
+    if [ -z "${RELEASE_VERSION}" ]; then
+      echo "ERROR: release-version metadata not set"
+      exit 1
+    fi
+    PRIMARY_TAGS=("latest" "v${RELEASE_VERSION}")
+    ;;
+  nightly)
+    # Commit-pinned manifest reuses nightly-{x86_64,aarch64} arch images.
+    PRIMARY_TAGS=("nightly")
+    EXTRA_MANIFEST_TAGS=("nightly-${COMMIT}")
+    ;;
+  *)
+    echo "Usage: $0 {release|nightly}"
+    exit 2
+    ;;
+esac

 echo "========================================"
-echo "Publishing release images v${RELEASE_VERSION}"
+echo "Publishing ${MODE} images to ${DOCKERHUB_REPO}"
 echo "  Commit: ${COMMIT}"
+echo "  Primary tags: ${PRIMARY_TAGS[*]}"
+if [ ${#EXTRA_MANIFEST_TAGS[@]} -gt 0 ]; then
+  echo "  Extra manifests: ${EXTRA_MANIFEST_TAGS[*]}"
+fi
 echo "========================================"

-# Login to ECR to pull staging images
 aws ecr-public get-login-password --region us-east-1 | \
  docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7

-docker pull "${ECR_REPO}:${COMMIT}-x86_64"
-docker pull "${ECR_REPO}:${COMMIT}-aarch64"
+SRC_X86="${ECR_REPO}:${COMMIT}-x86_64"
+SRC_ARM="${ECR_REPO}:${COMMIT}-aarch64"

-docker tag "${ECR_REPO}:${COMMIT}-x86_64" "${DOCKERHUB_REPO}:latest-x86_64"
-docker tag "${ECR_REPO}:${COMMIT}-x86_64" "${DOCKERHUB_REPO}:v${RELEASE_VERSION}-x86_64"
-docker push "${DOCKERHUB_REPO}:latest-x86_64"
-docker push "${DOCKERHUB_REPO}:v${RELEASE_VERSION}-x86_64"
+docker pull "${SRC_X86}"
+docker pull "${SRC_ARM}"

-docker tag "${ECR_REPO}:${COMMIT}-aarch64" "${DOCKERHUB_REPO}:latest-aarch64"
-docker tag "${ECR_REPO}:${COMMIT}-aarch64" "${DOCKERHUB_REPO}:v${RELEASE_VERSION}-aarch64"
-docker push "${DOCKERHUB_REPO}:latest-aarch64"
-docker push "${DOCKERHUB_REPO}:v${RELEASE_VERSION}-aarch64"
+publish_manifest() {
+  local tag="$1"
+  local arch_tag_base="$2"
+  docker manifest rm "${DOCKERHUB_REPO}:${tag}" || true
+  docker manifest create \
+    "${DOCKERHUB_REPO}:${tag}" \
+    "${DOCKERHUB_REPO}:${arch_tag_base}-x86_64" \
+    "${DOCKERHUB_REPO}:${arch_tag_base}-aarch64"
+  docker manifest push "${DOCKERHUB_REPO}:${tag}"
+}

-docker manifest rm "${DOCKERHUB_REPO}:latest" || true
-docker manifest rm "${DOCKERHUB_REPO}:v${RELEASE_VERSION}" || true
-docker manifest create "${DOCKERHUB_REPO}:latest" "${DOCKERHUB_REPO}:latest-x86_64" "${DOCKERHUB_REPO}:latest-aarch64"
-docker manifest create "${DOCKERHUB_REPO}:v${RELEASE_VERSION}" "${DOCKERHUB_REPO}:v${RELEASE_VERSION}-x86_64" "${DOCKERHUB_REPO}:v${RELEASE_VERSION}-aarch64"
-docker manifest push "${DOCKERHUB_REPO}:latest"
-docker manifest push "${DOCKERHUB_REPO}:v${RELEASE_VERSION}"
+for tag in "${PRIMARY_TAGS[@]}"; do
+  docker tag "${SRC_X86}" "${DOCKERHUB_REPO}:${tag}-x86_64"
+  docker tag "${SRC_ARM}" "${DOCKERHUB_REPO}:${tag}-aarch64"
+  docker push "${DOCKERHUB_REPO}:${tag}-x86_64"
+  docker push "${DOCKERHUB_REPO}:${tag}-aarch64"
+  publish_manifest "${tag}" "${tag}"
+done
+
+ARCH_BASE="${PRIMARY_TAGS[0]}"
+for tag in "${EXTRA_MANIFEST_TAGS[@]}"; do
+  publish_manifest "${tag}" "${ARCH_BASE}"
+done

 echo ""
-echo "Successfully published release images for v${RELEASE_VERSION}"
+echo "Successfully published ${MODE} images"
@@ -309,7 +309,7 @@ For Omni or custom models, create:

 Required updates:
 1. `docs/user_guide/diffusion/parallelism/overview.md` — parallelism support overview/table
-2. `docs/user_guide/diffusion/cpu_offload_diffusion.md` — if CPU offload supported (add to supported models table)
+2. `docs/user_guide/diffusion/cpu_offload.md` — if CPU offload supported (add to supported models table)
 3. `docs/user_guide/diffusion/cache_acceleration/teacache.md` — if TeaCache supported
 4. `docs/user_guide/diffusion/cache_acceleration/cache_dit.md` — if Cache-DiT supported
 5. Offline example docs under `examples/offline_inference/<name>/` (`README.md` or category-specific `.md`)
@@ -220,65 +220,70 @@ See `plan/voxcpm2_native_ar_design.md`.

 ### Steps

-1. **Register in `serving_speech.py`** — add all 5 points in a **single commit**;
-   partial integration causes hard-to-debug failures. This file is modified by every
-   model PR and is the most common source of rebase conflicts — see conflict note below.
+1. **Write one adapter** under `vllm_omni/entrypoints/openai/tts_adapters/`.
+   `serving_speech.py` should not need an edit — detection, stage discovery and
+   dispatch are all derived from what the adapter declares.

-   **Point 1** — stage constant (near the top, alongside the other `_*_TTS_MODEL_STAGES` sets):
+   Create `vllm_omni/entrypoints/openai/tts_adapters/your_model.py`:
   ```python
-   _YOUR_MODEL_TTS_MODEL_STAGES = {"your_stage_key"}
+   @register_tts_adapter
+   class YourModelAdapter(ARTTSAdapter):
+       name = "your_model"                              # registry key + log label
+       stage_keys = frozenset({"your_stage_key"})       # the deploy yaml's model_stage
+
+       def validate(self, request) -> str | None:
+           if not request.input or not request.input.strip():
+               return "Input text cannot be empty"
+           return None
+
+       async def build(self, request, sampling_params_list, has_inline_ref_audio):
+           params = {"text": [request.input]}
+           if request.voice is not None:
+               params["voice"] = [request.voice]
+           return PreparedRequest(
+               prompt={"prompt": request.input},
+               tts_params=params,
+               model_type=self.name,
+           )
   ```

-   **Point 2** — union into `_TTS_MODEL_STAGES`:
-   ```python
-   _TTS_MODEL_STAGES: set[str] = (
-       ...
-       | _YOUR_MODEL_TTS_MODEL_STAGES
-   )
-   ```
+   Then add the module to the import block at the bottom of
+   `tts_adapters/__init__.py` so it registers. That import line is the only shared
+   file a new model touches — which is also why the old rebase-conflict hotspot is
+   gone.

-   **Point 3** — model type detection in `_detect_tts_model_type()`:
-   ```python
-   if model_stage in _YOUR_MODEL_TTS_MODEL_STAGES:
-       return "your_model"
-   ```
+   > **Pure-diffusion TTS does not go through adapters yet.** Under
+   > `for_diffusion()`, `create_speech()` routes straight to
+   > `_create_diffusion_speech()` and never calls `validate()`/`build()`.
+   > `DiffusionTTSAdapter` is scaffolding with no production subclass, so logic
+   > placed in one would silently never run. Diffusion-engine models follow the
+   > existing diffusion path; wiring it through adapters is open work (#4855).

-   **Point 4** — validation dispatch in `_validate_tts_request()`:
-   ```python
-   if self._tts_model_type == "your_model":
-       return self._validate_your_model_request(request)
-   ```
+   **If a stage key alone cannot identify the model**, declare
+   `model_archs = frozenset({"YourModelForConditionalGeneration"})`; add
+   `arch_identifies_entry_stage = True` when the model owns no stage key at all
+   (Ming dense). For a rule that is not set membership, override `matches()` (see
+   `covo_audio.py`). For a genuine overlap with another adapter, give one an
+   explicit `detect_priority` — `test_tts_detection.py` fails on an unordered
+   overlap. Models that only serve speech in some topologies override
+   `stage_serves_speech()` (see `audex.py`).

-   **Point 5** — validation + parameter-builder methods:
-   ```python
-   def _validate_your_model_request(self, request) -> str | None:
-       if not request.input or not request.input.strip():
-           return "Input text cannot be empty"
-       return None
+   **Reuse shared helpers via `self.ctx.server`** rather than reimplementing them:
+   `_resolve_ref_audio`, `_apply_uploaded_speaker`, `_validate_ref_audio_format`,
+   `_max_instructions_length`. Read a comparable adapter first — `fish_speech.py`
+   (voice cloning), `higgs_audio_v3.py` (parameter-heavy), `moss_tts.py` (family
+   sharing a base class).

-   def _build_your_model_params(self, request) -> dict:
-       params = {"text": [request.input]}
-       if request.voice is not None:
-           params["voice"] = [request.voice]
-       return params
-   ```
-   Wire `_build_your_model_params` into `_create_tts_request()` alongside the other
-   model-specific param builders.
+   > **Do not add `self._tts_model_type == ...` branches to `serving_speech.py`.**
+   > Older models predate the adapter framework and still have them; they are being
+   > migrated out (RFC #4327, #4855). `tools/pre_commit/check_tts_adapter.py` is a
+   > ratchet on the remaining count and fails the commit if it grows. Behaviour that
+   > no adapter hook can express is a missing hook — propose it on the RFC.

-   > **Two dispatch patterns coexist**: Fish Speech uses a `self._is_fish_speech` boolean
-   > instance attribute checked before `elif self._is_tts`, while all newer models
-   > (CosyVoice3, MOSS-TTS-Nano) use the `_tts_model_type` string returned by
-   > `_detect_tts_model_type()`. For new models, always use the `_tts_model_type` string
-   > pattern — do not add new `_is_*` flags.
-
-   > **Unused variable rule**: only extract fields in `_build_your_model_params` that
-   > are actually forwarded to the model. Unused extractions fail `ruff F841`.
-   > For voice-cloning fields (`ref_audio` → `prompt_audio_path`, `ref_text` →
-   > `prompt_text`), add them to the param builder and verify they reach the model call.
-
-   **Rebase conflict note**: when rebasing onto `main` after another model was merged,
-   `serving_speech.py` will conflict. Resolution: always keep *both* the upstream
-   model's additions and your own — never discard either side.
+   > **Unused variable rule**: only extract fields in `build()` that are actually
+   > forwarded to the model. Unused extractions fail `ruff F841`. For voice-cloning
+   > fields (`ref_audio` -> `prompt_audio_path`, `ref_text` -> `prompt_text`), add
+   > them to the params and verify they reach the model call.

 2. **Handle model-specific parameters**:
   - Voice cloning: `ref_audio` encoding and prompt injection
@@ -295,7 +300,7 @@ import base64
 from pathlib import Path

 def build_voice_clone_prompt(ref_audio_path: str, text: str, codec) -> list:
-    """Build prompt with reference audio for voice cloning in serving_speech.py."""
+    """Build prompt with reference audio for voice cloning, called from the adapter."""
    audio_bytes = Path(ref_audio_path).read_bytes()
    codes = codec.encode(audio_bytes)  # Encode on CPU using model's codec (e.g., DAC)
    token_ids = [code + codec.vocab_offset for code in codes.flatten().tolist()]
@@ -321,7 +326,7 @@ Classify the model's **CI priority** first (high / medium / low). High-priority

 | Level | Location | Marker | CI pipeline | Notes |
 |-------|----------|--------|-------------|-------|
-| **L1** | `tests/model_executor/…`, `tests/entrypoints/openai_api/…`, stage-processor tests | `core_model` + `cpu` | `test-ready.yml` | Prompt assembly, async_chunk helpers, `serving_speech` validation — no GPU |
+| **L1** | `tests/model_executor/…`, `tests/entrypoints/openai_api/…`, stage-processor tests | `core_model` + `cpu` | `test-ready.yml` | Prompt assembly, async_chunk helpers, adapter validation — no GPU |
 | **L2** | `tests/e2e/online_serving/test_{slug}.py` | **`core_model` + `advanced_model`** (both on baseline smoke) + `tts` + `@hardware_test(...)` | `test-ready.yml` (`ready` label) | Default deploy smoke: single `/v1/audio/speech` or offline `OmniRunner` path |
 | **L3** | `tests/e2e/online_serving/test_{slug}.py` **and** `tests/e2e/offline_inference/test_{slug}.py` | Baseline smoke: **`core_model` + `advanced_model`**; heavier cases: `advanced_model` only (+ `tts`) | `test-merge.yml` **or** merged into nightly TTS function job | Streaming, voice clone, batch/queue, async_chunk |
 | **L4** | `tests/e2e/online_serving/test_{slug}_expansion.py`, optional offline expansion | `full_model` + `tts` | `test-nightly.yml` (`:full_moon: TTS · Function Test with L4`) | Feature matrix; perf → `tests/dfx/perf/tests/test_tts.json` |
@@ -366,7 +371,7 @@ See [vllm-omni-test skill](../vllm-omni-test/SKILL.md) § **Runtime send helpers

 ### Deliverables

- Updated `serving_speech.py` with all 5 integration points (single commit)
+- One adapter file under `tts_adapters/` plus its line in the package import block
 - Client scripts and server launcher under `examples/online_serving/text_to_speech/<model>/`
 - Gradio demo with streaming and voice cloning UI in the same dir
 - E2E tests per **Test Case Writing (CI Levels)** above (priority tier determines L1–L4 scope)
@@ -514,8 +519,8 @@ Use this checklist when integrating a new TTS model:
 - [ ] README.md written

 ### Phase 3: Online Serving
- [ ] All 5 `serving_speech.py` integration points added in one commit
- [ ] Only extract params in `_build_*_params` that are forwarded to the model call (ruff F841)
+- [ ] Adapter written under `tts_adapters/` and registered in the import block
+- [ ] Only extract params in `build()` that are forwarded to the model call (ruff F841)
 - [ ] Prompt builder handles text input correctly
 - [ ] Voice cloning works (if supported)
 - [ ] All response formats work (wav, mp3, flac, pcm)
@@ -1,6 +1,6 @@
 ---
 name: precheck-pr
-description: Self-check your branch before creating a PR — catch dead code, verify accuracy/perf claims, validate PR title format, and confirm merge readiness. Use when the user says "precheck", "self review", "pre-submit check", or "check my PR before I open it." Never posts to GitHub.
+description: Self-check your branch before creating a PR — catch dead code, prevent new model-specific Python examples, verify accuracy/perf claims, validate PR title format, and confirm merge readiness. Use when the user says "precheck", "self review", "pre-submit check", or "check my PR before I open it." Never posts to GitHub.
 ---

 # PR Pre-Check
@@ -65,6 +65,8 @@ Ask: "Quick mode or full mode?" Then walk the checklist for the detected PR type

 **Also run the Code-Quality sweep on every PR**, regardless of type or mode: the five diff-scoped checks in [references/code-quality.md](references/code-quality.md) — kwargs fragility, broad-except swallow, `Any`/wrong type hints, hot-path `.clone()`/`deepcopy`, and event-loop blocking — plus the advisory conventions (log level, structured logging, synchronization, cleanup, dependencies, naming) in [checklists.md](references/checklists.md). These count only lines the PR *adds* — the pre-existing backlog across the repo is out of scope.

