[CI][MiniCPM-o] Add NPU Video-MME accuracy nightly as a standalone job (#6596 )

Signed-off-by: amy-why-3459 <wuhaiyan17@huawei.com>
[CI][MiniCPM-o] Switch MiniCPM-o 4.5 perf Seed-TTS to Chinese and retune accuracy gates (#6447 )
2026-08-25 14:11:52 +08:00 · 2026-08-21 18:56:49 +08:00 · 2026-08-13 14:30:46 +08:00 · 2026-08-13 09:40:35 +08:00 · 2026-08-12 20:36:09 +08:00 · 2026-08-12 18:43:40 +08:00
1409 changed files with 25449 additions and 171175 deletions
@@ -6,6 +6,10 @@

 set -euo pipefail

+# minicpm-challenge branch: skip entire AMD CI (no pipeline upload => no image build / tests).
+echo "--- Skipping AMD CI on this branch (including image build)"
+exit 0
+
 source .buildkite/common/scripts/resolve_skip_ci.sh

 if [[ -z "${RUN_ALL:-}" ]]; then
@@ -1,103 +1,153 @@
 #!/bin/bash
+# vllm-omni customized version
+# Based on: vllm/.buildkite/scripts/hardware_ci/run-amd-test.sh
+# Last synced: 2025-12-15
+# Modifications: docker image name for vllm-omni

-# Run vLLM-Omni ROCm tests directly in the MI300 Kubernetes pod. The pod's
-# container image is selected by test-template-amd-omni.j2; MI300 has no DinD.
-set -euo pipefail
+# This script runs test inside the corresponding ROCm docker container.
+set -o pipefail

-: "${PYTHONFAULTHANDLER:=1}"
-: "${HF_HOME:=/home/buildkite-agent/huggingface}"
-: "${HF_HUB_DOWNLOAD_TIMEOUT:=300}"
-: "${HF_HUB_ETAG_TIMEOUT:=60}"
-: "${MIOPEN_DEBUG_CONV_DIRECT:=0}"
-: "${MIOPEN_DEBUG_CONV_GEMM:=0}"
-: "${VLLM_ROCM_USE_AITER:=0}"
-export PYTHONFAULTHANDLER HF_HOME HF_HUB_DOWNLOAD_TIMEOUT HF_HUB_ETAG_TIMEOUT
-export MIOPEN_DEBUG_CONV_DIRECT MIOPEN_DEBUG_CONV_GEMM VLLM_ROCM_USE_AITER
-export PYTORCH_ROCM_ARCH=""
-export PYTHONPATH="${PYTHONPATH:-..}"
-
-if [[ "${VLLM_CI_DOCKER_DISABLED:-0}" != "1" ]]; then
-    echo "Error: MI300 CI must run natively with Docker disabled." >&2
-    exit 1
-fi
-
-if [[ -n "${TEST_COMMANDS:-}" ]]; then
-    commands="${TEST_COMMANDS}"
-else
-    commands="$*"
-fi
-if [[ -z "${commands}" ]]; then
-    echo "Error: No test commands provided." >&2
-    exit 1
-fi
-
-echo "--- Native in-pod ROCm CI"
-
-job_id="${BUILDKITE_JOB_ID:-${BUILDKITE_PARALLEL_JOB:-local}}"
-job_id="${job_id//[^A-Za-z0-9_.-]/_}"
-native_cache_root="/tmp/vllm-omni-native-${job_id}"
-TMPDIR="${native_cache_root}/tmp"
-TORCHINDUCTOR_CACHE_DIR="${native_cache_root}/torchinductor"
-TRITON_CACHE_DIR="${native_cache_root}/triton"
-VLLM_CACHE_ROOT="${native_cache_root}/vllm"
-XDG_CACHE_HOME="${native_cache_root}/xdg"
-HF_DATASETS_CACHE="${native_cache_root}/huggingface/datasets"
-export TMPDIR TORCHINDUCTOR_CACHE_DIR TRITON_CACHE_DIR VLLM_CACHE_ROOT
-export XDG_CACHE_HOME HF_DATASETS_CACHE
-mkdir -p "${TMPDIR}" "${TORCHINDUCTOR_CACHE_DIR}" "${TRITON_CACHE_DIR}" \
-    "${VLLM_CACHE_ROOT}" "${XDG_CACHE_HOME}" "${HF_HOME}" \
-    "${HF_DATASETS_CACHE}"
-
-if [[ "${VLLM_CI_REQUIRE_PERSISTENT_HF_CACHE:-0}" == "1" ]]; then
-    if ! command -v findmnt >/dev/null 2>&1; then
-        echo "Error: findmnt is required to verify the Hugging Face cache mount." >&2
-        exit 1
-    fi
-    hf_mount=$(findmnt -n -T "${HF_HOME}" -o TARGET 2>/dev/null || true)
-    if [[ -z "${hf_mount}" || "${hf_mount}" == "/" ]]; then
-        echo "Error: MI300 CI requires a persistent volume at or above ${HF_HOME}." >&2
-        exit 1
-    fi
-fi
+# Export Python path
+export PYTHONPATH=".."

+# Print ROCm version
+echo "--- ROCm info"
 rocminfo

-expected_gpus="${VLLM_CI_EXPECTED_GPU_COUNT:-1}"
-python3 - <<'PY'
-import os
+# cleanup older docker images
+cleanup_docker() {
+  # Get Docker's root directory
+  docker_root=$(docker info -f '{{.DockerRootDir}}')
+  if [ -z "$docker_root" ]; then
+    echo "Failed to determine Docker root directory."
+    exit 1
+  fi
+  echo "Docker root directory: $docker_root"
+  # Check disk usage of the filesystem where Docker's root directory is located
+  disk_usage=$(df "$docker_root" | tail -1 | awk '{print $5}' | sed 's/%//')
+  # Define the threshold
+  threshold=70
+  if [ "$disk_usage" -gt "$threshold" ]; then
+    echo "Disk usage is above $threshold%. Cleaning up Docker images and volumes..."
+    # Remove dangling images (those that are not tagged and not used by any container)
+    docker image prune -f
+    # Remove unused volumes / force the system prune for old images as well.
+    docker volume prune -f && docker system prune --force --filter "until=72h" --all
+    echo "Docker images and volumes cleanup completed."
+  else
+    echo "Disk usage is below $threshold%. No cleanup needed."
+  fi
+}

-import torch
+# Call the cleanup docker function
+cleanup_docker

-expected = int(os.environ.get("VLLM_CI_EXPECTED_GPU_COUNT", "1"))
-assert torch.version.hip, "PyTorch is not a ROCm build"
-assert torch.cuda.is_available(), "ROCm GPU is not available to PyTorch"
-actual = torch.cuda.device_count()
-assert actual == expected, f"Expected {expected} ROCm GPU(s), found {actual}"
-PY
+echo "--- Pulling container"
+## Temporary change to use AMD Docker Hub to store the vllm-omni image
+# to bypass the rate limit issue with ECR Public Gallery.
+# Images are now stored in a separate repository for vllm-omni, instead of vllm-ci.
+# TODO: @tjtanaa point back to ECR Public Gallery
+# once the amd agents are configured to use ECR Public Gallery.
+# image_name="public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:${BUILDKITE_COMMIT}-rocm-omni"
+image_name="rocm/vllm-omni:${BUILDKITE_COMMIT}"
+container_name="rocm_${BUILDKITE_COMMIT}_$(tr -dc A-Za-z0-9 < /dev/urandom | head -c 10; echo)"

-echo "Commands:${commands}"
-if /bin/bash -o pipefail -c '
-set -E
-test_status=0
-trap '\''
-    command_status=$?
-    # Keep running subsequent commands, but retain a failure for the final exit.
-    # Prefer a test failure over pytest "no tests collected" when both occur.
-    if (( test_status == 0 || (test_status == 5 && command_status != 5) )); then
-        test_status=${command_status}
-    fi
-'\'' ERR
-eval "$1"
-exit "${test_status}"
-' _ "${commands}"; then
-    exit 0
+# TODO: @tjtanaa uncomment this once the amd agents are configured to use ECR Public Gallery.
+# # Install AWS CLI to authenticate to ECR Public Gallery to get higher rate limit for pulling images
+# sudo apt-get update && sudo apt-get install -y awscli
+##  Use safe docker login helper to prevent race conditions
+# source "$(dirname "${BASH_SOURCE[0]}")/../../common/scripts/docker_login_ecr_public.sh"
+# safe_docker_login_ecr_public
+
+## Pull the container from AMD Docker Hub
+
+docker pull "${image_name}"
+
+remove_docker_container() {
+   docker ps -aq --filter "name=${container_name}" | xargs -r docker rm -f || true
+   docker image rm -f "${image_name}" || true
+}
+trap remove_docker_container EXIT
+
+echo "--- Running container"
+
+HF_CACHE="$(realpath ~)/huggingface"
+mkdir -p "${HF_CACHE}"
+HF_MOUNT="/root/.cache/huggingface"
+
+if [[ -n "${TEST_COMMAND:-}" ]]; then
+    commands="$TEST_COMMAND"
 else
-    exit_code=$?
+    commands="$@"
+fi
+echo "Commands:$commands"
+
+PARALLEL_JOB_COUNT=8
+MYPYTHONPATH=".."
+
+# Test that we're launching on the machine that has
+# proper access to GPUs
+render_gid=$(getent group render | cut -d: -f3)
+if [[ -z "$render_gid" ]]; then
+  echo "Error: 'render' group not found. This is required for GPU access." >&2
+  exit 1
 fi

-if [[ ${exit_code} -eq 5 && "${VLLM_CI_ALLOW_NO_TESTS:-0}" == "1" ]]; then
-    echo "Pytest collected no tests; VLLM_CI_ALLOW_NO_TESTS=1, treating exit code 5 as success."
-    exit 0
+# check if the command contains shard flag, we will run all shards in parallel because the host have 8 GPUs.
+# TODO: @tjtanaa reenable to run VLLM_ROCM_USE_AITER=1 when AITER is shipped with prebuilt kernels.
+if [[ $commands == *"--shard-id="* ]]; then
+  # assign job count as the number of shards used
+  commands=$(echo "$commands" | sed -E "s/--num-shards[[:blank:]]*=[[:blank:]]*[0-9]*/--num-shards=${PARALLEL_JOB_COUNT} /g" | sed 's/ \\ / /g')
+  for GPU in $(seq 0 $(($PARALLEL_JOB_COUNT-1))); do
+    # assign shard-id for each shard
+    commands_gpu=$(echo "$commands" | sed -E "s/--shard-id[[:blank:]]*=[[:blank:]]*[0-9]*/--shard-id=${GPU} /g" | sed 's/ \\ / /g')
+    echo "Shard ${GPU} commands:$commands_gpu"
+    echo "Render devices: $BUILDKITE_AGENT_META_DATA_RENDER_DEVICES"
+    docker run \
+        --device /dev/kfd $BUILDKITE_AGENT_META_DATA_RENDER_DEVICES \
+        --network=host \
+        --shm-size=16gb \
+        --group-add "$render_gid" \
+        -e MIOPEN_DEBUG_CONV_DIRECT=0 \
+        -e MIOPEN_DEBUG_CONV_GEMM=0 \
+        -e VLLM_ROCM_USE_AITER=0 \
+        -e HIP_VISIBLE_DEVICES="${GPU}" \
+        -e HF_TOKEN \
+        -v "${HF_CACHE}:${HF_MOUNT}" \
+        -e "HF_HOME=${HF_MOUNT}" \
+        -e "PYTHONPATH=${MYPYTHONPATH}" \
+        --name "${container_name}_${GPU}" \
+        "${image_name}" \
+        /bin/bash -c "${commands_gpu}" \
+        |& while read -r line; do echo ">>Shard $GPU: $line"; done &
+    PIDS+=($!)
+  done
+  #wait for all processes to finish and collect exit codes
+  for pid in "${PIDS[@]}"; do
+    wait "${pid}"
+    STATUS+=($?)
+  done
+  for st in "${STATUS[@]}"; do
+    if [[ ${st} -ne 0 ]]; then
+      echo "One of the processes failed with $st"
+      exit "${st}"
+    fi
+  done
+else
+  echo "Render devices: $BUILDKITE_AGENT_META_DATA_RENDER_DEVICES"
+  docker run \
+          --device /dev/kfd $BUILDKITE_AGENT_META_DATA_RENDER_DEVICES \
+          --network=host \
+          --shm-size=16gb \
+          --group-add "$render_gid" \
+          -e MIOPEN_DEBUG_CONV_DIRECT=0 \
+          -e MIOPEN_DEBUG_CONV_GEMM=0 \
+          -e VLLM_ROCM_USE_AITER=0 \
+          -e HF_TOKEN \
+          -v "${HF_CACHE}:${HF_MOUNT}" \
+          -e "HF_HOME=${HF_MOUNT}" \
+          -e "PYTHONPATH=${MYPYTHONPATH}" \
+          --name "${container_name}" \
+          "${image_name}" \
+          /bin/bash -c "${commands}"
 fi
-
-exit "${exit_code}"
@@ -7,64 +7,54 @@ steps:

 - group: ":card_index_dividers: Simple Test"
  steps:
-    - label: "Simple · Model Executor Test · Shard %N/%t"
-      agent_pool: mi300_1
-      parallelism: 2
+    - label: "Simple · Diffusion & Model Executor Test (1/2)"
+      agent_pool: mi325_1
      depends_on: amd-build
+      timeout_in_minutes: 70
      mirror_hardwares: [amdproduction]
      grade: Blocking
      commands:
        - export VLLM_ROCM_USE_AITER=0
        # ignore test_teacache_extractors.py because it use rocm gemm kernel from vLLM
        # that is not supported on CPU
-        - "pytest -sv tests/model_executor -m 'core_model and cpu' --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml:coverage-model-executor-$$BUILDKITE_PARALLEL_JOB.xml"
+        # CPU tests on ROCm gpu machines are very slow, so we give them more time.
+        - "timeout 60m pytest -sv tests/diffusion -m 'core_model and cpu' --ignore=tests/diffusion/cache/test_teacache_extractors.py --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"

-    - label: "Simple · Diffusion Test · Shard %N/%t"
-      agent_pool: mi300_1
-      parallelism: 2
+    - label: "Simple · Diffusion & Model Executor Test (2/2)"
+      agent_pool: mi325_1
      depends_on: amd-build
+      timeout_in_minutes: 70
      mirror_hardwares: [amdproduction]
      grade: Blocking
      commands:
        - export VLLM_ROCM_USE_AITER=0
-        # ignore test_teacache_extractors.py because it use rocm gemm kernel from vLLM
-        # that is not supported on CPU
-        - "pytest -sv tests/diffusion -m 'core_model and cpu' --ignore=tests/diffusion/cache/test_teacache_extractors.py --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml:coverage-diffusion-$$BUILDKITE_PARALLEL_JOB.xml"
-
-    - label: "Simple · Engine&Entrypoints Test"
-      agent_pool: mi300_1
-      depends_on: amd-build
-      mirror_hardwares: [amdproduction]
-      grade: Blocking
-      commands:
-        - export VLLM_ROCM_USE_AITER=0
-        - "pytest -sv tests/entrypoints tests/engine -m 'core_model and cpu' --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
+        # CPU tests on ROCm gpu machines are very slow, so we give them more time.
+        - "timeout 60m pytest -sv tests/model_executor -m 'core_model and cpu' --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"

    - label: "Simple · Other Test"
-      agent_pool: mi300_1
+      agent_pool: mi325_1
      depends_on: amd-build
+      timeout_in_minutes: 50
      mirror_hardwares: [amdproduction]
      grade: Blocking
      commands:
        - export VLLM_ROCM_USE_AITER=0
        # ignore test_teacache_extractors.py because it use rocm gemm kernel from vLLM
        # that is not supported on CPU
-        - "pytest -sv tests/ -m 'core_model and cpu' --ignore=tests/diffusion --ignore=tests/model_executor --ignore=tests/entrypoints --ignore=tests/engine  --ignore=tests/diffusion/cache/test_teacache_extractors.py --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
+        - "timeout 40m pytest -sv tests/diffusion tests/model_executor -m 'core_model and cpu' --ignore=tests/diffusion/cache/test_teacache_extractors.py --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"

 - group: ":card_index_dividers: Diffusion Test"
  steps:
    - label: "Diffusion · LoRA Test"
-      agent_pool: mi300_1
+      agent_pool: mi325_1
      depends_on: amd-build
      mirror_hardwares: [amdproduction]
      grade: Blocking
-      artifact_paths:
-        - lora-server.log
      commands:
-        - pytest -sv tests/diffusion/lora/ -m 'advanced_model and cuda' --run-level "advanced_model" 2>&1 | tee lora-server.log
+        - timeout 20m pytest -s -v tests/diffusion/lora/test_images_generations_lora.py

    - label: "Diffusion Model CPU offloading Test"
-      agent_pool: mi300_1
+      agent_pool: mi325_1
      depends_on: amd-build
      mirror_hardwares: [amdproduction]
      grade: Blocking
@@ -72,7 +62,7 @@ steps:
        - export VLLM_LOGGING_LEVEL=DEBUG
        - export VLLM_WORKER_MULTIPROC_METHOD=spawn
        - |
-          bash -c '
+          timeout 20m bash -c '
            set +e
            pytest -s -v tests/diffusion/offloader/test_diffusion_cpu_offload.py
            EXIT1=\$?
@@ -82,7 +72,7 @@ steps:
          '

    - label: "Diffusion Sequence Parallelism Test (Need 4 GPUs)"
-      agent_pool: mi300_4
+      agent_pool: mi325_4
      depends_on: amd-build
      mirror_hardwares: [amdproduction]
      grade: Blocking
@@ -94,15 +84,15 @@ steps:

    # merge-only tests
    - label: "Diffusion Tensor Parallelism Test"
-      agent_pool: mi300_2
+      agent_pool: mi325_2
      depends_on: amd-build
      mirror_hardwares: [amdproduction]
      grade: Blocking
      commands:
-        - pytest -s -v tests/diffusion/distributed/test_tensor_parallel.py
+        - pytest -s -v tests/diffusion/distributed/test_zimage_parallelism.py

    - label: "Diffusion Model Test"
-      agent_pool: mi300_1
+      agent_pool: mi325_1
      depends_on: amd-build
      mirror_hardwares: [amdproduction]
      grade: Blocking
@@ -110,7 +100,7 @@ steps:
        - timeout 30m pytest -s -v tests/e2e/offline_inference/test_z_image.py

    - label: "Diffusion GPU Worker Test"
-      agent_pool: mi300_2
+      agent_pool: mi325_2
      depends_on: amd-build
      mirror_hardwares: [amdproduction]
      grade: Blocking
@@ -118,7 +108,7 @@ steps:
        - timeout 20m pytest -s -v tests/diffusion/test_diffusion_worker.py

    - label: "Diffusion · Wan22 Test"
-      agent_pool: mi300_1
+      agent_pool: mi325_1
      depends_on: amd-build
      mirror_hardwares: [amdproduction]
      grade: Blocking
@@ -131,7 +121,7 @@ steps:

 ## ISSUE depends on `diffusers` package: https://github.com/huggingface/diffusers/issues/13274
 # - label: "Audio Generation Model Test"
-#   agent_pool: mi300_1
+#   agent_pool: mi325_1
 #   depends_on: amd-build
 #   mirror_hardwares: [amdproduction]
 #   grade: Blocking
@@ -142,7 +132,7 @@ steps:
 #     - timeout 20m pytest -s -v tests/e2e/offline_inference/test_stable_audio_expansion.py -m "advanced_model and diffusion and L4" --run-level advanced_model

 - label: "Engine Test"
-  agent_pool: mi300_1
+  agent_pool: mi325_1
  depends_on: amd-build
  mirror_hardwares: [amdproduction]
  grade: Blocking
@@ -157,7 +147,7 @@ steps:
 - group: ":card_index_dividers: E2E Test"
  steps:
    - label: "Omni Model Test Qwen2-5-Omni"
-      agent_pool: mi300_4
+      agent_pool: mi325_4
      depends_on: amd-build
      mirror_hardwares: [amdproduction]
      commands:
@@ -165,7 +155,7 @@ steps:
        - pytest -s -v tests/e2e/*/test_qwen2_5_omni_expansion.py --run-level "core_model"

    - label: "Omni Model Test Qwen3-Omni"
-      agent_pool: mi300_2
+      agent_pool: mi325_2
      depends_on: amd-build
      mirror_hardwares: [amdproduction]
      grade: Blocking
@@ -173,23 +163,22 @@ steps:
        - export VLLM_WORKER_MULTIPROC_METHOD=spawn
        - pytest -s -v tests/e2e/offline_inference/test_qwen3_omni.py tests/e2e/online_serving/test_qwen3_omni.py -m 'advanced_model and cuda' --run-level 'advanced_model'

-    - label: "Qwen3-TTS CustomVoice E2E Test · Shard %N/%t"
-      agent_pool: mi300_1
-      parallelism: 2
+    - label: "Qwen3-TTS CustomVoice E2E Test"
+      agent_pool: mi325_1
      depends_on: amd-build
      mirror_hardwares: [amdproduction]
      grade: Blocking
      commands:
        - |
-          bash -c '
+          timeout 50m bash -c '
            export VLLM_LOGGING_LEVEL=DEBUG
            export VLLM_WORKER_MULTIPROC_METHOD=spawn
            export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
-            pytest -s -v tests/e2e/online_serving/test_qwen3_tts_customvoice.py tests/e2e/offline_inference/test_qwen3_tts_customvoice.py -m "advanced_model" --run-level "advanced_model" --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB
+            pytest -s -v tests/e2e/online_serving/test_qwen3_tts_customvoice.py tests/e2e/offline_inference/test_qwen3_tts_customvoice.py -m "advanced_model" --run-level "advanced_model"
          '

    - label: "Qwen3-TTS Base E2E Test"
-      agent_pool: mi300_1
+      agent_pool: mi325_1
      depends_on: amd-build
      mirror_hardwares: [amdproduction]
      grade: Blocking
@@ -207,7 +196,7 @@ steps:
 # Need to debug before reneable numerical changes across large PRs
 # # split Bagel Model Test with H100 (Real Weights) into three tests
 # - label: "Bagel Text2Img Model Test (1/3)"
-#   agent_pool: mi300_1
+#   agent_pool: mi325_1
 #   depends_on: amd-build
 #   mirror_hardwares: [amdproduction]
 #   grade: Blocking
@@ -219,7 +208,7 @@ steps:
 #     - timeout 30m pytest -s -v tests/e2e/offline_inference/test_bagel.py -m "advanced_model" --run-level "advanced_model" -k "shared_memory" -k "rocm"

 # - label: "Bagel Img2Img Model Test (2/3)"
-#   agent_pool: mi300_1
+#   agent_pool: mi325_1
 #   depends_on: amd-build
 #   mirror_hardwares: [amdproduction]
 #   grade: Blocking
@@ -231,7 +220,7 @@ steps:
 #     - timeout 30m pytest -s -v tests/e2e/offline_inference/test_bagel.py -m "advanced_model" --run-level "advanced_model" -k "rocm"

 # - label: "Bagel Online Serving Test (3/3)"
-#   agent_pool: mi300_1
+#   agent_pool: mi325_1
 #   depends_on: amd-build
 #   mirror_hardwares: [amdproduction]
 #   grade: Blocking
@@ -7,9 +7,8 @@ steps:

 - group: ":card_index_dividers: Simple Test"
  steps:
-    - label: "Simple · Model Executor Test · Shard %N/%t"
-      agent_pool: mi300_1
-      parallelism: 2
+    - label: "Simple · Diffusion & Model Executor Test"
+      agent_pool: mi325_1
      depends_on: amd-build
      mirror_hardwares: [amdproduction]
      grade: Blocking
@@ -17,30 +16,10 @@ steps:
        - export VLLM_ROCM_USE_AITER=0
        # ignore test_teacache_extractors.py because it use rocm gemm kernel from vLLM
        # that is not supported on CPU
-        - "pytest -sv tests/model_executor -m 'core_model and cpu' --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml:coverage-model-executor-$$BUILDKITE_PARALLEL_JOB.xml"
-
-    - label: "Simple · Diffusion Test"
-      agent_pool: mi300_1
-      depends_on: amd-build
-      mirror_hardwares: [amdproduction]
-      grade: Blocking
-      commands:
-        - export VLLM_ROCM_USE_AITER=0
-        # ignore test_teacache_extractors.py because it use rocm gemm kernel from vLLM
-        # that is not supported on CPU
-        - "pytest -sv tests/diffusion -m 'core_model and cpu'  --ignore=tests/diffusion/cache/test_teacache_extractors.py --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
-
-    - label: "Simple · Engine&Entrypoints Test"
-      agent_pool: mi300_1
-      depends_on: amd-build
-      mirror_hardwares: [amdproduction]
-      grade: Blocking
-      commands:
-        - export VLLM_ROCM_USE_AITER=0
-        - "pytest -sv tests/entrypoints tests/engine -m 'core_model and cpu' --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
+        - "timeout 40m pytest -sv tests/diffusion tests/model_executor -m 'core_model and cpu' --ignore=tests/diffusion/cache/test_teacache_extractors.py --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"

    - label: "Simple · Other Test"
-      agent_pool: mi300_1
+      agent_pool: mi325_1
      depends_on: amd-build
      mirror_hardwares: [amdproduction]
      grade: Blocking
@@ -48,19 +27,19 @@ steps:
        - export VLLM_ROCM_USE_AITER=0
        # ignore test_teacache_extractors.py because it use rocm gemm kernel from vLLM
        # that is not supported on CPU
-        - "pytest -sv tests/ -m 'core_model and cpu' --ignore=tests/diffusion --ignore=tests/model_executor --ignore=tests/entrypoints --ignore=tests/engine  --ignore=tests/diffusion/cache/test_teacache_extractors.py --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
+        - "timeout 40m pytest -sv tests/diffusion tests/model_executor -m 'core_model and cpu' --ignore=tests/diffusion/cache/test_teacache_extractors.py --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"