+**Also run the Examples-Policy check on every PR** using [references/examples-policy.md](references/examples-policy.md). Inspect only Python paths introduced by the diff. Treat a new model-, checkpoint-, vendor-, or family-specific Python example as blocking; do not report pre-existing example debt.
+
 ### Step 5: Print Report

 ```
@@ -77,6 +79,7 @@ Pre-check report for <branch>
  ─────────────────  ──────
  PR title format    ✓
  Code quality       ⚠ 1 broad except, 2 Any hints
+  Examples policy    ✗ new model-specific Python example
  PR desc integrity  ✓
  Registry/config    ✓
  Dead code          ⚠ 2 warnings
@@ -47,6 +47,19 @@ Diff-scoped sweep for five fragility patterns that are pervasive in the existing

 Roll the ⚠/✗ counts into the report as a single **Code quality** dimension row.

+---
+
+## All PRs: Examples Policy (run on every PR, quick & full)
+
+Inspect Python files introduced under `examples/` using
+[examples-policy.md](examples-policy.md). This is a diff-scoped ratchet: existing
+model-specific examples may be edited or removed without triggering the check.
+
+- [ ] **No new model-specific Python examples:** the diff adds, copies, or renames no Python file whose path or behavior is specific to one model, checkpoint, vendor, or model family. ✗ for any violation. Route inference through a shared task/protocol entrypoint, model request behavior through `vllm_omni/model_extras`, and commands through task docs or `recipes/`.
+- [ ] **New generic example code is justified:** any new task-neutral Python file under `examples/` is an actual user-facing entrypoint rather than reusable production logic, a benchmark, an app, a setup tool, or a reproducer. ⚠ when the placement is questionable.
+
+Roll the result into the report as a separate **Examples policy** dimension.
+
 ### Conventions (eyeball — no grep)

 Judgment calls that don't grep cleanly — apply them while reading the diff, not as a mechanical check:
@@ -0,0 +1,68 @@
+# Examples Python Policy
+
+Prevent new model-specific Python examples without forcing cleanup of the
+existing backlog. Run this check on every PR in quick and full modes.
+
+## Scope
+
+Set the merge base as in the main workflow, then list Python paths introduced
+by the PR, including additions, copies, and renames:
+
+```bash
+BASE="$(git merge-base HEAD origin/main)"
+git diff --name-status --diff-filter=ACR --find-renames --find-copies \
+  "${BASE}"...HEAD -- 'examples/**/*.py'
+```
+
+Inspect destination paths for copies and renames. Existing model-specific files
+that are only modified or deleted are grandfathered and must not be reported.
+
+## Blocking classification
+
+Mark ✗ when a newly introduced Python file does any of the following:
+
+- Names or scopes a path to one model, checkpoint, vendor, or model family.
+- Hard-codes a model identifier, model-specific prompt, request shape, output
+  conversion, or launch configuration behind an otherwise generic filename.
+- Duplicates an existing task/protocol runner to demonstrate one model.
+- Adds a per-model helper below a generic task hub such as `text_to_speech/`.
+
+Do not infer safety from a generic filename alone. For example, a file named
+`text_to_audio.py` is still model-specific if it only implements one model's
+contract.
+
+## Acceptable additions
+
+A new Python example may pass when it is a genuinely model-neutral,
+user-facing task or protocol entrypoint. It must accept model selection through
+configuration and keep model-specific behavior out of the script.
+
+Use ⚠ when the file is model-neutral but reusable logic appears misplaced under
+`examples/`. Route code by purpose:
+
+| Code purpose | Preferred location |
+|---|---|
+| Model prompt, defaults, request/output adaptation | `vllm_omni/model_extras/` or production model modules |
+| Runnable model command and validation evidence | Task documentation or `recipes/` |
+| UI or interactive application | `apps/` |
+| Benchmark or evaluator | `benchmarks/` |
+| Regression reproducer | `tests/` |
+| Download, conversion, or setup utility | `tools/` |
+
+If a model cannot use an existing shared runner, report the missing generic
+capability. Do not solve the gap by adding a model-specific Python example.
+
+## Report format
+
+Add one row to the pre-check report:
+
+```text
+Examples policy    ✓ no new Python example paths
+Examples policy    ⚠ generic helper belongs in tools/
+Examples policy    ✗ examples/offline_inference/new_model/end2end.py is model-specific
+Examples policy    ✗ examples/offline_inference/x_to_y_model_name.py is model-specific
+```
+
+For a blocker, name the path, the model-specific behavior, and the appropriate
+shared runner or destination. Do not propose deleting unrelated existing
+examples as part of the PR.
@@ -1,9 +1,9 @@
-# Claude Skills for vLLM-Omni
+# Repository Skills for vLLM-Omni

-This directory contains Claude Code skills maintained for the `vllm-omni`
-repository. These skills capture repeatable workflows for common contributor
-tasks such as model integration, pull request review, and release note
-generation.
+This directory contains repository-scale skills maintained for `vllm-omni`.
+They capture repeatable workflows for common contributor and maintainer tasks
+such as model integration, CI-aligned testing, performance optimization, and
+pull request review.

 ## Directory Structure

@@ -14,31 +14,41 @@ include:
 - `references/`: focused reference material used by the skill
 - `scripts/`: small helper scripts used by the skill

+## Using the Skills
+
+Coding agents that discover repository skills can invoke the relevant skill by
+name. For other agents, point them to the skill's `SKILL.md` and ask them to
+read it before changing code. Combine a domain skill with `vllm-omni-test` when
+tests or CI coverage change, then use `precheck-pr` for the contributor's final
+self-check.
+
 ## Available Skills

- `add-diffusion-model`: guides integration of a new diffusion model into
-  `vllm-omni`
- `diffusion-perf-opt`: guides diffusion model performance optimization,
-  including profiling traces, parallel strategies, stage timing analysis, and
-  benchmark-driven tuning
- `quantization`: guides quantization method selection, model integration,
-  checkpoint loading, and quality/performance validation for vLLM-Omni
- `add-omni-model`: covers addition of new omni-modality model support
- `add-tts-model`: covers integration of new TTS models and related serving
-  workflows
- `generate-release-note`: helps prepare release notes for repository changes
- `precheck-pr`: self-check a branch before creating a PR — validates PR title
-  format, catches dead code, verifies accuracy/perf claims, and confirms merge
-  readiness
- `vllm-omni-test`: guides generation and execution of CI-aligned tests (L1–L4),
-  pytest marker selection (`core_model` / `advanced_model` / `full_model`,
-  `omni` / `tts` / `diffusion`), Buildkite wiring (`test-ready.yml`,
-  `test-merge.yml`, `test-nightly.yml`, `test-weekly.yml`), and copy-paste
-  local plus CI-like `pytest` commands; see `references/test-routing.md` for
-  level-to-command mapping
- `review-pr`: provides a structured workflow for reviewing pull requests
- `vllm-omni-npu-model-runner-upgrade`: upgrades NPU model runners to align with the
-  latest vllm-ascend NPUModelRunner
+- [`add-diffusion-model`](add-diffusion-model/SKILL.md): guides integration of
+  a new diffusion model into `vllm-omni`
+- [`add-tts-model`](add-tts-model/SKILL.md): covers integration of new TTS
+  models and related serving workflows
+- [`diffusion-perf-opt`](diffusion-perf-opt/SKILL.md): guides diffusion model
+  performance optimization, including profiling traces, parallel strategies,
+  stage timing analysis, and benchmark-driven tuning
+- [`precheck-pr`](precheck-pr/SKILL.md): self-checks a branch before creating a
+  PR by validating title format, dead code, accuracy and performance claims,
+  and merge readiness
+- [`quantization`](quantization/SKILL.md): guides quantization method selection,
+  model integration, checkpoint loading, and quality/performance validation
+  for vLLM-Omni
+- [`review-pr`](review-pr/SKILL.md): provides a frozen-snapshot, contract-aware
+  workflow for maintainers and reviewers; contributors should use
+  `precheck-pr` for self-review
+- [`vllm-omni-npu-model-runner-upgrade`](vllm-omni-npu-upgrade/SKILL.md):
+  upgrades NPU model runners to align with the latest vllm-ascend
+  `NPUModelRunner`
+- [`vllm-omni-test`](vllm-omni-test/SKILL.md): guides generation and execution
+  of CI-aligned tests (L1–L4), pytest marker selection (`core_model` /
+  `advanced_model` / `full_model`, `omni` / `tts` / `diffusion`), Buildkite
+  wiring (`test-ready.yml`, `test-merge.yml`, `test-nightly.yml`,
+  `test-weekly.yml`), and copy-paste local plus CI-like `pytest` commands; see
+  `references/test-routing.md` for level-to-command mapping

 ## Maintenance Guidelines

@@ -0,0 +1,239 @@
+---
+name: review-pr
+description: Review pull requests and local branches for vllm-project/vllm-omni with a frozen snapshot, module-design ownership, feature-design overlays, targeted validation, and concise evidence-backed findings. Use for default, detailed, or repeat maintainer reviews; checking correctness, compatibility, tests, benchmarks, model additions, distributed changes, or breaking behavior; and identifying or explicitly requesting the most relevant code-owner reviewers. Use precheck-pr instead for an author's pre-submit self-check.
+---
+
+# Review vLLM-Omni Pull Requests
+
+Review like a maintainer: direct, selective, and focused on issues that CI does
+not prove. Prefer a few high-confidence findings over exhaustive commentary.
+Zero findings is a valid result.
+
+## Quality contract
+
+Make every finding:
+
+- **Correct:** prove a reachable failure, not a suspicion.
+- **Prioritized:** lead with merge blockers and high-impact defects.
+- **Actionable:** identify the smallest safe fix direction.
+- **Evidence-based:** cite code, tests, docs, CI, or measurements.
+- **Concise:** avoid review templates and repeated summaries.
+- **Calibrated:** match severity to user and maintainer impact.
+
+Do not report unrelated backlog, style already enforced by pre-commit, or a
+missing test that would not protect changed behavior.
+
+## Select the input and depth
+
+Use `vllm-project/vllm-omni` as the base repository. Accept its forks and local
+checkouts; use another skill for unrelated repositories.
+
+| Input | Review surface |
+| --- | --- |
+| PR number or URL | Frozen PR metadata, full diff, and relevant threads. |
+| Local branch/worktree | Frozen target-base SHA through committed, staged, unstaged, and in-scope untracked changes. |
+| Pre-filled context | Reuse supplied metadata; fetch only missing facts and the full diff. |
+
+Default to maintainer brevity. A detailed or audit request expands coverage and
+lists `path:line` findings, but keeps the same confidence and severity bar.
+
+## Reference guide
+
+Load references in review order: process, one primary module contract, matching
+feature designs, evidence checks, then delivery. Every file is linked directly
+below; do not load unrelated module references.
+
+Each concise reference links to the maintained
+[vLLM-Omni documentation](https://docs.vllm.ai/projects/vllm-omni/en/latest/).
+For branch-specific behavior, inspect the matching `docs/` file in the reviewed
+checkout first; use the published latest docs for current guidance and discovery.
+If docs and live code disagree, verify the code/tests and report the drift.
+
+### Review process
+
+| Reference | Read when |
+| --- | --- |
+| [review-execution.md](references/process/review-execution.md) | Every review; freeze inputs, inspect safely, and deliver against the same snapshot. |
+| [general-checks.md](references/process/general-checks.md) | Every review; apply repository-wide correctness and evidence rules. |
+| [design-contracts.md](references/process/design-contracts.md) | Every production review; resolve branch-local module and feature design status. |
+| [review-routing.md](references/process/review-routing.md) | After the diff census; select one primary module and conditional overlays. |
+
+### Primary module contract
+
+| Reference | Read when |
+| --- | --- |
+| [entrypoints.md](references/modules/entrypoints.md) | Offline/CLI/API ingress, validation, rendering, streaming, or sessions change. |
+| [configuration.md](references/modules/configuration.md) | Config construction, deploy/stage schema, defaults, registry, or topology changes. |
+| [input-output-modality.md](references/modules/input-output-modality.md) | Requests, messages, serialization, output types, accumulation, or completion change. |
+| [error-contracts.md](references/modules/error-contracts.md) | Error classification, fatality, propagation, sanitization, or rendering changes. |
+| [engine-orchestration.md](references/modules/engine-orchestration.md) | Cross-stage routing, request state, output ordering, RPC correlation, or terminal convergence changes. |
+| [stage-runtime.md](references/modules/stage-runtime.md) | Placement, startup, readiness, replica identity, affinity, membership, or shutdown changes. |
+| [omni-connector.md](references/modules/omni-connector.md) | Cross-stage/process/device/node transport or synchronization changes. |
+| [model-integration.md](references/modules/model-integration.md) | Registration, preprocessing, loading, runners, or model-specific execution changes. |
+| [ar-runtime.md](references/modules/ar-runtime.md) | AR scheduling, request/cache state, adapters, workers, or upstream vLLM semantics change. |
+| [diffusion.md](references/modules/diffusion.md) | Diffusion runtime, models, batching, parallelism, or offload changes. |
+| [execution-platforms.md](references/modules/execution-platforms.md) | Hardware selection, capabilities, vendor workers, kernels, or patches change. |
+| [cache-management.md](references/modules/cache-management.md) | Cache identity, reuse, validity, reset, eviction, or teardown changes. |
+| [quantization.md](references/modules/quantization.md) | Quantization selection, checkpoint metadata, layer mapping, precision, or constraints change. |
+| [observability.md](references/modules/observability.md) | Metrics, logs, units, labels, correlation, or lifecycle changes. |
+| [profiling.md](references/modules/profiling.md) | Profiling instrumentation, traces, start/stop lifecycle, or overhead changes. |
+| [benchmarking.md](references/modules/benchmarking.md) | Benchmark workload, metric calculation, CLI, or result metadata changes. |
+
+### Feature-design overlays
+
+| Reference | Read when |
+| --- | --- |
+| [runtime-stage-execution.md](references/features/runtime-stage-execution.md) | Disaggregated inference, async chunk/output/materialization, or prefix caching changes. |
+| [communication.md](references/features/communication.md) | A concrete OmniConnector backend or its deployment contract changes. |
+| [diffusion-acceleration.md](references/features/diffusion-acceleration.md) | Diffusion parallelism, attention, quantization, cache, batching, or offload changes. |
+| [infrastructure-performance.md](references/features/infrastructure-performance.md) | Metrics infrastructure or documented speech optimization stacks change. |
+
+### Evidence and quality checks
+
+| Reference | Read when |
+| --- | --- |
+| [model-addition-checklist.md](references/checks/model-addition-checklist.md) | A model, architecture, loader, processor, registry, or stage config is added. |
+| [perf-verification.md](references/checks/perf-verification.md) | The PR makes a latency, throughput, memory, or quality claim. |
+| [test-quality-evaluation.md](references/checks/test-quality-evaluation.md) | Tests change, are absent for risky code, or may not exercise production behavior. |
+| [tests-docs-checklist.md](references/checks/tests-docs-checklist.md) | Coverage, CI markers, examples, user docs, or PR evidence need review. |
+| [verification.md](references/checks/verification.md) | Hardware, a server, or a runnable affected path is available for active verification. |
+| [examples-policy.md](../precheck-pr/references/examples-policy.md) | The PR adds, copies, or renames Python under `examples/`; apply the canonical policy shared with `precheck-pr`. |
+
+### Delivery and reviewer coordination
+
+| Reference | Read when |
+| --- | --- |
+| [maintainer-style-study.md](references/delivery/maintainer-style-study.md) | Findings are ready for concise maintainer-style delivery. |
+| [review-requests.md](references/delivery/review-requests.md) | The user asks to identify, suggest, request, or ping code-owner reviewers. |
+
+## Workflow
+
+### 1. Freeze and report the snapshot
+
+Pin the base and head before reading source or running validation. Within 60 seconds,
+report the pinned head, CI, mergeability, and preliminary findings in the host
+conversation. Do not wait for CI or post this update to GitHub.
+
+If the target changes while fetching, discard the evidence and retry once. If
+it changes again, report the churn and wait for a stable target.
+
+For a trusted PR head, materialize the pinned head in an isolated detached
+worktree. A worktree freezes identity but is not a security sandbox. Treat fork
+heads as untrusted unless the user and environment policy explicitly establish
+otherwise: execute them only in a disposable, secret-free sandbox with restricted
+filesystem, network, and resources; without one, use static SHA-addressed reads
+and CI evidence only. For a local review, freeze the committed, index, worktree,
+and NUL-safe in-scope untracked contents. Follow
+[review-execution.md](references/process/review-execution.md) for trust gates,
+state fingerprints, and byte-for-byte staleness checks.
+
+### 2. Build the diff census
+
+Group files into production code, tests, docs, configuration, build/CI, and
+generated artifacts. Map each changed production file and test group to the PR
+goal. Compare the title/body claims with the actual diff; use linked issues only
+when they define the contract or reproduction.
+
+Mark unrelated scope and unexplained generated artifacts. Do not infer behavior
+from the PR description without tracing the live code.
+
+### 3. Route from the live behavior
+
+Trace each claimed behavior through the changed producer to its live consumer,
+then use [design-contracts.md](references/process/design-contracts.md) and
+[review-routing.md](references/process/review-routing.md) to select one primary
+module contract, a second only for a real documented cross-boundary call path,
+and every matching feature-design and evidence overlay. Treat titles and paths
+as hints; live behavior and the frozen head's current design metadata are
+authoritative. For docs-, tests-, or CI-only changes, route to the production
+contract they protect or use only the applicable evidence checks.
+
+### 4. Run the blocker scan
+
+Apply every category in [general-checks.md](references/process/general-checks.md) before
+lower-priority comments.
+
+If the diff census contains an added, copied, or renamed Python path under
+`examples/`, read and apply the canonical
+[examples policy](../precheck-pr/references/examples-policy.md). Treat a new
+model-specific Python example as blocking. Do not flag model-specific example
+debt that the PR only modifies or removes, and do not run the rest of the
+author-oriented `precheck-pr` workflow.
+
+For each changed value or behavior, trace:
+
+```text
+public ingress -> validation/defaulting -> producer -> transformations
+  -> stage/worker/connector boundary -> final consumer -> terminal cleanup
+```
+
+Cover every applicable offline/online, streaming/non-streaming, sync/async,
+feature-on/off, topology, and compatibility path. Search bounded callers and
+sibling implementations rather than assuming the changed hunk is the only path.
+
+### 5. Apply module and feature contracts
+
+Apply the reference set selected in step 3 and any matching repo-local skill.
+Read the exact module and feature pages in the frozen head, including status,
+ownership boundary, dependencies, candidate invariants, safe-change guide, and
+promotion gate. Candidate or draft rules are questions, not blockers, unless
+current code, tests, or policy enforce them. Inspect both sides of any config,
+registry, serialization, connector, cache, or stage boundary.
+
+When a diff adds or expands a helper, class, fallback, compatibility branch, or
+public behavior, run a subtraction pass: remove out-of-scope behavior and check
+whether each new abstraction can be deleted, merged, moved, or inlined.
+
+### 6. Verify the changed path
+
+Before each validation group, verify the frozen SHA plus the tracked, index,
+untracked, and ignored-file fingerprint, or recreate a pristine snapshot. On a
+trusted head or inside the required sandbox, run an import/version preflight,
+then the narrowest relevant tests and low-cost static checks. Bind every result
+to the head SHA, snapshot fingerprint, and environment fingerprint. Never run
+imports, tests, builds, hooks, or repo-configurable tooling from an untrusted
+head on the reviewer host.
+
+- Treat CI as status evidence; inspect only the first overlapping failure.
+- For docs-only changes, use diff hygiene, links/build checks, and bounded live
+  contract verification instead of dependency setup or pytest.
+- For hardware-dependent paths, run available static/CPU checks and name the
+  exact GPU/NPU gap. Never simulate device evidence.
+- For performance or accuracy claims, require comparable base/head runs with
+  the same environment, workload, warmup, repetitions, and quality criteria.
+
+Stop when each changed semantic path has a supported finding or an explicit
+no-issue conclusion. Do not search further only to increase confidence.
+
+### 7. Consolidate and deliver
+
+Verify each finding against the current diff, deduplicate by root cause, and
+order by severity.
+
+Re-read the remote head and reverify or recreate the pristine validation
+snapshot immediately before delivery. If either changed, mark the review stale
+and restart from the new snapshot.
+
+Return findings first. Use
+[maintainer-style-study.md](references/delivery/maintainer-style-study.md) to keep them
+direct and brief. Each finding must include an exact `path:line`, trigger or
+call path, current behavior, impact, and smallest fix direction. If there are
+no findings, say so briefly and name material validation gaps.
+
+Keep the review read-only unless the user explicitly authorizes posting. Do not
+submit `APPROVE`, `COMMENT`, or `REQUEST_CHANGES`, add labels, edit code, or push
+commits as an implied part of review.
+
+### 8. Optionally request focused owner reviews
+
+Only when the user asks to identify or request reviewers, read
+[review-requests.md](references/delivery/review-requests.md). Rank path-matched
+CODEOWNERS with the frozen module page's owners or required reviewers and
+documented governance expertise; propose one to three focused reviewers with an
+explicit contract rationale.
+
+Identifying or suggesting reviewers is read-only. Requesting reviewers or
+posting `@mention` comments changes external state and requires explicit user
+authorization. When authorized, recheck the head, deduplicate existing
+requests, and post at most one consolidated comment. Do not infer this
+permission from a request to review the code.
@@ -0,0 +1,4 @@
+interface:
+  display_name: "vLLM-Omni PR Review"
+  short_description: "Review vLLM-Omni PRs and route owner requests"
+  default_prompt: "Use $review-pr to review this vLLM-Omni pull request with owner-specific rules and evidence-backed findings."
@@ -0,0 +1,121 @@
+# Model Addition Checklist
+
+Read this reference when a PR adds or expands a model architecture, pipeline,
+loader, processor, registry entry, or stage configuration.
+
+Official docs: [model contribution guides](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/model/),
+[adding an omni model](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/model/adding_omni_model/),
+[adding a diffusion model](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/model/adding_diffusion_model/),
+[adding a TTS model](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/model/adding_tts_model/),
+[supported models](https://docs.vllm.ai/projects/vllm-omni/en/latest/models/supported_models/),
+[diffusion features](https://docs.vllm.ai/projects/vllm-omni/en/latest/user_guide/diffusion_features/),
+and [feature compatibility](https://docs.vllm.ai/projects/vllm-omni/en/latest/user_guide/feature_compatibility/).
+
+## Match claims to the diff
+
+- Reconcile every claimed architecture, modality, variant, feature, and example
+  with changed files and registry entries.
+- Resolve config model types, architecture names, callable paths, exports, and
+  dependency extras against live symbols.
+- Check sibling generations sharing an architecture name are disambiguated by
+  a stable config predicate rather than import order or an accidental default.
+
+## Close the integration chain
+
+Trace:
+
+```text
+public model id -> config/pipeline selection -> registry -> loader/processor
+  -> stage inputs -> model execution -> stage/public output
+```
+
+Verify optional dependencies fail with an actionable message, weight names and
+dtypes map correctly, and each advertised serving mode reaches the production
+dispatcher. Confirm outputs are non-empty and valid for their modality, shape,
+sample rate, or response schema.
+
+## Keep support documentation truthful
+
+- Build an evidence-backed inventory of the new model's exact architecture and
+  checkpoint identifiers, modalities and tasks, hardware backends, serving
+  modes, and supported acceleration, parallelism, cache, offload, and
+  quantization features. Treat untested support as unknown, not supported.
+- Update `docs/models/supported_models.md` with the architecture, model family,
+  example checkpoints, and only the backends validated by the PR.
+- For a diffusion model, add it to the applicable ImageGen, VideoGen, or
+  AudioGen table in `docs/user_guide/diffusion_features.md`. Fill every feature
+  column according to its legend and document partial support or constraints.
+- Update `docs/user_guide/feature_compatibility.md` when the model changes valid
+  feature combinations, configuration constraints, examples, or limitations,
+  and update any other branch-local support table affected by the claims.
+- Cross-check every documented support mark against the registry, configuration,
+  production-path tests, and PR evidence; do not copy a sibling model's support
+  claims without validation.
+
+## Ship the model recipe
+
+- Require every new model PR to add or update one model-family recipe under
+  `recipes/<vendor>/` and its row in the `recipes/README.md` Available Recipes
+  table. Follow [`recipes/TEMPLATE.md`](../../../../../recipes/TEMPLATE.md) and
+  the layout rules in
+  [`recipes/README.md`](../../../../../recipes/README.md); the
+  external recipes repository is a structural reference, not a substitute.
+- Include only tested task, serving-mode, and hardware configurations. Keep the
+  exact model identifiers, flags, commands, verification, feature support,
+  constraints, and limitations consistent with code, tests, examples, and the
+  support documentation.
+- Cover each validated platform in its own hardware section. Use one recipe per
+  model family by default; justify a split when configurations cannot be kept
+  clear in one file, and do not add placeholder sections for untested platforms.
+
+## Compare the reference implementation
+
+Require every new model PR to include a head-versus-reference accuracy or
+quality and performance comparison:
+
+- Name the canonical implementation and pin its repository revision, package
+  version, configuration, and checkpoint. If it cannot run, explain why and use
+  the closest runnable reference; do not silently omit the comparison.
+- Match inputs or dataset, seed, output settings, precision or quantization,
+  hardware, software, warmup, repetitions, synchronization, and timing and
+  memory scope. Include exact commands and environment details for both sides.
+- Compare accuracy or output quality with a task-appropriate metric and explicit
+  tolerance. When no reliable metric exists, provide paired fixed-input outputs,
+  artifacts, and evaluation criteria, and explain expected numerical or
+  qualitative differences.
+- Report end-to-end latency, task-appropriate throughput, peak device memory,
+  run-to-run variability, and head/reference ratios at equivalent output quality.
+- Prefer a timing split for preprocessing or encoding, each model stage,
+  inter-stage transfer or communication, decoding or postprocessing, and output
+  assembly. Define the boundaries, account for overlap, reconcile the split with
+  end-to-end time, and explain any unavailable stage measurement.
+
+Use [perf-verification.md](perf-verification.md) for the comparison protocol and
+repository benchmark and profiling links.
+
+## Remove accidental surface
+
+Search bounded call sites for:
+
+- inference-dead training `forward()` paths, unused factories/wrappers, never-set
+  branch keys, and immediately discarded parameters;
+- duplicate payload strings, validation, or shape coercion across stages;
+- compatibility aliases in brand-new code without an existing caller;
+- private symbols re-exported as public API or module-level side effects.
+
+Keep code only when it has a distinct live caller, invariant, or compatibility
+contract. Prefer one typed producer-consumer schema to repeated string keys.
+
+## Require proportionate evidence
+
+- Run a representative production-path inference and assert output content, not
+  only process survival.
+- Require profiling or A/B tables when the PR makes performance, memory,
+  precision, or quality claims, or when a suspected hot-path/device bug needs
+  that evidence. Do not impose fixed utilization or regression thresholds that
+  the repository does not define.
+- Require focused registry/config/loading tests, a representative E2E path, and
+  user-facing model/usage documentation.
+
+Use [diffusion.md](../modules/diffusion.md) for diffusion models and
+[perf-verification.md](perf-verification.md) for quantitative claims.
@@ -0,0 +1,51 @@
+# Performance and Accuracy Verification
+
+Activate this reference when the PR adds a model or claims or intentionally
+changes latency, throughput, memory, scaling, precision, or output quality.
+
+Official docs: [profiling](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/profiling/),
+[serving benchmarks](https://docs.vllm.ai/projects/vllm-omni/en/latest/cli/bench/serve/),
+and [metrics](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/metrics/).
+
+## Comparable A/B contract
+
+Compare frozen base and head for an existing path. For a new model absent from
+base, compare head with a pinned canonical reference implementation. Use the same:
+
+- hardware, driver, software versions, model/checkpoint, and dependencies;
+- input or dataset, seed, precision, batch/concurrency, topology, and feature
+  flags;
+- warmup, measured repetitions, synchronization, timing scope, and memory
+  collection method.
+
+Keep exact commands and report variability rather than a cherry-picked run.
+Use the repository's benchmark when it covers the claim; otherwise use the
+smallest reproducible workload that reaches the changed production path.
+
+## Evidence table
+
+| Dimension | Typical evidence |
+| --- | --- |
+| Latency | End-to-end latency and, when available, TTFT plus a defined per-stage and transfer-time split. |
+| Throughput/scaling | Requests, tokens, frames, or audio duration per second across the claimed concurrency/topology. |
+| Memory | Peak allocated/reserved device memory and OOM boundary if relevant. |
+| Quality/accuracy | Repository metric or known-good output comparison with an explicit tolerance; paired samples when no metric exists. |
+
+Every optimization needs correctness or quality evidence. A faster result on a
+different workload, precision, seed, or topology is not a valid comparison.
+
+## Reviewer verification ladder
+
+1. Run base/head A/B on suitable hardware when affordable.
+2. If only one side can run, verify the script and request a comparable pair.
+3. If hardware or assets are unavailable, audit methodology, code path, and
+   contributor evidence; name the exact unverified claim.
+
+Classify discrepancies before reporting: implementation regression, benchmark
+bug, environmental drift, noise, or unsupported claim. Explain material
+regressions against the PR's stated goal or repository contract; do not invent
+universal percentage thresholds.
+
+Report claimed and measured values together, bind them to the frozen SHAs and
+environment, and keep unavailable hardware as a validation gap rather than a
+fabricated pass.
@@ -0,0 +1,39 @@
+# Test Quality Evaluation
+
+Read this reference when tests change, a risky production path has no test, or
+a test-only PR may not prove the intended behavior.
+
+Official docs: [test system overview](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/ci/test_system_overview/)
+and [test writing guide](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/ci/test_writing_guide/).
+
+## Static proof check
+
+For each changed semantic path, ask whether the test would fail if that behavior
+were reverted or broken. Check:
+
+- assertions pin the contract, not only non-null output, logs, or “no crash”;
+- the production dispatcher, registry, connector, or scheduler is reached
+  instead of the changed behavior being mocked away;
+- fakes preserve relevant types, MRO, shapes, devices, async behavior, and
+  lifecycle transitions;
+- seeds, ordering, timing, synchronization, external services, and numeric
+  tolerances are deterministic or explicitly controlled;
+- normal, invalid, boundary, feature-off, failure/cancellation, and regression
+  paths are covered where the diff changes them;
+- run-level and domain markers place hardware/model tests in the intended CI
+  lane without silently skipping the contract.
+
+Map source symbols to tests with bounded `rg` searches; do not assume the test
+tree mirrors production paths.
+
+## Runtime check
+
+Run the narrowest affected tests supported by the environment. Record skipped
+hardware/model cases as gaps. Classify failures as code, test, infrastructure,
+or flaky before turning them into findings; one passing rerun does not erase a
+flakiness signal.
+
+Keep grades or matrices internal. Report only concrete code bugs or the one or
+two test defects that leave changed behavior materially unprotected. Use
+[tests-docs-checklist.md](tests-docs-checklist.md) for CI and documentation
+coverage.
@@ -0,0 +1,48 @@
+# Tests and Documentation Checklist
+
+Use this reference to connect changed behavior to coverage, CI selection,
+examples, documentation, and reproducible PR evidence.
+
+Official docs: [test writing guide](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/ci/test_writing_guide/),
+[test execution guide](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/ci/test_execution_guide/),
+and [documentation guide](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/DOCS_GUIDE/).
+
+## Coverage packet
+
+For each high-risk change, record a compact internal packet:
+
+```text
+change and failure risk -> existing unit/E2E coverage -> uncovered boundary
+  -> smallest test and stable assertion that closes the gap
+```
+
+Prefer a regression test that fails on the frozen base for bug fixes. Check
+numeric tolerances, deterministic inputs, realistic mocks, hardware/domain
+markers, and Buildkite/CI wiring using the documentation present on the reviewed
+branch. A skipped test is not a pass.
+
+## Documentation sync
+
+Require user-facing updates when the diff changes a model, feature, CLI/API,
+config key, default, compatibility behavior, or supported platform. Verify:
+
+- the actual docs navigation and support tables used by the branch;
+- exact identifiers, defaults, constraints, known limitations, and migration;
+- a minimal runnable example for newly exposed behavior;
+- examples and docs agree on commands, parameters, output schema, and supported
+  modes;
+- links and the bounded live contract build or resolve correctly.
+
+Do not request docs for internal behavior with no user-visible contract.
+
+## PR evidence
+
+For contributor-run tests or benchmarks, require enough provenance to evaluate
+the claim: hardware, relevant software versions, exact commands, inputs/model,
+result, and approximate runtime. Performance or accuracy evidence must compare
+base and head under equivalent conditions; use
+[perf-verification.md](perf-verification.md).
+
+Turn a missing test or document into a finding only when it protects or explains
+changed behavior. Name the smallest useful addition rather than requesting a
+broad coverage expansion.
@@ -0,0 +1,36 @@
+# Active Verification
+
+Read this reference when a runnable affected path and suitable local or remote
+hardware are available. Never claim device evidence from static analysis or a
+simulated backend.
+
+Official docs: [installation](https://docs.vllm.ai/projects/vllm-omni/en/latest/getting_started/installation/)
+and [test execution guide](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/ci/test_execution_guide/).
+
+## Choose the narrowest level
+
+| Available environment | Verification |
+| --- | --- |
+| Matching accelerator, model/assets, and server | Targeted unit/integration test plus one representative E2E request. |
+| Matching accelerator without serving setup | Targeted model/runtime test and output or metric inspection. |
+| CPU/static environment only | Import/version preflight, focused CPU tests, config/registry resolution, and static contract checks. |
+| Missing dependencies or inaccessible hardware | Record the exact gap and evaluate contributor/CI evidence. |
+
+## Execute safely
+
+1. Bind the run to the frozen head SHA and record Python, platform, dependency,
+   model, device, and relevant environment fingerprints.
+2. Run the smallest affected unit test before an E2E workload.
+3. Exercise the changed public or production-dispatch path with bounded inputs.
+4. Compare actual output, metrics, cleanup, and exit behavior with the PR claim.
+5. For base/head claims, use the equivalent A/B protocol in
+   [perf-verification.md](perf-verification.md).
+
+Avoid disturbing shared servers, ports, models, or accelerators without user
+authorization. Do not install large dependencies, download gated assets, or
+start remote workloads merely because hardware might exist.
+
+Report the command, environment, result, and any skipped path. Classify failures
+as change-induced, pre-existing, infrastructure, environment, or flaky before
+making a finding. A hardware gap lowers confidence; it is not automatic proof
+of a defect.
@@ -0,0 +1,105 @@
+# Maintainer Review Style
+
+Use this reference only when turning proved findings into comments. The baseline
+maintainer samples favor short, direct comments and reserve long explanations
+for architecture or non-obvious failure paths.
+
+Official docs: [contributing and review process](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/).
+
+## Evidence basis
+
+This calibration used 928 PRs merged from 2026-05-10 through 2026-08-08.
+Candidates were ranked by substantive human inline-review threads, then by
+distinct reviewers and review rounds. Author self-comments, bots and AI
+reviewers, and bodyless approvals were excluded. Counts selected the sample;
+the practices below came from reading root comments and follow-up replies.
+
+The main set contains the six leaders after filtering; three other high-ranked
+PRs add core-runtime and benchmark coverage. All were merged.
+
+| PR | Review boundary |
+| --- | --- |
+| [#2906](https://github.com/vllm-project/vllm-omni/pull/2906) | New TTS model, shared utilities, recipes, and request state. |
+| [#3907](https://github.com/vllm-project/vllm-omni/pull/3907) | Full-duplex API, session lifecycle, concurrency, and realtime evidence. |
+| [#2162](https://github.com/vllm-project/vllm-omni/pull/2162) | Diffusion world model, OpenPI serving, tests, examples, and docs. |
+| [#3454](https://github.com/vllm-project/vllm-omni/pull/3454) | Diffusion model, generic CLI surface, loaders, and configuration scope. |
+| [#4425](https://github.com/vllm-project/vllm-omni/pull/4425) | Structured configuration, typing, defaults, and compatibility. |
+| [#5397](https://github.com/vllm-project/vllm-omni/pull/5397) | Distributed offload, collectives, failure handling, and load semantics. |
+| [#4281](https://github.com/vllm-project/vllm-omni/pull/4281) | Composable parallelism, configuration contracts, and examples. |
+| [#3628](https://github.com/vllm-project/vllm-omni/pull/3628) | Stage metrics, benchmark definitions, opt-in API behavior, and cleanup. |
+| [#3855](https://github.com/vllm-project/vllm-omni/pull/3855) | Stage runtime, thread safety, startup, and compatibility. |
+
+Historical comments calibrate delivery; they do not create merge policy. Live
+code, tests, repository docs, and the current `CODEOWNERS` file remain
+authoritative. When refreshing this study, promote a practice only when it
+recurs across at least three PRs or is supported by current repository guidance.
+
+## Recurring review behavior
+
+- Start from a changed contract, not a style preference. State the exact
+  request, configuration, topology, or failure path that triggers the issue.
+- Protect repository boundaries. Keep model-specific behavior in its domain,
+  move genuinely reusable logic to a shared module, and reuse existing loaders,
+  protocols, definitions, and config sources instead of bypassing them.
+- Challenge unrelated scope and abstractions. Ask why a new flag, config, class,
+  helper, fallback, or file is needed when an existing path appears sufficient.
+- Trace concurrency and terminal paths: overlapping requests, disconnect,
+  cancellation, partial failure, stale replies, collective schedule mismatch,
+  cleanup, and retry. Prefer an explicit startup error for unsupported
+  combinations over silently running with incomplete state or wrong weights.
+- Preserve user-visible behavior across variants. An accepted CLI/API/config
+  field must be applied, explicitly rejected, or visibly warned about; compare
+  normal and headless, offline and online, and streaming and non-streaming paths.
+- Ask for evidence at the affected boundary: a regression test using the real
+  tensor or protocol shape, correct pytest markers so CI runs it, comparable A/B
+  performance results, or an SLO-oriented latency/quality measurement.
+- Keep interfaces explicit: avoid `Any` and opaque strings where a precise
+  union, enum, or typed envelope exists; remove dead code and duplicated
+  constants; validate requests before expensive execution.
+- Document module scope and non-obvious invariants. Update examples, recipes,
+  supported-model/feature docs, and hardware assumptions when users rely on
+  them.
+- When expertise crosses an ownership boundary, ask a named owner to inspect a
+  specific file or contract and explain why. Do not send a generic `PTAL` list.
+- Keep non-blocking cleanup in a linked follow-up only after the current PR is
+  safe. If a revision fixes only part of a failure chain, continue the existing
+  thread with the remaining trigger instead of opening a duplicate finding.
+
+## Write the finding
+
+- Lead with the issue or decisive question; omit a review preamble.
+- Name the concrete trigger and impact, then the smallest fix direction.
+- Keep obvious fixes to one sentence. Use more detail only when the call path or
+  ownership boundary is otherwise unclear.
+- Prefer one root-cause comment over repeated symptom comments.
+- Hedge only when evidence is genuinely incomplete; mark that item as a
+  question or validation gap rather than overstating severity.
+- Say briefly that no findings were found when the review is clean; zero
+  comments is a valid outcome when posting is authorized.
+
+Useful forms:
+
+```text
+This leaves <state> allocated when <failure/cancellation path>. Release it in
+the shared terminal cleanup path.
+```
+
+```text
+Does this producer also update <consumer>? It still reads the old field here.
+```
+
+```text
+With <configuration>, this bypasses <existing loader/validator>, so <impact>.
+Reuse that path or reject this combination before startup.
+```
+
+```text
+@<owner>, could you check <specific boundary>? This changes <contract> used by
+<consumer>.
+```
+
+Use a priority label only for a proved high-impact defect. Avoid generic praise,
+routine “Nit:” prefixes, decorative severity labels, audit templates, rule IDs,
+comment-count narration, and “I left a few comments.” Keep approvals, review
+events, reviewer requests, mentions, and GitHub replies subject to the user's
+explicit authorization.
@@ -0,0 +1,99 @@
+# Focused Code-Owner Review Requests
+
+Read only when the user asks to identify, suggest, request, add, or ping
+reviewers. Finding candidates is read-only; GitHub review requests and
+`@mention` comments are external writes.
+
+Owner sources, in order:
+
+1. the reviewed head's
+   [CODEOWNERS](https://github.com/vllm-project/vllm-omni/blob/main/.github/CODEOWNERS)
+   for path ownership;
+2. the active module design page's `owners` and `required_reviewers` metadata
+   for contract ownership and mandatory design review; and
+3. documented
+   [governance expertise](https://docs.vllm.ai/projects/vllm-omni/en/latest/community/governance/)
+   for specialization and tie-breaking.
+
+Resolve all three from the reviewed head when available because ownership
+changes over time. Use published docs only to discover a page absent from an
+older checkout, never to override the frozen head, and do not copy a static
+owner list into review output. CODEOWNERS remains the path authority; module
+metadata identifies contract experts and required design reviewers.
+
+## Rank candidates
+
+1. Freeze the PR head and collect changed files, author, requested reviewers,
+   submitted reviews, and existing comments.
+2. Resolve the **last matching CODEOWNERS rule** for each changed file. Do not
+   union an earlier broad rule with a later specific rule.
+3. Resolve the primary module and feature overlays through
+   [design-contracts.md](../process/design-contracts.md). Read the active module
+   page's status, `owners`, and `required_reviewers`; do not treat owners on an
+   archived, superseded, or unrelated page as current authority.
+4. Group files by the selected primary owner and the exact module or feature
+   contract needing review. Weight production owners and live consumers above
+   tests/docs/CI owners and the repository-wide fallback. Include an applicable
+   `required_reviewers` entry even when it is not the path owner, and label why.
+5. Use documented governance specialties only to choose among matching owners:
+   for example serving versus configuration, diffusion model versus cache or
+   parallelism, TTS versus generic model execution, or CI versus tests.
+6. Exclude the PR author, bots, users who already completed the needed review,
+   and duplicate pending requests. Do not infer availability or invent
+   expertise from a username.
+7. Select one reviewer for a single-owner change and at most three for a real
+   cross-module change. If more are plausible, rank them but do not ping a
+   broad group; ask the user before exceeding three.
+
+If every specific owner is excluded, consider one repository-wide fallback
+owner and label that choice as a fallback rather than a path match. Never fan
+out to the full fallback list. Prefer a qualified owner who covers multiple
+critical path groups over separate reviewers for each file. If CODEOWNERS is
+missing or invalid, state that gap and use governance only as a labeled fallback.
+
+Recent file history may break a tie only when CODEOWNERS and governance remain
+ambiguous. Never use commit count alone to override an explicit current owner.
+
+## Preview before writing
+
+Show the user the exact candidate set and rationale:
+
+```text
+@owner — <owned paths or module role>; please verify <specific module or feature contract>.
+```
+
+Draft one consolidated comment tied to the frozen head:
+
+```text
+Requesting focused review for `<short-head-sha>`:
+
+- @owner1: could you check the <module contract> in `<path/group>`?
+- @owner2: could you check the <feature or cross-boundary contract> in `<path/group>`?
+
+Relevant validation: <one concise result or exact remaining gap>.
+```
+
+Mention only owners being requested. Keep the reason specific enough that each
+person knows what decision or invariant needs attention; do not post a generic
+“please review” or a generated ownership audit.
+
+## External-write gate
+
+- “Who should review?”, “suggest reviewers”, or “draft a request” authorizes
+  only the preview.
+- “Request/add these reviewers” authorizes the GitHub review-request mutation.
+- “Ping/comment/tag the owners” authorizes the consolidated comment.
+- When the user explicitly asks to request reviews **with an owner comment**,
+  both writes are authorized. Obey narrower wording when only one action is
+  requested.
+
+Immediately before an authorized write, verify the PR is still open, the head
+SHA is unchanged, and the candidate is not the author or already satisfied.
+Report draft/WIP state and avoid notifications unless the user explicitly wants
+reviewers requested before ready-for-review.
+
+Use the GitHub connector for reviewer requests and the single top-level comment;
+use `gh` only when connector coverage is unavailable. Do not submit an approval,
+request-changes event, label, assignment, or multiple comments as part of this
+action. After writing, reread the PR and report the reviewers requested and the
+comment URL. If the head changed, discard the draft and rerank before posting.
@@ -0,0 +1,24 @@
+# Communication Features
+
+Use with the OmniConnector, stage-runtime, configuration, and I/O module
+contracts. Load the concrete backend design from the reviewed head:
+
+| Backend | Design |
+| --- | --- |
+| Mooncake store | [MooncakeStoreConnector](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/omni_connectors/mooncake_store_connector/) |
+| Mooncake transfer engine | [MooncakeTransferEngineConnector](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/omni_connectors/mooncake_transfer_engine_connector/) |
+| Mori transfer engine | [MoriTransferEngineConnector](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/omni_connectors/mori_transfer_engine_connector/) |
+| Shared memory | [SharedMemoryConnector](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/omni_connectors/shared_memory_connector/) |
+| Yuanrong store | [YuanrongConnector](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/omni_connectors/yuanrong_connector/) |
+| Yuanrong transfer engine | [YuanrongTransferEngineConnector](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/omni_connectors/yuanrong_transfer_engine_connector/) |
+
+## Feature checks
+
+- Verify dependency, service, device/network, port, memory-pool, and environment
+  prerequisites with actionable startup errors.
+- Match key/metadata construction, sender and receiver roles, allocation,
+  ownership, completion, and supported fast/serialized paths.
+- Exercise timeout, retry, stale sender/buffer reclamation, partial transfer,
+  cancellation, and connector close semantics.
+- Validate the smallest supported same-host or cross-node topology and document
+  backend limitations and operational diagnostics.
@@ -0,0 +1,26 @@
+# Diffusion Acceleration Features
+
+Use with the exact diffusion submodule plus platform, cache, quantization, or
+benchmarking contracts selected by the live path.
+
+| Feature family | Designs |
+| --- | --- |
+| Parallelism | [CFG](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/cfg_parallel/), [Expert](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/expert_parallel/), [HSDP](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/hsdp/), [Pipeline](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/pipeline_parallel/), [Sequence](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/sequence_parallel/), [Tensor](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/tensor_parallel/), [VAE patch](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/vae_parallel/) |
+| Attention | [Skip-Softmax](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/skip_softmax/) |
+| Quantization | [Quantization](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/quantization/) |
+| Cache | [Cache-DiT](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/cache_dit/), [TeaCache](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/teacache/) |
+| Batching | [Diffusion Continuous Batching](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/diffusion_continuous_batching/) |
+| Offload | [Distributed Layerwise Offload](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/distributed_layerwise_offload/) |
+
+## Feature checks
+
+- Verify feature selection, supported models/platforms/topologies, defaults,
+  dependencies, and every touched feature combination.
+- Preserve feature-off and single-rank correctness; reject unsupported
+  combinations before collectives, loading, or execution begin.
+- Trace shapes, shards, ranks, collectives, precision, cache validity, residency,
+  and per-request state through the selected runtime hooks.
+- Compare output quality or accuracy against the disabled/reference path before
+  accepting latency or memory improvements.
+- Require exact commands, topology, environment, warmup, repetitions, and
+  measurements for performance claims; update feature compatibility docs.
@@ -0,0 +1,22 @@
+# Infrastructure and Performance Features
+
+Load the matching design from the reviewed head:
+
+- [Prometheus Metrics](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/metrics/)
+  for logger wrapping, gating, lifecycle, stage transfer, and metric definitions.
+- [Speech Generation Performance Optimizations](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/qwen3_omni_tts_performance_optimization/)
+  for stage batching, CUDA graphs, async chunk, streaming, re-prefill, compile,
+  and deployment evidence.
+
+## Feature checks
+
+- Route instrumentation changes through observability or profiling and result
+  methodology through benchmarking and the performance evidence check.
+- Preserve opt-in/default behavior, units, labels, aggregation, timing scope,
+  lifecycle resets, and bounded overhead.
+- For stacked optimizations, measure each increment against the same baseline
+  and explain interactions, quality guards, regressions, and limitations.
+- Require exact model, checkpoint, hardware, software, workload, topology,
+  concurrency, warmup, repetitions, and production commands.
+- Update the design page when metric meaning, supported optimization stack, or
+  deployment guidance changes.
@@ -0,0 +1,24 @@
+# Runtime and Stage Execution Features
+
+Load the matching design from the reviewed head:
+
+| Feature | Design | Primary module intersections |
+| --- | --- | --- |
+| Disaggregated inference | [Design](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/disaggregated_inference/) | Configuration, stage runtime, OmniConnector, I/O contracts |
+| Async chunk | [Design](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/async_chunk/) | Model integration, orchestration, stage runtime, connector, I/O |
+| Async diffusion output | [Design](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/async_diffusion_output/) | Diffusion runtime, I/O contracts, entrypoints |
+| Async Omni output materialization | [Design](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/omni_async_output_materialization/) | AR runtime, model integration, I/O contracts, orchestration |
+| Automatic prefix caching | [Design](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/prefix_caching/) | AR runtime, cache management, I/O contracts |
+
+## Feature checks
+
+- Confirm the feature's supported models, entrypoints, stage types, topology,
+  configuration, default, and compatibility limitations.
+- Trace enablement from public/config ingress through every participating module
+  and verify the advertised production dispatch actually selects it.
+- Preserve a correct disabled or fallback path and make unsupported combinations
+  fail or warn as documented.
+- Test overlap, ordering, identity, backpressure, cancellation, failure, final
+  partial output, and cleanup at each asynchronous boundary.
+- Update the feature page, support matrices, examples, and recipes when behavior
+  or coverage changes.
@@ -0,0 +1,22 @@
+# Autoregressive Runtime
+
+Primary design: [Autoregressive Runtime](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/ar_runtime/).
+
+Use for AR request queues, scheduling state, token and memory budgets, KV/input
+readiness, workers, runners, and upstream vLLM request/cache transitions.
+
+## Contract checks
+
+- Preserve upstream request-state, scheduling, cache, and preemption semantics
+  unless an Omni-specific difference is documented and tested.
+- Convert modality-specific data through explicit input or output adapters; do
+  not inject it through unrelated scheduler state.
+- Keep workers and model runners focused on assigned execution rather than
+  admission or cross-stage routing.
+- Synchronize token, sequence, batch, and memory budgets with worker-visible
+  state and prevent starvation, double counting, or stale readiness.
+- Preserve legal transitions and one terminal cleanup path across completion,
+  abort, timeout, preemption, worker failure, and shutdown.
+
+Test request lifecycle, scheduling decisions, abort, cache state, adapters, and
+every affected worker execution mode against the pinned upstream contract.
@@ -0,0 +1,24 @@
+# Benchmarking
+
+Primary design: [Benchmarking](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/benchmarking/).
+
+Use when benchmark code, workloads, metric calculations, CLI benchmark
+entrypoints, or result metadata change. Use the performance check separately
+when a PR uses benchmark results as evidence.
+
+## Contract checks
+
+- Record model and revision, code SHA, hardware, software, configuration,
+  workload, precision, topology, concurrency, warmup, repetitions, and
+  measurement window.
+- Validate successful and semantically acceptable outputs before accepting
+  timing, memory, or scaling results.
+- Define every metric's unit, population, aggregation, synchronization, and
+  timing scope.
+- Keep workload parsing and metric computation deterministic and reusable
+  across base and head.
+- Reject comparisons that change material workload or runtime settings without
+  isolating and explaining that difference.
+
+Test workload parsing and metric calculations with deterministic fixtures and
+publish exact commands plus variability for quantitative claims.
@@ -0,0 +1,22 @@
+# Cache Management
+
+Primary design: [Cache Management](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/cache_management/).
+
+Use for AR or diffusion cache identity, reusable state, validity, lifecycle,
+reset, eviction, and teardown.
+
+## Contract checks
+
+- Include every model, request, input, timestep, precision, device, topology,
+  and feature property that can change a reused value in cache identity.
+- Preserve a correct disabled and miss path; never reuse after validity inputs
+  change.
+- Isolate request-scoped state across concurrent requests and define ownership
+  for model-scoped state.
+- Make reset, invalidation, eviction, capacity, and teardown explicit on
+  completion, cancellation, failure, model reload, and shutdown.
+- Keep cache optimizations behind the owning runtime's hooks rather than adding
+  a second request lifecycle.
+
+Test hit, miss, invalidation, disabled behavior, concurrency, capacity, reset,
+and teardown, with quality comparison when reuse is approximate.
@@ -0,0 +1,24 @@
+# vLLM-Omni Configuration
+
+Primary design: [vLLM-Omni Configuration](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/vllm_omni_config/).
+
+Use for config construction, deploy YAML, registries, schema, defaults,
+aliases, CLI projection, stage config, and topology. Inspect the reviewed
+page's status: while its contract remains deferred or draft, current code and
+tests—not proposed precedence rules—are authoritative.
+
+## Contract checks
+
+- Resolve each supported structured, legacy, CLI, environment, default, alias,
+  and stage-override producer through its actual runtime consumer.
+- Reject unknown or owner-mismatched fields explicitly; keep process-local
+  runtime objects out of transportable configuration.
+- Verify default, explicit, and feature-off values reach every live factory and
+  consumer without silent reinterpretation.
+- Treat stage count, placement, connector, parallelism, device, and memory
+  fields as one topology contract and reject impossible combinations early.
+- Preserve supported construction-path parity until a documented migration
+  removes a path.
+
+Require normalization and schema tests, a live-consumer assertion, and docs for
+public keys, defaults, constraints, precedence, or migration behavior.
@@ -0,0 +1,33 @@
+# Diffusion Module Family
+
+Primary design: [Diffusion Modules](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/diffusion/).
+
+Use for diffusion-stage behavior, then load the exact submodule page from the
+reviewed head:
+
+| Submodule | Signals | Design |
+| --- | --- | --- |
+| Runtime | Admission, scheduling, execution, progress, outputs, cancellation, hooks, cleanup | [Diffusion Runtime](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/diffusion/diffusion_runtime/) |
+| Model Integration | Pipelines, registry, loader, adapters, shared layers, model-specific processing | [Diffusion Model Integration](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/diffusion/diffusion_model_integration/) |
+| Continuous Batching | Compatibility, request/step batches, per-request progress, capacity | [Continuous Batching](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/diffusion/continuous_batching/) |
+| Parallelism | Rank topology, process groups, sharding, collectives, distributed execution | [Parallelism](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/diffusion/parallelism/) |
+| Offloader | Residency, transfers, memory accounting, prefetch, teardown | [Offloader](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/diffusion/offloader/) |
+
+## Family-wide checks
+
+- Keep one scheduler-owned lifecycle from admission through completion,
+  cancellation, failure, and cleanup; optional features must use defined hooks.
+- Select pipelines through registry and loader contracts instead of scattered
+  model-name conditionals; keep genuine model differences in model directories.
+- Trace latent, conditioning, timestep, generator, shape, layout, dtype, device,
+  batch expansion, and output conversion through the actual consumer.
+- Make batch compatibility and per-request isolation explicit; never associate
+  output by unstable batch position.
+- Derive topology from validated configuration, define every sharding boundary,
+  keep collectives symmetric, and preserve a supported single-rank path.
+- Track one residency owner per offloaded component, wait for transfer readiness,
+  preserve model state, bound retained copies, and clean up deterministically.
+
+Load matching feature designs for cache, quantization, parallelism, continuous
+batching, or offload. Require feature-off correctness, representative inference,
+terminal-path tests, and quality evidence for optimizations.
@@ -0,0 +1,23 @@
+# Engine Orchestration
+
+Primary design: [Engine Orchestration](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/engine_orchestration/).
+
+Use for request-state creation, cross-stage routing, output ordering, companion
+tracking, RPC correlation, cancellation propagation, and terminal convergence.
+Orchestration does not own replica placement, stage-process startup, public
+rendering, connector transport, or payload and error schemas.
+
+## Contract checks
+
+- Keep one owner for cross-stage routing; entrypoints, models, and stage clients
+  must not independently select the next logical stage.
+- Preserve request identity and output order across concurrent stages and
+  correlate every control or RPC result to the originating request and wave.
+- Make terminal state monotonic: no new forwarding after success, abort,
+  request failure, or fatal engine failure.
+- Propagate cancellation and failure to every active stage and release
+  orchestrator-owned tasks, queues, companions, and request state.
+- Preserve shutdown ordering across the orchestrator and stage runtime.
+
+Test representative multi-stage routing, ordering, correlation, cancellation,
+failure, and repeated shutdown.
@@ -0,0 +1,25 @@
+# Entrypoints and Serving Boundaries
+
+Primary design: [Entrypoints and Serving Boundaries](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/entrypoints/).
+
+Use for offline APIs, CLI and server composition, OpenAI-compatible routes,
+request validation, response conversion, streaming, sessions, and engine
+handoff. Entrypoints adapt public contracts; they do not own cross-stage
+routing, stage lifecycle, configuration precedence, payload schemas, or error
+classification.
+
+## Contract checks
+
+- Normalize and validate public values once before engine submission.
+- Preserve request identity, modality, ordering, and exactly one terminal event
+  across streaming, disconnect, cancellation, and failure.
+- Translate internal outputs and errors explicitly for each advertised offline,
+  HTTP, SSE, WebSocket, or asynchronous-job path.
+- Keep model-specific behavior behind a common adapter or processor instead of
+  adding model-name policy to generic entrypoints.
+- Require API or CLI changes to explain necessity, alternatives, compatibility,
+  migration, and the supported route or transport matrix.
+
+Test the affected public protocol, normalization, engine handoff, rendering,
+and terminal outcomes. Route orchestration, I/O schema, and semantic error
+changes to their module contracts.
@@ -0,0 +1,24 @@
+# Error Classification, Propagation, and Rendering
+
+Primary design: [Error Classification, Propagation, and Rendering](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/error_contracts/).
+
+Use when a change classifies failures, crosses process boundaries, decides
+fatal versus request-scoped impact, sanitizes context, or renders offline,
+HTTP, SSE, WebSocket, or asynchronous-job errors. The I/O module owns the wire
+schema; this module owns the meaning of its error fields.
+
+## Contract checks
+
+- Keep request-scoped failure isolated unless the underlying engine is no
+  longer usable.
+- Preserve stable classification and request context across process and
+  protocol boundaries without exposing prompts, outputs, secrets, or internal
+  stack details.
+- Keep cancellation distinguishable from internal failure.
+- Terminate each public transport exactly once with consistent status, body,
+  event, finish reason, and metrics classification.
+- Maintain compatibility across mixed or older producers only when the
+  reviewed contract defines that behavior.
+
+Test table-driven classification, wire round trips, fatal/request-scoped
+propagation, cancellation, sanitization, and every affected public renderer.
@@ -0,0 +1,23 @@
+# Execution Platforms
+
+Primary design: [Execution Platforms](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/execution_platforms/).
+
+Use for hardware capability detection, worker selection, patches, kernels,
+attention backends, and vendor-specific configuration or connectors.
+
+## Contract checks
+
+- Guard hardware behavior through platform selection or capability detection
+  and fail clearly when unsupported.
+- Keep portable modules free of direct vendor imports when a common selector
+  exists; verify portable imports without the target accelerator.
+- Override only behavior that differs from common or upstream implementations,
+  and compare sibling worker hooks when inheritance does not provide parity.
+- Preserve dtype, layout, rank, stream, synchronization, workspace,
+  graph-capture, and resource-lifetime contracts.
+- Do not let simplified CPU doubles hide platform MRO, initialization, or
+  dispatch failures.
+
+Run portable checks first, then the smallest matching-device test in a fresh
+environment. Report unavailable hardware as a validation gap, never simulated
+device evidence.
@@ -0,0 +1,23 @@
+# Input, Output, and Modality Contracts
+
+Primary design: [Input, Output, and Modality Contracts](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/input_output_modality_contracts/).
+
+Use for requests, prompt and modality types, message schemas, serialization,
+output types, accumulation, completion, and compatibility shims crossing
+entrypoint, orchestration, stage, connector, or model boundaries.
+
+## Contract checks
+
+- Give every boundary value an explicit modality and validated typed schema.
+- Preserve request and stage identity across conversion, serialization,
+  streaming updates, batching, cancellation, and errors.
+- Distinguish partial, final, empty, and failed output states for every modality.
+- Verify producer and consumer agreement on shape, layout, dtype, device,
+  ownership, metadata, ordering, and optional or unknown fields.
+- Keep internal objects out of public protocols; translate them at the owning
+  entrypoint.
+- Preserve wire compatibility deliberately and give deprecated shims a bounded
+  migration path.
+
+Require construction, round-trip, accumulation, completion, rejection, and
+compatibility tests at each changed producer-consumer boundary.
@@ -0,0 +1,25 @@
+# Model Integration
+
+Primary design: [Model Integration](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/model_integration/).
+
+Use for model registration, configuration adapters, preprocessing, checkpoint
+loading, stage inputs, model-specific hooks, shared layers, runners, and model
+execution. Use the diffusion family reference for diffusion models.
+
+## Contract checks
+
+- Declare explicitly how the model class, loader, input processor, stage
+  configuration, and runtime capabilities are selected.
+- Preserve weight-name mapping, dtype/device placement, optional-dependency
+  errors, and checkpoint-specific loading semantics.
+- Trace each typed input and output through preprocessing, runner state, model
+  call, and its actual consumer.
+- Reuse upstream vLLM behavior when sufficient; document and test the precise
+  difference for every override or patch.
+- Keep model code out of admission, cross-stage routing, public policy, and
+  terminal lifecycle ownership.
+- Place reusable behavior in shared modules and test it with more than one
+  integration.
+
+Require focused registry, loader, processor, runner, and representative model
+execution tests. Apply the model-addition check for new or expanded support.
@@ -0,0 +1,21 @@
+# Observability
+
+Primary design: [Observability](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/observability/).
+
+Use for metrics, logs, correlation fields, and cross-stage request visibility.
+
+## Contract checks
+
+- Preserve stable request and stage identity across entrypoints, orchestration,
+  stages, workers, errors, and terminal cleanup.
+- Treat metric names, units, labels, aggregation, and lifecycle as public
+  contracts; document any semantic change.
+- Keep labels bounded: never use request IDs, prompts, outputs, paths, or other
+  unbounded user values.
+- Register and reset metrics exactly once across repeated startup and shutdown,
+  and keep disabled instrumentation inexpensive.
+- Align emitted values with their actual timing and lifecycle owner rather than
+  reconstructing them from ambiguous client strings.
+
+Test registration, label sets, units, resets, error classification, and
+representative multi-stage emission.
@@ -0,0 +1,24 @@
+# OmniConnector
+
+Primary design: [OmniConnector](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/omni_connector/).
+
+Use for connector contracts and model-agnostic data transfer across stages,
+processes, devices, or nodes. Connector implementations transport and
+synchronize data; they do not choose the next stage or implement model policy.
+
+## Contract checks
+
+- Prove producer and consumer agreement on request/stage identity, shape,
+  layout, dtype, placement, ownership, ordering, and completion.
+- Keep backend selection and metadata/key construction explicit; do not silently
+  reinterpret or drop payloads across SHM, store, network, or vendor paths.
+- Bound connect, put/get, retry, synchronization, and readiness waits with
+  backpressure, cancellation, and actionable errors.
+- Prevent route, endpoint, port, key, and buffer collisions across repeated
+  startup, concurrent requests, and replica replacement.
+- Release connections, handles, buffers, listener work, and stale sender state
+  on success, cancellation, failure, and shutdown.
+
+Load the matching communication feature design for a concrete backend. Require
+focused producer-consumer tests and the smallest runnable multi-process or
+multi-node path.
@@ -0,0 +1,21 @@
+# Profiling
+
+Primary design: [Profiling](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/profiling/).
+
+Use for opt-in instrumentation and trace collection across supported profiler
+backends.
+
+## Contract checks
+
+- Keep expensive collection disabled by default and bounded by explicit start,
+  stop, timeout, and cleanup behavior.
+- Preserve scheduling, generated outputs, resource ownership, and concurrency
+  semantics apart from documented synchronization overhead.
+- Include stage, rank, worker, operation, and request correlation where useful
+  without embedding user payloads or secrets.
+- Make repeated start/stop and partial failure safe, including multi-rank trace
+  naming and artifact ownership.
+- Measure disabled and enabled overhead before making performance claims.
+
+Test the disabled path, repeated lifecycle, failure cleanup, trace context, and
+each affected backend.
@@ -0,0 +1,22 @@
+# Quantization
+
+Primary design: [Quantization](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/quantization/).
+
+Use for method registration, checkpoint interpretation, layer replacement,
+reduced precision, platform capability, and runtime constraints.
+
+## Contract checks
+
+- Validate checkpoint metadata, selected method, supported layers, device
+  capability, required dependencies, and parallel layout before loading.
+- Preserve loader and weight-name mapping semantics, dtype, scale/granularity,
+  zero point, padding, and buffer handling through the real kernel consumer.
+- Fail clearly for unsupported combinations; do not silently choose a different
+  method or precision.
+- Keep online and pre-quantized checkpoint paths explicit and ensure patches or
+  fallbacks do not bypass model-owned loaders.
+- Require numerical comparison against a defined reference with explicit
+  tolerance before treating performance results as valid.
+
+Test registration, loading, representative layers, metadata, platform guards,
+parallel combinations, and numerical correctness separately from performance.
@@ -0,0 +1,23 @@
+# Stage Runtime and Replica Lifecycle
+
+Primary design: [Stage Runtime and Replica Lifecycle](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/stage_runtime/).
+
+Use for local or distributed placement, startup, readiness, stage pools,
+replica identity and membership, selection, affinity, liveness, draining, and
+stage-client or process lifecycle. Cross-stage request policy belongs to engine
+orchestration; model scheduling belongs to the owning runtime.
+
+## Contract checks
+
+- Acquire, replace, and retire replicas through one stage-runtime lifecycle.
+- Preserve stable replica identity and request affinity until termination or a
+  declared replica loss.
+- Make readiness explicit before admission; surface partial-startup and
+  membership failures without leaving half-initialized capacity.
+- Keep local and distributed semantics aligned for supported modes.
+- Make drain, shutdown, and cleanup idempotent across repeated signals and
+  process failure.
+
+Test startup, readiness, selection, affinity, membership changes, abort,
+process loss, draining, and repeated shutdown on affected local/distributed
+paths.
@@ -0,0 +1,34 @@
+# Resolve Design Contracts
+
+Use after freezing the review head and before selecting module or feature
+references. The maintained [design index](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/)
+defines two routing axes:
+
+- **Module designs** own code boundaries and invariants. Select one primary
+  module and a second only when the live producer-consumer path crosses it.
+- **Feature designs** describe behavior spanning modules. Load every feature
+  page whose implementation, default, support, or contract changes.
+
+## Resolution order
+
+1. Open `docs/design/index.md` in the reviewed head, not the reviewer's checkout
+   or published latest tree.
+2. Match changed behavior against each module page's `primary_code_paths`,
+   `primary_path_exceptions`, ownership boundary, and `depends_on` metadata.
+3. Read `status`, `architecture_state`, `last_verified_commit`, and any linked
+   in-flight work before treating prose as current behavior.
+4. Use current code and tests when a page is draft, deferred, stale, absent, or
+   conflicts with the frozen implementation. Report material documentation
+   drift separately.
+5. Treat candidate invariants and promotion gates as review questions until
+   repository policy, current code/tests, or owner-approved normative text
+   makes them enforceable. Do not turn a draft identifier into an automatic
+   blocker.
+6. Use published latest docs only for discovery when the reviewed head lacks a
+   page; never import a newer contract into an older branch silently.
+
+When behavior changes an active module or feature contract, require the same PR
+to update that page. Require a new page only when repository policy, an accepted
+RFC/issue, or explicit maintainer acceptance criteria do so; otherwise treat it
+as a non-blocking design suggestion. Do not demand a second page when an existing
+design already owns the exact contract.
@@ -0,0 +1,129 @@
+# General Review Checks
+
+Apply these checks to every PR after freezing the review surface. A pattern is
+not a finding until the diff introduces or exposes a reachable trigger, impact,
+and smallest safe fix. Do not report unrelated backlog.
+
+Official docs: [contributing guide](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/)
+[feature compatibility](https://docs.vllm.ai/projects/vllm-omni/en/latest/user_guide/feature_compatibility/),
+and [design documents](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/).
+
+## Contract and scope
+
+- Match the title/body claims to every changed production path, test, config,
+  dependency, generated artifact, and user-visible document.
+- Trace each changed value through public ingress, validation/defaulting,
+  producer, transformations, process or device boundaries, final consumer, and
+  terminal cleanup.
+- Check every applicable sync/async, offline/online, streaming/non-streaming,
+  feature-off, topology, and compatibility path without demanding unsupported
+  modes merely for symmetry.
+
+## Module, API, and class design
+
+Apply these rules to new or materially changed production code; do not require
+unrelated cleanup from untouched files.
+
+1. Start each source module with a concise module docstring that defines its
+   responsibility and boundaries and identifies its primary classes and key
+   functions. Place it after any required license header and before imports.
+2. Give every added or materially changed class and function a clear docstring
+   covering its responsibility, parameters, return value, raised exceptions,
+   and non-obvious invariants. Use explicit typed parameters at owned API
+   boundaries; avoid opaque `**kwargs` and `Any`. Allow them only when an
+   upstream or compatibility interface requires them, and document and validate
+   the accepted keys and concrete types.
+3. Move stable helpers shared by multiple features into an appropriately scoped
+   utility module. Do not let feature modules accumulate large collections of
+   unrelated private utility functions; keep a private helper local only when
+   it is single-purpose and tightly coupled to that feature.
+4. Keep every method on a key class within that class's stated responsibility,
+   and document non-obvious design decisions and invariants. Before adding a
+   method, prefer reuse, consolidation, or extension of an existing abstraction;
+   split the class when new behavior creates a separate responsibility or makes
+   the class unnecessarily long.
+5. Require changes to user-facing behavior to update `docs/`. When an existing
+   active module or feature design page owns the changed contract, update that
+   page. Require a new design page only when the reviewed branch's repository