 - label: "Custom Pipeline Test"
-  agent_pool: mi300_1
+  agent_pool: mi325_1
  depends_on: amd-build
  mirror_hardwares: [amdproduction]
  grade: Blocking
  commands:
-    - pytest -s -v tests/e2e/features/custom_pipeline/ -m "core_model"
+    - timeout 20m pytest -s -v tests/e2e/offline_inference/custom_pipeline/ -m "core_model"


 - label: "Voxtral TTS CUDA Unit Test"
-  agent_pool: mi300_1
+  agent_pool: mi325_1
  depends_on: amd-build
  mirror_hardwares: [amdproduction]
  grade: Blocking
@@ -70,25 +49,33 @@ steps:
 - group: ":card_index_dividers: Diffusion Test"
  steps:
    - label: "Diffusion · Batch Test"
-      agent_pool: mi300_1
+      agent_pool: mi325_1
      depends_on: amd-build
      mirror_hardwares: [amdproduction]
      grade: Blocking
      commands:
-        - pytest -sv tests/diffusion/batching -m 'core_model and cuda' --run-level "core_model"
+        - timeout 20m pytest -sv tests/diffusion/batching -m 'core_model and cuda' --run-level "core_model"

    - label: "Diffusion · Offloader Test"
-      agent_pool: mi300_1
+      agent_pool: mi325_1
      depends_on: amd-build
      mirror_hardwares: [amdproduction]
      grade: Blocking
      commands:
        - export VLLM_LOGGING_LEVEL=DEBUG
        - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-        - pytest -s -v tests/diffusion/offloader -m "core_model and cuda" --run-level "core_model"
+        - |
+          timeout 20m bash -c '
+            set +e
+            pytest -s -v tests/diffusion/offloader/test_diffusion_cpu_offload.py
+            EXIT1=\$?
+            pytest -s -v tests/diffusion/offloader/test_diffusion_layerwise_offload.py
+            EXIT2=\$?
+            exit \$((EXIT1 | EXIT2))
+          '

    - label: "Diffusion · Cache Test"
-      agent_pool: mi300_1
+      agent_pool: mi325_1
      depends_on: amd-build
      mirror_hardwares: [amdproduction]
      grade: Blocking
@@ -96,10 +83,10 @@ steps:
        - export GPU_ARCHS=gfx942
        - export VLLM_LOGGING_LEVEL=DEBUG
        - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-        - pytest -sv tests/diffusion/cache -m 'core_model and cuda' --run-level "core_model"
+        - timeout 15m pytest -s -v -m "core_model and cache and diffusion and not distributed_cuda and L4"

    - label: "Diffusion Sequence Parallelism Test"
-      agent_pool: mi300_2
+      agent_pool: mi325_2
      depends_on: amd-build
      mirror_hardwares: [amdproduction]
      grade: Blocking
@@ -109,7 +96,7 @@ steps:
        - timeout 20m pytest -s -v tests/diffusion/distributed/test_sequence_parallel.py -m core_model

    - label: "Diffusion GPU Worker Test"
-      agent_pool: mi300_2
+      agent_pool: mi325_2
      depends_on: amd-build
      mirror_hardwares: [amdproduction]
      grade: Blocking
@@ -117,7 +104,7 @@ steps:
        - timeout 20m pytest -s -v tests/diffusion/test_diffusion_worker.py

    - label: "Diffusion · Model Test"
-      agent_pool: mi300_1
+      agent_pool: mi325_1
      depends_on: amd-build
      mirror_hardwares: [amdproduction]
      grade: Blocking
@@ -125,7 +112,7 @@ steps:
        - timeout 15m pytest -sv tests/diffusion/models/ -m 'core_model and cuda' --run-level "core_model"

    - label: "Diffusion · Wan22 Test"
-      agent_pool: mi300_1
+      agent_pool: mi325_1
      depends_on: amd-build
      mirror_hardwares: [amdproduction]
      grade: Blocking
@@ -139,7 +126,7 @@ steps:

 ## ISSUE depends on `diffusers` package: https://github.com/huggingface/diffusers/issues/13274
 # - label: "Audio Generation Model Test"
-#   agent_pool: mi300_1
+#   agent_pool: mi325_1
 #   depends_on: amd-build
 #   mirror_hardwares: [amdproduction]
 #   grade: Blocking
@@ -150,7 +137,7 @@ steps:
 #     - timeout 20m pytest -s -v tests/e2e/offline_inference/test_stable_audio_expansion.py -m "advanced_model and diffusion and L4" --run-level advanced_model

 - label: "Engine Test"
-  agent_pool: mi300_1
+  agent_pool: mi325_1
  depends_on: amd-build
  mirror_hardwares: [amdproduction]
  grade: Blocking
@@ -162,7 +149,7 @@ steps:
      '

 # - label: "Omni Model Test Qwen3-Omni"
-#   agent_pool: mi300_2
+#   agent_pool: mi325_2
 #   depends_on: amd-build
 #   mirror_hardwares: [amdproduction]
 #   grade: Blocking
@@ -175,7 +162,7 @@ steps:
 - group: ":card_index_dividers: E2E Test"
  steps:
    - label: "Qwen3-TTS E2E Test"
-      agent_pool: mi300_1
+      agent_pool: mi325_1
      depends_on: amd-build
      mirror_hardwares: [amdproduction]
      grade: Blocking
@@ -186,7 +173,7 @@ steps:
        - timeout 30m pytest -s -v tests/e2e/online_serving/test_qwen3_tts_customvoice.py -m "core_model" --run-level "core_model"

    - label: "CosyVoice3-TTS E2E Test"
-      agent_pool: mi300_1
+      agent_pool: mi325_1
      depends_on: amd-build
      mirror_hardwares: [amdproduction]
      grade: Blocking
@@ -199,7 +186,7 @@ steps:
 # TODO: Bagel test on ROCm is very unstable. @tjtanaa
 # Need to debug before reneable numerical changes across large PRs
 # - label: "Bagel Text2Img Model Test"
-#   agent_pool: mi300_1
+#   agent_pool: mi325_1
 #   depends_on: amd-build
 #   mirror_hardwares: [amdproduction]
 #   grade: Blocking
@@ -211,7 +198,7 @@ steps:
 #     - timeout 30m pytest -s -v tests/e2e/offline_inference/test_bagel.py -m "core_model" --run-level "core_model" -k "rocm"

 # - label: "Bagel Img2Img Model Test"
-#   agent_pool: mi300_1
+#   agent_pool: mi325_1
 #   depends_on: amd-build
 #   mirror_hardwares: [amdproduction]
 #   grade: Blocking
@@ -223,7 +210,7 @@ steps:
 #     - timeout 30m pytest -s -v tests/e2e/offline_inference/test_bagel.py -m "core_model" --run-level "core_model" -k "rocm"

 # - label: "Bagel Online Serving Test"
-#   agent_pool: mi300_1
+#   agent_pool: mi325_1
 #   depends_on: amd-build
 #   mirror_hardwares: [amdproduction]
 #   grade: Blocking
@@ -1,65 +1,11 @@
 {# vllm-omni customized version
   Based on: https://github.com/vllm-project/ci-infra/blob/main/buildkite/test-template-amd.j2
-   Last synced: 2026-08-04
-   Modifications: Use the shared MI300 queues and Omni command-passing contract
+   Last synced: 2025-12-15
+   Modifications: Removed unused CUDA/NVIDIA logic, keeping only AMD tests
 #}
 {% set docker_image_amd = "rocm/vllm-omni:$BUILDKITE_COMMIT" %}
 {% set default_working_dir = "/app/vllm-omni" %}

-{# MI300 runs tests natively in Kubernetes; Docker-in-Docker is unavailable. #}
-{% macro amd_native_k8s_plugin(indent, step) -%}
-{{ indent }}plugins:
-{{ indent }}  - kubernetes:
-{{ indent }}      podSpecPatch:
-{{ indent }}        automountServiceAccountToken: false
-{{ indent }}        securityContext:
-{{ indent }}          seccompProfile:
-{{ indent }}            type: RuntimeDefault
-{{ indent }}        imagePullSecrets:
-{{ indent }}          - name: docker-config
-{{ indent }}        containers:
-{{ indent }}          - name: container-0
-{{ indent }}            image: {{ docker_image_amd }}
-{{ indent }}            imagePullPolicy: Always
-{{ indent }}            securityContext:
-{{ indent }}              allowPrivilegeEscalation: false
-{{ indent }}              capabilities:
-{{ indent }}                add:
-{{ indent }}                  - IPC_LOCK
-{{ indent }}            resources:
-{{ indent }}              limits:
-{{ indent }}                amd.com/gpu: "{{ step.agent_pool[-1:] }}"
-{{ indent }}              requests:
-{{ indent }}                amd.com/gpu: "{{ step.agent_pool[-1:] }}"
-{{ indent }}            volumeMounts:
-{{ indent }}              - name: devshm
-{{ indent }}                mountPath: /dev/shm
-{{ indent }}            env:
-{{ indent }}              - name: AMD_CI_RUNTIME
-{{ indent }}                value: native
-{{ indent }}              - name: NATIVE_CI
-{{ indent }}                value: "true"
-{{ indent }}              - name: VLLM_CI_DOCKER_DISABLED
-{{ indent }}                value: "1"
-{{ indent }}              - name: VLLM_CI_EXPECTED_GPU_COUNT
-{{ indent }}                value: "{{ step.agent_pool[-1:] }}"
-{{ indent }}              - name: HF_HOME
-{{ indent }}                value: /home/buildkite-agent/huggingface
-{{ indent }}              - name: MIOPEN_DEBUG_CONV_DIRECT
-{{ indent }}                value: "0"
-{{ indent }}              - name: MIOPEN_DEBUG_CONV_GEMM
-{{ indent }}                value: "0"
-{{ indent }}              - name: VLLM_ROCM_USE_AITER
-{{ indent }}                value: "0"
-{{ indent }}              - name: PYTORCH_ROCM_ARCH
-{{ indent }}                value: ""
-{{ indent }}        volumes:
-{{ indent }}          - name: devshm
-{{ indent }}            emptyDir:
-{{ indent }}              medium: Memory
-{{ indent }}              sizeLimit: 16Gi
-{%- endmacro %}
-
 {% if env %}
 env:
 {% for name in env %}
@@ -107,31 +53,19 @@ steps:
          {% if step.agent_pool %}
          queue: amd_{{ step.agent_pool }}
          {% else %}
-          queue: amd_mi300_1
+          queue: amd_mi325_1
          {% endif %}
-{{ amd_native_k8s_plugin("        ", step) }}
 {% set cmd_body = (step.command or (step.commands | join("\n"))) | trim %}
 {% set indented_cmd = cmd_body | replace("\n", "\n            ") %}
        command: bash .buildkite/amd/scripts/run-amd-test.sh
        env:
-          AMD_CI_RUNTIME: "native"
-          NATIVE_CI: "true"
-          VLLM_CI_DOCKER_DISABLED: "1"
-          VLLM_CI_EXPECTED_GPU_COUNT: "{{ step.agent_pool[-1:] }}"
-          VLLM_CI_REQUIRE_PERSISTENT_HF_CACHE: "1"
-          HF_HOME: "/home/buildkite-agent/huggingface"
-          HF_HUB_DOWNLOAD_TIMEOUT: "300"
-          HF_HUB_ETAG_TIMEOUT: "60"
-          MIOPEN_DEBUG_CONV_DIRECT: "0"
-          MIOPEN_DEBUG_CONV_GEMM: "0"
-          VLLM_ROCM_USE_AITER: "0"
-          PYTORCH_ROCM_ARCH: ""
+          DOCKER_BUILDKIT: "1"
 {% if step.env %}
 {% for name in step.env %}
          {{ name }}: "{{ step.env[name] }}"
 {% endfor %}
 {% endif %}
-          TEST_COMMANDS: |-
+          TEST_COMMAND: |-
            (command rocm-smi || true) && cd {{ (step.working_dir or default_working_dir) | safe }}
 {% if env %}
 {% for name in env %}
@@ -148,15 +82,6 @@ steps:
 {% endif %}
            {{ indented_cmd | safe }}
        priority: 100
-{% if step.parallelism %}
-        parallelism: {{ step.parallelism }}
-{% endif %}
-{% if step.artifact_paths %}
-        artifact_paths:
-{% for path in step.artifact_paths %}
-          - "{{ path }}"
-{% endfor %}
-{% endif %}
        {% if step.timeout_in_minutes %}
        timeout_in_minutes: {{ step.timeout_in_minutes }}
        {% endif %}
@@ -175,31 +100,19 @@ steps:
      {% if step.agent_pool %}
      queue: amd_{{ step.agent_pool }}
      {% else %}
-      queue: amd_mi300_1
+      queue: amd_mi325_1
      {% endif %}
-{{ amd_native_k8s_plugin("    ", step) }}
 {% set cmd_body = (step.command or (step.commands | join("\n"))) | trim %}
 {% set indented_cmd = cmd_body | replace("\n", "\n        ") %}
    command: bash .buildkite/amd/scripts/run-amd-test.sh
    env:
-      AMD_CI_RUNTIME: "native"
-      NATIVE_CI: "true"
-      VLLM_CI_DOCKER_DISABLED: "1"
-      VLLM_CI_EXPECTED_GPU_COUNT: "{{ step.agent_pool[-1:] }}"
-      VLLM_CI_REQUIRE_PERSISTENT_HF_CACHE: "1"
-      HF_HOME: "/home/buildkite-agent/huggingface"
-      HF_HUB_DOWNLOAD_TIMEOUT: "300"
-      HF_HUB_ETAG_TIMEOUT: "60"
-      MIOPEN_DEBUG_CONV_DIRECT: "0"
-      MIOPEN_DEBUG_CONV_GEMM: "0"
-      VLLM_ROCM_USE_AITER: "0"
-      PYTORCH_ROCM_ARCH: ""
+      DOCKER_BUILDKIT: "1"
 {% if step.env %}
 {% for name in step.env %}
      {{ name }}: "{{ step.env[name] }}"
 {% endfor %}
 {% endif %}
-      TEST_COMMANDS: |-
+      TEST_COMMAND: |-
        (command rocm-smi || true) && cd {{ (step.working_dir or default_working_dir) | safe }}
 {% if env %}
 {% for name in env %}
@@ -216,15 +129,6 @@ steps:
 {% endif %}
        {{ indented_cmd | safe }}
    priority: 100
-{% if step.parallelism %}
-    parallelism: {{ step.parallelism }}
-{% endif %}
-{% if step.artifact_paths %}
-    artifact_paths:
-{% for path in step.artifact_paths %}
-      - "{{ path }}"
-{% endfor %}
-{% endif %}
    {% if step.timeout_in_minutes %}
    timeout_in_minutes: {{ step.timeout_in_minutes }}
    {% endif %}
@@ -8,12 +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
-#
-# The ``l4_*`` presets run under the ``docker`` plugin, which does not put
-# ``buildkite-agent`` on PATH inside the container the way the ``kubernetes``
-# presets do, so they set ``mount-buildkite-agent: true``; without it any step
-# calling ``buildkite-agent`` (artifact upload, annotate, meta-data) fails there.
+#   a3_npu_2    — ascend-a3, resource_class npu-2 (A3 CI image tag …:$BUILDKITE_COMMIT-a3)