+   policy, an accepted RFC/issue, or explicit maintainer acceptance criteria do
+   so; otherwise suggest it as non-blocking. For a major architectural change,
+   apply the branch's contributing policy for a design RFC or issue; the current
+   policy uses more than 500 changed production-code lines, excluding kernel,
+   data, config, and test changes. Resolve page status and authority through
+   [design-contracts.md](design-contracts.md), and do not demand a duplicate page
+   when an existing design owns the exact contract. When required, cover scope,
+   data flow, public contracts, failure behavior, alternatives, validation,
+   limitations, and rollout.
+6. Require every performance PR to provide reproducible base-versus-head A/B
+   results under the same hardware, software, model, workload, precision,
+   topology, warmup, repetitions, and measurement scope. Include exact commands,
+   variability, and a correctness or quality guard; one-sided or non-comparable
+   numbers are incomplete evidence. For a new model absent from the frozen base,
+   compare head with its pinned canonical reference implementation under rule 12
+   instead. Follow
+   [perf-verification.md](../checks/perf-verification.md) for the full protocol.
+7. Require every addition, removal, default change, or semantic change to a
+   user-facing inference API or CLI to explain why it is necessary, which
+   alternatives were considered, and why the chosen interface is preferable.
+   Record compatibility, deprecation or migration behavior, and the rationale
+   in the PR. Update the corresponding module and feature design documents when
+   they define the changed interface or workflow, or when policy requires one.
+8. Require every bug-fix PR to add an automated regression test that reproduces
+   the defect on the frozen base and passes on the fixed head. Exercise the
+   production path or nearest stable boundary and assert the corrected behavior,
+   not only process survival. Record the exact base/head command and result, and
+   place hardware- or model-specific coverage in the correct CI lane. A manual
+   reproduction supplements but does not replace the regression test.
+9. Require every new or materially changed recipe to follow
+   [`recipes/TEMPLATE.md`](../../../../../recipes/TEMPLATE.md) and the naming and
+   layout conventions in
+   [`recipes/README.md`](../../../../../recipes/README.md).
+   Complete every applicable summary, use-case, reference, hardware,
+   environment, exact-command, verification, and notes section with tested,
+   reproducible details; explain any intentional deviation in the PR.
+10. Require every new model to verify its supported modalities, platforms,
+    execution modes, and features with production-path evidence. Update
+    `docs/models/supported_models.md` and every applicable feature support or
+    compatibility table; do not claim support that the PR does not validate.
+    Follow
+    [model-addition-checklist.md](../checks/model-addition-checklist.md) for the
+    model-specific documentation checks.
+11. Require every new model PR to add or update its in-repository model-family
+    recipe under `recipes/` and its `recipes/README.md` index entry. Apply rule
+    9, and keep the recipe's model identifiers, tasks, serving modes, hardware,
+    commands, supported features, and limitations aligned with validated PR
+    evidence. An external recipe link does not replace the in-repository recipe.
+12. Require every new model to compare accuracy or output quality and performance
+    against a pinned baseline or canonical reference implementation. Match the
+    checkpoint, inputs, precision, hardware, workload, and measurement method;
+    provide exact commands, results, and explanations for material differences.
+    Prefer a detailed timing split for each stage and explain when it is
+    unavailable. Follow
+    [model-addition-checklist.md](../checks/model-addition-checklist.md) and
+    [perf-verification.md](../checks/perf-verification.md) for the evidence
+    contract.
+
+## Blocking risk scan
+
+| Risk | Prove before reporting |
+| --- | --- |
+| Correctness | A live input reaches a wrong output, exception, silent drop, shape/dtype/device mismatch, or partially initialized state. |
+| Compatibility | A supported API, config key, default, serialization schema, model identifier, or caller breaks without validation, migration, or deprecation. |
+| Lifecycle | Allocation or startup lacks cleanup on partial failure, timeout, cancellation, shutdown, or repeated requests. |
+| Concurrency/distribution | Ordering, identity, rank, world size, completion, timeout, backpressure, or collective assumptions fail on a supported path. |
+| Cache/shared state | Identity omits a correctness-affecting input, or isolation, invalidation, disabled behavior, eviction, or cleanup is incomplete. |
+| Async behavior | Blocking I/O, sleep, a lock across `await`, or unnecessary serialization harms a reachable async path. |
+| Security/data handling | Secrets, user payloads, unsafe deserialization/eval/shell, unvalidated paths, or unbounded metric labels are reachable. |
+| Validation evidence | Tests bypass the production dispatcher, mock away the change, use unrealistic types/MRO, or cannot fail when the defect returns. |
+| User contract | User-visible model, feature, API, CLI, config, default, or compatibility behavior changes without accurate docs or examples. |
+
+Search bounded callers and sibling implementations before reporting dead code,
+duplication, or a missed consumer. Keep new abstractions only when they have a
+distinct live caller, invariant, or compatibility need.
+
+## Finding bar
+
+Anchor each finding to a changed `path:line`; name the trigger or call path,
+current behavior, impact, and smallest fix direction. Treat pending CI, missing
+hardware, or unsupported measurements as validation gaps unless the repository
+contract makes that evidence a merge requirement.
@@ -0,0 +1,191 @@
+# Review Execution and Delivery
+
+Use this reference for every review. It defines snapshot collection, gates,
+validation records, delivery permissions, maintainer tone, and re-review.
+
+Official docs: [contributing guide](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/)
+and [CI failure triage](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/ci/failures/).
+
+## Contents
+
+- [Freeze the review surface](#freeze-the-review-surface)
+- [Report status before analysis](#report-status-before-analysis)
+- [Apply review gates](#apply-review-gates)
+- [Run bounded validation](#run-bounded-validation)
+- [Deliver maintainer-style findings](#deliver-maintainer-style-findings)
+- [Re-review safely](#re-review-safely)
+
+## Freeze the review surface
+
+For a GitHub PR, read the base/head SHA before and after fetching metadata and
+the diff:
+
+```bash
+gh api "repos/vllm-project/vllm-omni/pulls/<PR>" \
+  --jq '{base_sha: .base.sha, head_sha: .head.sha}'
+REVIEW_FIELDS="number,url,title,body,isDraft,baseRefName,headRefName,mergeable,mergeStateStatus,statusCheckRollup,files"
+gh pr view <PR> --repo vllm-project/vllm-omni \
+  --json "${REVIEW_FIELDS}"
+gh pr diff <PR> --repo vllm-project/vllm-omni
+gh api "repos/vllm-project/vllm-omni/pulls/<PR>" \
+  --jq '{base_sha: .base.sha, head_sha: .head.sha}'
+```
+
+Discard the snapshot if either SHA changed. Do not mix comments or validation
+from different heads.
+
+After the SHAs stabilize, fetch and materialize a trusted `head_sha` in an
+isolated detached worktree. Run every source read, `rg` search, import, and test
+from that snapshot, not the caller's checkout. A detached worktree provides
+snapshot isolation, not a security boundary.
+
+Treat a fork head as untrusted unless the user and environment policy explicitly
+establish trust. Never run its imports, tests, builds, hooks, package setup, or
+repo-configurable linters/plugins on the reviewer host. Execute them only in a
+disposable sandbox or VM with no credentials or inherited secrets, no host agent
+or service sockets, minimal read-only host mounts, disabled or explicitly
+allowlisted network, resource/time limits, and destruction after the run. If
+that boundary is unavailable, limit the review to the remote diff, SHA-addressed
+reads such as `git show <head_sha>:<path>`, and existing CI evidence; report all
+executable validation as a gap.
+
+Before the first source read, record a pristine snapshot fingerprint outside
+the reviewed worktree. Include `HEAD`, the NUL-delimited index entries and blob
+IDs, and a NUL-safe manifest of every worktree entry except Git metadata,
+including tracked, untracked, and ignored paths with file type, mode, symlink
+target, and content hash. Before each validation group and delivery, recompute
+and byte-compare the fingerprint as well as asserting `HEAD == head_sha`. If a
+tool changes or creates any entry, discard affected evidence and recreate the
+snapshot before another group; do not rely on `HEAD` alone or clean an unknown
+worktree in place. If an exact snapshot cannot be created, use SHA-addressed
+reads and report filesystem-dependent validation as a gap.
+
+For a local branch/worktree, determine the target ref from the user, current PR,
+or configured upstream; never infer it from the branch name. Resolve the target
+once and include all task-owned worktree state:
+
+```bash
+git status --porcelain=v2 -z
+git rev-parse HEAD
+git rev-parse <target-ref>
+git merge-base <target-base-sha> HEAD
+git diff --stat <comparison-commit>
+git diff --name-status <comparison-commit>
+git diff --binary <comparison-commit>
+git diff --cached --binary <comparison-commit>
+git diff --binary
+git ls-files --others --exclude-standard -z
+```
+
+Freeze the exact bytes of every in-scope untracked file from the NUL-delimited
+list, plus a NUL-safe path, file-type, mode, and content-hash manifest. Also
+fingerprint `HEAD`, the target and merge base, porcelain status, and both binary
+index and worktree patches. Recording only untracked names is insufficient.
+Keep these snapshots in the evidence packet and do not mutate the reviewed
+checkout during a read-only review.
+
+For local changes without PR metadata, mark the title/body as synthetic. Base
+them on the user request and commit messages without inventing validation claims.
+
+If no PR, branch, or usable worktree is identifiable, ask for a PR URL/number or
+explicit base and head rather than guessing.
+
+## Report status before analysis
+
+Within 60 seconds of starting, and before source searches or tests, send this
+host update:
+
+```text
+Pinned head: <SHA>
+Base/comparison: <ref and SHA>
+CI: <pass/fail/pending/not applicable>
+Mergeability: <state/not applicable>
+Preliminary findings: <brief finding or none yet>
+```
+
+Mark early findings as preliminary and continue. This is not a GitHub comment.
+
+## Apply review gates
+
+- Report draft/WIP state. Continue a local review when explicitly requested,
+  but do not publish review events without separate authorization.
+- Record DCO, pre-commit, required CI, and mergeability. Pending or unknown gates
+  do not block source review.
+- Treat a failed gate as its own evidence. Do not restate its formatting/lint
+  output as a new code-review finding.
+- Open CI logs only when the first failing step overlaps the frozen diff or
+  blocks the verdict. Start with the first error, not the last cascade.
+- Treat the PR body as navigation. Commands, benchmark tables, and claimed tests
+  become evidence only after their provenance and relevance are checked.
+
+## Run bounded validation
+
+Keep one evidence packet for files read, bounded searches, callers, tests, CI,
+hardware, routes, and findings. Reuse it rather than refetching facts.
+
+Before pytest, run a short import/version compatibility check. Record each
+validation result with:
+
+```text
+repo, head SHA, command, result, Python/platform, dependency or lock fingerprint
+```
+
+After preflight, run targeted tests and low-cost static checks alongside source
+inspection when possible. Imports, tests, builds, and linters may create caches
+or rewrite files, so isolate each potentially mutating group in a disposable
+snapshot or recreate the pinned snapshot before continuing. Map source symbols
+to tests with bounded `rg` searches rather than assuming the test directory
+mirrors production paths.
+
+Classify failures as code, test, infrastructure, or flaky before reporting.
+Skipped hardware tests are gaps, not passes. For available hardware, verify the
+smallest representative unit/E2E path and compare actual output with PR claims.
+
+## Deliver maintainer-style findings
+
+Default to roughly 1-5 short comments, but treat that as calibration rather than
+a quota: report every real blocker and report no finding when there is no issue.
+
+Write each finding as:
+
+```text
+[P1] Short imperative title — path/to/file.py:<line>
+<Trigger or call path>. <Current behavior and impact>. <Smallest fix direction>.
+```
+
+Use [maintainer-style-study.md](../delivery/maintainer-style-study.md) for
+comment tone.
+Keep rule IDs, grades, and audit matrices internal unless the user asks for the
+complete audit.
+
+Verify every inline `path:line` against the frozen diff before delivery. For a
+PR, re-read the remote head and byte-compare the detached snapshot fingerprint,
+or recreate it from `head_sha`, before asserting current lines. For a local
+review, recompute and byte-compare the frozen `HEAD`, target, merge base, status,
+index patch, worktree patch, and untracked-content manifest. Any mismatch makes
+the review stale; discard the affected evidence and restart. Prefer one
+root-cause comment to several symptom comments.
+
+Local presentation is the default. Only explicit authorization permits GitHub
+posting. When authorized, publish one consolidated final review after
+validation; do not post preliminary or incremental comments. Submit a review
+event (`APPROVE`, `COMMENT`, or `REQUEST_CHANGES`) only when the user explicitly
+chooses that event.
+
+Reviewer requests and owner `@mention` comments are separate external writes;
+follow [review-requests.md](../delivery/review-requests.md) when the user asks
+for them.
+
+## Re-review safely
+
+Freeze the new head and compare it with the previously reviewed SHA. Then:
+
+1. inspect the delta and every file affected by conflict resolution or rebase;
+2. revalidate earlier findings against current lines and behavior;
+3. read unresolved, non-outdated threads and the author's evidence;
+4. rerun only checks invalidated by the delta;
+5. look for new regressions introduced by the fix;
+6. do not repeat resolved or outdated comments.
+
+If the target head changes during re-review, discard stale validation and start
+again from the new snapshot.
@@ -0,0 +1,76 @@
+# Module and Feature Review Routing
+
+Read [design-contracts.md](design-contracts.md) first. Route from the frozen
+head's live producer-consumer behavior, then confirm the result against its
+`docs/design/` metadata. Titles and paths are evidence, not the deciding rule.
+
+## Primary module contract
+
+Choose one primary module and a second only when the live call path crosses a
+documented dependency or primary-path exception.
+
+| Module | Live behavior signals | Read |
+| --- | --- | --- |
+| Entrypoints | Offline/CLI/API ingress, validation, rendering, streaming, sessions | [entrypoints.md](../modules/entrypoints.md) |
+| Configuration | Schema, defaults, deploy/stage construction, registry, topology | [configuration.md](../modules/configuration.md) |
+| I/O and Modality | Requests, messages, serialization, output types, accumulation, completion | [input-output-modality.md](../modules/input-output-modality.md) |
+| Error Contracts | Classification, fatality, propagation, sanitization, public rendering | [error-contracts.md](../modules/error-contracts.md) |
+| Engine Orchestration | Cross-stage routing, request state, ordering, RPC correlation, terminal convergence | [engine-orchestration.md](../modules/engine-orchestration.md) |
+| Stage Runtime | Placement, startup, readiness, replicas, affinity, membership, shutdown | [stage-runtime.md](../modules/stage-runtime.md) |
+| OmniConnector | Cross-stage/process/device/node transport and synchronization | [omni-connector.md](../modules/omni-connector.md) |
+| Model Integration | Registration, preprocessing, loading, runners, model-specific execution | [model-integration.md](../modules/model-integration.md) |
+| AR Runtime | Scheduling, request/cache state, adapters, workers, upstream vLLM semantics | [ar-runtime.md](../modules/ar-runtime.md) |
+| Diffusion Family | Diffusion runtime, models, batching, parallelism, or offload | [diffusion.md](../modules/diffusion.md) |
+| Execution Platforms | Hardware selection, capabilities, vendor workers, kernels, patches | [execution-platforms.md](../modules/execution-platforms.md) |
+| Cache Management | Cache identity, validity, reuse, eviction, reset, teardown | [cache-management.md](../modules/cache-management.md) |
+| Quantization | Method/metadata selection, layer mapping, reduced precision, constraints | [quantization.md](../modules/quantization.md) |
+| Observability | Metrics, logs, labels, units, correlation, lifecycle | [observability.md](../modules/observability.md) |
+| Profiling | Opt-in instrumentation, traces, start/stop lifecycle, overhead | [profiling.md](../modules/profiling.md) |
+| Benchmarking | Workloads, metric computation, benchmark CLI, result metadata | [benchmarking.md](../modules/benchmarking.md) |
+
+For tests-, docs-, CI-, or recipe-only changes, route to the production module
+whose contract they protect. When none exists, use the applicable evidence
+check without inventing a production owner.
+
+## Feature-design overlays
+
+Load every category whose documented behavior, enablement, default, support, or
+compatibility changes.
+
+| Feature-design section | Read |
+| --- | --- |
+| Runtime and stage execution | [runtime-stage-execution.md](../features/runtime-stage-execution.md) |
+| Communication and concrete connectors | [communication.md](../features/communication.md) |
+| Diffusion acceleration | [diffusion-acceleration.md](../features/diffusion-acceleration.md) |
+| Infrastructure and performance | [infrastructure-performance.md](../features/infrastructure-performance.md) |
+
+## Evidence and change overlays
+
+| Signal | Read | Optional repo-local skill |
+| --- | --- | --- |
+| New or expanded model, loader, processor, registry, or stage config | [model-addition-checklist.md](../checks/model-addition-checklist.md) | [`add-tts-model`](../../../add-tts-model/SKILL.md) or [`add-diffusion-model`](../../../add-diffusion-model/SKILL.md) |
+| Latency, throughput, memory, scaling, precision, or quality claim | [perf-verification.md](../checks/perf-verification.md) | [`diffusion-perf-opt`](../../../diffusion-perf-opt/SKILL.md) for diffusion |
+| Tests changed, absent for risky behavior, or test-only | [test-quality-evaluation.md](../checks/test-quality-evaluation.md) | [`vllm-omni-test`](../../../vllm-omni-test/SKILL.md) |
+| CI, examples, docs, public behavior, or contributor evidence | [tests-docs-checklist.md](../checks/tests-docs-checklist.md) | None |
+| Suitable hardware/server and runnable affected path | [verification.md](../checks/verification.md) | None |
+| User asks who should review or requests owner notification | [review-requests.md](../delivery/review-requests.md) | None |
+
+Use [`quantization`](../../../quantization/SKILL.md) for a quantization module
+change and [`$vllm-omni-npu-model-runner-upgrade`](../../../vllm-omni-npu-upgrade/SKILL.md)
+for an NPU execution-platform change when those skills are available.
+
+For a bug fix, require a reachable reproduction and regression test. For a
+refactor, prove parity and remove obsolete paths. For a feature, verify the
+public/config contract, module integration, compatibility/default behavior,
+production dispatch, feature design, and docs.
+
+## Calibrate findings
+
+- **P0:** security exposure, data corruption, or broad project unusability.
+- **P1:** reachable runtime failure, wrong output, compatibility break, or
+  unsafe lifecycle in the changed behavior.
+- **P2:** real non-blocking defect with a concrete future failure mode.
+
+Treat draft candidate invariants, missing hardware, and unsupported claims as
+questions or validation gaps unless current code, tests, or policy makes them a
+merge requirement.
@@ -1,6 +1,11 @@
 # CODEOWNERS is used to automatically assign PR reviewers
 # Format: path pattern @username or @team
 # More info: https://docs.github.com/en/repositories/managing-your-repositorys-settings-and-features/customizing-your-repository/about-code-owners
+#
+# The vllm_omni/** source-tree rules below are derived from the `owners` and
+# `primary_code_paths` front matter in docs/design/module/*.md. When updating
+# ownership, update the design doc first, then regenerate the matching rule here.
+# Last-match-wins: more specific rules must come AFTER broader ones.

 # Default owners (fallback reviewers for all files)
 * @Gaohan123 @hsliuustc0106 @ywang96
@@ -16,30 +21,15 @@
 /.claude/ @hsliuustc0106

 # Tests
-/tests/ @yenuo26
+/tests/ @yenuo26 @NickCao

 # CI
-/.buildkite/ @yenuo26 @congw729
+/.buildkite/ @yenuo26 @congw729 @NickCao

-# Engine and model_executor
-/vllm_omni/core/ @tzhouam @yinpeiqi @fake0fan @Sy0307
-/vllm_omni/engine/ @tzhouam @yinpeiqi @fake0fan @Sy0307
-/vllm_omni/entrypoints/ @tzhouam @yinpeiqi @fake0fan @alex-jw-brooks
-/vllm_omni/model_executor/ @tzhouam @gcanlin
-/vllm_omni/worker/ @tzhouam @gcanlin
+# ComfyUI / external apps
+/apps/ @fhfuih @alex-jw-brooks

-# Diffusion
-/vllm_omni/diffusion/ @Isotr0py @princepride @SamitHuang @wtomin @ZJY0516 @RuixiangMa @david6666666 @xuechendi
-/vllm_omni/lora/ @princepride @SamitHuang @wtomin @ZJY0516 @RuixiangMa @yuanheng-zhao
-
-# Diffusion CPU offloading
-/vllm_omni/diffusion/offloader/ @Isotr0py @princepride @SamitHuang @wtomin @ZJY0516 @RuixiangMa @yuanheng-zhao @xuechendi
-
-# Omni and TTS support
-/vllm_omni/transformers_utils/ @tzhouam @yuanheng-zhao
-/vllm_omni/model_executor/models/ @linyueqian @ZeldaHuang @princepride @yuanheng-zhao @amy-why-3459 @Bounty-hunter @Sy0307 @gcanlin
-
-# Benchmarks
+# Benchmarks (out-of-tree)
 /benchmarks/accuracy/ @david6666666
 /benchmarks/build_dataset/ @linyueqian @yuanheng-zhao
 /benchmarks/diffusion/ @Isotr0py @princepride @SamitHuang @wtomin @ZJY0516 @RuixiangMa @xuechendi
@@ -47,20 +37,112 @@
 /benchmarks/glm_image/ @princepride @JaredforReal
 /benchmarks/tts/ @linyueqian @ZeldaHuang @princepride @Sy0307 @gcanlin

-# Usability and Observability
-/vllm_omni/deploy/ @tzhouam @lishunyang12
-/vllm_omni/metrics/ @lishunyang12
-/vllm_omni/profiler/ @gcanlin
+# =====================================================================
+# Module design ownership (docs/design/module/*.md)
+# Each section header cites the source design doc.
+# =====================================================================

-# ComfyUI
-/apps/ @fhfuih @alex-jw-brooks
+# Autoregressive runtime — docs/design/module/ar_runtime.md
+/vllm_omni/core/ @tzhouam @yinpeiqi @fake0fan @Sy0307 @Gaohan123
+/vllm_omni/worker/ @tzhouam @yinpeiqi @fake0fan @Sy0307 @Gaohan123

-# Large Scale Serving
+# Engine orchestration & stage runtime — docs/design/module/engine_orchestration.md, stage_runtime.md
+/vllm_omni/engine/ @tzhouam @fake0fan
+# File-level overrides: input/output modality contracts own these engine files
+# — docs/design/module/input_output_modality_contracts.md
+/vllm_omni/engine/messages.py @Sy0307 @amy-why-3459 @Gaohan123 @alex-jw-brooks
+/vllm_omni/engine/serialization.py @Sy0307 @amy-why-3459 @Gaohan123 @alex-jw-brooks
+/vllm_omni/engine/mm_outputs.py @Sy0307 @amy-why-3459 @Gaohan123 @alex-jw-brooks
+/vllm_omni/engine/output_modality.py @Sy0307 @amy-why-3459 @Gaohan123 @alex-jw-brooks
+/vllm_omni/engine/output_processor.py @Sy0307 @amy-why-3459 @Gaohan123 @alex-jw-brooks
+
+# Input/output modality contracts — docs/design/module/input_output_modality_contracts.md
+/vllm_omni/inputs/ @Sy0307 @amy-why-3459 @Gaohan123 @alex-jw-brooks
+/vllm_omni/outputs/ @Sy0307 @amy-why-3459 @Gaohan123 @alex-jw-brooks
+/vllm_omni/request.py @Sy0307 @amy-why-3459 @Gaohan123 @alex-jw-brooks
+/vllm_omni/data_entry_keys.py @Sy0307 @amy-why-3459 @Gaohan123 @alex-jw-brooks
+
+# Entrypoints and serving boundaries — docs/design/module/entrypoints.md
+/vllm_omni/entrypoints/ @alex-jw-brooks @linyueqian @NickCao
+# File-level overrides within entrypoints/
+/vllm_omni/entrypoints/openai/errors.py @alex-jw-brooks @NickCao
+/vllm_omni/entrypoints/cli/benchmark/ @alex-jw-brooks @Bounty-hunter
+
+# Error contracts — docs/design/module/error_contracts.md
+/vllm_omni/errors.py @alex-jw-brooks @NickCao
+
+# OmniConnector (inter-stage transport) — docs/design/module/omni_connector.md
 /vllm_omni/distributed/ @princepride @yuanheng-zhao @xuechendi @natureofnature @fake0fan

-# Quantization and configuration
+# Model integration — docs/design/module/model_integration.md
+/vllm_omni/model_executor/ @tzhouam @gcanlin
+/vllm_omni/model_executor/models/ @tzhouam @gcanlin @Sy0307 @amy-why-3459 @linyueqian
+/vllm_omni/model_extras/ @tzhouam @gcanlin @princepride
+/vllm_omni/plugins/ @gcanlin @tjtanaa @xuechendi
+# Config override within model_executor — docs/design/module/vllm_omni_config.md
+/vllm_omni/model_executor/stage_configs/ @lishunyang12 @alex-jw-brooks
+
+# Execution platforms (hardware backends) — docs/design/module/execution_platforms.md
+/vllm_omni/platforms/ @gcanlin @tjtanaa @xuechendi
+/vllm_omni/attention/ @gcanlin @tjtanaa @xuechendi @david6666666 @lishunyang12
+
+# Configuration & deploy — docs/design/module/vllm_omni_config.md
 /vllm_omni/config/ @lishunyang12 @alex-jw-brooks
+/vllm_omni/deploy/ @lishunyang12 @alex-jw-brooks
+
+# Quantization — docs/design/module/quantization.md
 /vllm_omni/quantization/ @david6666666 @Isotr0py @lishunyang12

-# Hardware plugins and accelerators
-/vllm_omni/platforms/ @gcanlin @tjtanaa @xuechendi
+# Observability — docs/design/module/observability.md
+/vllm_omni/metrics/ @lishunyang12 @vraiti
+
+# Profiling — docs/design/module/profiling.md
+/vllm_omni/profiler/ @gcanlin
+
+# Benchmarking (in-tree) — docs/design/module/benchmarking.md
+/vllm_omni/benchmarks/ @alex-jw-brooks @Bounty-hunter
+
+# LoRA (no dedicated design doc; ownership intentionally narrowed)
+/vllm_omni/lora/ @SamitHuang @wtomin
+
+# Transformers utils (related path only in design docs; preserves prior ownership)
+/vllm_omni/transformers_utils/ @tzhouam @yuanheng-zhao
+
+# =====================================================================
+# Diffusion module family — docs/design/module/diffusion/
+# Umbrella rule first; sub-module rules after it win via last-match.
+# =====================================================================
+
+# Diffusion umbrella (top-level files: data.py, forward_context.py, etc.)
+# — docs/design/module/diffusion/index.md
+/vllm_omni/diffusion/ @Isotr0py @princepride @SamitHuang @wtomin @ZJY0516 @RuixiangMa @david6666666 @xuechendi
+
+# Cache management — docs/design/module/cache_management.md
+/vllm_omni/diffusion/cache/ @Isotr0py @princepride @SamitHuang
+
+# Diffusion quantization — docs/design/module/quantization.md
+/vllm_omni/diffusion/quantization/ @david6666666 @Isotr0py @lishunyang12
+
+# Diffusion profiling — docs/design/module/profiling.md
+/vllm_omni/diffusion/profiler/ @gcanlin
+
+# Diffusion runtime (scheduler, executor, worker)
+# — docs/design/module/diffusion/diffusion_runtime.md, continuous_batching.md
+/vllm_omni/diffusion/sched/ @Isotr0py @princepride @SamitHuang @fhfuih
+/vllm_omni/diffusion/executor/ @Isotr0py @princepride @SamitHuang @fhfuih
+/vllm_omni/diffusion/worker/ @Isotr0py @princepride @SamitHuang @fhfuih
+
+# Diffusion model integration (pipelines, registration, checkpoint loading)
+# — docs/design/module/diffusion/diffusion_model_integration.md
+/vllm_omni/diffusion/models/ @fhfuih @Bounty-hunter @wtomin
+/vllm_omni/diffusion/model_loader/ @fhfuih @Bounty-hunter @wtomin @RuixiangMa
+/vllm_omni/diffusion/lora/ @fhfuih @Bounty-hunter @wtomin @RuixiangMa
+
+# Diffusion parallelism (rank topology, TP/SP/PP/VAE patch)
+# — docs/design/module/diffusion/parallelism.md
+/vllm_omni/diffusion/distributed/ @princepride @yuanheng-zhao @xuechendi @Bounty-hunter
+/vllm_omni/diffusion/attention/parallel/ @princepride @yuanheng-zhao @xuechendi @Bounty-hunter
+
+# Diffusion offloader (CPU offloading, DLO)
+# — docs/design/module/diffusion/offloader.md
+/vllm_omni/diffusion/offloader/ @yuanheng-zhao @david6666666 @lishunyang12
@@ -61,6 +61,16 @@ repos:
      types: [python]
      files: ^tests/

+    - id: check-tts-adapter-migration
+      name: Ratchet TTS per-model branches out of serving_speech.py
+      entry: python tools/pre_commit/check_tts_adapter.py
+      language: python
+      pass_filenames: false
+      files: ^(vllm_omni/entrypoints/openai/(serving_speech\.py|tts_adapters/__init__\.py)|tools/pre_commit/check_tts_adapter\.py)$
+      # The budget-is-now-slack reminder is printed on a PASSING run, and
+      # pre-commit hides output from passing hooks unless the hook is verbose.
+      verbose: true
+
    # Keep `suggestion` last
    - id: suggestion
      name: Suggestion
@@ -11,10 +11,15 @@ build:
    post_checkout:
      - git fetch --unshallow || true
    pre_install:
+      # `pre_install` runs before RTD upgrades pip, and the seeded pip 23.1
+      # rejects the `typing_extensions` wheel torch needs (fixed in pip 23.2).
+      - python -m pip install --upgrade pip
      # Pre-install CPU torch so the subsequent `pip install .[docs]` does not
      # pull ~2 GB of CUDA 13 libs (cuBLAS/cuDNN/NCCL/...) that exceed RTD's
-      # 1 GB memory cap and time out the mkdocs build.
-      - python -m pip install --index-url https://download.pytorch.org/whl/cpu torch
+      # 1 GB memory cap and time out the mkdocs build. Wheel-only: `--index-url`
+      # replaces PyPI, so an sdist fallback cannot reach its build deps. The
+      # quotes keep YAML from reading `:all:` as a mapping key.
+      - "python -m pip install --only-binary=:all: --index-url https://download.pytorch.org/whl/cpu torch"

 mkdocs:
  configuration: mkdocs.yml
@@ -9,21 +9,17 @@ Easy, fast, and cheap omni-modality model serving for everyone
 </h3>

 <p align="center">
-| <a href="https://vllm-omni.readthedocs.io/en/latest/"><b>Documentation</b></a> | <a href="https://deepwiki.com/vllm-project/vllm-omni"><b>DeepWiki</b></a> | <a href="https://discuss.vllm.ai"><b>User Forum</b></a> | <a href="https://slack.vllm.ai"><b>Developer Slack</b></a> | <a href="docs/assets/WeChat.jpg"><b>WeChat</b></a> | <a href="https://arxiv.org/abs/2602.02204"><b>Paper</b></a> | <a href="https://docs.google.com/presentation/d/111-L8zF7A1j_YI_cR8JsblofdScdRr2f/edit?usp=sharing&ouid=110473603432222024453&rtpof=true&sd=true"><b>Slides</b></a> |
+| <a href="https://vllm-omni.readthedocs.io/en/latest/"><b>Documentation</b></a> | <a href="https://deepwiki.com/vllm-project/vllm-omni"><b>DeepWiki</b></a> | <a href="https://discuss.vllm.ai"><b>User Forum</b></a> | <a href="https://slack.vllm.ai"><b>Developer Slack</b></a> | <a href="docs/assets/WeChat.jpg"><b>WeChat</b></a> | <a href="https://arxiv.org/abs/2602.02204"><b>Paper</b></a> | <a href="https://docs.google.com/presentation/d/1aPj0OGl_-ZVoib-Qne5dGDAlrRFB-PdHl6E-EE99g8E/edit?usp=sharing"><b>Slides</b></a> |
 </p>


 ---

 *Latest News* 🔥
+- [2026/08] We released [0.26.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.26.0) - aligned with the vLLM 0.26 release line, featuring [MiniMax H3](recipes/MiniMaxAI/MiniMax-H3.md) joint video/audio generation, an experimental full-duplex realtime runtime for [MiniCPM-o 4.5](recipes/OpenBMB/MiniCPM-o-4_5.md), distributed layerwise diffusion offload, and broader model, hardware, streaming, TTS, and quantization support.
 - [2026/07] We released [0.24.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.24.0) - aligned with the vLLM 0.24 release line, expanding production-ready coverage across TTS, speech, diffusion, image/video generation, and robot-policy serving, with major Omni stage runtime refactoring, diffusion request-level batching, async output materialization, quantization/cache/memory improvements, and broad CUDA/ROCm/XPU/NPU support.
- [2026/06] We released [0.22.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.22.0) - an **omnimodal world-model** release aligned with vLLM 0.22, featuring [Nvidia Cosmos3](recipes/cosmos3/Cosmos3-Nano.md)/DreamZero world model support, expanded quantization coverage across Blackwell/NPU/XPU, TTS production improvements, new models including MiniCPM-o 4.5, MOSS-TTS, and Lance, plus RL integration with [VeRL-Omni](https://github.com/verl-project/verl-omni).
- [2026/05] We released [0.20.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.20.0) - refreshes the serving/runtime stack for large-scale omni workloads, and improves diffusion model performance, quantization, and hardware readiness across CUDA, ROCm, MUSA, NPU, and XPU backends.
- [2026/03] We released [0.18.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.18.0) - strengthens the core runtime through a large entrypoint refactor and scheduler/runtime cleanups, expands unified quantization and diffusion execution, broadens multimodal model coverage, and improves production readiness across audio, omni, image, video, RL, and multi-platform deployments.
+- [2026/06] Starting with [0.14.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.14.0), vLLM-Omni publishes a stable release aligned with every even-numbered upstream vLLM minor version. [0.16.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.16.0), [0.18.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.18.0), [0.20.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.20.0), and [0.22.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.22.0) continued this cadence, expanding omni and world-model support with [NVIDIA Cosmos3](recipes/cosmos3/Cosmos3-Nano.md) and DreamZero, adding models such as MiniCPM-o 4.5, MOSS-TTS, and Lance, and advancing TTS, diffusion, distributed execution, quantization, RL integration through [VeRL-Omni](https://github.com/verl-project/verl-omni), and CUDA/ROCm/MUSA/NPU/XPU coverage.
 - [2026/03] Check out our first public [project deepdive](https://youtu.be/sgwNfsNnR9I) at the vLLM Hong Kong Meetup!
- [2026/03] **[vllm-omni-skills](https://github.com/hsliuustc0106/vllm-omni-skills)** is a community-driven collection of AI assistant skills that help developers work with vLLM-Omni more effectively. These skills can be used with popular agentic AI coding assistants like **Cursor IDE**, **Claude**, **Codex**, and more.
- [2026/02] We released [0.16.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.16.0) - A major alignment + capability release that rebases onto **upstream vLLM v0.16.0** and significantly expands performance, distributed execution, and production readiness across **Qwen3-Omni / Qwen3-TTS**, **Bagel**, **MiMo-Audio**, **GLM-Image** and the **Diffusion (DiT) image/video stack**—while also improving platform coverage (CUDA / ROCm / NPU / XPU), CI quality, and documentation.
- [2026/02] We released [0.14.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.14.0) - This is the first **stable release** of vLLM-Omni that expands Omni’s diffusion / image-video generation and audio / TTS stack, improves distributed execution and memory efficiency, and broadens platform/backend coverage (GPU/ROCm/NPU/XPU). It also brings meaningful upgrades to serving APIs, profiling & benchmarking, and overall stability. Please check our latest [paper](https://arxiv.org/abs/2602.02204) for architecture design and performance results.
 - [2025/11] vLLM community officially released [vllm-project/vllm-omni](https://github.com/vllm-project/vllm-omni) in order to support omni-modality models serving.

 ---
@@ -55,12 +51,13 @@ vLLM-Omni is flexible and easy to use with:
 - Tensor, pipeline, data and expert parallelism support for distributed inference
 - Streaming outputs
 - OpenAI-compatible API server
+- Full-duplex realtime serving with streaming audio input and output (experimental)

 vLLM-Omni seamlessly supports most popular open-source models on HuggingFace, including:

- **Omni-modality models** (e.g. Qwen3-Omni, Cosmos3, HunyuanImage, BAGEL)
+- **Omni-modality models** (e.g. Qwen3-Omni, MiniCPM-o 4.5, Cosmos3, HunyuanImage, BAGEL)
 - **TTS models** (e.g. Qwen3-TTS, VoxCPM2, Ming-Omni-TTS, CosyVoice3)
- **Diffusion models** — image, video, and audio generation (e.g. Qwen-Image, Wan2.2, FLUX)
+- **Diffusion models** — image, video, and audio generation (e.g. MiniMax H3, Qwen-Image, Wan2.2, FLUX)
 - **Robot-policy and action models** (e.g. GR00T-N1.7, DreamZero-DROID, InternVLA-A1, Cosmos3 action policy)

 ## Getting Started
@@ -47,7 +47,7 @@ If no, check your shell running the ComfyUI process. There may be some error mes
 This extension offers the following nodes based on the output modalities (at **ComfyUI sidebar -> Node Library**):

 - **Generate Image** for text-to-image and image-to-image tasks
- **Generate Video** for text-to-video and image-to-video tasks
+- **Generate Video** for text-to-video, first-frame/image-to-video, and reference-conditioned video
 - **Multimodality Understanding** for multimodality-to-text and multimodality-to-audio tasks
 - **TTS** and **TTS Voice Clone** for TTS tasks

@@ -105,7 +105,7 @@ You can configure per-stage sampling parameters for multi-stage models.
 > [!TIP]
 > The node automatically choose text-to-image or image-to-image API endpoints depending on whether you connect an image input or not.

-### Text-to-video and image-to-video generation (e.g., Wan)
+### Text-to-video and image-to-video generation (e.g., Wan, MiniMax-H3)

 (Also available at **ComfyUI sidebar->Template->vLLM-Omni->vLLM-Omni Video Generation**)

@@ -117,7 +117,11 @@ You can configure per-stage sampling parameters for multi-stage models.
 </p>

 > [!TIP]
-> The node automatically choose text-to-video or image-to-video API endpoints depending on whether you connect an image input or not.
+> Connect a **frame** image for first-frame / image-to-video (e.g., Wan I2V, MiniMax-H3 FL2VA).
+>
+> For reference-conditioned generation (MiniMax-H3 Ref2VA), connect a **Video References** node instead.
+>
+> Do not use `frame` and `references` together. Task routing is automatic from which inputs you connect.

 ### TTS (e.g., Qwen TTS series)

@@ -15,11 +15,13 @@ from .comfyui_vllm_omni.nodes import (
    VLLMOmniDiffusionSampling,
    VLLMOmniGenerateImage,
    VLLMOmniGenerateVideo,
+    VLLMOmniMiniMaxH3Params,
    VLLMOmniQwenTTSParams,
    VLLMOmniRemoteLoRA,
    VLLMOmniSamplingParamsList,
    VLLMOmniTTS,
    VLLMOmniUnderstanding,
+    VLLMOmniVideoReferences,
    VLLMOmniVoiceClone,
    VLLMOmniWanParams,
 )
@@ -32,6 +34,7 @@ NODE_CLASS_MAPPINGS = {
    "VLLMOmniUnderstanding": VLLMOmniUnderstanding,
    "VLLMOmniTTS": VLLMOmniTTS,
    "VLLMOmniVoiceClone": VLLMOmniVoiceClone,
+    "VLLMOmniVideoReferences": VLLMOmniVideoReferences,
    # === Params ===
    "VLLMOmniARSampling": VLLMOmniARSampling,
    "VLLMOmniDiffusionSampling": VLLMOmniDiffusionSampling,
@@ -39,6 +42,7 @@ NODE_CLASS_MAPPINGS = {
    "VLLMOmniRemoteLoRA": VLLMOmniRemoteLoRA,
    "VLLMOmniQwenTTSParams": VLLMOmniQwenTTSParams,
    "VLLMOmniWanParams": VLLMOmniWanParams,
+    "VLLMOmniMiniMaxH3Params": VLLMOmniMiniMaxH3Params,
 }

 # A dictionary that contains the friendly/humanly readable titles for the nodes
@@ -49,6 +53,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
    "VLLMOmniUnderstanding": "Multimodality Understanding",
    "VLLMOmniTTS": "TTS (Text to Speech)",
    "VLLMOmniVoiceClone": "TTS Voice Cloning",
+    "VLLMOmniVideoReferences": "Video References",
    # === Params ===
    "VLLMOmniARSampling": "AR Sampling Params",
    "VLLMOmniDiffusionSampling": "Diffusion Sampling Params",
@@ -56,6 +61,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
    "VLLMOmniRemoteLoRA": "LoRA",
    "VLLMOmniQwenTTSParams": "Qwen TTS Params",
    "VLLMOmniWanParams": "Wan Video Params",
+    "VLLMOmniMiniMaxH3Params": "MiniMax-H3 Video Params",
 }

 WEB_DIRECTORY = "./web"
@@ -10,7 +10,9 @@ from .utils.types import (
    AudioFormat,
    AutoregressionSamplingParams,
    DiffusionSamplingParams,
+    MiniMaxH3ModelSpecificParams,
    QwenTTSModelSpecificParams,
+    VideoReferences,
    WanModelSpecificParams,
 )
 from .utils.validators import (
@@ -163,7 +165,8 @@ class VLLMOmniGenerateVideo(_VLLMOmniGenerateBase):
                "num_frames": ("INT", {"default": 41, "min": 1}),
            },
            "optional": {
-                "image": ("IMAGE",),
+                "frame": ("IMAGE",),
+                "references": ("VIDEO_REFERENCES",),
                "sampling_params": ("SAMPLING_PARAMS",),
                "lora": ("REMOTE_LORA",),
                "model_params": ("VIDEO_PARAMS",),
@@ -174,6 +177,15 @@ class VLLMOmniGenerateVideo(_VLLMOmniGenerateBase):
    RETURN_NAMES = ("video",)
    FUNCTION = "generate"

+    @classmethod
+    def VALIDATE_INPUTS(cls, url, model, frame=None, references=None, **_kwargs) -> str | Literal[True]:
+        base = super().VALIDATE_INPUTS(url, model)
+        if base is not True:
+            return base
+        if frame is not None and references is not None:
+            return "Provide only one of frame or references, not both."
+        return True
+
    async def generate(
        self,
        url: str,
@@ -184,7 +196,8 @@ class VLLMOmniGenerateVideo(_VLLMOmniGenerateBase):
        fps: int,
        num_frames: int,
        negative_prompt: str | None = None,
-        image: torch.Tensor | None = None,
+        frame: torch.Tensor | None = None,
+        references: dict | None = None,
        sampling_params: dict | list[dict] | None = None,
        model_params: dict | None = None,
        lora: dict | None = None,
@@ -207,14 +220,16 @@ class VLLMOmniGenerateVideo(_VLLMOmniGenerateBase):

        if sampling_params is not None:
            sampling_params.pop("type", None)  # internal fields
-        if model_params is not None:
-            model_params.pop("type", None)  # internal fields
+        # model_params["type"] is also an internal field, but is used in api_client
+        # if model_params is not None:
+        #     model_params.pop("type", None)  # internal fields

        client = VLLMOmniClient(url)
        output = await client.generate_video(
            model=model,
            prompt=prompt,
-            image=image,  # image present => i2v, absent => t2v
+            frame=frame,  # frame present => fl2va / Wan I2V
+            references=references,
            width=width,
            height=height,
            num_frames=num_frames,
@@ -734,3 +749,78 @@ class VLLMOmniWanParams:

    def get_params(self, **kwargs):
        return (WanModelSpecificParams(kwargs),)
+
+
+class VLLMOmniMiniMaxH3Params:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "audio_flow_shift": (
+                    "FLOAT",
+                    {"default": 3.0, "min": 0.0, "max": 100.0, "step": 0.1},
+                ),
+                "flow_shift": (
+                    "FLOAT",
+                    {"default": 12.0, "min": 0.0, "max": 100.0, "step": 0.1},
+                ),
+            }
+        }
+
+    RETURN_TYPES = ("VIDEO_PARAMS",)
+    RETURN_NAMES = ("MiniMax-H3 video params",)
+    FUNCTION = "get_params"
+    CATEGORY = "vLLM-Omni/Video Params"
+
+    def get_params(self, **kwargs):
+        params = MiniMaxH3ModelSpecificParams(kwargs)
+        params["type"] = "minimax_h3"
+        return (params,)
+
+
+class VLLMOmniVideoReferences:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {},
+            "optional": {
+                "image_1": ("IMAGE",),
+                "image_2": ("IMAGE",),
+                "audio_1": ("AUDIO",),
+                "audio_2": ("AUDIO",),
+                "video_1": ("VIDEO",),
+                "video_2": ("VIDEO",),
+            },
+        }
+
+    RETURN_TYPES = ("VIDEO_REFERENCES",)
+    RETURN_NAMES = ("references",)
+    FUNCTION = "get_references"
+    CATEGORY = "vLLM-Omni"
+
+    def get_references(
+        self,
+        image_1: torch.Tensor | None = None,
+        image_2: torch.Tensor | None = None,
+        audio_1: AudioInput | None = None,
+        audio_2: AudioInput | None = None,
+        video_1: VideoInput | None = None,
+        video_2: VideoInput | None = None,
+        **kwargs,
+    ):
+        if kwargs:
+            logger.info("Uncaught kwargs: %s", kwargs)
+        refs = VideoReferences()
+        if image_1 is not None:
+            refs["image_1"] = image_1
+        if image_2 is not None:
+            refs["image_2"] = image_2
+        if audio_1 is not None:
+            refs["audio_1"] = audio_1
+        if audio_2 is not None:
+            refs["audio_2"] = audio_2
+        if video_1 is not None:
+            refs["video_1"] = video_1
+        if video_2 is not None:
+            refs["video_2"] = video_2
+        return (refs,)
@@ -24,6 +24,7 @@ from .format import (
    image_tensor_to_base64,
    image_tensor_to_png_bytes,
    video_to_base64,
+    video_to_bytes,
 )
 from .logger import get_logger, pretty_printer
 from .models import lookup_model_spec
@@ -63,7 +64,11 @@ async def url_bytes(session: aiohttp.ClientSession, url: str, verb: str = "get",

 class VLLMOmniClient:
    def __init__(
-        self, base_url: str, timeout: float | None = None, poll_interval: float = 5.0, max_poll_duration: float = 60 * 5
+        self,
+        base_url: str,
+        timeout: float | None = None,
+        poll_interval: float = 5.0,
+        max_poll_duration: float = 60 * 30,
    ):
        self.base_url = base_url
        self.timeout = aiohttp.ClientTimeout(total=timeout)
@@ -261,12 +266,17 @@ class VLLMOmniClient:
        num_frames: int,
        fps: int,
        negative_prompt: str | None = None,
-        image: torch.Tensor | None = None,
+        frame: torch.Tensor | None = None,
+        references: dict | None = None,
        sampling_params: dict | None = None,
        model_params: dict | None = None,
        lora: dict | None = None,
-        **extra_body,
+        **extra_params,
    ) -> VideoInput:
+        if frame is not None and references is not None:
+            raise ValueError("Provide only one of frame or references, not both.")
+
+        # === regular payload fields ===
        form = aiohttp.FormData()
        form.add_field("model", model)
        form.add_field("prompt", prompt)
@@ -279,23 +289,91 @@ class VLLMOmniClient:
        if sampling_params is not None:
            for k, v in sampling_params.items():
                form.add_field(k, str(v))
-        if model_params is not None:
-            for k, v in model_params.items():
-                form.add_field(k, str(v))
        if lora is not None:
            form.add_field("lora", json.dumps(lora, ensure_ascii=False))
-        if extra_body:
-            form.add_field("extra_body", json.dumps(extra_body, ensure_ascii=False))

-        if image is not None:
+        # === multimodal inputs (first-last-frames, references, etc.) ===
+        input_reference_image: torch.Tensor | None = None
+        audio_reference: AudioInput | None = None
+        reference_videos: list[VideoInput] = []
+        video_task: str | None = None
+
+        if frame is not None:
+            input_reference_image = frame
+            video_task = "fl2va"
+        elif references is not None:
+            images = [references[k] for k in ("image_1", "image_2") if k in references and references[k] is not None]
+            audios = [references[k] for k in ("audio_1", "audio_2") if k in references and references[k] is not None]
+            videos = [references[k] for k in ("video_1", "video_2") if k in references and references[k] is not None]
+
+            if not images and not audios and not videos:
+                raise ValueError("references is empty; connect at least one image/audio/video.")
+
+            if videos:
+                if images or audios:
+                    raise ValueError(
+                        "Video references cannot be combined with image or audio references. "
+                        "Connect only video_1/video_2 for multi-video Ref2VA."
+                    )
+                reference_videos = videos
+                video_task = "ref2va"
+            elif len(images) == 1 and len(audios) == 1:
+                input_reference_image = images[0]
+                audio_reference = audios[0]
+                video_task = "ref2va"
+            else:
+                raise ValueError(
+                    "Invalid references combination. Supported modes: "
+                    "(1) one or more videos only, or (2) exactly one image and one audio."
+                )
+        else:
+            video_task = "t2va"
+
+        if input_reference_image is not None:
            image_filename = "image.png"  # Required for multipart form
            form.add_field(
                "input_reference",
-                image_tensor_to_png_bytes(image, image_filename),
+                image_tensor_to_png_bytes(input_reference_image, image_filename),
                filename=image_filename,
                content_type="image/png",
            )

+        if audio_reference is not None:
+            form.add_field(
+                "audio_reference",
+                json.dumps({"audio_url": audio_to_base64(audio_reference)}, ensure_ascii=False),
+            )
+
+        for idx, video in enumerate(reference_videos, start=1):
+            video_filename = f"reference_{idx}.mp4"
+            form.add_field(
+                "input_references",
+                video_to_bytes(video, video_filename),
+                filename=video_filename,
+                content_type="video/mp4",
+            )
+
+        # === model specific params. Either use a specialized builder, or add flattened fields as-is ===
+        if model_params is not None:
+            model_params = dict(model_params)
+            model_params.pop("type", None)
+
+        spec, _ = lookup_model_spec(model)
+        params_builder = spec.get("params_builder") if spec else None
+        if params_builder is not None:
+            form_fields = params_builder(
+                model_params or {},
+                extra_params={**extra_params, "task": video_task},
+            )
+            for k, v in form_fields.items():
+                form.add_field(k, v if isinstance(v, str) else str(v))
+        else:
+            if model_params is not None:
+                for k, v in model_params.items():
+                    form.add_field(k, str(v))
+            if extra_params:
+                form.add_field("extra_params", json.dumps(extra_params, ensure_ascii=False))
+
        async with aiohttp.ClientSession(timeout=self.timeout) as session:
            # Start the video generation job
            url = f"{self.base_url}/videos"
@@ -1,7 +1,12 @@
+import json
 import re
+from typing import Any

+from .logger import get_logger
 from .types import Modality, ModelMode, Spec

+logger = get_logger(__name__)
+

 def _bagel_payload_preprocessor(payload: dict) -> dict:
    try:
@@ -29,6 +34,30 @@ def _qwen25_payload_preprocessor(payload: dict) -> dict:
    return payload


+def _minimaxh3_params_builder(
+    model_params: dict[str, Any],
+    *,
+    extra_params: dict[str, Any],
+) -> dict[str, Any]:
+    """Build multipart form fields for MiniMax-H3 from model_params + routed task."""
+    params = dict(model_params)
+    params.pop("type", None)
+    form_fields: dict[str, Any] = {}
+    merged_extra_params: dict[str, Any] = dict(extra_params or {})
+
+    if "flow_shift" in params:
+        form_fields["flow_shift"] = params.pop("flow_shift")
+    for key in ("audio_flow_shift",):
+        if key in params:
+            merged_extra_params[key] = params.pop(key)
+    if params:
+        logger.warning("Unused MiniMax-H3 model params ignored: %s", sorted(params))
+
+    if merged_extra_params:
+        form_fields["extra_params"] = json.dumps(merged_extra_params, ensure_ascii=False)
+    return form_fields
+
+
 _MODEL_PIPELINE_SPECS: dict[str, Spec] = {
    r"BAGEL-7B-MoT": {
        "stages": [
@@ -71,6 +100,21 @@ _MODEL_PIPELINE_SPECS: dict[str, Spec] = {
            }
        ],
    },
+    r"MiniMax-H3(/FL2VA|/Ref2VA)?": {
+        "stages": ["diffusion"],
+        "modes": [
+            {
+                "mode": ModelMode.VIDEO_GENERATION,
+                "input_modalities": [
+                    Modality.TEXT,
+                    Modality.IMAGE,
+                    Modality.VIDEO,
+                    Modality.AUDIO,
+                ],
+            }
+        ],
+        "params_builder": _minimaxh3_params_builder,
+    },
 }
 # Convert dict keys to regex patterns
 MODEL_PIPELINE_SPECS: dict[re.Pattern, Spec] = {}
@@ -80,12 +124,10 @@ del _MODEL_PIPELINE_SPECS


 def lookup_model_spec(model: str) -> tuple[Spec | None, str | None]:
-    try:
-        last_component = model.rstrip("/").rsplit("/", 1)[-1]
-    except IndexError:
-        last_component = model
+    normalized = model.rstrip("/").lstrip("/")
+    lookup_key = normalized.split("/", 1)[-1]
    for pattern, spec in MODEL_PIPELINE_SPECS.items():
-        if pattern.search(last_component):
+        if pattern.search(lookup_key):
            return spec, pattern.pattern
    return None, None

@@ -101,6 +143,9 @@ if __name__ == "__main__":
        "Qwen/Qwen3-Omni-30B-A3B-Instruct",
        "Custom/Path/UnknownModel-Instruct",
        "Not/Matching/Anything",
+        "/path/to/MiniMax-H3/FL2VA",
+        "/path/to/MiniMax-H3/Ref2VA",
+        "MiniMaxAI/MiniMax-H3",
    ]

    test_payload = {"messages": [{"role": "user", "content": "prompt"}]}
@@ -112,7 +157,13 @@ if __name__ == "__main__":
            if preprocessor := spec.get("payload_preprocessor"):
                result = preprocessor(test_payload)
                print(f"✓ {path:<40} → {result}")
+            elif params_builder := spec.get("params_builder"):
+                result = params_builder(
+                    {"audio_flow_shift": 3.0, "flow_shift": 12.0, "type": "minimax_h3"},
+                    extra_params={"task": "t2va"},
+                )
+                print(f"✓ {path:<40} → {result}")
            else:
-                print(f"✓ {path:<40} → No preprocessor")
+                print(f"✓ {path:<40} → No preprocessor/params_builder")
        else:
            print(f"✗ {path:<40} → No match")
@@ -1,12 +1,8 @@
 from collections.abc import Callable
 from enum import Enum, auto
-from typing import (
-    Any,
-    Literal,
-    NotRequired,
-    TypeAlias,
-    TypedDict,
-)
+from typing import Any, Literal, TypeAlias
+
+from typing_extensions import NotRequired, TypedDict

 AudioFormat: TypeAlias = Literal["mp3", "opus", "aac", "flac", "wav", "pcm"]

@@ -27,6 +23,14 @@ class WanModelSpecificParams(dict):
    pass


+class MiniMaxH3ModelSpecificParams(dict):
+    pass
+
+
+class VideoReferences(dict):
+    pass
+
+
 class ModelMode(Enum):
    IMAGE_GENERATION = auto()
    VIDEO_GENERATION = auto()
@@ -47,9 +51,11 @@ class ModelModeSpec(TypedDict):


 PayloadPreprocessor: TypeAlias = Callable[[dict[str, Any]], dict[str, Any]]
+ParamsBuilder: TypeAlias = Callable[..., dict[str, Any]]


 class Spec(TypedDict):
    stages: list[Literal["diffusion", "autoregression"]]
    modes: list[ModelModeSpec]
    payload_preprocessor: NotRequired[PayloadPreprocessor]
+    params_builder: NotRequired[ParamsBuilder]
@@ -1,513 +1 @@
-{
-    "id": "3aea1d08-7de5-4997-ba71-b2f66ad72a21",
-    "revision": 0,
-    "last_node_id": 14,
-    "last_link_id": 28,
-    "nodes": [
-        {
-            "id": 10,
-            "type": "MarkdownNote",
-            "pos": [
-                227.99306819575278,
-                -231.24143306069843
-            ],
-            "size": [
-                240.3326478922474,
-                136.3505820791881
-            ],
-            "flags": {},
-            "order": 0,
-            "mode": 0,
-            "inputs": [],
-            "outputs": [],
-            "title": "Note: Task and Input",
-            "properties": {},
-            "widgets_values": [
-                "vLLM-Omni nodes are categorized based on the output modality. The \"Generate Video\" node supports both text-to-video generation and image-to-video generation. The node will create corresponding payload depending on whether an input image is present or not."
-            ],
-            "color": "#432",
-            "bgcolor": "#000"
-        },
-        {
-            "id": 4,
-            "type": "LoadImage",
-            "pos": [
-                496.31859627609606,
-                -229.71277089860084
-            ],
-            "size": [
-                270,
-                314
-            ],
-            "flags": {},
-            "order": 1,
-            "mode": 0,
-            "inputs": [
-                {
-                    "localized_name": "image",
-                    "name": "image",
-                    "type": "COMBO",
-                    "widget": {
-                        "name": "image"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "choose file to upload",
-                    "name": "upload",
-                    "type": "IMAGEUPLOAD",
-                    "widget": {
-                        "name": "upload"
-                    },
-                    "link": null
-                }
-            ],
-            "outputs": [
-                {
-                    "localized_name": "IMAGE",
-                    "name": "IMAGE",
-                    "type": "IMAGE",
-                    "links": [
-                        25
-                    ]
-                },
-                {
-                    "localized_name": "MASK",
-                    "name": "MASK",
-                    "type": "MASK",
-                    "links": null
-                }
-            ],
-            "properties": {
-                "Node name for S&R": "LoadImage"
-            },
-            "widgets_values": [
-                "cute-cat.jpg",
-                "image"
-            ]
-        },
-        {
-            "id": 8,
-            "type": "VLLMOmniDiffusionSampling",
-            "pos": [
-                465.5140218220927,
-                148.80072646828987
-            ],
-            "size": [
-                284.205078125,
-                226
-            ],
-            "flags": {},
-            "order": 2,
-            "mode": 0,
-            "inputs": [
-                {
-                    "localized_name": "n",
-                    "name": "n",
-                    "type": "INT",
-                    "widget": {
-                        "name": "n"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "num_inference_steps",
-                    "name": "num_inference_steps",
-                    "type": "INT",
-                    "widget": {
-                        "name": "num_inference_steps"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "guidance_scale",
-                    "name": "guidance_scale",
-                    "type": "FLOAT",
-                    "widget": {
-                        "name": "guidance_scale"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "true_cfg_scale",
-                    "name": "true_cfg_scale",
-                    "type": "FLOAT",
-                    "widget": {
-                        "name": "true_cfg_scale"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "vae_use_slicing",
-                    "name": "vae_use_slicing",
-                    "type": "BOOLEAN",
-                    "widget": {
-                        "name": "vae_use_slicing"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "vae_use_tiling",
-                    "name": "vae_use_tiling",
-                    "type": "BOOLEAN",
-                    "widget": {
-                        "name": "vae_use_tiling"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "seed",
-                    "name": "seed",
-                    "type": "INT",
-                    "widget": {
-                        "name": "seed"
-                    },
-                    "link": null
-                }
-            ],
-            "outputs": [
-                {
-                    "localized_name": "diffusion sampling params",
-                    "name": "diffusion sampling params",
-                    "type": "SAMPLING_PARAMS",
-                    "links": [
-                        27
-                    ]
-                }
-            ],
-            "properties": {
-                "Node name for S&R": "VLLMOmniDiffusionSampling"
-            },
-            "widgets_values": [
-                1,
-                50,
-                1,
-                1,
-                false,
-                false,
-                1992,
-                "randomize"
-            ]
-        },
-        {
-            "id": 14,
-            "type": "VLLMOmniWanParams",
-            "pos": [
-                518.6005521845747,
-                428.4455663719664
-            ],
-            "size": [
-                270,
-                106
-            ],
-            "flags": {},
-            "order": 3,
-            "mode": 0,
-            "inputs": [
-                {
-                    "localized_name": "guidance_scale_2",
-                    "name": "guidance_scale_2",
-                    "type": "FLOAT",
-                    "widget": {
-                        "name": "guidance_scale_2"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "boundary_ratio",
-                    "name": "boundary_ratio",
-                    "type": "FLOAT",
-                    "widget": {
-                        "name": "boundary_ratio"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "flow_shift",
-                    "name": "flow_shift",
-                    "type": "FLOAT",
-                    "widget": {
-                        "name": "flow_shift"
-                    },
-                    "link": null
-                }
-            ],
-            "outputs": [
-                {
-                    "localized_name": "Wan video params",
-                    "name": "Wan video params",
-                    "type": "VIDEO_PARAMS",
-                    "links": [
-                        28
-                    ]
-                }
-            ],
-            "properties": {
-                "Node name for S&R": "VLLMOmniWanParams"
-            },
-            "widgets_values": [
-                4,
-                0.875,
-                5
-            ]
-        },
-        {
-            "id": 12,
-            "type": "VLLMOmniGenerateVideo",
-            "pos": [
-                827.2566336087867,
-                -73.77449831827545
-            ],
-            "size": [
-                400,
-                346
-            ],
-            "flags": {},
-            "order": 4,
-            "mode": 0,
-            "inputs": [
-                {
-                    "localized_name": "image",
-                    "name": "image",
-                    "shape": 7,
-                    "type": "IMAGE",
-                    "link": 25
-                },
-                {
-                    "localized_name": "sampling_params",
-                    "name": "sampling_params",
-                    "shape": 7,
-                    "type": "SAMPLING_PARAMS",
-                    "link": 27
-                },
-                {
-                    "localized_name": "lora",
-                    "name": "lora",
-                    "shape": 7,
-                    "type": "REMOTE_LORA",
-                    "link": null
-                },
-                {
-                    "localized_name": "model_params",
-                    "name": "model_params",
-                    "shape": 7,
-                    "type": "VIDEO_PARAMS",
-                    "link": 28
-                },
-                {
-                    "localized_name": "url",
-                    "name": "url",
-                    "type": "STRING",
-                    "widget": {
-                        "name": "url"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "model",
-                    "name": "model",
-                    "type": "STRING",
-                    "widget": {
-                        "name": "model"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "prompt",
-                    "name": "prompt",
-                    "type": "STRING",
-                    "widget": {
-                        "name": "prompt"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "negative_prompt",
-                    "name": "negative_prompt",
-                    "type": "STRING",
-                    "widget": {
-                        "name": "negative_prompt"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "width",
-                    "name": "width",
-                    "type": "INT",
-                    "widget": {
-                        "name": "width"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "height",
-                    "name": "height",
-                    "type": "INT",
-                    "widget": {
-                        "name": "height"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "fps",
-                    "name": "fps",
-                    "type": "INT",
-                    "widget": {
-                        "name": "fps"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "num_frames",
-                    "name": "num_frames",
-                    "type": "INT",
-                    "widget": {
-                        "name": "num_frames"
-                    },
-                    "link": null
-                }
-            ],
-            "outputs": [
-                {
-                    "localized_name": "video",
-                    "name": "video",
-                    "type": "VIDEO",
-                    "links": [
-                        26
-                    ]
-                }
-            ],
-            "properties": {
-                "Node name for S&R": "VLLMOmniGenerateVideo"
-            },
-            "widgets_values": [
-                "http://localhost:8000/v1",
-                "Wan-AI/Wan2.2-TI2V-5B-Diffusers",
-                "Make the cat in the attached video yawning. Eye closed and mouth wide open. Keep the original realistic style.",
-                "Cartoonish",
-                900,
-                1200,
-                12,
-                41
-            ]
-        },
-        {
-            "id": 13,
-            "type": "SaveVideo",
-            "pos": [
-                1257.5441369501657,
-                -44.12956394419863
-            ],
-            "size": [
-                374,
-                372.34896826553796
-            ],
-            "flags": {},
-            "order": 5,
-            "mode": 0,
-            "inputs": [
-                {
-                    "localized_name": "video",
-                    "name": "video",
-                    "type": "VIDEO",
-                    "link": 26
-                },
-                {
-                    "localized_name": "filename_prefix",
-                    "name": "filename_prefix",
-                    "type": "STRING",
-                    "widget": {
-                        "name": "filename_prefix"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "format",
-                    "name": "format",
-                    "type": "COMBO",
-                    "widget": {
-                        "name": "format"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "codec",
-                    "name": "codec",
-                    "type": "COMBO",
-                    "widget": {
-                        "name": "codec"
-                    },
-                    "link": null
-                }
-            ],
-            "outputs": [],
-            "properties": {},
-            "widgets_values": [
-                "video/ComfyUI",
-                "auto",
-                "auto"
-            ]
-        }
-    ],
-    "links": [
-        [
-            25,
-            4,
-            0,
-            12,
-            0,
-            "IMAGE"
-        ],
-        [
-            26,
-            12,
-            0,
-            13,
-            0,
-            "VIDEO"
-        ],
-        [
-            27,
-            8,
-            0,
-            12,
-            1,
-            "SAMPLING_PARAMS"
-        ],
-        [
-            28,
-            14,
-            0,
-            12,
-            3,
-            "VIDEO_PARAMS"
-        ]
-    ],
-    "groups": [
-        {
-            "id": 1,
-            "title": "Input",
-            "bounding": [
-                213.1706010087147,
-                -313.20095750667554,
-                560.8246471437027,
-                407.4246532472182
-            ],
-            "color": "#3f789e",
-            "font_size": 24,
-            "flags": {}
-        }
-    ],
-    "config": {},
-    "extra": {
-        "workflowRendererVersion": "LG",
-        "ds": {
-            "scale": 1.1469075819486894,
-            "offset": [
-                101.7632950847089,
-                492.29121889347806
-            ]
-        }
-    },
-    "version": 0.4
-}
+{"id":"3aea1d08-7de5-4997-ba71-b2f66ad72a21","revision":0,"last_node_id":24,"last_link_id":35,"nodes":[{"id":13,"type":"SaveVideo","pos":[1315.729153459605,-137.92038948394355],"size":[374,313.7692307692308],"flags":{},"order":9,"mode":0,"inputs":[{"localized_name":"video","name":"video","type":"VIDEO","link":32},{"localized_name":"filename_prefix","name":"filename_prefix","type":"STRING","widget":{"name":"filename_prefix"},"link":null},{"localized_name":"format","name":"format","type":"COMBO","widget":{"name":"format"},"link":null},{"localized_name":"codec","name":"codec","type":"COMFY_DYNAMICCOMBO_V3","widget":{"name":"codec"},"link":null}],"outputs":[{"localized_name":"video","name":"video","type":"VIDEO","links":null}],"properties":{},"widgets_values":["video/ComfyUI","auto","auto"]},{"id":10,"type":"MarkdownNote","pos":[183.74636797766794,-571.2529158046766],"size":[240.3326478922474,136.3505820791881],"flags":{},"order":0,"mode":0,"inputs":[],"outputs":[],"title":"Note: Task and Input","properties":{},"widgets_values":["vLLM-Omni nodes are categorized based on the output modality. The \"Generate Video\" node supports both *text-to-video*, *frame-to-video*, and *reference-to-video* generation. The node will create corresponding payload depending on whether an input image is present or not."],"color":"#432","bgcolor":"#000"},{"id":4,"type":"LoadImage","pos":[452.07189605801125,-569.7242536425791],"size":[270,314],"flags":{},"order":1,"mode":0,"inputs":[{"localized_name":"image","name":"image","type":"COMBO","widget":{"name":"image"},"link":null},{"localized_name":"choose file to upload","name":"upload","type":"IMAGEUPLOAD","widget":{"name":"upload"},"link":null}],"outputs":[{"localized_name":"IMAGE","name":"IMAGE","type":"IMAGE","links":[29]},{"localized_name":"MASK","name":"MASK","type":"MASK","links":null}],"properties":{"Node name for S&R":"LoadImage"},"widgets_values":["rap.png","image"]},{"id":23,"type":"LoadImage","pos":[9.206110246057657,-7.6955291509170785],"size":[270,314],"flags":{},"order":2,"mode":0,"inputs":[{"localized_name":"image","name":"image","type":"COMBO","widget":{"name":"image"},"link":null},{"localized_name":"choose file to upload","name":"upload","type":"IMAGEUPLOAD","widget":{"name":"upload"},"link":null}],"outputs":[{"localized_name":"IMAGE","name":"IMAGE","type":"IMAGE","links":[34]},{"localized_name":"MASK","name":"MASK","type":"MASK","links":null}],"properties":{"Node name for S&R":"LoadImage"},"widgets_values":["white-cat.jpg","image"]},{"id":22,"type":"MarkdownNote","pos":[-9.783113411416666,-148.8790300038605],"size":[297.3012007349086,88],"flags":{},"order":3,"mode":0,"inputs":[],"outputs":[],"title":"Note: Reference to video (r2v)","properties":{},"widgets_values":["For r2v tasks, connect this Video Reference block to the main Generate Video block. Then disconnect the \"frame\" input above."],"color":"#432","bgcolor":"#000"},{"id":24,"type":"LoadAudio","pos":[317.9929742385805,-141.44821500065095],"size":[282.798828125,136],"flags":{},"order":4,"mode":0,"inputs":[{"localized_name":"audio","name":"audio","type":"COMBO","widget":{"name":"audio"},"link":null},{"localized_name":"audioUI","name":"audioUI","type":"AUDIO_UI","widget":{"name":"audioUI"},"link":null},{"localized_name":"choose file to upload","name":"upload","type":"AUDIOUPLOAD","widget":{"name":"upload"},"link":null}],"outputs":[{"localized_name":"AUDIO","name":"AUDIO","type":"AUDIO","links":[35]}],"properties":{"Node name for S&R":"LoadAudio"},"widgets_values":["nothing-beats-a-jet2-holiday.mp3",null,""]},{"id":21,"type":"VLLMOmniVideoReferences","pos":[376.6129172897595,105.41557277556292],"size":[156.9380859375,126],"flags":{},"order":7,"mode":0,"inputs":[{"localized_name":"image_1","name":"image_1","shape":7,"type":"IMAGE","link":34},{"localized_name":"image_2","name":"image_2","shape":7,"type":"IMAGE","link":null},{"localized_name":"audio_1","name":"audio_1","shape":7,"type":"AUDIO","link":35},{"localized_name":"audio_2","name":"audio_2","shape":7,"type":"AUDIO","link":null},{"localized_name":"video_1","name":"video_1","shape":7,"type":"VIDEO","link":null},{"localized_name":"video_2","name":"video_2","shape":7,"type":"VIDEO","link":null}],"outputs":[{"localized_name":"references","name":"references","type":"VIDEO_REFERENCES","links":null}],"properties":{"Node name for S&R":"VLLMOmniVideoReferences"},"widgets_values":[]},{"id":8,"type":"VLLMOmniDiffusionSampling","pos":[83.24620208113915,422.08543477542605],"size":[284.205078125,226],"flags":{},"order":5,"mode":0,"inputs":[{"localized_name":"n","name":"n","type":"INT","widget":{"name":"n"},"link":null},{"localized_name":"num_inference_steps","name":"num_inference_steps","type":"INT","widget":{"name":"num_inference_steps"},"link":null},{"localized_name":"guidance_scale","name":"guidance_scale","type":"FLOAT","widget":{"name":"guidance_scale"},"link":null},{"localized_name":"true_cfg_scale","name":"true_cfg_scale","type":"FLOAT","widget":{"name":"true_cfg_scLine truncated
@@ -1,5 +1,6 @@
 import asyncio
 import base64
+import json
 import mimetypes
 import os
 import time
@@ -13,6 +14,17 @@ from tqdm import tqdm
 DEFAULT_EDITS_BOT_TASK = "think"


+def _is_minimax_h3_mixed_reference(model: str) -> bool:
+    """MiniMax-H3 Ref2VA consumes reference videos as file paths.
+
+    Its reference preparer ffprobes / ffmpegs the reference file, so single
+    ``video_reference`` uploads are sent as a base64 data URL: the server
+    persists them to disk and keeps ``source_path``, handing the pipeline a
+    real file path instead of a decoded frame list.
+    """
+    return "MiniMax-H3" in (model or "")
+
+
@dataclass
 class RequestFuncInput:
    prompt: str
@@ -355,13 +367,21 @@ async def async_request_v1_videos(
        )
    elif input.video_paths and len(input.video_paths) > 0:
        video_path = input.video_paths[0]
-        image_file = open(video_path, "rb")
-        form.add_field(
-            "input_reference",
-            image_file,
-            filename=os.path.basename(video_path),
-            content_type=_guess_mime_type(video_path),
-        )
+        if _is_minimax_h3_mixed_reference(input.model):
+            with open(video_path, "rb") as reference_file:
+                video_b64 = base64.b64encode(reference_file.read()).decode("ascii")
+            form.add_field(
+                "video_reference",
+                json.dumps([{"video_url": f"data:video/mp4;base64,{video_b64}"}]),
+            )
+        else:
+            image_file = open(video_path, "rb")
+            form.add_field(
+                "input_reference",
+                image_file,
+                filename=os.path.basename(video_path),
+                content_type=_guess_mime_type(video_path),
+            )

    job_id = None
    job_status = None
@@ -858,7 +858,12 @@ class RandomDataset(BaseDataset):
            height = int(self.args.height or profile.get("height") or 720)
            num_frames = int(self.args.num_frames or profile.get("num_frames") or 16)
            fps = float(self.args.fps or profile.get("fps") or 8)
-            writer = cv2.VideoWriter(video_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (width, height))
+            # cv2 typically writes MPEG-4 Part 2 (``mp4v``), which pipelines such
+            # as MiniMax-H3 Ref2VA reject because they require H.264/H.265
+            # reference videos. Write the synthetic clip first, then re-encode
+            # it to H.264 when ffmpeg is available; otherwise keep ``mp4v``.
+            raw_path = video_path + ".raw.mp4"
+            writer = cv2.VideoWriter(raw_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (width, height))
            if not writer.isOpened():
                raise RuntimeError("cv2.VideoWriter failed to open")
            for frame_idx in range(num_frames):
@@ -871,6 +876,10 @@ class RandomDataset(BaseDataset):
                frame[max(y - 36, 0) : min(y + 36, height), x : min(x + 96, width), :] = (64, 128, 220)
                writer.write(frame)
            writer.release()
+            if _reencode_video_to_h264(raw_path, video_path):
+                os.unlink(raw_path)
+            else:
+                os.replace(raw_path, video_path)
            return [video_path]
        except Exception as e:
            raise RuntimeError(
@@ -878,6 +887,40 @@ class RandomDataset(BaseDataset):
            ) from e


+def _reencode_video_to_h264(source: str, destination: str) -> bool:
+    """Re-encode a video to H.264/MP4 using ffmpeg when available.
+
+    Returns False when ffmpeg is missing or the re-encode fails so callers can
+    fall back to the original container/codec.
+    """
+    import shutil
+    import subprocess
+
+    if shutil.which("ffmpeg") is None:
+        return False
+    try:
+        result = subprocess.run(
+            [
+                "ffmpeg",
+                "-y",
+                "-loglevel",
+                "error",
+                "-i",
+                source,
+                "-c:v",
+                "libx264",
+                "-pix_fmt",
+                "yuv420p",
+                destination,
+            ],
+            capture_output=True,
+            text=True,
+        )
+        return result.returncode == 0 and os.path.exists(destination)
+    except Exception:
+        return False
+
+
 def _compute_expected_latency_ms_from_base(req: RequestFuncInput, args, base_time_ms: float | None) -> float | None:
    """Compute expected execution time (ms) based on a base per-step-per-frame unit time.

@@ -158,7 +158,6 @@ 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)
@@ -176,7 +175,7 @@ def _generate_image(omni, args, prompt, seed):
    elapsed = time.perf_counter() - start
    peak_mem = torch.accelerator.max_memory_allocated() / (1024**3)

-    req_out = OmniRequestOutput.unwrap_result(outputs)
+    req_out = outputs[0] if isinstance(outputs, list) else outputs
    if not req_out.images:
        raise ValueError("Could not extract image output from result.")
    img = req_out.images[0]
@@ -206,15 +205,15 @@ def _generate_video(omni, args, prompt, seed):
    elapsed = time.perf_counter() - start
    peak_mem = torch.accelerator.max_memory_allocated() / (1024**3)

-    first = outputs[0]
-    if hasattr(first, "request_output") and isinstance(first.request_output, list):
-        inner = first.request_output[0]
-        if isinstance(inner, OmniRequestOutput) and hasattr(inner, "images"):
-            frames = inner.images[0] if inner.images else None
-        else:
-            frames = inner
-    elif hasattr(first, "images") and first.images:
-        frames = first.images[0]
+    if isinstance(outputs, list) and isinstance(outputs[0], OmniRequestOutput):
+        first = outputs[0]
+    elif isinstance(outputs, OmniRequestOutput):
+        first = outputs
+    else:
+        raise ValueError("Could not extract video frames from output.")
+
+    if hasattr(first, "images"):
+        frames = first.images[0] if first.images else None
    else:
        raise ValueError("Could not extract video frames from output.")

@@ -300,7 +300,7 @@ def benchmark(args: argparse.Namespace) -> None:
        output_idx = 0
        for stage_outputs in omni.generate(all_prompts, sampling_params_list, py_generator=True):
            if stage_outputs.final_output_type == "image":
-                request_output = stage_outputs.request_output
+                request_output = stage_outputs
                request_id = getattr(request_output, "request_id", "")

                images = getattr(request_output, "images", [])
@@ -167,9 +167,11 @@ def main() -> None:
    ]
    native_cmd = [
        sys.executable,
-        "examples/offline_inference/text_to_video/text_to_video_lingbot.py",
+        "examples/offline_inference/text_to_video/text_to_video.py",
        "--model",
        args.model,
+        "--model-class-name",
+        "LingBotVideoPipeline",
        "--prompt",
        args.prompt,
        "--output",
@@ -139,6 +139,34 @@ python benchmarks/tts/bench_tts.py \
    --output-dir ./results
 ```