 x-npu-k8s-plugins: &npu_k8s_plugins
  - kubernetes:
@@ -31,7 +26,6 @@ mirror_hardwares:
          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
          always-pull: true
          propagate-environment: true
-          mount-buildkite-agent: true
          shm-size: "8gb"
          environment:
            - HF_HOME=/fsx/hf_cache
@@ -47,7 +41,6 @@ mirror_hardwares:
          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
          always-pull: true
          propagate-environment: true
-          mount-buildkite-agent: true
          shm-size: "8gb"
          environment:
            - HF_HOME=/fsx/hf_cache
@@ -1,82 +0,0 @@
-#!/usr/bin/env bash
-# Run a model's E2E tests once per entry mode as separate coverage runs and upload
-# the reports, so per-mode attribution survives (see vllm-project/vllm-omni#5332).
-#
-# Usage: run_cov_split.sh --model-id <id> [--offline <paths>] [--online <paths>] \
-#                         -- <pytest args...>
-#
-#   --model-id  Names the artifacts: coverage-<id>-{offline,online}-<step id>.xml.gz.
-#               Use the model's directory name under vllm_omni/*/models/.
-#   --offline   Pytest path(s) for the offline half. Repeatable.
-#   --online    Pytest path(s) for the online half. Repeatable.
-#   --          Everything after this is forwarded verbatim to every pytest run,
-#               quoting preserved: the job's own -m expression, --run-level, and
-#               anything else it needs (e.g. --test-config-file).
-#
-# At least one of --offline/--online is required, so a job with only one mode runs
-# just that half. Total runtime is bounded by the step's timeout_in_minutes.
-#
-# Exits non-zero if any half or the upload failed.
-set -uo pipefail
-
-MODEL_ID=""
-OFFLINE=()
-ONLINE=()
-PYTEST_ARGS=()
-
-while [[ $# -gt 0 ]]; do
-    case "$1" in
-        --model-id) MODEL_ID="$2"; shift 2 ;;
-        --offline)  OFFLINE+=("$2"); shift 2 ;;
-        --online)   ONLINE+=("$2"); shift 2 ;;
-        --)         shift; PYTEST_ARGS=("$@"); break ;;
-        *) echo "run_cov_split.sh: unknown argument: $1" >&2; exit 2 ;;
-    esac
-done
-
-if [[ -z "${MODEL_ID}" ]]; then
-    echo "run_cov_split.sh: missing --model-id" >&2
-    exit 2
-fi
-
-if ((${#OFFLINE[@]} == 0 && ${#ONLINE[@]} == 0)); then
-    echo "run_cov_split.sh: need at least one of --offline / --online" >&2
-    exit 2
-fi
-
-# Two steps can test the same model, and artifacts are scoped to the build (a
-# nightly one carries the ready, merge and nightly tiers at once), so unsuffixed
-# reports would share a path -- a glob download then races to rename onto one file.
-# Not BUILDKITE_JOB_ID: the step id survives retries, so attempts collapse under
-# Buildkite's latest-attempt lookup instead of accumulating. A parallel step would
-# need BUILDKITE_PARALLEL_JOB as well; no pilot is one.
-REPORT_SUFFIX="${BUILDKITE_STEP_ID:-local}"
-OFFLINE_REPORT="coverage-${MODEL_ID}-offline-${REPORT_SUFFIX}.xml"
-ONLINE_REPORT="coverage-${MODEL_ID}-online-${REPORT_SUFFIX}.xml"
-
-run_half() {
-    local mode="$1"
-    shift
-    local report="coverage-${MODEL_ID}-${mode}-${REPORT_SUFFIX}.xml"
-    echo "--- coverage: ${MODEL_ID} ${mode}"
-    pytest -s -v "$@" "${PYTEST_ARGS[@]}" --cov=vllm_omni --cov-report="xml:${report}"
-    local status=$?
-    # Cobertura lists every statement in vllm_omni whether or not it ran, so the
-    # report is ~7MB no matter how little the job covers. The repetition is what
-    # dominates it, and gzip takes that to ~400KB.
-    gzip -9 -f "${report}" || status=1
-    return "${status}"
-}
-
-# Clear both modes, not just the ones being run: the upload glob matches both, so
-# a report left by an earlier run would otherwise be published as if the mode it
-# belongs to had run this time.
-rm -f "${OFFLINE_REPORT}" "${ONLINE_REPORT}" "${OFFLINE_REPORT}.gz" "${ONLINE_REPORT}.gz"
-
-EXIT=0
-((${#OFFLINE[@]})) && { run_half offline "${OFFLINE[@]}" || EXIT=1; }
-((${#ONLINE[@]})) && { run_half online "${ONLINE[@]}" || EXIT=1; }
-
-buildkite-agent artifact upload "coverage-${MODEL_ID}-*-${REPORT_SUFFIX}.xml.gz" || EXIT=1
-
-exit "${EXIT}"
@@ -282,7 +282,7 @@ def _git(*args: str) -> subprocess.CompletedProcess[str]:


 def _resolve_diff_range() -> str | None:
-    """Return a git diff range for PR or main builds, or None when unavailable."""
+    """Return a git diff range for PR or push-branch builds, or None when unavailable."""
    is_pr = os.environ.get("BUILDKITE_PULL_REQUEST", "false") != "false" and os.environ.get(
        "BUILDKITE_PULL_REQUEST", ""
    )
@@ -302,13 +302,15 @@ def _resolve_diff_range() -> str | None:
                return None
        return f"{base_ref}...{commit}"

-    if os.environ.get("BUILDKITE_BRANCH", "") == "main":
+    # main: ready/nightly/weekly push CI; minicpm-challenge: merge (L3) push CI
+    branch = os.environ.get("BUILDKITE_BRANCH", "")
+    if branch in ("main", "minicpm-challenge"):
        if _git("rev-parse", "--verify", f"{commit}^").returncode != 0:
-            _log("main commit has no parent; using safe defaults")
+            _log(f"{branch} commit has no parent; using safe defaults")
            return None
        return f"{commit}^..{commit}"

-    _log("not PR/main build; using safe defaults")
+    _log("not PR/main/minicpm-challenge build; using safe defaults")
    return None


@@ -59,11 +59,12 @@ E2E_GROUP_MARKER = "E2E Test"
 CI_MIRROR_HARDWARES_PATH = ROOT / ".buildkite/common/ci_mirror_hardwares.yml"

 CUDA_NIGHTLY_ONLY = (
-    '(build.pull_request.labels includes "nightly-test") || (build.branch == "main" && build.env("NIGHTLY") == "1")'
+    '(build.pull_request.labels includes "nightly-test") || '
+    '(build.branch == "minicpm-challenge" && build.env("NIGHTLY") == "1")'
 )
 NPU_NIGHTLY_ONLY = (
-    '(build.branch == "main" && build.env("NIGHTLY") == "1") || '
-    '(build.branch != "main" && ('
+    '(build.branch == "minicpm-challenge" && build.env("NIGHTLY") == "1") || '
+    '(build.branch != "minicpm-challenge" && ('
    'build.pull_request.labels includes "nightly-test" || '
    'build.pull_request.labels includes "omni-test" || '
    'build.pull_request.labels includes "tts-test" || '
@@ -103,7 +104,8 @@ def _format_bootstrap_if(expr: str) -> str:

 def _compute_bootstrap_if_exprs(*, decision, platform: str) -> dict[str, str]:
    disabled = "false"
-    nightly_main = 'build.branch == "main" && build.env("NIGHTLY") == "1"'
+    # L4 nightly scheduled push CI runs on minicpm-challenge (not main).
+    nightly_main = 'build.branch == "minicpm-challenge" && build.env("NIGHTLY") == "1"'
    nightly_only = NPU_NIGHTLY_ONLY if platform == "npu" else CUDA_NIGHTLY_ONLY

    if platform == "npu":
@@ -118,11 +120,12 @@ def _compute_bootstrap_if_exprs(*, decision, platform: str) -> dict[str, str]:
        merge_base = disabled
    else:
        ready_pr = 'build.branch != "main" && build.pull_request.labels includes "ready"'
-        merge_main = 'build.branch == "main" && build.env("NIGHTLY") != "1" && build.env("WEEKLY") != "1"'
-        merge_pr = 'build.branch != "main" && build.pull_request.labels includes "merge-test"'
+        # L3 merge push CI runs on minicpm-challenge (not main).
+        merge_main = 'build.branch == "minicpm-challenge" && build.env("NIGHTLY") != "1" && build.env("WEEKLY") != "1"'
+        merge_pr = 'build.branch != "minicpm-challenge" && build.pull_request.labels includes "merge-test"'
        nightly_label_if = (
-            '(build.branch == "main" && build.env("NIGHTLY") == "1") || '
-            '(build.branch != "main" && ('
+            '(build.branch == "minicpm-challenge" && build.env("NIGHTLY") == "1") || '
+            '(build.branch != "minicpm-challenge" && ('
            'build.pull_request.labels includes "nightly-test" || '
            'build.pull_request.labels includes "omni-test" || '
            'build.pull_request.labels includes "tts-test" || '
@@ -140,8 +143,8 @@ def _compute_bootstrap_if_exprs(*, decision, platform: str) -> dict[str, str]:
    weekly_main = 'build.branch == "main" && build.env("WEEKLY") == "1"'

    if decision.skip_all:
-        # Docs / skip-mark only: no PR-label escape hatch. Main scheduled
-        # NIGHTLY=1 still runs L4 + L2/L3 (--e2e); WEEKLY=1 still runs L5.
+        # Docs / skip-mark only: no PR-label escape hatch. Scheduled
+        # NIGHTLY=1 (minicpm-challenge) still runs L4 + L2/L3 (--e2e); WEEKLY=1 (main) still runs L5.
        image_expr = f"({nightly_main}) || ({weekly_main})" if platform == "cuda" else nightly_main
        ready_expr = nightly_main
        merge_expr = nightly_main if platform == "cuda" else disabled
@@ -8,35 +8,35 @@ steps:
    agents:
      queue: "cpu_queue_premerge"

-  # L2 Test — PR ready label (diff-aware) or main+NIGHTLY=1 (--e2e, full E2E group)
+  # L2 Test — PR ready label (diff-aware) or minicpm-challenge+NIGHTLY=1 (--e2e, full E2E group)
  - label: "Upload Ready Pipeline"
    depends_on: image-build
    key: upload-ready-pipeline
    commands:
      - |
        EXTRA=""
-        if [ "${NIGHTLY:-}" = "1" ] && [ "${BUILDKITE_BRANCH}" = "main" ]; then
+        if [ "${NIGHTLY:-}" = "1" ] && [ "${BUILDKITE_BRANCH}" = "minicpm-challenge" ]; then
          EXTRA="--e2e"
        fi
        python3 .buildkite/common/scripts/upload_pipeline.py --upload $$EXTRA .buildkite/cuda/test-ready.yml
    agents:
      queue: "cpu_queue_premerge"