+#### Fixed IndexTTS 2.5 performance sweep
+
+IndexTTS 2.5 is registered in `model_configs.yaml` like the other TTS models.
+Use `--served-model-name` when the server was launched from a local native
+bundle instead of the registry key. The command below reproduces the acceptance
+workload: Seed-TTS Eval EN, `n=500`, concurrency `4/8/16/32`, five warmups, and
+dataset seed 0.
+
+```bash
+python benchmarks/tts/bench_tts.py \
+    --model IndexTeam/IndexTTS-2.5 \
+    --served-model-name /path/to/indextts-2.5 \
+    --task voice_clone --locale en \
+    --dataset-path /path/to/seedtts_testset \
+    --host 127.0.0.1 --port 8092 \
+    --concurrency 4 8 16 32 \
+    --num-prompts 500 --num-warmups 5 \
+    --output-dir ./results/indextts2_5 \
+    -- \
+    --tokenizer /path/to/indextts-2.5/qwen0.6bemo4-merge \
+    --seed 0 --metric-percentiles 50,95,99 --disable-tqdm --save-detailed
+```
+
+The wrapper benchmarks an already-running server. For reproducible quality
+comparisons, add `--request-seed 42`; production-performance runs should omit
+it. The trailing `--seed 0` controls Seed-TTS row selection rather than model
+sampling.
+
 ### 4. Plot a sweep

 ```bash
@@ -84,12 +84,17 @@ def build_bench_args(
    result_filename: str | None,
    extra_cli_args: list[str],
    output_len: int | None = None,
+    served_model_name: str | None = None,
+    num_warmups: int = 2,
+    request_seed: int | None = None,
 ) -> list[str]:
    """Build the ``vllm bench serve --omni`` command for one (task, concurrency) run."""
    dataset_name = _TASK_TO_DATASET[task]
    backend: str = model_cfg["backend"]
    endpoint: str = model_cfg["endpoint"]
-    task_extra_body: dict[str, Any] = (model_cfg.get("task_extra_body") or {}).get(task) or {}
+    task_extra_body: dict[str, Any] = dict((model_cfg.get("task_extra_body") or {}).get(task) or {})
+    if request_seed is not None:
+        task_extra_body["seed"] = request_seed

    # Resolve dataset path
    if dataset_path:
@@ -109,7 +114,7 @@ def build_bench_args(
        "--port",
        str(port),
        "--model",
-        model,
+        served_model_name or model,
        "--backend",
        backend,
        "--endpoint",
@@ -119,7 +124,7 @@ def build_bench_args(
        "--num-prompts",
        str(num_prompts),
        "--num-warmups",
-        "2",
+        str(num_warmups),
        "--percentile-metrics",
        "ttft,e2el,audio_rtf,audio_ttfp,audio_duration,audio_underrun",
    ]
@@ -152,7 +157,8 @@ def build_bench_args(
        if result_filename:
            cmd += ["--result-filename", result_filename]

-    cmd += extra_cli_args
+    passthrough = extra_cli_args[1:] if extra_cli_args[:1] == ["--"] else extra_cli_args
+    cmd += passthrough
    return cmd


@@ -225,6 +231,12 @@ def main() -> None:
    parser.add_argument(
        "--model", required=True, help="HuggingFace model ID (e.g. Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice)"
    )
+    parser.add_argument(
+        "--served-model-name",
+        default=None,
+        help="Model name/path accepted by the running server. Use this when --model selects a registry entry "
+        "but the server was launched from a local bundle path.",
+    )
    parser.add_argument("--task", default="all", help="Task type: voice_clone | default_voice | voice_design | all")
    parser.add_argument("--locale", default="en", choices=["en", "zh"])
    parser.add_argument("--concurrency", type=int, nargs="+", default=[1, 4], metavar="N")
@@ -236,6 +248,13 @@ def main() -> None:
        metavar="N",
        help="Number of prompts per run. If one value, applied to all concurrency levels.",
    )
+    parser.add_argument("--num-warmups", type=int, default=2, help="Warmup requests before each measured run")
+    parser.add_argument(
+        "--request-seed",
+        type=int,
+        default=None,
+        help="Optional model sampling seed added to every request body for reproducible quality runs",
+    )
    parser.add_argument(
        "--dataset-path", default=None, help="Root of seed-tts-eval dataset (required for voice_clone/default_voice)"
    )
@@ -256,6 +275,9 @@ def main() -> None:
    parser.add_argument("extra", nargs=argparse.REMAINDER, help="Extra args passed directly to vllm bench serve")
    args = parser.parse_args()

+    if args.num_warmups < 0:
+        parser.error("--num-warmups cannot be negative")
+
    model_configs = load_model_configs(Path(args.model_configs))
    if args.model not in model_configs:
        known = "\n  ".join(model_configs.keys())
@@ -308,6 +330,9 @@ def main() -> None:
                result_filename=result_filename,
                extra_cli_args=args.extra or [],
                output_len=args.output_len,
+                served_model_name=args.served_model_name,
+                num_warmups=args.num_warmups,
+                request_seed=args.request_seed,
            )
            result = run_one_benchmark(cmd)
            if result is not None:
@@ -7,6 +7,17 @@
 # is tracked here.

 models:
+  IndexTeam/IndexTTS-2.5:
+    supported_tasks: [voice_clone]
+    backend: openai-audio-speech
+    endpoint: /v1/audio/speech
+    trust_remote_code: true
+    task_extra_body:
+      voice_clone:
+        extra_params:
+          lang: en
+          text_normalization: true
+
  # -CustomVoice checkpoints lack speaker_encoder weights, so voice_clone is
  # NOT supported (an attempt raises ValueError from _extract_speaker_embedding
  # at model runtime).  Use -Base for voice_clone.
@@ -1,7 +1,24 @@
-# Target vllm version: v0.26.0 (releases/v0.26.0 branch).
+# Target vllm version: v0.27.0 (releases/v0.27.0 branch).
 # Keep VLLM_BASE_TAG aligned with the target vLLM release on every rebase.
+#
+# Release-boundary form: there is no precompiled-wheel pin here, because the
+# base image already ships exactly the target vLLM. Everything the runtime
+# needs — torch, flashinfer, nvJitLink, numpy — comes from that image as one
+# self-consistent set.
+#
+# This block reappears only when the rebase targets a vLLM commit that is NOT a
+# released tag. During the 0.27 rebase, targeting v0.27.0rc1 on top of the
+# v0.26.0 base image required reinstalling vLLM from wheels.vllm.ai and then
+# hand-repairing the fallout: pinning torch ahead of flashinfer (installing it
+# after bound flashinfer's TVM FFI runtime to the old torch and aborted engine
+# core init), registering libnvJitLink.so.13 with ldconfig, and re-pinning numpy
+# under numba's ceiling. All of it was compensation for a base image and a vLLM
+# that disagreed. At a real release boundary they agree, so none of it is
+# needed. If you pin an untagged commit again, restore the reinstall block —
+# agent/tasks/20_update_wheel_url.sh fails the run rather than letting the
+# Dockerfile silently ship the wrong vLLM.
 ARG VLLM_BASE_IMAGE=vllm/vllm-openai
-ARG VLLM_BASE_TAG=v0.26.0
+ARG VLLM_BASE_TAG=v0.27.0

 # We use registry-based layer caching for the dependencies,
 # where the tag is determined based on the hash of the contents
@@ -1,5 +1,5 @@
 ARG VLLM_ASCEND_IMAGE=quay.io/atlas-ci/vllm-ascend
-ARG VLLM_ASCEND_TAG=v0.25.0
+ARG VLLM_ASCEND_TAG=v0.26.0
 FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}

 # WORKDIR /vllm-workspace/vllm
@@ -26,6 +26,10 @@ RUN export PIP_EXTRA_INDEX_URL=https://mirrors.huaweicloud.com/ascend/repos/pypi
    export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/Ascend/ascend-toolkit/latest/`uname -i`-linux/devlib && \
    python3 -m pip install -v -e /vllm-workspace/vllm-omni/ --no-build-isolation

+RUN python3 -m pip install -v --no-deps \
+      "stepaudio2-minicpmo>=0.1.1" \
+      "step-audio2>=1.0.0"
+
 ENV VLLM_WORKER_MULTIPROC_METHOD=spawn

 ENTRYPOINT []
@@ -1,5 +1,5 @@
 ARG VLLM_ASCEND_IMAGE=quay.io/atlas-ci/vllm-ascend
-ARG VLLM_ASCEND_TAG=v0.25.0-a3
+ARG VLLM_ASCEND_TAG=v0.26.0-a3
 FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}

 # WORKDIR /vllm-workspace/vllm
@@ -26,6 +26,10 @@ RUN export PIP_EXTRA_INDEX_URL=https://mirrors.huaweicloud.com/ascend/repos/pypi
    export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/Ascend/ascend-toolkit/latest/`uname -i`-linux/devlib && \
    python3 -m pip install -v -e /vllm-workspace/vllm-omni/ --no-build-isolation

+RUN python3 -m pip install -v --no-deps \
+      "stepaudio2-minicpmo>=0.1.1" \
+      "step-audio2>=1.0.0"
+
 ENV VLLM_WORKER_MULTIPROC_METHOD=spawn

 ENTRYPOINT []
@@ -1,5 +1,5 @@
 ARG VLLM_ASCEND_IMAGE=quay.io/atlas-ci/vllm-ascend
-ARG VLLM_ASCEND_TAG=v0.25.0
+ARG VLLM_ASCEND_TAG=v0.26.0
 FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}

 ARG APP_DIR=/vllm-workspace/vllm-omni
@@ -35,11 +35,17 @@ RUN source /usr/local/Ascend/ascend-toolkit/set_env.sh && \
      "psutil>=7.2.0" \
      "soundfile>=0.13.1" \
      "pyttsx3>=2.99" \
+      "opencc==1.4.1" \
      "opencv-python>=4.12.0.88" \
      "imageio[ffmpeg]>=0.6.0" \
+      "jiwer>=3.0.0" \
+      "zhon>=2.0.0" \
+      "zhconv>=1.4.2" \
+      "funasr>=1.0.0" \
      "hyperpyyaml>=1.2.0" \
      "onnx>=1.16.0" \
      "librosa>=0.10.0" \
+      "datasets>=3.0.0" \
      --index-url http://cache-service.nginx-pypi-cache.svc.cluster.local/pypi/simple \
      --trusted-host cache-service.nginx-pypi-cache.svc.cluster.local \
      --default-timeout=60 && \
@@ -1,5 +1,5 @@
 ARG VLLM_ASCEND_IMAGE=quay.io/atlas-ci/vllm-ascend
-ARG VLLM_ASCEND_TAG=v0.25.0-a3
+ARG VLLM_ASCEND_TAG=v0.26.0-a3
 FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}

 ARG APP_DIR=/vllm-workspace/vllm-omni
@@ -35,11 +35,18 @@ RUN source /usr/local/Ascend/ascend-toolkit/set_env.sh && \
      "psutil>=7.2.0" \
      "soundfile>=0.13.1" \
      "pyttsx3>=2.99" \
+      "opencc==1.4.1" \
      "opencv-python>=4.12.0.88" \
      "imageio[ffmpeg]>=0.6.0" \
+      "jiwer>=3.0.0" \
+      "zhon>=2.0.0" \
+      "zhconv>=1.4.2" \
+      "funasr>=1.0.0" \
      "hyperpyyaml>=1.2.0" \
      "onnx>=1.16.0" \
      "librosa>=0.10.0" \
+      "torchmetrics>=1.9.0" \
+      "datasets>=3.0.0" \
      --index-url http://cache-service.nginx-pypi-cache.svc.cluster.local/pypi/simple \
      --trusted-host cache-service.nginx-pypi-cache.svc.cluster.local \
      --default-timeout=60 && \
@@ -88,7 +88,7 @@ ENV UV_EXTRA_INDEX_URL=${PIP_EXTRA_INDEX_URL}
 ENV UV_INDEX_STRATEGY="unsafe-best-match"
 ENV UV_LINK_MODE="copy"

-ARG VLLM_VERSION=v0.26.0
+ARG VLLM_VERSION=v0.27.0
 RUN git clone -b ${VLLM_VERSION} https://github.com/vllm-project/vllm
 WORKDIR /workspace/vllm

@@ -108,7 +108,7 @@ ENV VLLM_WORKER_MULTIPROC_METHOD=spawn

 RUN --mount=type=cache,target=/root/.cache/uv \
    uv pip uninstall triton triton-xpu && \
-    uv pip install triton-xpu==3.7.1 && \
+    uv pip install triton-xpu==3.7.2 && \
    uv pip install --no-build-isolation --no-deps .

 CMD ["/bin/bash"]
@@ -144,12 +144,13 @@ WORKDIR /workspace/vllm-omni
 COPY . .

 ENV VLLM_OMNI_TARGET_DEVICE=xpu
+# Keep the upstream oneCCL packages paired with the XPU torch runtime.
 RUN --mount=type=cache,target=/root/.cache/uv \
    uv pip install --no-build-isolation \
    --extra-index-url https://mirrors.ustc.edu.cn/pypi/simple/ \
    ".[dev]" && \
-    uv pip uninstall triton oneccl oneccl-devel && \
-    uv pip install triton-xpu==3.7.1 --reinstall
+    uv pip uninstall triton && \
+    uv pip install triton-xpu==3.7.2 --reinstall

 FROM vllm-omni AS vllm-omni-openai

@@ -16,31 +16,21 @@ nav:
  - Examples:
    - examples/README.md
    - Offline Inference:
-      - GLM-Image Multistage End-to-End Inference: user_guide/examples/offline_inference/glm_image.md
-      - HunyuanImage-3.0 Image-to-Text Inference: user_guide/examples/offline_inference/hunyuan_image3.md
      - Image-To-Image: user_guide/examples/offline_inference/image_to_image.md
      - Image-To-Video: user_guide/examples/offline_inference/image_to_video.md
-      - InternVLA-A1: user_guide/examples/offline_inference/internvla_a1.md
-      - MiMo-Audio: user_guide/examples/offline_inference/mimo_audio.md
-      - Qwen2.5-Omni: user_guide/examples/offline_inference/qwen2_5_omni.md
-      - Qwen3-Omni: user_guide/examples/offline_inference/qwen3_omni.md
+      - Speech-To-Video: user_guide/examples/offline_inference/speech_to_video.md
      - Text-To-Audio: user_guide/examples/offline_inference/text_to_audio.md
      - Text-To-Image: user_guide/examples/offline_inference/text_to_image.md
      - Text-To-Speech: user_guide/examples/offline_inference/text_to_speech.md
      - Text-To-Video: user_guide/examples/offline_inference/text_to_video.md
+      - X-To-Text: user_guide/examples/offline_inference/x_to_text.md
      - X-To-Video-Audio: user_guide/examples/offline_inference/x_to_video_audio.md
    - Online Serving:
-      - BAGEL-7B-MoT: user_guide/examples/online_serving/bagel.md
-      - vLLM-Omni Helm Chart: user_guide/examples/online_serving/chart-helm.md
-      - Diffusers Backend Adapter: user_guide/examples/online_serving/diffusers_pipeline_adapter.md
-      - GLM-Image Online Serving: user_guide/examples/online_serving/glm_image.md
      - Image-To-Image: user_guide/examples/online_serving/image_to_image.md
      - Image-To-Video: user_guide/examples/online_serving/image_to_video.md
-      - MiMo-Audio: user_guide/examples/online_serving/mimo_audio.md
-      - Qwen2.5-Omni: user_guide/examples/online_serving/qwen2_5_omni.md
-      - Qwen3-Omni: user_guide/examples/online_serving/qwen3_omni.md
-      - Text-To-Speech: user_guide/examples/online_serving/text_to_speech.md
+      - Speech-To-Video: user_guide/examples/online_serving/speech_to_video.md
      - Text-To-Image: user_guide/examples/online_serving/text_to_image.md
+      - Text-To-Speech: user_guide/examples/online_serving/text_to_speech.md
      - Text-To-Video: user_guide/examples/online_serving/text_to_video.md
  - General:
    - usage/*
@@ -50,29 +40,10 @@ nav:
  - Models:
    - models/supported_models.md
  - Features:
-    - Sleep Mode: features/sleep_mode.md
-    - Diffusion Features:
-      - Overview: user_guide/diffusion_features.md
-      - Feature Compatibility: user_guide/feature_compatibility.md
-      - Regional Compilation: user_guide/diffusion/regional_compilation.md
-      - Cache Acceleration:
-        - TeaCache: user_guide/diffusion/cache_acceleration/teacache.md
-        - Cache-DiT: user_guide/diffusion/cache_acceleration/cache_dit.md
-      - Attention Backends: user_guide/diffusion/attention_backends.md
-      - Frame Interpolation: user_guide/diffusion/frame_interpolation.md
-      - Parallelism:
-        - Overview: user_guide/diffusion/parallelism/overview.md
-        - CFG Parallel: user_guide/diffusion/parallelism/cfg_parallel.md
-        - Expert Parallel: user_guide/diffusion/parallelism/expert_parallel.md
-        - Hybrid Sharded Data Parallel: user_guide/diffusion/parallelism/hsdp.md
-        - Sequence Parallel: user_guide/diffusion/parallelism/sequence_parallel.md
-        - Tensor Parallel: user_guide/diffusion/parallelism/tensor_parallel.md
-        - VAE Parallelism: user_guide/diffusion/parallelism/vae_parallelism.md
-      - CPU Offloading: user_guide/diffusion/cpu_offload_diffusion.md
-      - LoRA: user_guide/diffusion/lora.md
-      - Custom Pipeline: features/custom_pipeline.md
-      - Request-Level Batching: user_guide/diffusion/request_batching.md
-      - Step Execution: user_guide/diffusion/step_execution.md
+    - Overview: features/README.md
+    - Runtime and Stage Execution:
+      - Execution Modes and Streaming: user_guide/diffusion/execution_modes.md
+      - Sleep Mode: features/sleep_mode.md
    - Quantization:
      - Overview: user_guide/quantization/overview.md
      - Online Quantization: user_guide/quantization/online.md
@@ -83,9 +54,33 @@ nav:
      - MXFP8 W8A8: user_guide/quantization/mxfp8.md
      - MXFP4 W4A4: user_guide/quantization/mxfp4.md
      - ModelOpt: user_guide/quantization/modelopt.md
-      - GGUF: user_guide/quantization/gguf.md
      - AutoRound: user_guide/quantization/autoround.md
      - msModelSlim: user_guide/quantization/msmodelslim.md
+    - Diffusion Acceleration:
+      - Overview and Compatibility:
+        - Overview: user_guide/diffusion_features.md
+        - Feature Compatibility: user_guide/feature_compatibility.md
+      - CPU Offloading: user_guide/diffusion/cpu_offload.md
+      - Cache Acceleration:
+        - TeaCache: user_guide/diffusion/cache_acceleration/teacache.md
+        - Cache-DiT: user_guide/diffusion/cache_acceleration/cache_dit.md
+      - Parallelism:
+        - Overview: user_guide/diffusion/parallelism/overview.md
+        - CFG Parallel: user_guide/diffusion/parallelism/cfg_parallel.md
+        - Expert Parallel: user_guide/diffusion/parallelism/expert_parallel.md
+        - Hybrid Sharded Data Parallel: user_guide/diffusion/parallelism/hsdp.md
+        - Sequence Parallel: user_guide/diffusion/parallelism/sequence_parallel.md
+        - Tensor Parallel: user_guide/diffusion/parallelism/tensor_parallel.md
+        - VAE Parallelism: user_guide/diffusion/parallelism/vae_parallelism.md
+      - Attention Backends: user_guide/diffusion/attention_backends.md
+      - Regional Compilation: user_guide/diffusion/regional_compilation.md
+      - Frame Interpolation: user_guide/diffusion/frame_interpolation.md
+      - Startup and Loading: user_guide/diffusion/startup_and_loading.md
+      - LoRA: user_guide/diffusion/lora.md
+    - Experimental:
+      - Session State Manager: features/session_state_manager.md
+  - Integrations:
+    - Custom Diffusion Pipeline: features/custom_pipeline.md
    - ComfyUI: features/comfyui.md
  - Fault Tolerance:
    - Failure Modes: user_guide/fault_injection_reliability_matrix.md
@@ -102,31 +97,65 @@ nav:
  - CI: contributing/ci
  - Design Documents:
    - design/index.md
-    - design/architecture_overview.md
+    - Architecture Documents:
+      - Architecture Overview: design/architecture_overview.md
    - Feature Design:
-      - design/feature/disaggregated_inference.md
-      - design/feature/ray_based_execution.md
-      - design/feature/omni_connectors/
-      - design/feature/prefix_caching.md
-      - design/feature/cfg_parallel.md
-      - design/feature/expert_parallel.md
-      - design/feature/sequence_parallel.md
-      - design/feature/tensor_parallel.md
-      - design/feature/vae_parallel.md
-      - design/feature/hsdp.md
-      - design/feature/cache_dit.md
-      - design/feature/teacache.md
-      - design/feature/skip_softmax.md
-      - design/feature/async_chunk.md
-      - design/feature/vae_parallel.md
-      - design/feature/diffusion_step_execution.md
-      - design/feature/diffusion_request_level_batching.md
-      - design/feature/diffusion_continuous_batching.md
+      - Runtime and Stage Execution:
+        - Disaggregated Inference: design/feature/disaggregated_inference.md
+        - Async Chunk: design/feature/async_chunk.md
+        - Async Diffusion Output: design/feature/async_diffusion_output.md
+        - Async Omni Output Materialization: design/feature/omni_async_output_materialization.md
+        - Automatic Prefix Caching: design/feature/prefix_caching.md
+      - Communication:
+        - OmniConnector Implementations:
+          - Mooncake Store Connector: design/feature/omni_connectors/mooncake_store_connector.md
+          - Mooncake Transfer Engine Connector: design/feature/omni_connectors/mooncake_transfer_engine_connector.md
+          - Mori Transfer Engine Connector: design/feature/omni_connectors/mori_transfer_engine_connector.md
+          - Shared Memory Connector: design/feature/omni_connectors/shared_memory_connector.md
+          - Yuanrong Store Connector: design/feature/omni_connectors/yuanrong_connector.md
+          - Yuanrong Transfer Engine Connector: design/feature/omni_connectors/yuanrong_transfer_engine_connector.md
+      - Quantization: design/feature/quantization.md
+      - Diffusion Acceleration:
+        - Parallelism:
+          - CFG-Parallel: design/feature/cfg_parallel.md
+          - Expert Parallel: design/feature/expert_parallel.md
+          - Hybrid Sharded Data Parallel (HSDP): design/feature/hsdp.md
+          - Pipeline Parallel: design/feature/pipeline_parallel.md
+          - Sequence Parallel: design/feature/sequence_parallel.md
+          - Tensor Parallel: design/feature/tensor_parallel.md
+          - VAE Patch Parallelism: design/feature/vae_parallel.md
+        - Attention Optimization:
+          - Skip-Softmax: design/feature/skip_softmax.md
+        - Cache-DiT: design/feature/cache_dit.md
+        - TeaCache: design/feature/teacache.md
+        - Diffusion Continuous Batching: design/feature/diffusion_continuous_batching.md
+        - Distributed Layerwise Offload: design/feature/distributed_layerwise_offload.md
+    - Infrastructure and Performance:
+      - Prometheus Metrics: design/metrics.md
+      - Speech Generation Performance Optimizations: design/qwen3_omni_tts_performance_optimization.md
    - Module Design:
-      - design/module/ar_module.md
-      - design/module/dit_module.md
-      - design/module/entrypoint_module.md
-      - design/module/async_omni_architecture.md
+      - Entrypoints: design/module/entrypoints.md
+      - vLLM-Omni Configuration: design/module/vllm_omni_config.md
+      - Input, Output, and Modality Contracts: design/module/input_output_modality_contracts.md
+      - Error Contracts: design/module/error_contracts.md
+      - Engine Orchestration: design/module/engine_orchestration.md
+      - Stage Runtime: design/module/stage_runtime.md
+      - OmniConnector: design/module/omni_connector.md
+      - Model Integration: design/module/model_integration.md
+      - Autoregressive Runtime: design/module/ar_runtime.md
+      - Diffusion:
+        - Overview: design/module/diffusion/index.md
+        - Runtime: design/module/diffusion/diffusion_runtime.md
+        - Model Integration: design/module/diffusion/diffusion_model_integration.md
+        - Continuous Batching: design/module/diffusion/continuous_batching.md
+        - Parallelism: design/module/diffusion/parallelism.md
+        - Offloader: design/module/diffusion/offloader.md
+      - Execution Platforms: design/module/execution_platforms.md
+      - Cache Management: design/module/cache_management.md
+      - Quantization: design/module/quantization.md
+      - Observability: design/module/observability.md
+      - Profiling: design/module/profiling.md
+      - Benchmarking: design/module/benchmarking.md
  - Docs Guide: contributing/DOCS_GUIDE.md
 - API Reference:
  - api/README.md
@@ -134,7 +163,8 @@ nav:
 - CLI Reference: cli
 - Community:
  - Governance: community/governance.md
-  - community/*
+  - Contact Us: community/contact_us.md
+  - Community Meetings: community/meetings.md
  - Slack: https://slack.vllm.ai
  - Blog: https://blog.vllm.ai
  - Forum: https://discuss.vllm.ai
@@ -52,15 +52,16 @@ vLLM-Omni is flexible and easy to use with:
 - Tensor, pipeline, data and expert parallelism support for distributed inference
 - Streaming outputs
 - OpenAI-compatible API server
+- Full-duplex realtime serving with streaming audio input and output (experimental)

 vLLM-Omni seamlessly supports most popular open-source models on HuggingFace, including:

- Omni-modality models (e.g. Qwen3-Omni, Cosmos3, HunyuanImage, BAGEL)
+- Omni-modality models (e.g. Qwen3-Omni, MiniCPM-o 4.5, Cosmos3, HunyuanImage, BAGEL)
 - TTS models (e.g. Qwen3-TTS, VoxCPM2, Ming-Omni-TTS, CosyVoice3)
- Diffusion models — image, video, and audio generation (e.g. Qwen-Image, Wan2.2, FLUX)
+- Diffusion models — image, video, and audio generation (e.g. MiniMax H3, Qwen-Image, Wan2.2, FLUX)
 - Robot-policy and action models (e.g. GR00T-N1.7, DreamZero-DROID, InternVLA-A1, Cosmos3 action policy)

 For more information, checkout the following:

- [vllm-omni architecture design and recent roadmaps](https://docs.google.com/presentation/d/1XJWgv79lORl8rbaVvp2d5Sqs6ZEBgAgj/edit?slide=id.p1#slide=id.p1)
+- [vllm-omni architecture design and recent roadmaps](https://docs.google.com/presentation/d/1aPj0OGl_-ZVoib-Qne5dGDAlrRFB-PdHl6E-EE99g8E/edit?usp=sharing)
 - [vllm-omni announcement blogpost](https://blog.vllm.ai/2025/11/30/vllm-omni.html)
@@ -18,18 +18,12 @@ Specify the port:
 vllm serve Qwen/Qwen2.5-Omni-7B --omni --port 8091
 ```