-  # L3 Test — PR merge-test label (diff-aware) or main+NIGHTLY=1 (--e2e, full E2E group)
+  # L3 Test — PR merge-test label (diff-aware) or minicpm-challenge+NIGHTLY=1 (--e2e, full E2E group)
  - label: "Upload Merge Pipeline"
    depends_on: image-build
    key: upload-merge-pipeline
    commands:
      - |
        EXTRA=""
-        if [ "${NIGHTLY:-}" = "1" ] && [ "${BUILDKITE_BRANCH}" = "main" ]; then
+        if [ "${NIGHTLY:-}" = "1" ] && [ "${BUILDKITE_BRANCH}" = "minicpm-challenge" ]; then
          EXTRA="--e2e"
        fi
        python3 .buildkite/common/scripts/upload_pipeline.py --upload $$EXTRA .buildkite/cuda/test-merge.yml
    agents:
      queue: "cpu_queue_premerge"

-  # L4 Test — main+NIGHTLY=1 (scheduled), or PR with specific label (e.g. add label then Rebuild)
+  # L4 Test — minicpm-challenge+NIGHTLY=1 (scheduled), or PR with specific label (e.g. add label then Rebuild)
  - label: "Upload Nightly Pipeline"
    depends_on: image-build
    key: upload-nightly-pipeline
@@ -8,206 +8,237 @@ steps:
  - group: ":card_index_dividers: Simple Test"
    depends_on: upload-merge-pipeline
    steps:
-      - label: "Simple · Model Executor Test"
+      - label: "Simple · Diffusion & Model Executor Test"
        commands:
-          - "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"
+          - "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"
        mirror_hardwares: l4_1

      - label: "Simple · Other Test"
        commands:
-          - "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"
+          - "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"
        mirror_hardwares: l4_1

-  - group: ":card_index_dividers: Diffusion Test"
-    depends_on: upload-merge-pipeline
-    steps:
-      - label: "Diffusion · LoRA Test"
-        timeout_in_minutes: 30
-        commands:
-          - pytest -sv tests/diffusion/lora/ -m 'advanced_model and cuda' --run-level "advanced_model"
-        mirror_hardwares: l4_1
+  # - group: ":card_index_dividers: Diffusion Test"
+  #   depends_on: upload-merge-pipeline
+  #   steps:
+  #     - label: "Diffusion · LoRA Test"
+  #       timeout_in_minutes: 30
+  #       commands:
+  #         - pytest -sv tests/diffusion/lora/ -m 'advanced_model and cuda' --run-level "advanced_model"
+  #       mirror_hardwares: l4_1

-  - label: "Distributed Test"
-    depends_on: upload-merge-pipeline
-    timeout_in_minutes: 60
-    commands:
-      - |
-        timeout 55m bash -c "
-          set -e
-          export VLLM_IMAGE_FETCH_TIMEOUT=60
-          pytest -s -v tests/distributed -m 'advanced_model and cuda and H100' --run-level 'advanced_model'
-        "
-    mirror_hardwares: h100_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

-  - group: ":card_index_dividers: Entrypoints Test"
-    depends_on: upload-merge-pipeline
-    steps:
-      - label: "Entrypoint Test with H100"
-        # TEMPORARY 60 -> 90: engine startup/sleep-wake is ~13-15 min slower per
-        # run on the vLLM 0.26 image (model loading ~2x, init ~2x; see builds
-        # 2915 vs 2916), which pushed this suite past 60 min. Revert once the
-        # 0.26 startup regression is understood and fixed.
-        timeout_in_minutes: 90
-        commands:
-          - pytest -s -v tests/entrypoints/ -m "advanced_model and cuda and H100" --run-level "advanced_model"
-        mirror_hardwares: h100_2
+  # - label: "Distributed Test"
+  #   depends_on: upload-merge-pipeline
+  #   timeout_in_minutes: 60
+  #   commands:
+  #     - |
+  #       timeout 55m bash -c "
+  #         set -e
+  #         export VLLM_IMAGE_FETCH_TIMEOUT=60
+  #         pytest -s -v tests/distributed -m 'advanced_model and cuda and H100' --run-level 'advanced_model'
+  #       "
+  #   mirror_hardwares: h100_1

-      - label: "Entrypoint Test with L4"
-        timeout_in_minutes: 60
-        commands:
-          - pytest -s -v tests/entrypoints/ -m "advanced_model and cuda and L4" --run-level "advanced_model"
-        mirror_hardwares: l4_1
+  # - group: ":card_index_dividers: Entrypoints Test"
+  #   depends_on: upload-merge-pipeline
+  #   steps:
+  #     - label: "Entrypoint Test with H100"
+  #       timeout_in_minutes: 60
+  #       commands:
+  #         - pytest -s -v tests/entrypoints/ -m "advanced_model and cuda and H100" --run-level "advanced_model"
+  #       mirror_hardwares: h100_2

-  - group: ":card_index_dividers: E2E Test"
-    depends_on: upload-merge-pipeline
-    steps:
-      - label: "TTS · Qwen3-TTS CustomVoice Test"
-        source_file_dependencies:
-          - tests/e2e/online_serving/test_qwen3_tts_customvoice.py
-          - tests/e2e/offline_inference/test_qwen3_tts_customvoice.py
-          - vllm_omni/model_executor/models/qwen3_tts/
-          - vllm_omni/model_executor/models/common/qwen3_code_predictor.py
-          - vllm_omni/model_executor/models/common/snake_activation.py
-          - vllm_omni/model_executor/models/common/alias_free_activation.py
-          - vllm_omni/model_executor/stage_input_processors/qwen3_tts.py
-          - vllm_omni/deploy/qwen3_tts.yaml
-        commands:
-          - |
-            timeout 40m bash -c "
-              export VLLM_LOGGING_LEVEL=DEBUG
-              export VLLM_ALLOW_LONG_MAX_MODEL_LEN=1
-              pytest -s -v tests/e2e/online_serving/test_qwen3_tts_customvoice.py tests/e2e/offline_inference/test_qwen3_tts_customvoice.py -m 'advanced_model and cuda' --run-level 'advanced_model'
-            "
-        mirror_hardwares: l4_1
+  #     - label: "Entrypoint Test with L4"
+  #       timeout_in_minutes: 30
+  #       commands:
+  #         - pytest -s -v tests/entrypoints/ -m "advanced_model and cuda and L4" --run-level "advanced_model"
+  #       mirror_hardwares: l4_1

-      - label: "TTS · Qwen3-TTS Base Test"
-        source_file_dependencies:
-          - tests/e2e/online_serving/test_qwen3_tts_base.py
-          - tests/e2e/offline_inference/test_qwen3_tts_base.py
-          - vllm_omni/model_executor/models/qwen3_tts/
-          - vllm_omni/model_executor/models/common/qwen3_code_predictor.py
-          - vllm_omni/model_executor/models/common/snake_activation.py
-          - vllm_omni/model_executor/models/common/alias_free_activation.py
-          - vllm_omni/model_executor/stage_input_processors/qwen3_tts.py
-          - vllm_omni/deploy/qwen3_tts.yaml
-          - .buildkite/common/scripts/run_cov_split.sh
-          - pyproject.toml
-        # 40 -> 60: the coverage split runs the model twice.
-        timeout_in_minutes: 60
-        commands:
-          - |
-            export VLLM_LOGGING_LEVEL=DEBUG
-            export VLLM_ALLOW_LONG_MAX_MODEL_LEN=1
-            .buildkite/common/scripts/run_cov_split.sh \
-              --model-id qwen3_tts \
-              --offline tests/e2e/offline_inference/test_qwen3_tts_base.py \
-              --online tests/e2e/online_serving/test_qwen3_tts_base.py \
-              -- -m 'advanced_model and cuda' --run-level 'advanced_model'
-        mirror_hardwares: l4_1
+  # - group: ":card_index_dividers: E2E Test"
+  #   depends_on: upload-merge-pipeline
+  #   steps:
+  #     - label: "TTS · Qwen3-TTS CustomVoice Test"
+  #       source_file_dependencies:
+  #         - tests/e2e/online_serving/test_qwen3_tts_customvoice.py
+  #         - tests/e2e/offline_inference/test_qwen3_tts_customvoice.py
+  #         - vllm_omni/model_executor/models/qwen3_tts/
+  #         - vllm_omni/model_executor/stage_input_processors/qwen3_tts.py
+  #         - vllm_omni/deploy/qwen3_tts.yaml
+  #       commands:
+  #         - |
+  #           timeout 40m bash -c "
+  #             export VLLM_LOGGING_LEVEL=DEBUG
+  #             export VLLM_ALLOW_LONG_MAX_MODEL_LEN=1
+  #             pytest -s -v tests/e2e/online_serving/test_qwen3_tts_customvoice.py tests/e2e/offline_inference/test_qwen3_tts_customvoice.py -m 'advanced_model and cuda' --run-level 'advanced_model'
+  #           "
+  #       mirror_hardwares: l4_1

-      - label: "Omni · Qwen3-Omni Test"
-        source_file_dependencies:
-          - tests/e2e/offline_inference/test_qwen3_omni.py
-          - tests/e2e/online_serving/test_qwen3_omni.py
-          - vllm_omni/model_executor/models/qwen3_omni/
-          - vllm_omni/model_executor/models/common/qwen3_code_predictor.py
-          - vllm_omni/model_executor/models/common/snake_activation.py
-          - vllm_omni/model_executor/models/qwen2_5_omni/qwen2_5_omni_thinker.py
-          - vllm_omni/model_executor/models/qwen3_tts/cuda_graph_decoder_wrapper.py
-          - vllm_omni/model_executor/stage_input_processors/qwen3_omni.py
-          - vllm_omni/deploy/qwen3_omni_moe.yaml
-        timeout_in_minutes: 50
-        commands:
-          - pytest -s -v tests/e2e/offline_inference/test_qwen3_omni.py tests/e2e/online_serving/test_qwen3_omni.py -m 'advanced_model and cuda' --run-level 'advanced_model'
-        mirror_hardwares: h100_2
+  #     - label: "TTS · Qwen3-TTS Base Test"
+  #       source_file_dependencies:
+  #         - tests/e2e/online_serving/test_qwen3_tts_base.py
+  #         - tests/e2e/offline_inference/test_qwen3_tts_base.py
+  #         - vllm_omni/model_executor/models/qwen3_tts/
+  #         - vllm_omni/model_executor/stage_input_processors/qwen3_tts.py
+  #         - vllm_omni/deploy/qwen3_tts.yaml
+  #       commands:
+  #         - |
+  #           timeout 40m bash -c "
+  #             export VLLM_LOGGING_LEVEL=DEBUG
+  #             export VLLM_ALLOW_LONG_MAX_MODEL_LEN=1
+  #             pytest -s -v tests/e2e/online_serving/test_qwen3_tts_base.py tests/e2e/offline_inference/test_qwen3_tts_base.py -m 'advanced_model and cuda' --run-level 'advanced_model'
+  #           "
+  #       mirror_hardwares: l4_1
+
+  #     - label: "TTS · Higgs-Audio-V3 Test"
+  #       source_file_dependencies:
+  #         - tests/e2e/online_serving/test_higgs_audio_v3.py
+  #         - vllm_omni/model_executor/models/higgs_audio_v3/
+  #         - vllm_omni/model_executor/stage_input_processors/higgs_audio_v3.py
+  #         - vllm_omni/deploy/higgs_multimodal_qwen3.yaml
+  #       commands:
+  #         - |
+  #           timeout 20m bash -c "
+  #             export VLLM_WORKER_MULTIPROC_METHOD=spawn
+  #             export VLLM_USE_DEEP_GEMM=0
+  #             export VLLM_MOE_USE_DEEP_GEMM=0
+  #             pytest -s -v tests/e2e/online_serving/test_higgs_audio_v3.py -m 'advanced_model' --run-level 'advanced_model'
+  #           "
+  #       mirror_hardwares: l4_1
+
+  #     - label: "Omni · Qwen3-Omni Test"
+  #       source_file_dependencies:
+  #         - tests/e2e/offline_inference/test_qwen3_omni.py
+  #         - tests/e2e/online_serving/test_qwen3_omni.py
+  #         - vllm_omni/model_executor/models/qwen3_omni/
+  #         - vllm_omni/model_executor/stage_input_processors/qwen3_omni.py
+  #         - vllm_omni/deploy/qwen3_omni_moe.yaml
+  #       timeout_in_minutes: 50
+  #       commands:
+  #         - pytest -s -v tests/e2e/offline_inference/test_qwen3_omni.py tests/e2e/online_serving/test_qwen3_omni.py -m 'advanced_model and cuda' --run-level 'advanced_model'
+  #       mirror_hardwares: h100_2

      - label: "Omni · MiniCPM-o 4.5 Test"
-        source_file_dependencies:
-          - tests/e2e/offline_inference/test_minicpmo_4_5.py
-          - tests/e2e/online_serving/test_minicpmo_4_5.py
-          - vllm_omni/model_executor/models/minicpmo_4_5/
-          - vllm_omni/model_executor/models/step_audio2/
-          - vllm_omni/model_executor/models/cosyvoice3/code2wav_core/
-          - vllm_omni/model_executor/models/cosyvoice3/utils.py
-          - vllm_omni/model_executor/stage_input_processors/minicpmo_4_5_omni.py
-          - vllm_omni/deploy/minicpmo_4_5.yaml
        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'
-        # 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
+        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
-          - vllm_omni/deploy/minicpmo_4_5.yaml
-          - 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/models/step_audio2/
-          - vllm_omni/model_executor/models/cosyvoice3/code2wav_core/
-          - vllm_omni/model_executor/models/cosyvoice3/utils.py
-          - 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/engine/orchestrator.py
-          - vllm_omni/entrypoints/cli/serve.py
-          - vllm_omni/entrypoints/openai/api_server.py
-          - vllm_omni/entrypoints/openai/duplex_capability.py
-          - vllm_omni/config/stage_config.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: "Diffusion · Qwen Image Test"
-        source_file_dependencies:
-          - tests/e2e/online_serving/test_qwen_image.py
-          - vllm_omni/diffusion/models/qwen_image/
-          - vllm_omni/diffusion/models/dmd2/
-          - vllm_omni/diffusion/distributed/autoencoders/autoencoder_kl_qwenimage.py
-        commands:
-          - |
-            timeout 40m bash -c '
-              export VLLM_IMAGE_FETCH_TIMEOUT=60
-              pytest -s -v tests/e2e/online_serving/test_qwen_image.py -m 'advanced_model and cuda' --run-level 'advanced_model'
-            '
-        mirror_hardwares: h100_1
+      # - label: "TTS · VoxCPM2 Test"
+      #   source_file_dependencies:
+      #     - tests/e2e/offline_inference/test_voxcpm2_tts.py
+      #     - tests/e2e/online_serving/test_voxcpm2_tts.py
+      #     - vllm_omni/model_executor/models/voxcpm2/
+      #     - vllm_omni/deploy/voxcpm2.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_voxcpm2_tts.py tests/e2e/online_serving/test_voxcpm2_tts.py -m 'advanced_model and cuda' --run-level 'advanced_model'
+      #       "
+      #   mirror_hardwares: l4_1

-      - label: "Diffusion · Wan22 Test"
-        source_file_dependencies:
-          - tests/e2e/offline_inference/test_wan22_t2v.py
-          - tests/e2e/online_serving/test_wan22_t2v.py
-          - vllm_omni/diffusion/models/wan2_2/
-          - vllm_omni/diffusion/models/dmd2/
-        commands:
-          - |
-            timeout 40m bash -c "
-              export VLLM_IMAGE_FETCH_TIMEOUT=60
-              pytest -s -v tests/e2e/offline_inference/test_wan22_t2v.py tests/e2e/online_serving/test_wan22_t2v.py -m 'advanced_model and cuda and diffusion' --run-level 'advanced_model'
-            "
-        mirror_hardwares: h100_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
+      #     - vllm_omni/diffusion/models/qwen_image/
+      #   timeout_in_minutes: 20
+      #   commands:
+      #     - pytest -s -v tests/e2e/online_serving/test_qwen_image_edit.py -m 'advanced_model and cuda' --run-level 'advanced_model'
+      #   mirror_hardwares: h100_1
+      # - label: "Diffusion · Bagel Test"
+      #   source_file_dependencies:
+      #     - tests/e2e/offline_inference/test_bagel.py
+      #     - tests/e2e/online_serving/test_bagel.py
+      #     - vllm_omni/model_executor/models/bagel/
+      #     - vllm_omni/diffusion/models/bagel/
+      #     - vllm_omni/model_executor/stage_input_processors/bagel.py
+      #     - vllm_omni/deploy/bagel.yaml
+      #   timeout_in_minutes: 20
+      #   commands:
+      #     - pytest -s -v tests/e2e/offline_inference/test_bagel.py tests/e2e/online_serving/test_bagel.py -m 'advanced_model and cuda' --run-level 'advanced_model'
+      #   mirror_hardwares: h100_1
+
+      # - label: "Diffusion · Qwen Image Test"
+      #   source_file_dependencies:
+      #     - tests/e2e/online_serving/test_qwen_image.py
+      #     - vllm_omni/diffusion/models/qwen_image/
+      #   commands:
+      #     - |
+      #       timeout 40m bash -c '
+      #         export VLLM_IMAGE_FETCH_TIMEOUT=60
+      #         pytest -s -v tests/e2e/online_serving/test_qwen_image.py -m 'advanced_model and cuda' --run-level 'advanced_model'
+      #       '
+      #   mirror_hardwares: h100_1
+
+      # - label: "Diffusion · Qwen Image Layered Test"
+      #   source_file_dependencies:
+      #     - tests/e2e/online_serving/test_qwen_image_layered.py
+      #     - vllm_omni/diffusion/models/qwen_image/
+      #   commands:
+      #     - |
+      #       timeout 40m bash -c "
+      #         export VLLM_IMAGE_FETCH_TIMEOUT=60
+      #         pytest -s -v tests/e2e/online_serving/test_qwen_image_layered.py -m 'advanced_model and cuda' --run-level 'advanced_model'
+      #       "
+      #   mirror_hardwares: h100_1
+
+      # - label: "Diffusion · Wan22 Test"
+      #   source_file_dependencies:
+      #     - tests/e2e/offline_inference/test_wan22_t2v.py
+      #     - tests/e2e/online_serving/test_wan22_t2v.py
+      #     - vllm_omni/diffusion/models/wan2_2/
+      #   commands:
+      #     - |
+      #       timeout 40m bash -c "
+      #         export VLLM_IMAGE_FETCH_TIMEOUT=60
+      #         pytest -s -v tests/e2e/offline_inference/test_wan22_t2v.py tests/e2e/online_serving/test_wan22_t2v.py -m 'advanced_model and cuda and diffusion' --run-level 'advanced_model'
+      #       "
+      #   mirror_hardwares: h100_1
+
+      # - label: "Diffusion · Z Image Test"
+      #   source_file_dependencies:
+      #     - tests/e2e/offline_inference/test_z_image.py
+      #     - tests/e2e/online_serving/test_z_image.py
+      #     - vllm_omni/diffusion/models/z_image/
+      #   timeout_in_minutes: 30
+      #   commands:
+      #     - pytest -s -v tests/e2e/offline_inference/test_z_image.py tests/e2e/online_serving/test_z_image.py -m 'advanced_model and cuda and diffusion' --run-level 'advanced_model'
+      #   mirror_hardwares: l4_1
+      # - label: "Diffusion · FLUX.2 Klein Test"
+      #   source_file_dependencies:
+      #     - tests/e2e/offline_inference/test_flux2_klein.py
+      #     - vllm_omni/diffusion/models/flux2_klein/
+      #   timeout_in_minutes: 30
+      #   commands:
+      #     - pytest -s -v tests/e2e/offline_inference/test_flux2_klein.py -m 'advanced_model and cuda and diffusion' --run-level 'advanced_model'
+      #   mirror_hardwares: l4_1
@@ -15,7 +15,7 @@ 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 --ignore=tests/e2e/online_serving/test_minicpmo_4_5_duplex_expansion.py
+          - pytest -sv tests/e2e/online_serving/test_minicpmo_4_5_expansion.py tests/e2e/online_serving/test_minicpmo_4_5.py tests/e2e/offline_inference/test_minicpmo_4_5.py --run-level "full_model" -m "full_model and H100 and omni"
        mirror_hardwares: h100_2

      - label: ":full_moon: Omni · MiniCPM-o 4.5 Duplex Test"
@@ -24,36 +24,38 @@ steps:
          - 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:
-          - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
-          - pytest -s -v tests/examples/ -m "full_model and omni and L4" --run-level "full_model"
-        mirror_hardwares: l4_4
+  #     - label: ":full_moon: Omni · Doc Test with L4"
+  #       timeout_in_minutes: 90
+  #       commands:
+  #         - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
+  #         - pytest -s -v tests/examples/ -m "full_model and omni and L4" --run-level "full_model"
+  #       mirror_hardwares: l4_4

-      - label: ":full_moon: Omni · Doc Test with H100"
-        timeout_in_minutes: 90
-        commands:
-          - pytest -s -v tests/examples/ -m "full_model and omni and H100" --run-level "full_model"
-        mirror_hardwares: h100_2
+  #     - label: ":full_moon: Omni · Doc Test with H100"
+  #       timeout_in_minutes: 90
+  #       commands:
+  #         - pytest -s -v tests/examples/ -m "full_model and omni and H100" --run-level "full_model"
+  #       mirror_hardwares: h100_2

-      - label: ":full_moon: Omni · 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/qwen3_omni/test_qwen3_omni.py -m "full_model" --run-level full_model
-            EXIT=$$?
-            buildkite-agent artifact upload "tests/e2e/accuracy/qwen3_omni/results/qwen_omni_acc/*.json"
-            exit $$EXIT
-        mirror_hardwares: h100_2
+  #     - label: ":full_moon: Omni · Accuracy Test"
+  #       timeout_in_minutes: 180
+  #       commands:
+  #         - export SEED_TTS_WER_EVAL=1
+  #         - export SEED_TTS_SIM_EVAL=1
+  #         - export SEED_TTS_EVAL_DEVICE=cuda:1
+  #         - |
+  #           set +e
+  #           pytest -s -v tests/e2e/accuracy/qwen3_omni/test_qwen3_omni.py -m "full_model" --run-level full_model
+  #           EXIT=$$?
+  #           buildkite-agent artifact upload "tests/e2e/accuracy/qwen3_omni/results/qwen_omni_acc/*.json"
+  #           exit $$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_SIM_EVAL=1
          - export SEED_TTS_EVAL_DEVICE=cuda:1
          - |
            set +e
@@ -63,57 +65,57 @@ steps:
            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
-        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_qwen3_omni_no_async_chunk.json
-            EXIT=$$?
-            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
-            exit $$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
+  #       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_qwen3_omni_no_async_chunk.json
+  #           EXIT=$$?
+  #           buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
+  #           exit $$EXIT
+  #       mirror_hardwares: h100_2

-      - label: ":full_moon: Omni · Perf Test · Async Chunk"
-        key: nightly-omni-performance-async-chunk
-        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_qwen3_omni_async_chunk.json
-            EXIT=$$?
-            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
-            exit $$EXIT
-        mirror_hardwares: h100_2
+  #     - label: ":full_moon: Omni · Perf Test · Async Chunk"
+  #       key: nightly-omni-performance-async-chunk
+  #       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_qwen3_omni_async_chunk.json
+  #           EXIT=$$?
+  #           buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
+  #           exit $$EXIT
+  #       mirror_hardwares: h100_2

-      - label: ":full_moon: Omni · Perf Test · vLLM Text"
-        key: nightly-omni-performance-vllm-text
-        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_qwen3_omni_vllm_text.json
-            EXIT=$$?
-            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
-            exit $$EXIT
-        mirror_hardwares: h100_2
+  #     - label: ":full_moon: Omni · Perf Test · vLLM Text"
+  #       key: nightly-omni-performance-vllm-text
+  #       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_qwen3_omni_vllm_text.json
+  #           EXIT=$$?
+  #           buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
+  #           exit $$EXIT
+  #       mirror_hardwares: h100_2

-      - label: ":full_moon: Omni · Perf Test · Multi-Replica"
-        key: nightly-omni-performance-multi-replicas
-        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_qwen3_omni_multi_replicas.json
-            EXIT=$$?
-            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
-            exit $$EXIT
-        mirror_hardwares: h100_3
+  #     - label: ":full_moon: Omni · Perf Test · Multi-Replica"
+  #       key: nightly-omni-performance-multi-replicas
+  #       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_qwen3_omni_multi_replicas.json
+  #           EXIT=$$?
+  #           buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
+  #           exit $$EXIT
+  #       mirror_hardwares: h100_3

      - label: ":full_moon: Omni · MiniCPM-o 4.5 · Perf Test"
        key: nightly-omni-performance-minicpmo-4-5
@@ -122,7 +124,7 @@ steps:
          - 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
+            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py -m "cuda and H100" --test-config-file tests/dfx/perf/tests/test_minicpmo_4_5.json
            EXIT=$$?
            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
            exit $$EXIT
@@ -141,292 +143,439 @@ steps:
            exit $$EXIT
        mirror_hardwares: h100_1

-      - label: ":full_moon: Omni · Multi-Replica Startup Test with 4x H100"
-        timeout_in_minutes: 45
-        commands:
-          - pytest -s -v tests/e2e/online_serving/test_qwen3_omni_multi_replicas.py -m "full_model" --run-level "core_model"
-        mirror_hardwares: h100_4
+  #     - label: ":full_moon: Omni · Multi-Replica Startup Test with 4x H100"
+  #       timeout_in_minutes: 45
+  #       commands:
+  #         - pytest -s -v tests/e2e/online_serving/test_qwen3_omni_multi_replicas.py -m "full_model" --run-level "core_model"
+  #       mirror_hardwares: h100_4