-For a migrated model, load a custom deploy configuration with `--deploy-config`:
+Load a custom deploy configuration with `--deploy-config`:

 ```bash
 vllm serve Qwen/Qwen2.5-Omni-7B --omni --deploy-config /path/to/deploy_config.yaml
 ```

-The deprecated `--stage-configs-path` flag is retained for models that still use the legacy `stage_args` schema:
-
-```bash
-vllm serve ByteDance-Seed/BAGEL-7B-MoT --omni --stage-configs-path /path/to/legacy_stage_config.yaml
-```
-
 ## bench

 Run benchmark tests for online serving throughput.
@@ -8,7 +8,6 @@ The stage-based CLI is designed for deployments that require launching each pipe
 - For **migrated models** that utilize the bundled deployment YAML configurations located in
  `vllm_omni/deploy/`, the `--deploy-config` flag is only required to override the default configuration. By default, executing `vllm serve MODEL --omni ...`
  automatically loads the bundled deployment configuration.
- For custom legacy `stage_args` YAMLs, pass the file with `--stage-configs-path`.

 Example: Initializing Stage 0 (Orchestrator and API Server):
 The commands below show a common device mapping where Stage 0 uses GPU 0 and
@@ -32,7 +31,7 @@ CUDA_VISIBLE_DEVICES=1 vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni \
    --omni-master-port 26000
 ```

-When utilizing a custom deployment YAML based on the new schema, append `--deploy-config /path/to/override.yaml` to each command execution. Conversely, for legacy models, substitute this parameter with `--stage-configs-path /path/to/stage_configs.yaml`.
+When utilizing a custom deployment YAML, append `--deploy-config /path/to/override.yaml` to each command execution.

 In the standard execution paradigm, the `--stage-overrides` argument is utilized to apply stage-specific configurations from a single CLI command.
 However, under the **stage-based CLI** paradigm, where each process strictly encapsulates a single stage, it is recommended to specify tuning parameters directly via discrete command-line flags for the respective stage, rather than constructing a composite `--stage-overrides` JSON string.
@@ -1,5 +1,9 @@
 # Contact Us

- For technical questions and feature requests, please use GitHub [Issues](https://github.com/vllm-project/vllm-omni/issues)
- For coordinating contributions and development and discussing with other users and developers, please join `sig-omni` channel in our [Slack](https://slack.vllm.ai/) or use the [vLLM Forum](https://discuss.vllm.ai/)
- For security disclosures, please use GitHub's [Security Advisories](https://github.com/vllm-project/vllm-omni/security/advisories) feature
+- For bugs and feature requests, search or open a GitHub [issue](https://github.com/vllm-project/vllm-omni/issues).
+- For technical and usage questions, join the `sig-omni` channel in [vLLM Slack](https://slack.vllm.ai/) or use the [vLLM Forum](https://discuss.vllm.ai/).
+- For coordinating contributions and development, use `sig-omni`, the forum, or join a [community meeting](meetings.md).
+
+## Security Disclosures
+
+Do not share vulnerability details in public GitHub issues, pull requests, discussions, or Slack channels. To arrange a private report, send a direct message to one of the [lead maintainers](governance.md#lead-maintainers) in [vLLM Slack](https://slack.vllm.ai/). If you cannot send a direct message, you may post a detail-free request in `sig-omni` asking a lead maintainer to contact you privately; do not include any vulnerability details in that request.
@@ -40,6 +40,7 @@ Committers have write access and merge rights. They typically have deep expertis
 - [@Isotr0py](https://github.com/Isotr0py): Diffusion and Quantization
 - [@linyueqian](https://github.com/linyueqian): TTS and Omni Support
 - [@lishunyang12](https://github.com/lishunyang12): Quantization and Configuration
+- [@NickCao](https://github.com/NickCao): CI, Tests, Entrypoint, and TTS Model Support
 - [@princepride](https://github.com/princepride): Diffusion and Omni Support
 - [@RuixiangMa](https://github.com/RuixiangMa): Diffusion models, parallel, cache, and docs
 - [@SamitHuang](https://github.com/SamitHuang): RL, Diffusion, and cache
@@ -51,31 +52,11 @@ Committers have write access and merge rights. They typically have deep expertis
 - [@ZJY0516](https://github.com/ZJY0516): Diffusion attention backend, kernel fusion, and CustomOp
 - [@yuanheng-zhao](https://github.com/yuanheng-zhao): Diffusion cache, offload, and Omni Support

-## Diffusion Workload Division
+## Reviewer Routing

-This section breaks down diffusion-related responsibilities. If you have PRs touching these areas, please ping the listed owners for review.
+For path-based reviewer routing, consult [`.github/CODEOWNERS`](https://github.com/vllm-project/vllm-omni/blob/main/.github/CODEOWNERS). Detailed subsystem boundaries and safe-change guidance live in the [module design documents](../design/index.md#module-design-documents).

-### Runtime-related
-
- Diffusion models: @RuixiangMa, @wtomin, @xuechendi
-
-### Optimization
-
- Parallel: @RuixiangMa, @wtomin
- Attention backend: @ZJY0516
- Cache: @yuanheng-zhao, @RuixiangMa, @SamitHuang
- Offload: @yuanheng-zhao
- Intel GPU support: @xuechendi
- Quantization: @lishunyang12, @david6666666
- Kernel fusion / communication-computation: @ZJY0516
-
-### Docs & Test
-
- Diffusion docs: @RuixiangMa, @wtomin
-
-## Meetings
-
-Committers hold **bi-weekly meetings** to discuss future directions and collaborations of the project.
+`CODEOWNERS` is operational routing metadata, not a governance or approval policy. Being listed does not grant committer status or merge rights, and it does not mean every listed reviewer must approve a change. The maintainer roster above is authoritative for project roles and merge rights.

 ## Committer Nomination Process

@@ -0,0 +1,8 @@
+# Community Meetings
+
+vLLM-Omni hosts developer-facing meetings for Chinese- and English-language audiences. Schedules and access details can change, so use these maintained documents as the source of truth:
+
+- [Chinese-language meeting document](https://docs.google.com/document/d/1pdUBiS_7mdOUNDtdwy-9OUf7jsMWN324BIbps5olDME/edit?tab=t.0)
+- [English-language meeting document](https://docs.google.com/document/d/19AlD08T8uJ7wqfs8VtYCi0iGMTwlTYVVlAxFZ3S7OIg/edit?tab=t.0)
+
+Each document contains the current schedule, access details, agendas, and past meeting notes. For broader vLLM community events, see the [vLLM events calendar](https://vllm.ai/events/).
@@ -1 +0,0 @@
-# Meetups
@@ -1,12 +0,0 @@
-# Volunteers for Bugfix and CI
-
-We encourage you to check current docs and [issues](https://github.com/vllm-project/vllm-omni/issues) to find possible solutions for your questions. If non of these can solve it, please propose an issue to describe your questions about bug or CI problems for developing.
-
-If you have urgent need for locating and solving bugfix or CI problems, please find community volunteers below.
-
-| Dec 4-Dec 12 | Dec 15-Dec 19 | Dec 22-Dec 26 | Dec 29- Jan 2, 2026| Jan 5-Jan 9 | Jan 12-Jan 16 |
-|----------|----------|----------|----------|----------|----------|
-| <a href="https://github.com/congw729">Conw729</a> | <a href="https://github.com/yinpeiqi">yinpeiqi</a> | <a href="https://github.com/tzhouam">tzhouam</a> | <a href="https://github.com/SamitHuang">SamitHuang</a> | <a href="https://github.com/gcanlin">gcanlin</a> | <a href="https://github.com/natureofnature">natureofnature</a> |
-| <a href="https://github.com/david6666666">david6666666</a> | <a href="https://github.com/R2-Y">R2-Y</a> | <a href="https://github.com/hsliuustc0106">hsliuustc0106</a> | <a href="https://github.com/Gaohan123">Gaohan123</a> | <a href="https://github.com/ZJY0516">ZJY0516</a> | <a href="https://github.com/qibaoyuan">qibaoyuan</a> |
-
-We kindly welcome more contributors to fix bugs and contribute new features!
@@ -2,7 +2,7 @@

 This section lists the most common options for running vLLM-Omni.

-For options within a vLLM Engine. Please refer to [vLLM Configuration](https://docs.vllm.ai/en/v0.16.0/configuration/index.html)
+For options within a vLLM Engine, please refer to the [vLLM 0.26 configuration guide](https://docs.vllm.ai/en/v0.26.0/configuration/index.html).

 Currently, the main options are maintained by stage configs for each model.

@@ -34,12 +34,8 @@ Two additional `StrategySpec` slots — `layer_hook_specs` and `kernel_specs`
 | Flag | Description |
 |------|-------------|
 | `--strategy-config PATH` | Path to a `strategy.yaml`. Only takes effect on registry-based deploy paths (`--deploy-config` or the bundled default deploy YAML). Its derived sizing is overlaid onto the merged stages *before* CLI overrides. |
-| `--stage-configs-path PATH` | **Legacy.** Loads the old `stage_args` YAML schema for un-migrated models. Passing `--strategy-config` together with this path is silently inapplicable — vLLM-Omni emits a warning and the strategy is ignored (see `vllm_omni/entrypoints/utils.py`, lines 372–380). |
 | `--omni-lb-policy POLICY` | Orchestrator-wide load-balancer policy. Strategy-derived from any `stage_replica` axis; an explicit CLI value wins and must match the derived one or `AsyncOmniEngine` raises `Conflicting load-balancer policy` at construction. |

-!!! warning
-    `--strategy-config` cannot be used with `--stage-configs-path` (the legacy deploy path). If your model has not been migrated to the registry-based pipeline yet, the strategy is silently dropped with a warning. Migrate to a registry-based model or use `--deploy-config` to apply a strategy.
-
 ## Sub-schemas

 ### `axis`
@@ -216,17 +212,6 @@ Strategy keys MUST be `model_stage` *names* (strings), the same labels stage con
 !!! warning "Mixing CLI overrides with a strategy on the same axis"
    A strategy is meant to be the single writer for the axes it declares. If a CLI override (`--tensor-parallel-size 2`, `--stage-overrides '{"0": {"tensor_parallel_size": 2}}'`) sets the same axis, the **CLI value wins** and a warning is emitted from `_reconcile_strategy_with_cli`. The device-layout guard then re-runs against the effective layout, so a CLI override that breaks `tp * dp * pp * num_replicas` will still fail fast at config time.

-!!! warning "Passing `--strategy-config` against the legacy `--stage-configs-path`"
-    Composable parallel only applies on the registry-based deploy path. If a model still resolves through the legacy `stage_args` YAML (loaded via `--stage-configs-path`), `--strategy-config` is silently inapplicable and emits:
-
-    ```text
-    --strategy-config (<path>) was provided but model '<model>' resolves via the
-    legacy stage_configs YAML path, which does not support composable-parallel
-    strategies; the strategy is ignored. Use a registry-based model to apply it.
-    ```
-
-    Migrate the model to the new deploy schema (see `configuration/stage_configs.md`) before applying a strategy.
-
 !!! warning "Conflicting `omni_lb_policy` across stages"
    `omni_lb_policy` is a single pipeline-wide knob. If two `stage_replica` axes in different stages derive different policies, `apply_strategy_specs` raises:

@@ -158,7 +158,7 @@ runtime:

 ```bash
 vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091 \
-    --stage-configs-path /path/to/qwen3_omni_pd.yaml
+    --deploy-config /path/to/qwen3_omni_pd.yaml
 ```

 ## Operational Notes
@@ -1,9 +1,9 @@
-# Deploy and legacy stage configurations
+# Pipeline and deploy configurations

-In vLLM-Omni, a model's `PipelineConfig` defines its fixed stage topology, while a deploy configuration controls how those stages run. Models that have not migrated to this split still use legacy stage configurations with a `stage_args` schema.
+In vLLM-Omni, a model's `PipelineConfig` defines its fixed stage topology, while a deploy configuration controls how those stages run.

 !!! note
-    Default deploy config YAMLs (for example, `vllm_omni/deploy/qwen2_5_omni.yaml`, `vllm_omni/deploy/qwen3_omni_moe.yaml`, and `vllm_omni/deploy/qwen3_tts.yaml`) are bundled and loaded automatically when neither `--stage-configs-path` nor `--deploy-config` is provided — the model registry resolves the right pipeline + deploy YAML by `model_type`. Custom legacy `stage_args` YAMLs are still accepted via `--stage-configs-path`.
+    Default deploy config YAMLs (for example, `vllm_omni/deploy/qwen2_5_omni.yaml`, `vllm_omni/deploy/qwen3_omni_moe.yaml`, and `vllm_omni/deploy/qwen3_tts.yaml`) are bundled and loaded automatically when `--deploy-config` is omitted. The model registry resolves the right pipeline and deploy YAML by `model_type`.

 ## Deploy configuration schema

@@ -86,10 +86,9 @@ stages:

 | Flag | Description |
 |------|-------------|
-| `--deploy-config PATH` | Load a new-schema deploy YAML. It is mutually exclusive with `--stage-configs-path`. **Optional** — when omitted, the bundled `vllm_omni/deploy/<model_type>.yaml` is auto-loaded by the model registry. |
+| `--deploy-config PATH` | Load a deploy YAML. **Optional** — when omitted, the bundled `vllm_omni/deploy/<model_type>.yaml` is auto-loaded by the model registry. |
 | `--stage-overrides JSON` | Per-stage JSON overrides, e.g. `'{"0":{"gpu_memory_utilization":0.5}}'`. Per-stage values always win over global flags. |
 | `--async-chunk` / `--no-async-chunk` | Flip the deploy YAML's `async_chunk:` bool. Unset (default) leaves the YAML value in force. |
-| `--stage-configs-path` | **Deprecated.** Load a legacy `stage_args` YAML. New-schema YAMLs passed to this flag are auto-detected for compatibility, but should use `--deploy-config`. It is mutually exclusive with `--deploy-config` and will be removed in a future release. |

 ### Stage-Based CLI Paradigm

@@ -119,8 +118,7 @@ CUDA_VISIBLE_DEVICES=1 vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni \
    --omni-master-port 26000
 ```

-When instantiating a custom deployment YAML conforming to the updated schema, append the `--deploy-config /path/to/override.yaml` directive
-to all node invocations. For legacy architectures (e.g., BAGEL) configured via deprecated `stage_args:` schemas, continue to specify the relevant configuration via `--stage-configs-path /path/to/config.yaml`.
+When instantiating a custom deployment YAML, append the `--deploy-config /path/to/override.yaml` directive to all node invocations.

 In the context of standard initialization architectures, utilizing the `--stage-overrides` parameter operates as the optimal methodology
 for delineating stage-specific tuning from the CLI interface:
@@ -220,8 +218,7 @@ Therefore, as a core part of vLLM-Omni, a model's pipeline and deployment config

 To override specific parameters, explicitly inject the customized configuration schema
 in both online and offline instantiation flows. Use the `--deploy-config` flag
-when loading a deploy configuration, reserving the `--stage-configs-path` parameter
-exclusively to maintain compatibility with legacy `stage_args` YAML constructs.
+when loading a deploy configuration.

 Examples:

@@ -236,11 +233,6 @@ For online serving:
 vllm serve Qwen/Qwen2.5-Omni-7B --omni --port 8091 --deploy-config /path/to/deploy_config.yaml
 ```

-Legacy online serving:
-
-```bash
-vllm serve ByteDance-Seed/BAGEL-7B-MoT --omni --port 8091 --stage-configs-path /path/to/stage_configs_file
-```
 !!! important
    We are actively iterating on the definition of deployment configurations, and we welcome feedback from users and developers.

@@ -395,8 +387,8 @@ that support request-level batching with `step_execution` disabled.

 Use this together with `max_num_seqs > 1` for bursty serving traffic. `0`
 disables admission waiting and preserves the lowest first-request latency.
-For diffusion request-level batching tuning, see
-[Request-Level Batching](../user_guide/diffusion/request_batching.md).
+For diffusion execution and batching tuning, see
+[Diffusion Execution Modes](../user_guide/diffusion/execution_modes.md).

 Default: `0.0`

@@ -3,7 +3,7 @@
 Thank you for your interest in contributing to vLLM-Omni! This document provides guidelines and instructions for contributing.

 !!! note
-    We host weekly developer-facing online meetings to discuss milestones and updates **every Tuesday at 19:30 PDT**. Meeting link as well as the past meeting notes can be found [here](https://tinyurl.com/vllm-omni-meeting).
+    vLLM-Omni hosts developer-facing meetings for Chinese- and English-language audiences. See [Community Meetings](../community/meetings.md) for current schedules, access details, agendas, and past notes.

 ## Getting Started

@@ -76,12 +76,53 @@ Please refer to the [test instructions](./ci/test_execution_guide.md) for detail
 !!! warning
    Currently, not all unit tests pass when run on CPU platforms. If you don't have access to a GPU platform to run unit tests locally, rely on the continuous integration system to run the tests for now.

+### Using repository skills with coding agents
+
+vLLM-Omni maintains [repository-scale skills](https://github.com/vllm-project/vllm-omni/tree/main/.claude/skills) for common coding, testing, performance, and review workflows. These skills capture repository-specific structure, validation requirements, and evidence standards that a general coding agent may not know.
+
+If your coding agent discovers repository skills automatically, ask it to use the relevant skill by name. Otherwise, point it to the linked `SKILL.md` and ask it to read the file before changing code. Skills can be combined: use a domain skill for the implementation, `vllm-omni-test` for test and CI decisions, and `precheck-pr` before opening the pull request.
+
+| Task | Repository skill |
+| --- | --- |
+| Add or extend a diffusion model | [`add-diffusion-model`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/add-diffusion-model/SKILL.md) |
+| Add or extend a text-to-speech model | [`add-tts-model`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/add-tts-model/SKILL.md) |
+| Add, select, or debug quantization | [`quantization`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/quantization/SKILL.md) |
+| Diagnose or optimize diffusion performance | [`diffusion-perf-opt`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/diffusion-perf-opt/SKILL.md) |
+| Upgrade an NPU model runner | [`vllm-omni-npu-model-runner-upgrade`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/vllm-omni-npu-upgrade/SKILL.md) |
+| Add a regression test, choose L1-L4 coverage, or wire Buildkite | [`vllm-omni-test`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/vllm-omni-test/SKILL.md) |
+| Self-check a branch before opening a pull request | [`precheck-pr`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/precheck-pr/SKILL.md) |
+| Perform a maintainer or reviewer review of an existing pull request or local branch | [`review-pr`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/review-pr/SKILL.md) |
+
+Use the following workflow when collaborating with a coding agent:
+
+1. **Ground the task.** Provide the issue, RFC, or design document that defines the expected behavior. Ask the agent to state its assumptions and identify the affected module or feature contracts before editing code.
+2. **Select the narrowest applicable skills.** Start with the domain skill that owns the change. Add `vllm-omni-test` whenever production behavior or tests change. For a bug fix, ask for the smallest reproducer and a regression test tied to the original symptom.
+3. **Require executable evidence.** Ask the agent to run the narrowest relevant checks and report the exact commands and results. It must identify tests it could not run because of unavailable hardware, model weights, credentials, or dependencies; an unrun check is not a pass.
+4. **Inspect the resulting diff.** Confirm that the implementation stays within the issue or RFC, does not overwrite unrelated work, and includes the required tests, documentation, and benchmark or accuracy evidence.
+5. **Run the pre-submit workflow.** Use `precheck-pr`, then run the applicable local tests and pre-commit hooks before opening the pull request.
+
+For example:
+
+```text
+Use add-diffusion-model and vllm-omni-test to implement the linked issue.
+Read the relevant design documents first, keep the change within the issue,
+and report every test command, result, and hardware validation gap.
+```
+
+```text
+Use vllm-omni-test to turn this bug reproducer into the smallest deterministic
+regression test. Prefer L1 CPU coverage; if the failure requires real weights
+or serving, explain the required L2/L3 environment and provide the exact command.
+```
+
+Repository skills guide the agent, but they do not replace contributor judgment, the accepted issue or RFC, required test evidence, or maintainer review. Review all generated changes before submitting them.
+
 ## Issues

 If you encounter a bug or have a feature request, please search existing issues first to see if it has already been reported. If not, please file a new issue, providing as much relevant information as possible.

 !!! important
-    If you discover a security vulnerability, please report it by creating a GitHub issue with the `security` label.
+    Do not report suspected security vulnerabilities through a public issue, pull request, discussion, or Slack channel. Follow the [security disclosure instructions](../community/contact_us.md#security-disclosures) to arrange a private report.

 ## Pull Requests & Code Reviews

@@ -1,6 +1,6 @@
 **Scope**

-For diffusion models, the L4 functionality test covers all or common *diffusion features* that are supported by this model, including several [parallelism acceleration methods](https://docs.vllm.ai/projects/vllm-omni/en/latest/user_guide/diffusion/parallelism_acceleration/), [CPU offloading](https://docs.vllm.ai/projects/vllm-omni/en/latest/user_guide/diffusion/cpu_offload_diffusion/), [TeaCache](https://docs.vllm.ai/projects/vllm-omni/en/latest/user_guide/diffusion/teacache/) and [Cache-DiT](https://docs.vllm.ai/projects/vllm-omni/en/latest/user_guide/diffusion/cache_dit_acceleration/) cache backends, [quantization methods](https://docs.vllm.ai/projects/vllm-omni/en/latest/user_guide/quantization/overview/).
+For diffusion models, the L4 functionality test covers all or common *diffusion features* that are supported by this model, including several [parallelism acceleration methods](https://docs.vllm.ai/projects/vllm-omni/en/latest/user_guide/diffusion/parallelism_acceleration/), [CPU offloading](https://docs.vllm.ai/projects/vllm-omni/en/latest/user_guide/diffusion/cpu_offload/), [TeaCache](https://docs.vllm.ai/projects/vllm-omni/en/latest/user_guide/diffusion/teacache/) and [Cache-DiT](https://docs.vllm.ai/projects/vllm-omni/en/latest/user_guide/diffusion/cache_dit_acceleration/) cache backends, [quantization methods](https://docs.vllm.ai/projects/vllm-omni/en/latest/user_guide/quantization/overview/).

 **Test Case Design**

@@ -44,9 +44,11 @@ Pass **`--test-config-file`** to run one JSON file as-is, or omit it for the bul
            "ignore_eos": true,
            "percentile-metrics": "ttft,tpot,itl,e2el,audio_rtf,audio_ttfp,audio_duration",
            "baseline": {
-                "mean_ttft_ms": [500, 800],
-                "mean_audio_ttfp_ms": [2000, 3500],
-                "mean_audio_rtf": [0.25, 0.35]
+                "H100": {
+                    "mean_ttft_ms": [500, 800],
+                    "mean_audio_ttfp_ms": [2000, 3500],
+                    "mean_audio_rtf": [0.25, 0.35]
+                }
            }
        }
    ]
@@ -142,10 +144,6 @@ pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py \
  --test-config-file tests/dfx/perf/tests/test_bagel_vllm_omni.json
 pytest -s -v tests/dfx/perf/scripts/run_benchmark.py \
  --test-config-file tests/dfx/perf/tests/test_qwen3_omni_async_chunk.json
-
-# Optional baseline assertion (default off)
-pytest -sv tests/dfx/perf/scripts/run_diffusion_benchmark.py --assert-baseline \
-  --test-config-file tests/dfx/perf/tests/test_qwen_image_vllm_omni.json
 ```