-  - 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:
+  # - 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 · Function Test with L4"
-        timeout_in_minutes: 120
-        commands:
-          - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
-          - export VLLM_USE_DEEP_GEMM="0"
-          - export VLLM_MOE_USE_DEEP_GEMM="0"
-          - pytest -s -v tests/e2e/ -m "full_model and L4 and tts" --run-level "full_model" --ignore=tests/e2e/accuracy
-        mirror_hardwares: l4_1
+  #     - label: ":full_moon: TTS · Function Test with L4"
+  #       timeout_in_minutes: 120
+  #       commands:
+  #         - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
+  #         - export VLLM_USE_DEEP_GEMM="0"
+  #         - export VLLM_MOE_USE_DEEP_GEMM="0"
+  #         - pytest -s -v tests/e2e/ -m "full_model and L4 and tts" --run-level "full_model" --ignore=tests/e2e/accuracy --ignore=tests/e2e/online_serving/test_voxcpm2_tts_expansion.py
+  #       mirror_hardwares: l4_1

-      - label: ":full_moon: TTS · Perf Test"
-        key: nightly-tts-performance
-        timeout_in_minutes: 180
-        commands:
-          - export BENCHMARK_DIR=tests/dfx/perf/results
-          - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
-          - |
-            set +e
-            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_tts.json
-            EXIT=$$?
-            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
-            exit $$EXIT
-        mirror_hardwares: h100_2
+  #     - label: ":full_moon: TTS · Accuracy Test · VoxCPM2"
+  #       timeout_in_minutes: 30
+  #       commands:
+  #         - |
+  #           timeout 30m bash -c "
+  #             export VLLM_LOGGING_LEVEL=DEBUG
+  #             export VLLM_WORKER_MULTIPROC_METHOD=spawn
+  #             export VLLM_ALLOW_LONG_MAX_MODEL_LEN=1
+  #             pytest -s -v tests/e2e/online_serving/test_voxcpm2_tts_expansion.py -m 'full_model and cuda and tts' --run-level 'full_model'
+  #           "
+  #       mirror_hardwares: l4_1

-  - group: ":card_index_dividers: Tiny Model Tests [Multi-GPU]"
-    key: nightly-tiny-model-test-group
-    depends_on: upload-nightly-pipeline
-    if: >-
-      build.env("NIGHTLY") == "1" ||
-      build.pull_request.labels includes "nightly-test"
-    steps:
+  #     - label: ":full_moon: TTS · Perf Test"
+  #       key: nightly-tts-performance
+  #       timeout_in_minutes: 180
+  #       commands:
+  #         - export BENCHMARK_DIR=tests/dfx/perf/results
+  #         - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
+  #         - |
+  #           set +e
+  #           pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_tts.json
+  #           EXIT=$$?
+  #           buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
+  #           exit $$EXIT
+  #       mirror_hardwares: h100_2

-      - label: ":full_moon: Tiny Model Tests · Diffusion (Multi-GPU accelerations)"
-        timeout_in_minutes: 30
-        # NOTE: Single-GPU (core_model) tiny model tests run on the ready label,
-        # so nightly only adds the multi-GPU parallelism configurations.
-        #
-        # This is a bit confusing, but `full_model` is used to mark the multigpu tests, and
-        # --run-level `core_model` is used to indicate that we should use tiny model weights
-        # for the CI, since these tests should pass irrespective of whether or not we use
-        # the full weights or not.
-        commands:
-          - timeout 25m pytest -sv tests/model_tests/diffusion -m 'full_model and cuda' --run-level "core_model"
-        mirror_hardwares: l4_4
+  #     - label: ":full_moon: TTS · VoxCPM2 · Perf Test"
+  #       key: nightly-tts-performance-voxcpm2
+  #       timeout_in_minutes: 180
+  #       commands:
+  #         - export BENCHMARK_DIR=tests/dfx/perf/results
+  #         - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
+  #         - |
+  #           set +e
+  #           pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_voxcpm2.json
+  #           EXIT=$$?
+  #           buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
+  #           exit $$EXIT
+  #       mirror_hardwares: h100_1

-  - 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 "diffusion-x2iat-test"
-    steps:
-      # Use explicit file shards so single-card cases do not reserve 2+ GPUs.
+  #     - label: ":full_moon: TTS · Higgs-Audio-V3 · Perf Test"
+  #       key: nightly-tts-performance-higgs-audio-v3
+  #       timeout_in_minutes: 180
+  #       commands:
+  #         - export BENCHMARK_DIR=tests/dfx/perf/results
+  #         - export VLLM_USE_DEEP_GEMM="0"
+  #         - export VLLM_MOE_USE_DEEP_GEMM="0"
+  #         - |
+  #           set +e
+  #           pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_higgs_audio_v3.json
+  #           EXIT=$$?
+  #           buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
+  #           exit $$EXIT
+  #       mirror_hardwares: h100_1

-      - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with H100 · Single-GPU"
-        timeout_in_minutes: 120
-        commands:
-          - >-
-            pytest -sv
-            tests/e2e/online_serving/test_qwen_image_expansion.py
-            -m "full_model and diffusion and H100 and not distributed_cuda"
-            --run-level "full_model"
-        mirror_hardwares: h100_1
+  # - group: ":card_index_dividers: Tiny Model Tests [Multi-GPU]"
+  #   key: nightly-tiny-model-test-group
+  #   depends_on: upload-nightly-pipeline
+  #   if: >-
+  #     build.env("NIGHTLY") == "1" ||
+  #     build.pull_request.labels includes "nightly-test"
+  #   steps:

-      - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with H100 · Multi-GPU Qwen-Image"
-        timeout_in_minutes: 120
-        commands:
-          - >-
-            pytest -sv
-            tests/e2e/online_serving/test_qwen_image_expansion.py
-            -m "full_model and diffusion and H100 and distributed_cuda"
-            --run-level "full_model"
-        mirror_hardwares: h100_2
+  #     - label: ":full_moon: Tiny Model Tests · Diffusion (Multi-GPU accelerations)"
+  #       timeout_in_minutes: 30
+  #       # NOTE: Single-GPU (core_model) tiny model tests run on the ready label,
+  #       # so nightly only adds the multi-GPU parallelism configurations.
+  #       #
+  #       # This is a bit confusing, but `full_model` is used to mark the multigpu tests, and
+  #       # --run-level `core_model` is used to indicate that we should use tiny model weights
+  #       # for the CI, since these tests should pass irrespective of whether or not we use
+  #       # the full weights or not.
+  #       commands:
+  #         - timeout 25m pytest -sv tests/model_tests/diffusion -m 'full_model and cuda' --run-level "core_model"
+  #       mirror_hardwares: l4_4

-      - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with L4"
-        timeout_in_minutes: 120
-        commands:
-          - pytest -sv tests/e2e/ -k "not test_wan and not hunyuan" -m "full_model and diffusion and L4" --run-level "full_model" --ignore=tests/e2e/accuracy
-        mirror_hardwares: l4_4
+  # - 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 "diffusion-x2iat-test"
+  #   steps:
+  #     # Use explicit file shards so single-card cases do not reserve 2+ GPUs,
+  #     # and Bagel feature coverage is not duplicated via a broad tests/e2e sweep.

-      - label: ":full_moon: Diffusion X2I(&A&T) · Doc Test"
-        timeout_in_minutes: 60
-        commands:
-          - pytest -s -v tests/examples/*/test_text_to_image.py -m "full_model and example and H100" --run-level "full_model"
-        mirror_hardwares: h100_2
+  #     - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with H100 · Single-GPU"
+  #       timeout_in_minutes: 120
+  #       commands:
+  #         - >-
+  #           pytest -sv
+  #           tests/e2e/online_serving/test_qwen_image_expansion.py
+  #           tests/e2e/online_serving/test_qwen_image_edit_expansion.py
+  #           tests/e2e/online_serving/test_qwen_image_layered_expansion.py
+  #           tests/e2e/online_serving/test_boogu_image.py
+  #           tests/e2e/online_serving/test_boogu_image_edit.py
+  #           tests/e2e/online_serving/test_boogu_image_expansion.py
+  #           tests/e2e/online_serving/test_boogu_image_edit_expansion.py
+  #           tests/e2e/online_serving/test_bagel_expansion.py
+  #           tests/e2e/online_serving/test_krea2_expansion.py
+  #           tests/e2e/offline_inference/test_krea2_expansion.py
+  #           -m "full_model and diffusion and H100 and not distributed_cuda"
+  #           --run-level "full_model"
+  #       mirror_hardwares: h100_1

-      - label: ":full_moon: Diffusion X2I(&A&T) · Accuracy Test"
-        timeout_in_minutes: 180
-        commands:
-          - export VLLM_HTTP_TIMEOUT_KEEP_ALIVE=120
-          - pytest -s -v tests/e2e/accuracy/test_qwen_image.py --run-level full_model
-          - pytest -s -v tests/e2e/accuracy/test_diffusers_backend_similarity.py -k '2i_matches_diffusers' -m full_model --run-level full_model
-        mirror_hardwares: h100_1
+  #     - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with H100 · Multi-GPU Qwen-Image"
+  #       timeout_in_minutes: 120
+  #       commands:
+  #         - >-
+  #           pytest -sv
+  #           tests/e2e/online_serving/test_qwen_image_expansion.py
+  #           tests/e2e/online_serving/test_qwen_image_edit_expansion.py
+  #           tests/e2e/offline_inference/test_krea2_expansion.py
+  #           -m "full_model and diffusion and H100 and distributed_cuda"
+  #           --run-level "full_model"
+  #       mirror_hardwares: h100_2

-      - label: ":full_moon: HunyuanImage3-DIT · Accuracy Test"
-        timeout_in_minutes: 180
-        commands:
-          - export HUNYUAN_IMAGE3_MODEL="tencent/HunyuanImage-3.0-Instruct"
-          - export HUNYUAN_IMAGE3_DEVICES="0,1,2,3"
-          - pytest -s -v tests/e2e/accuracy/test_hunyuan_image3_pixel_accuracy.py -m full_model --run-level full_model
-        mirror_hardwares: h100_4
+  #     - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with H100 · Multi-GPU Layered/LongCat/FLUX"
+  #       timeout_in_minutes: 90
+  #       commands:
+  #         - >-
+  #           pytest -sv
+  #           tests/e2e/online_serving/test_qwen_image_layered_expansion.py
+  #           -m "full_model and diffusion and H100 and distributed_cuda"
+  #           --run-level "full_model"
+  #       mirror_hardwares: h100_2

-      - label: ":full_moon: Diffusion X2I(&A&T) · Perf Test · Qwen-Image"
-        key: nightly-diffusion-x2iat-performance-qwen-image
-        timeout_in_minutes: 180
-        commands:
-          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
-          - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
-          - 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
-            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
+  #     - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with H100 · Multi-GPU Bagel"
+  #       timeout_in_minutes: 120
+  #       commands:
+  #         - >-
+  #           pytest -sv
+  #           tests/e2e/online_serving/test_bagel_expansion.py
+  #           -m "full_model and diffusion and H100 and distributed_cuda"
+  #           --run-level "full_model"
+  #       mirror_hardwares: h100_2

-  - 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 X2I(&A&T) · GLM-Image Function Test with H100"
+  #       timeout_in_minutes: 120
+  #       commands:
+  #         - >-
+  #           pytest -sv
+  #           tests/e2e/online_serving/test_glm_image_expansion.py
+  #           tests/e2e/offline_inference/test_glm_image_autoround_w4a16_expansion.py
+  #           -m "full_model and diffusion and H100"
+  #           --run-level "full_model"
+  #       mirror_hardwares: h100_2

-      - label: ":full_moon: Diffusion X2V · Wan2.2 T2V Function Test"
-        timeout_in_minutes: 90
-        commands:
-          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "t2v" -m "full_model and cuda" --run-level "full_model"
-        mirror_hardwares: h100_2
+  #     - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with L4"
+  #       timeout_in_minutes: 120
+  #       commands:
+  #         - pytest -sv tests/e2e/ -k "not test_wan and not test_bagel_expansion and not hunyuan" -m "full_model and diffusion and L4" --run-level "full_model" --ignore=tests/e2e/accuracy
+  #       mirror_hardwares: l4_4

-      - label: ":full_moon: Diffusion X2V · Wan2.2 I2V/TI2V Function Test"
-        timeout_in_minutes: 90
-        commands:
-          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "i2v" -m "full_model and cuda" --run-level "full_model"
-        mirror_hardwares: h100_2
+  #     - label: ":full_moon: Diffusion X2I(&A&T) · Doc Test"
+  #       timeout_in_minutes: 60
+  #       commands:
+  #         - pytest -s -v tests/examples/*/test_text_to_image.py -m "full_model and example and H100" --run-level "full_model"
+  #       mirror_hardwares: h100_2