 See also [Markers for Tests](#markers-for-tests) for registered hardware markers (`H100`, `L4`, `cuda`, `distributed_cuda`, …).
@@ -202,4 +200,4 @@ You can add any benchmark running parameters you need here. For all optional par
 | num_prompts     | int / array | Yes      | 10,[10, 20, 30] | Number of requests. Supports single values or arrays. If a single value is used, it will be automatically expanded to match the number of qps or max_concurrency, e.g., [10,10,10]. If an array is used, its length must match the number of qps or max_concurrency. |
 | request_rate    | float / array | No  | 0.5, [0.5, 1, inf] | Queries per second. Supports single values or arrays. If a single value is used, it will be automatically expanded to match the number of num_prompts, e.g., [1,1,1]. If an array is used, its length must match the number of num_prompts.                          |
 | max_concurrency | int / array | No       | 1, [1, 2, 3]    | Maximum concurrent in-flight requests. Same array / expansion rules as `request_rate` (mutually exclusive with QPS mode).                                                                                                                                                                                                             |
-| baseline        | object      | No       | see above       | Optional per-metric thresholds; keys must match benchmark output fields. Scalar, list (per sweep step), or object (keyed by concurrency or QPS string).  
+| baseline        | object      | No       | see below       | Optional thresholds. **Hardware-nested only**: top-level keys must be `[hardware-resource]` pytest markers from `pyproject.toml` (`get_hardware_mark_list()`). Under each hardware key, metric names are free-form; values are scalars or sweep-aligned lists. Example: `{"H100": {"throughput_qps": […], "custom_stage_ms": 1.0}, "A3": {…}}`. Runtime filename aliases (`_RUNTIME_DEVICE_ALIASES`) are separate and do not expand this allowlist. |
@@ -56,7 +56,11 @@ Through five levels (L1-L5) and common (Common) specifications, the system clari
      <td>/</td>
      <td><code>core_model and cpu</code></td>
      <td rowspan="2">&lt;15min</td>
-      <td>/tests/{component_name}/test_xxx</td>
+      <td>
+        <code>tests/{component}/…</code> mirroring <code>vllm_omni/{component}/</code><br>
+        (e.g. <code>tests/diffusion/</code>, <code>tests/engine/</code>, <code>tests/entrypoints/</code>)<br>
+        Do <strong>not</strong> add new top-level <code>tests/</code> dirs unrelated to a component.
+      </td>
      <td>
        <a href="test_writing_guide.md#l1--l2-level-testing-unit-testing-and-basic-end-to-end-verification">L1 &amp; L2</a><br>
        Section 1 L1&amp;L2: Purpose, Test Content, Directory Location, Example
@@ -68,16 +72,18 @@ Through five levels (L1-L5) and common (Common) specifications, the system clari
      <td><strong>L2</strong><br>(E2E across models & GPU-required UT)</td>
      <td>Online (basic deployment scenarios):<br>dummy, normal inference function (output format, stream), some instance startup UT</td>
      <td>High-priority models + online basic scenarios; request success, non-empty output, format match (no Whisper/accuracy)</td>
-      <td>High-priority features (using random lightweight models)</td>
+      <td>High-priority features and component tests that require launching instances</td>
      <td>High-priority interfaces (using random lightweight models)</td>
      <td><code>core_model and hardware_test(H100, L4, etc.) and omni/tts/diffusion</code></td>
      <td>
-        <strong>Model tests:</strong><br>
-        /tests/e2e/online_serving/test_{model_name}.py<br>
-        <strong>Feature tests:</strong><br>
-        /tests/{component_name}/test_xxx<br>
-        <strong>Interface tests:</strong><br>
-        /tests/entrypoints/test_xxx
+        <strong>Model E2E:</strong><br>
+        <code>tests/e2e/online_serving/test_{model}.py</code><br>
+        <code>tests/e2e/offline_inference/test_{model}.py</code><br>
+        <strong>Feature integration:</strong><br>
+        <code>tests/e2e/features/&lt;feature&gt;/</code><br>
+        (e.g. <code>fullduplex/</code>, <code>custom_pipeline/</code>, <code>rlhf_test/</code>)<br>
+        <strong>Component / interface Test (GPU):</strong><br>
+        <code>tests/{component}/…</code>, <code>tests/entrypoints/…</code>
      </td>
      <td>
        <a href="test_writing_guide.md#l1--l2-level-testing-unit-testing-and-basic-end-to-end-verification">L1 &amp; L2</a><br>
@@ -90,18 +96,19 @@ Through five levels (L1-L5) and common (Common) specifications, the system clari
      <td><strong>L3</strong><br>(Important Perf & Integration & Accuracy)</td>
      <td>Online & Offline (multiple deployment scenarios):<br>real model, normal inference function, normal accuracy</td>
      <td>High/medium-priority models + key online/offline scenarios; real weights, Whisper/similarity, preset voice gender, basic accuracy</td>
-      <td>Medium-priority features (using random lightweight models)</td>
+      <td>Medium-priority features and component tests that require launching instances</td>
      <td>Medium-priority interfaces (using random lightweight models)</td>
      <td><code>advanced_model and hardware_test(H100, L4, etc.) and omni/tts/diffusion</code></td>
      <td>&lt;30min</td>
      <td>
-        <strong>Model tests:</strong><br>
-        /tests/e2e/online_serving/test_{model_name}.py<br>
-        /tests/e2e/offline_inference/test_{model_name}.py<br>
-        <strong>Feature tests:</strong><br>
-        /tests/{component_name}/test_xxx<br>
-        <strong>Interface tests:</strong><br>
-        /tests/entrypoints/test_xxx
+        <strong>Model E2E:</strong><br>
+        <code>tests/e2e/online_serving/test_{model}.py</code><br>
+        <code>tests/e2e/offline_inference/test_{model}.py</code><br>
+        <code>tests/e2e/accuracy/</code><br>
+        <strong>Feature integration:</strong><br>
+        <code>tests/e2e/features/&lt;feature&gt;/</code><br>
+        <strong>Component / interface Test:</strong><br>
+        <code>tests/{component}/…</code>, <code>tests/entrypoints/…</code>
      </td>
      <td>
        <a href="test_writing_guide.md#l3-level-testing-core-integration-performance-and-accuracy-verification">L3</a><br>
@@ -114,26 +121,23 @@ Through five levels (L1-L5) and common (Common) specifications, the system clari
      <td><strong>L4</strong><br>(Perf & Integration & Accuracy)</td>
      <td>Online: full functional scenarios + performance test + doc test + accuracy test</td>
      <td>High-priority models: function, performance, accuracy, and doc testing<br>Medium-priority models: function and doc testing</td>
-      <td>Low-priority features (using real weights)</td>
+      <td>Low-priority features and component tests that require launching instances</td>
      <td>Low-priority interfaces (using real weights)</td>
      <td><code>full_model and hardware_test(H100, L4, etc.) and omni/tts/diffusion</code></td>
      <td>&lt;3 hour</td>
      <td>
-        <strong>Model tests:</strong><br>
-        /tests/e2e/online_serving/test_{model_name}_expansion.py<br>
-        <strong>Feature tests:</strong><br>
-        /tests/{component_name}/test_xxx<br>
-        <strong>Interface tests:</strong><br>
-        /tests/entrypoints/test_xxx<br>
+        <strong>Model E2E:</strong><br>
+        <code>tests/e2e/online_serving/test_{model}_expansion.py</code><br>
+        <code>tests/e2e/offline_inference/test_{model}_expansion.py</code><br>
+        <code>tests/e2e/accuracy/test_{model}.py</code><br>
+        <strong>Feature integration:</strong><br>
+        <code>tests/e2e/features/&lt;feature&gt;/</code><br>
+        <strong>Component / interface Test:</strong><br>
+        <code>tests/{component}/…</code>, <code>tests/entrypoints/…</code><br>
        <strong>Performance:</strong><br>
-        /tests/dfx/perf/tests/test_qwen3_omni_*.json (Omni), test_tts.json (TTS),<br>
-        test_voxcpm2.json, test_higgs_audio_v3.json, and<br>
-        /tests/dfx/perf/tests/test_{diffusion_model}_vllm_omni.json (Diffusion)<br>
-        <strong>Doc Test:</strong><br>
-        tests/examples/online_serving/test_{model_name}.py<br>
-        tests/examples/offline_inference/test_{model_name}.py<br>
-        <strong>Accuracy Test:</strong><br>
-        /tests/e2e/accuracy/test_{model_name}.py
+        <code>tests/dfx/perf/tests/</code><br>
+        <strong>Doc examples:</strong><br>
+        <code>tests/examples/online_serving/</code>, <code>tests/examples/offline_inference/</code>
      </td>
      <td>
        <a href="test_writing_guide.md#l4-level-testing-full-functionality-performance-and-documentation-testing">L4</a><br>
@@ -152,11 +156,9 @@ Through five levels (L1-L5) and common (Common) specifications, the system clari
      <td> Depends on reality </td>
      <td>
        <strong>Stability:</strong><br>
-        /tests/dfx/stability/tests/test_qwen3_omni.json<br>
-        /tests/dfx/stability/tests/test_wan22.json<br>
+        <code>tests/dfx/stability/tests/</code><br>
        <strong>Reliability:</strong><br>
-        tests/dfx/reliability/test_reliability_{model_key}.py<br>
-        (e.g. <code>test_reliability_qwen3_omni.py</code>, <code>test_reliability_wan22.py</code>, <code>test_reliability_hunyuan_image.py</code>, <code>test_reliability_voxcpm2.py</code>)
+        <code>tests/dfx/reliability/test_reliability_{model}.py</code>
      </td>
      <td>
        <a href="test_writing_guide.md#l5-level-testing-stability-and-reliability-testing">L5</a><br>
@@ -168,7 +170,9 @@ Through five levels (L1-L5) and common (Common) specifications, the system clari
  </tbody>
 </table>

-For per-level test authoring (directories, markers, examples), see [Test Writing Guide](./test_writing_guide.md).
+**Test Dir placement (summary):** component / unit under `tests/{component}/` mirroring `vllm_omni/`; model E2E under `tests/e2e/online_serving/`, `tests/e2e/offline_inference/`, and `tests/e2e/accuracy/`; feature integration under `tests/e2e/features/<feature>/`; doc example tests under `tests/examples/online_serving/` and `tests/examples/offline_inference/`; performance under `tests/dfx/perf/`; stability under `tests/dfx/stability/`; reliability under `tests/dfx/reliability/`. Do **not** add new top-level directories under `tests/` that are unrelated to a `vllm_omni` component (or to the established `e2e` / `dfx` / `helpers` / `examples` / `buildkite` layout).
+
+For per-level test authoring (markers, examples), see [Test Writing Guide](./test_writing_guide.md).

 ## Common Specifications

@@ -4,137 +4,20 @@ This document describes how to author tests for each CI level (L1–L5). For the

 ## Test Directory Structure

-Legend: `✅` = test exists, `⬜` = suggested to add.
-```
-vllm_omni/                                    tests/
-├── config/                             →     ├── config/
-│   ├── model.py                              │   └── test_model.py                    ⬜
-│   └── lora.py                               │   └── test_lora.py                      ⬜
-│
-├── core/                               →     ├── core/
-│   └── sched/                                 │   └── sched/
-│       ├── omni_ar_scheduler.py               │       ├── test_omni_ar_scheduler.py    ⬜
-│       ├── omni_generation_scheduler.py       │       ├── test_omni_generation_scheduler.py  ⬜
-│       └── output.py                          │       └── test_output.py               ✅ currently in entrypoints/test_omni_new_request_data.py (tests output.OmniNewRequestData)
-│
-├── diffusion/                          →     ├── diffusion/
-│   ├── diffusion_engine.py                    │   ├── test_diffusion_engine.py          ⬜
-│   ├── attention/                             │   ├── attention/
-│   │   ├── layer.py                            │   │   ├── test_attention_sp.py         ✅
-│   │   └── backends/                           │   │   └── test_flash_attn.py           ✅
-│   ├── distributed/                           │   ├── distributed/
-│   │   └── ...                                 │   │   ├── test_comm.py                 ✅
-│   │                                            │   │   ├── test_cfg_parallel.py        ✅
-│   │                                            │   │   └── test_sp_plan_hooks.py       ✅
-│   ├── lora/                                   │   ├── lora/
-│   │   └── ...                                 │   │   ├── test_base_linear.py          ✅
-│   │                                            │   │   └── test_lora_manager.py        ✅
-│   ├── models/                                 │   ├── models/
-│   │   ├── qwen_image/                         │   │   ├── qwen_image/                 (e2e coverage)
-│   │   ├── ovis_image/                         │   │   ├── ovis_image/
-│   │   │   └── ...                             │   │   │   └── test_ovis_image.py     ✅
-│   │   ├── z_image/                            │   │   └── z_image/
-│   │   └── ...                                 │   │       └── test_zimage_tp_constraints.py  ✅
-│   └── worker/                                 │   └── worker/
-│       ├── diffusion_worker.py                 │       └── test_diffusion_worker.py   ✅ file at diffusion/test_diffusion_worker.py
-│       └── diffusion_model_runner.py            │
-│
-├── distributed/                         →     ├── distributed/
-│   └── omni_connectors/                         │   └── omni_connectors/
-│       ├── adapter.py                           │       ├── test_adapter_and_flow.py   ✅
-│       ├── kv_transfer_manager.py               │       ├── test_basic_connectors.py   ✅
-│       ├── connectors/                           │       ├── test_kv_flow.py             ✅
-│       └── utils/                               │       └── test_omni_connector_configs.py  ✅
-│
-├── engine/                             →     ├── engine/
-│   ├── input_processor.py                      │   ├── test_input_processor.py         ⬜  (no processor.py in source)
-│   ├── output_processor.py                     │   └── test_output_processor.py         ⬜
-│   └── arg_utils.py                            │   └── test_arg_utils.py               ⬜
-│
-├── entrypoints/                        →     ├── entrypoints/
-│   ├── stage_utils.py                          │   ├── test_stage_utils.py            ✅
-│   ├── cli/                                     │   ├── cli/                           (benchmarks/test_serve_cli.py covers CLI serve)
-│   │   └── ...                                  │   │   └── test_*.py                  ⬜
-│   └── openai/                                  │   └── openai_api/                    # maps to entrypoints/openai/
-│       ├── api_server.py                        │       ├── test_api_server.py         ⬜  (e2e indirect coverage)
-│       ├── serving_chat.py                       │       ├── test_serving_chat_sampling_params.py  ✅
-│       ├── serving_speech.py                     │       ├── test_serving_speech.py     ✅
-│       └── image_api_utils.py                   │       └── test_image_server.py      ✅
-│
-├── inputs/                             →     ├── inputs/
-│   ├── data.py                                 │   ├── test_data.py                   ⬜
-│   ├── parse.py                                │   ├── test_parse.py                 ⬜
-│   └── preprocess.py                            │   └── test_preprocess.py            ✅ currently in entrypoints/test_omni_input_preprocessor.py
-│
-├── model_executor/                     →     ├── model_executor/
-│   ├── layers/                                  │   ├── layers/
-│   │   └── mrope.py                             │   │   └── test_mrope.py              ⬜
-│   ├── model_loader/                            │   ├── model_loader/
-│   │   └── weight_utils.py                      │   │   └── test_weight_utils.py      ⬜
-│   ├── models/                                  │   ├── models/
-│   │   ├── qwen2_5_omni/                         │   │   ├── qwen2_5_omni/
-│   │   │   ├── qwen2_5_omni_thinker.py           │   │   │   ├── test_audio_length.py  ✅
-│   │   │   ├── qwen2_5_omni_talker.py            │   │   │   ├── test_qwen2_5_omni_thinker.py  ⬜
-│   │   │   └── qwen2_5_omni_token2wav.py         │   │   │   ├── test_qwen2_5_omni_talker.py  ⬜
-│   │   └── qwen3_omni/                          │   │   │   └── test_qwen2_5_omni_token2wav.py  ⬜
-│   │       └── ...                               │   │   └── qwen3_omni/
-│   ├── stage_configs/                           │   │       └── test_*.py              ⬜
-│   │   └── *.yaml                               │   └── stage_configs/                 (used by e2e, test_*.py can be added)  ⬜
-│   └── stage_input_processors/                  │   └── stage_input_processors/
-│       └── ...                                  │       └── test_*.py                 ⬜
-│
-├── sample/                             →     ├── sample/
-│   └── __init__.py                             │   └── test_*.py                      ⬜
-│
-├── utils/                              →     ├── utils/
-│   └── __init__.py                             │   └── test_*.py                       ⬜  (no platform_utils.py currently)
-│
-├── worker/                             →     ├── worker/
-│   ├── gpu_ar_model_runner.py                  │   ├── test_gpu_ar_model_runner.py    ⬜
-│   ├── gpu_ar_worker.py                        │   ├── test_gpu_ar_worker.py           ⬜
-│   ├── gpu_generation_model_runner.py          │   ├── test_gpu_generation_model_runner.py  ✅
-│   ├── gpu_generation_worker.py                │   ├── test_gpu_generation_worker.py  ⬜
-│   ├── gpu_model_runner.py                     │   ├── test_omni_gpu_model_runner.py   ✅
-│   └── mixins.py                               │   └── (npu under platforms/npu/worker/)  # not worker/npu/
-│
-├── platforms/                          →     (no tests/platforms/, e2e and stage_configs provide indirect coverage)
-│   ├── cuda/
-│   ├── npu/worker/                             # NPU worker here, not vllm_omni/worker/npu/
-│   ├── rocm/
-│   └── xpu/worker/
-│
-├── outputs.py                          →     test_outputs.py                         ✅ (at tests root)
-├── (logger, patch, request, version)    →     (no corresponding unit test)
-│
-└── e2e (tests side only)               →     ├── e2e/
-                                               ├── online_serving/                     ✅ non-empty
-                                               │   ├── test_qwen2_5_omni.py
-                                               │   ├── test_async_omni.py
-                                               │   ├── test_qwen3_omni.py
-                                               │   ├── test_qwen3_omni_expansion.py
-                                               │   ├── test_mimo_audio.py
-                                               │   └── test_images_generations_lora.py
-                                               └── offline_inference/                  ✅
-                                                   ├── test_qwen2_5_omni.py
-                                                   ├── test_qwen3_omni.py
-                                                   ├── test_bagel_text2img.py
-                                                   ├── test_z_image.py
-                                                   ├── test_wan22.py
-                                                   ├── test_zimage_tensor_parallel.py
-                                                   ├── test_cache_dit.py
-                                                   ├── test_teacache.py
-                                                   ├── test_stable_audio_expansion.py
-                                                   ├── test_diffusion_cpu_offload.py
-                                                   ├── test_diffusion_layerwise_offload.py
-                                                   ├── test_diffusion_lora.py
-                                                   ├── test_sequence_parallel.py
-                                                   └── stage_configs/                  (legacy schema, still
-                                                       ├── bagel_*.yaml                 present for unmigrated
-                                                       └── npu/, rocm/, etc.            models)
+Canonical placement by CI level is in the **Test Dir** column of the level matrix in [Test System Overview](./test_system_overview.md).

-# Migrated models (qwen3_omni_moe, qwen2_5_omni, qwen3_tts) live under
-# vllm_omni/deploy/ instead — see docs/configuration/stage_configs.md.
-```
+Summary:
+
+- **Component / unit:** `tests/{component}/…` mirroring `vllm_omni/{component}/`
+- **Model E2E:** `tests/e2e/online_serving/`, `tests/e2e/offline_inference/`, `tests/e2e/accuracy/`
+- **Feature integration:** `tests/e2e/features/<feature>/`
+- **Doc example tests:** `tests/examples/online_serving/`, `tests/examples/offline_inference/`
+- **Performance:** `tests/dfx/perf/tests/` (JSON configs; runners under `tests/dfx/perf/scripts/`)
+- **Stability:** `tests/dfx/stability/tests/`
+- **Reliability:** `tests/dfx/reliability/`
+- Do **not** add new top-level `tests/` directories unrelated to a component (or to `e2e` / `dfx` / `helpers` / `examples` / `buildkite`)
+
+Deploy YAMLs: `vllm_omni/deploy/` via `tests.helpers.stage_config.get_deploy_config_path` (see [stage configs](../../configuration/stage_configs.md)).

 ## Markers for Tests

@@ -373,13 +256,13 @@ L2 level testing builds upon L1 by introducing GPU resources and verifying that

 #### 1.3 Test Directory and Execution Files

-A clear directory structure is key to managing test cases efficiently.
+A clear directory structure is key to managing test cases efficiently. See the **Test Dir** column in [Test System Overview](./test_system_overview.md) for the canonical placement rules.

-   ***L1 Test Directory***: `/tests/{component_name}/test_xxx.py`
-   -   Here, `{component_name}` corresponds to modules in the source code, such as `distributed`, `entrypoints`, etc., and `test_xxx.py` is the specific test file.
-   ***L2 Test Directory***:
-   -   Online Serving: `/tests/e2e/online_serving/test_{model_name}.py`
-    -   Offline Inference: `/tests/e2e/offline_inference/test_{model_name}.py`
+-   ***L1 (component / unit)***: `tests/{component_name}/…` mirroring `vllm_omni/{component_name}/` (e.g. `tests/diffusion/`, `tests/entrypoints/`). Do **not** invent new top-level `tests/` folders unrelated to a component.
+-   ***L2 (basic model E2E)***:
+    -   Online serving: `tests/e2e/online_serving/test_{model_name}.py`
+    -   Offline inference: `tests/e2e/offline_inference/test_{model_name}.py`
+-   ***Feature-level integration*** (cross-cutting, not a single-model smoke): `tests/e2e/features/<feature>/` (e.g. `fullduplex/`, `custom_pipeline/`, `rlhf_test/`).

 #### 1.4 Execution Method and Example

@@ -485,10 +368,12 @@ L3 level testing executes after code is merged into the main branch. Its core pu

 #### 2.3 Test Directory and Execution Files

-   ***Functional Testing***:
-   -   Online Serving: `/tests/e2e/online_serving/test_{model_name}_expansion.py`
-    -   Offline Inference: `/tests/e2e/offline_inference/test_{model_name}_expansion.py`
-    -   (Note: `_expansion.py` likely means it contains more comprehensive scenario cases compared to L2 tests).
+-   ***Functional / model E2E***:
+    -   Online serving: `tests/e2e/online_serving/test_{model_name}_expansion.py`
+    -   Offline inference: `tests/e2e/offline_inference/test_{model_name}_expansion.py`
+    -   Accuracy / similarity: `tests/e2e/accuracy/`
+    -   (`_expansion.py` holds broader scenario coverage than the L2 smoke file.)
+-   ***Feature-level integration***: `tests/e2e/features/<feature>/` (not under a new top-level `tests/` directory).

 #### 2.4 Execution Method and Example

@@ -653,7 +653,7 @@ python end2end.py --model your-org/your-model-name --modality text2img --prompts
 Mirror BAGEL’s online serving setup:

 - Server launcher: `examples/online_serving/your_model_name/run_server.sh`
-  - Wrap `vllm serve ... --omni --port ...` (and `--stage-configs-path ...` if needed)
+  - Wrap `vllm serve ... --omni --port ...` (and `--deploy-config ...` if needed)
 - Client: `examples/online_serving/your_model_name/openai_chat_client.py`
  - Send requests to `POST /v1/chat/completions`
  - Support multimodal inputs (e.g., base64 image) if your model needs it
@@ -755,7 +755,8 @@ omni = Omni(model="your-model", ulysses_degree=2, ring_degree=2)

 ### Step Execution

-See detailed design guide: [How to add step execution support](../../design/feature/diffusion_step_execution.md)
+See the detailed
+[Diffusion Continuous Batching design guide](../../design/feature/diffusion_continuous_batching.md).

 Use this only when your pipeline can be split into stable request-scoped and
 step-scoped phases. The reference implementation is
@@ -769,9 +770,8 @@ step-scoped phases. The reference implementation is
 Do not enable `step_execution=True` until those four methods are implemented
 and validated against the request-level path.

-If you want the pipeline to work with the experimental batched step-wise path
-(`max_num_seqs > 1`), also see:
-[Continuous Batching for Step-Wise Diffusion](../../design/feature/diffusion_continuous_batching.md).
+The same design guide covers the experimental batched step-wise path used when
+`max_num_seqs > 1`.

 If you expose this in example scripts or recipes, keep it opt-in. Surface
 runtime features like `step_execution` as optional flags instead of silently
@@ -824,7 +824,7 @@ omni = Omni(model="your-model",

 ### CPU Offload

-See detailed guide: [CPU Offloading for Diffusion Models](../../user_guide/diffusion/cpu_offload_diffusion.md)
+See detailed guide: [CPU Offloading for Diffusion Models](../../user_guide/diffusion/cpu_offload.md)

 vLLM-Omni provides two offloading strategies to reduce GPU memory usage:

@@ -48,8 +48,8 @@ vllm_omni/deploy/
 ```

 New in-tree models should register their `PipelineConfig` in
-`vllm_omni/config/pipeline_registry.py`; use `--stage-configs-path` only for
-custom legacy `stage_args` YAMLs.
+`vllm_omni/config/pipeline_registry.py`; use `--deploy-config` for custom
+deployment YAMLs.

 ## Step-by-Step Implementation

@@ -449,9 +449,8 @@ Key configuration fields:
 !!! note
    New in-tree models should define frozen topology in `models/<model>/pipeline.py`,
    register it in `vllm_omni/config/pipeline_registry.py`, and put deployment
-    knobs in `vllm_omni/deploy/<model>.yaml`. Legacy `stage_args` YAMLs are still
-    accepted for custom configs via `--stage-configs-path`, but should not be used
-    for new bundled defaults.
+    knobs in `vllm_omni/deploy/<model>.yaml`. Use `--deploy-config` to load a
+    custom deployment YAML.

 ### Batch mode

@@ -673,100 +672,147 @@ down and spawn a new one mid-module. A few rules that save real CI debugging tim

 ## Online Serving Integration

-To expose your model through the `/v1/audio/speech` OpenAI-compatible endpoint, add
-**all five** of the following integration points to
-`vllm_omni/entrypoints/openai/serving_speech.py` in a **single commit**. Adding them
-piecemeal causes partial-integration failures that are hard to debug.
+To expose your model through the `/v1/audio/speech` OpenAI-compatible endpoint, write
+one adapter under `vllm_omni/entrypoints/openai/tts_adapters/`. The adapter declares
+how your model is recognized and owns its request handling; `serving_speech.py` should
+not need an edit.

-### 1. Stage constant
+### 1. Write the adapter

-Near the top of the file, alongside the other `_*_TTS_MODEL_STAGES` constants:
+Create `vllm_omni/entrypoints/openai/tts_adapters/your_model.py`:

 ```python
-_YOUR_MODEL_TTS_MODEL_STAGES = {"your_model_stage_key"}
+# SPDX-License-Identifier: Apache-2.0
+"""YourModel serving adapter."""
+
+from typing import TYPE_CHECKING
+
+from vllm_omni.entrypoints.openai.tts_adapters import register_tts_adapter
+from vllm_omni.entrypoints.openai.tts_adapters.base import ARTTSAdapter, PreparedRequest
+
+if TYPE_CHECKING:
+    from vllm_omni.entrypoints.openai.protocol.audio import OpenAICreateSpeechRequest
+
+
+@register_tts_adapter
+class YourModelAdapter(ARTTSAdapter):
+    # The registry key, and the model-type string used in logs.
+    name = "your_model"
+    # The engine ``model_stage`` value(s) your deployment yaml sets. Detection,
+    # stage discovery and `/v1/audio/speech` routing all derive from this.
+    stage_keys = frozenset({"your_model_stage_key"})
+
+    def validate(self, request: "OpenAICreateSpeechRequest") -> str | None:
+        """Return an error string, or None if the request is valid."""
+        if not request.input or not request.input.strip():
+            return "Input text cannot be empty"
+        return None
+
+    async def build(
+        self,
+        request: "OpenAICreateSpeechRequest",
+        sampling_params_list: list,
+        has_inline_ref_audio: bool,
+    ) -> PreparedRequest:
+        """Build the engine prompt and per-model parameters."""
+        params: dict[str, list] = {"text": [request.input]}
+        if request.voice is not None:
+            params["voice"] = [request.voice]
+        return PreparedRequest(
+            prompt={"prompt": request.input},
+            tts_params=params,
+            model_type=self.name,
+        )
 ```

-### 2. Union into `_TTS_MODEL_STAGES`
+Then add it to the import block at the bottom of
+`vllm_omni/entrypoints/openai/tts_adapters/__init__.py` so it registers on import.
+That import line is the only shared file a new model has to touch.

-Add to the `_TTS_MODEL_STAGES` set union:
+> **Pure-diffusion TTS does not go through adapters yet.** When the server is built
+> by `OmniOpenAIServingSpeech.for_diffusion()`, `create_speech()` routes straight to
+> `_create_diffusion_speech()` and never calls `validate()` or `build()`.
+> `DiffusionTTSAdapter` is scaffolding for that migration and has no production
+> subclass today, so validation or prompt construction placed in one would silently
+> never run. If your model is served by the diffusion engine rather than
+> `engine_client`, follow the existing diffusion path and say so on #4855 — wiring
+> that path through the adapter layer is open work, not something to do inside a
+> new-model PR.
+
+### 2. If a stage key alone cannot identify your model
+
+Two escape hatches, in order of preference:

 ```python
-_TTS_MODEL_STAGES: set[str] = (
-    ...
-    | _YOUR_MODEL_TTS_MODEL_STAGES
-)
+# The architecture is authoritative (e.g. your model has no dedicated
+# model_stage value, or shares one with another model).
+model_archs = frozenset({"YourModelForConditionalGeneration"})
+
+# Set this too if your model owns NO stage key, so stage discovery has to find
+# your AR entry stage by architecture.
+arch_identifies_entry_stage = True
 ```

-### 3. Model type detection
-
-In `_detect_tts_model_type()`, add before the final `return None`:
+If your rule is not a set-membership test, override `matches()` — see
+`covo_audio.py`, which shares the generic `fused_thinker_talker` stage key with
+non-CoVo deployments and confirms with the architecture:

 ```python
-if model_stage in _YOUR_MODEL_TTS_MODEL_STAGES:
-    return "your_model"
+@classmethod
+def matches(cls, model_stage: str | None, model_arch: str | None) -> bool:
+    return super().matches(model_stage, model_arch) and bool(model_arch) and "CovoAudio" in model_arch
 ```

-### 4. Request validation dispatch
+If your model can match the same deployment as another adapter, give one of them an
+explicit `detect_priority` (lower runs first). `tests/entrypoints/openai_api/
+test_tts_detection.py` fails if two same-priority detectors can both match one input,
+so an unordered overlap cannot be merged.

-In `_validate_tts_request()`, add before the fallback `return`:
+Models that are speech-capable only in some deployment topologies override
+`stage_serves_speech()` — see `audex.py`, whose omni thinker is text-final unless the
+speech decoder is deployed alongside it.

-```python
-if self._tts_model_type == "your_model":
-    return self._validate_your_model_request(request)
-```
+### 3. Reuse the shared helpers

-### 5. Validation and parameter-builder methods
+Adapters reach shared serving helpers through `self.ctx.server`: reference-audio
+resolution (`_resolve_ref_audio`), uploaded-speaker handling
+(`_apply_uploaded_speaker`), format validation (`_validate_ref_audio_format`), and
+prompt-length limits (`_max_instructions_length`). Read a comparable adapter before
+writing your own — `fish_speech.py` for voice cloning, `higgs_audio_v3.py` for
+parameter-heavy models, `moss_tts.py` for a family sharing one base class.

-Add two new methods:
+### 4. Test it

-```python
-def _validate_your_model_request(
-    self, request: OpenAICreateSpeechRequest
-) -> str | None:
-    """Validate YourModel request. Returns an error string or None."""
-    if not request.input or not request.input.strip():
-        return "Input text cannot be empty"
-    return None
+`tests/entrypoints/openai_api/test_tts_adapter.py` covers registry invariants and
+`test_tts_detection.py` covers detection. Add your model to
+`EXPECTED_MODEL_TYPES` in the former, and a case to the latter if you added a
+`model_archs` or `matches()` rule.

-def _build_your_model_params(
-    self, request: OpenAICreateSpeechRequest
-) -> dict[str, Any]:
-    """Build additional_information dict for YourModel."""
-    params: dict[str, Any] = {"text": [request.input]}
-    if request.voice is not None:
-        params["voice"] = [request.voice]
-    # Add any other model-specific fields here
-    return params
-```
+### Do not add branches to `serving_speech.py`

-Then wire `_build_your_model_params` into the request-dispatch block in
-`_create_tts_request()` (search for the equivalent `_build_*_params` call for an
-existing model to find the right location). If the model supports voice cloning
-(`ref_audio` → `prompt_audio_path`, `ref_text` → `prompt_text`), add those mappings
-here too — follow any existing `_build_<model>_params` in `serving_speech.py` (e.g.
-`_build_moss_tts_params` for the voice-cloning variant) for the pattern.
+Older models predate the adapter framework and still have per-model branches in
+`serving_speech.py`; they are being migrated out (RFC #4327, #4855). Do not copy
+that pattern. `tools/pre_commit/check_tts_adapter.py` is a ratchet on the remaining
+`self._tts_model_type == ...` comparisons and fails the commit if the count grows.

-> **Two dispatch patterns coexist:** Fish Speech uses a `self._is_fish_speech` boolean
-> checked *before* `elif self._is_tts`. All newer models use the `_tts_model_type`
-> string pattern shown above. For new models, always use the string pattern — do not
-> add new `_is_*` boolean flags.
-
-> **Note on unused variables:** Only extract parameters in `_build_your_model_params`
-> that you actually pass to the model's generate / `inference_stream` call. Extracting
-> a variable without forwarding it will trigger a `ruff F841` pre-commit failure.
+If your model genuinely needs behaviour that no adapter hook can express, that is a
+missing hook — propose it on the RFC rather than adding a branch.

 ### Merge conflicts

-`serving_speech.py` is modified by every new model PR and is the most common source of
-rebase conflicts. When rebasing onto `main` and a conflict appears here, the resolution
-is always to **keep both** the upstream model's additions and your own — never discard
-either side. After resolving:
+Your adapter is a new file, so it cannot conflict. The one shared line is your entry
+in the import block at the bottom of `tts_adapters/__init__.py`, which conflicts only
+when another model lands at the same time. The resolution is always to **keep both**
+imports — never discard either side. After resolving:

 ```bash
-git add vllm_omni/entrypoints/openai/serving_speech.py
+git add vllm_omni/entrypoints/openai/tts_adapters/__init__.py
 git rebase --continue
 ```

+If you find yourself resolving a conflict inside `serving_speech.py`, something in
+your model is still going through the legacy path; see the note above.
+
 ## Single-Stage Models

 Some TTS models (e.g. MOSS-TTS-Nano) do not use a two-stage pipeline. Instead the