-      - label: ":full_moon: Diffusion X2V · Doc Test"
-        timeout_in_minutes: 60
-        commands:
-          - pytest -s -v tests/examples/offline_inference/test_image_to_video.py -m "full_model and example and H100" --run-level "full_model"
-        mirror_hardwares: h100_1
+  #     - label: ":full_moon: Diffusion X2I(&A&T) · Accuracy Test"
+  #       timeout_in_minutes: 180
+  #       commands:
+  #         - export VLLM_HTTP_TIMEOUT_KEEP_ALIVE=120
+  #         - pytest -s -v tests/e2e/accuracy/test_qwen_image*.py --run-level full_model
+  #         - pytest -s -v tests/e2e/accuracy/test_diffusers_backend_similarity.py -k '2i_matches_diffusers' -m full_model --run-level full_model
+  #       mirror_hardwares: h100_1

-      - label: ":full_moon: Diffusion X2V · Other Function Test"
-        timeout_in_minutes: 90
-        commands:
-          - pytest -s -v tests/e2e/offline_inference/test_wan22_autoround_w4a16_expansion.py -m "full_model and cuda" --run-level "full_model"
-        mirror_hardwares: h100_2
+  #     - label: ":full_moon: HunyuanImage3-DIT · Accuracy Test"
+  #       timeout_in_minutes: 180
+  #       commands:
+  #         - export HUNYUAN_IMAGE3_MODEL="tencent/HunyuanImage-3.0-Instruct"
+  #         - export HUNYUAN_IMAGE3_DEVICES="0,1,2,3"
+  #         - pytest -s -v tests/e2e/accuracy/test_hunyuan_image3_pixel_accuracy.py -m full_model --run-level full_model
+  #       mirror_hardwares: h100_4

-      - label: ":full_moon: Diffusion X2V · Wan2.2 Accuracy Test"
-        timeout_in_minutes: 180
-        commands:
-          - pytest -s -v tests/e2e/accuracy/wan22_i2v/test_wan22_i2v_video_similarity.py -m full_model --run-level full_model
-          - 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 X2I(&A&T) · Perf Test · Qwen-Image"
+  #       key: nightly-diffusion-x2iat-performance-qwen-image
+  #       timeout_in_minutes: 180
+  #       commands:
+  #         - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
+  #         - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
+  #         - export CACHE_DIT_VERSION=1.3.0
+  #         - |
+  #           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
+  #           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

-      - 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 X2I(&A&T) · Perf Test · Qwen-Image-Edit"
+  #       key: nightly-diffusion-x2iat-performance-qwen-image-edit
+  #       timeout_in_minutes: 180
+  #       commands:
+  #         - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
+  #         - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
+  #         - export CACHE_DIT_VERSION=1.3.0
+  #         - |
+  #           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
+  #           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

-      - 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
-        mirror_hardwares: h100_2
+  #     - 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
+  #       commands:
+  #         - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
+  #         - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
+  #         - export CACHE_DIT_VERSION=1.3.0
+  #         - |
+  #           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
+  #           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

-      - label: ":full_moon: Diffusion X2V · Perf Test"
-        key: nightly-diffusion-x2v-performance
-        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_wan22_i2v_vllm_omni.json
-            EXIT1=$$?
-            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_cosmos3_vllm_omni.json
-            EXIT2=$$?
-            buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
-            buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
-            if [ $$EXIT1 -ne 0 ]; then exit $$EXIT1; fi
-            exit $$EXIT2
-        mirror_hardwares: h100_2
+  #     - label: ":full_moon: Diffusion X2I(&A&T) · Perf Test · Qwen-Image-Layered"
+  #       key: nightly-diffusion-x2iat-performance-qwen-image-layered
+  #       timeout_in_minutes: 120
+  #       commands:
+  #         - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
+  #         - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
+  #         - export CACHE_DIT_VERSION=1.3.0
+  #         - |
+  #           set +e
+  #           pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_layered_vllm_omni.json
+  #           EXIT=$$?
+  #           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_1

-      - 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
+  #     - label: ":full_moon: Diffusion X2I(&A&T) · Perf Test · BAGEL"
+  #       key: nightly-diffusion-x2iat-performance-bagel
+  #       timeout_in_minutes: 180
+  #       commands:
+  #         - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
+  #         - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
+  #         - export CACHE_DIT_VERSION=1.3.0
+  #         - |
+  #           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
+  #           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_1

-  - 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"
-    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
+  # - 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 · 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 X2V · Wan2.2 T2V Function Test"
+  #       timeout_in_minutes: 90
+  #       commands:
+  #         - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "t2v" -m "full_model and cuda" --run-level "full_model"
+  #       mirror_hardwares: h100_2

-      - label: ":full_moon: Diffusion · Offloader Test"
-        timeout_in_minutes: 60
-        commands:
-          - pytest -sv tests/diffusion/offloader -m "full_model and cuda"
-        mirror_hardwares: l4_1
+  #     - label: ":full_moon: Diffusion X2V · Wan2.2 I2V/TI2V Function Test"
+  #       timeout_in_minutes: 90
+  #       commands:
+  #         - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "i2v" -m "full_model and cuda" --run-level "full_model"
+  #       mirror_hardwares: h100_2

-      - label: ":full_moon: Diffusion · Quantization Test with H100 · Single-GPU"
-        timeout_in_minutes: 60
-        commands:
-          - pytest -sv tests/diffusion/quantization -m 'full_model and cuda and H100' --run-level "full_model"
-        mirror_hardwares: h100_1
+  #     - 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"
+  #       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 X2V · Doc Test"
+  #       timeout_in_minutes: 60
+  #       commands:
+  #         - pytest -s -v tests/examples/offline_inference/test_image_to_video.py -m "full_model and example and H100" --run-level "full_model"
+  #       mirror_hardwares: h100_1

-      - 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 X2V · Other Function Test"
+  #       timeout_in_minutes: 90
+  #       commands:
+  #         - pytest -s -v tests/e2e/offline_inference/test_wan22_autoround_w4a16_expansion.py -m "full_model and cuda" --run-level "full_model"
+  #         - pytest -s -v tests/e2e/online_serving/test_wan_2_1_vace_expansion.py tests/e2e/online_serving/test_ltx2_expansion.py -m "full_model and cuda" --run-level "full_model"
+  #       mirror_hardwares: h100_2

-  # NOTE: ":email: Nightly Collection & Email" is deprecated and will be removed soon.
-  # Do not add new depends_on entries or extend this step.
-  - label: ":email: Nightly Collection & Email"
-    key: nightly-perf-distribution
-    depends_on:
-      - nightly-omni-performance-no-async-chunk
-      - nightly-omni-performance-async-chunk
-      - nightly-omni-performance-vllm-text
-      - nightly-omni-performance-multi-replicas
-      - nightly-tts-performance
-      - nightly-diffusion-x2iat-performance-qwen-image
-      - nightly-diffusion-x2v-performance
-    if: build.env("NIGHTLY") == "1" && build.env("EMAIL_DISTRIBUTION") == "1"
-    commands:
-      - pip install openpyxl
-      - export DEFAULT_INPUT_DIR=tests/dfx/perf/results
-      - export DEFAULT_OUTPUT_DIR=tests/dfx/perf/results
-      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-tts-performance
-      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-omni-performance-no-async-chunk
-      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-omni-performance-async-chunk
-      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-omni-performance-vllm-text
-      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-omni-performance-multi-replicas
-      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2iat-performance-qwen-image
-      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2v-performance
-      - python tools/nightly/generate_nightly_perf_excel.py
-      - python tools/nightly/generate_nightly_perf_html.py
-      - python tools/nightly/send_nightly_email.py --report-file "tests/dfx/perf/results/*.xlsx, tests/dfx/perf/results/*.html"
-      - buildkite-agent artifact upload "tests/dfx/perf/results/*.xlsx"
-      - buildkite-agent artifact upload "tests/dfx/perf/results/*.html"
-    agents:
-      queue: "cpu_queue_premerge"
+  #     - label: ":full_moon: Diffusion X2V · Wan2.2 Accuracy Test"
+  #       timeout_in_minutes: 180
+  #       commands:
+  #         - pytest -s -v tests/e2e/accuracy/wan22_i2v/test_wan22_i2v_video_similarity.py -m full_model --run-level full_model
+  #         - 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 · 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/test_video_streaming_output_similarity.py -m full_model --run-level full_model
+  #       mirror_hardwares: h100_2
+
+  #     - label: ":full_moon: Diffusion X2V · Perf Test"
+  #       key: nightly-diffusion-x2v-performance
+  #       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_wan22_i2v_vllm_omni.json
+  #           EXIT1=$$?
+  #           pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_cosmos3_vllm_omni.json
+  #           EXIT2=$$?
+  #           buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
+  #           buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
+  #           if [ $$EXIT1 -ne 0 ]; then exit $$EXIT1; fi
+  #           exit $$EXIT2
+  #       mirror_hardwares: h100_2
+
+  # - group: ":card_index_dividers: Diffusion Test"
+  #   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 · Offloader Test"
+  #       timeout_in_minutes: 40
+  #       commands:
+  #         - timeout 40m pytest -s -v tests/diffusion/offloader -m "full_model and cuda"
+  #       mirror_hardwares: l4_1
+
+  #     - label: ":full_moon: Diffusion · Quantization Test with H100"
+  #       commands:
+  #         - timeout 60m pytest -sv tests/diffusion/quantization -m 'full_model and cuda and H100' --run-level "full_model"
+  #       mirror_hardwares: h100_1
+
+  #     - label: ":full_moon: Diffusion · Quantization Test with L4"
+  #       timeout_in_minutes: 60
+  #       commands:
+  #         - 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
+  #   depends_on: upload-nightly-pipeline
+  #   if: build.env("NIGHTLY") == "1"
+  #   commands:
+  #     - python tools/nightly/buildkite_testcase_statistics.py -o tests/dfx/perf/results/buildkite_testcase_statistics.html
+  #     - buildkite-agent artifact upload "tests/dfx/perf/results/*.html"
+  #   mirror_hardwares: h100_1
+
+  # # NOTE: ":email: Nightly Collection & Email" is deprecated and will be removed soon.
+  # # Do not add new depends_on entries or extend this step.
+  # - label: ":email: Nightly Collection & Email"
+  #   key: nightly-perf-distribution
+  #   depends_on:
+  #     - nightly-omni-performance-no-async-chunk
+  #     - nightly-omni-performance-async-chunk
+  #     - nightly-omni-performance-vllm-text
+  #     - nightly-omni-performance-multi-replicas
+  #     - nightly-tts-performance
+  #     - nightly-tts-performance-higgs-audio-v3
+  #     - nightly-diffusion-x2iat-performance-qwen-image
+  #     - nightly-diffusion-x2iat-performance-qwen-image-edit
+  #     - nightly-diffusion-x2iat-performance-qwen-image-edit-2511
+  #     - nightly-diffusion-x2iat-performance-qwen-image-layered
+  #     - nightly-diffusion-x2iat-performance-bagel
+  #     - nightly-diffusion-x2v-performance
+  #     - nightly-testcase-statistics
+  #   if: build.env("NIGHTLY") == "1" && build.env("EMAIL_DISTRIBUTION") == "1"
+  #   commands:
+  #     - pip install openpyxl
+  #     - export DEFAULT_INPUT_DIR=tests/dfx/perf/results
+  #     - export DEFAULT_OUTPUT_DIR=tests/dfx/perf/results
+  #     - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-tts-performance
+  #     - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-tts-performance-higgs-audio-v3
+  #     - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-omni-performance-no-async-chunk
+  #     - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-omni-performance-async-chunk
+  #     - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-omni-performance-vllm-text
+  #     - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-omni-performance-multi-replicas
+  #     - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2iat-performance-qwen-image
+  #     - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2iat-performance-qwen-image-edit
+  #     - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2iat-performance-qwen-image-edit-2511
+  #     - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2iat-performance-qwen-image-layered
+  #     - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2iat-performance-bagel
+  #     - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2v-performance
+  #     - buildkite-agent artifact download "tests/dfx/perf/results/*.html" . --step nightly-testcase-statistics
+  #     - python tools/nightly/generate_nightly_perf_excel.py
+  #     - python tools/nightly/generate_nightly_perf_html.py
+  #     - python tools/nightly/send_nightly_email.py --report-file "tests/dfx/perf/results/*.xlsx, tests/dfx/perf/results/*.html"
+  #     - buildkite-agent artifact upload "tests/dfx/perf/results/*.xlsx"
+  #     - buildkite-agent artifact upload "tests/dfx/perf/results/*.html"
+  #   agents:
+  #     queue: "cpu_queue_premerge"
@@ -8,189 +8,133 @@ steps:
  - group: ":card_index_dividers: Simple Test"
    depends_on: upload-ready-pipeline
    steps:
-      - label: "Simple · Model Executor Test"
+      - label: "Simple · Diffusion & Model Executor Test"
        commands:
-          - "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"
+          - "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"
        mirror_hardwares: l4_1

      - label: "Simple · Other Test"
        commands:
-          - "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"
+          - "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"
        mirror_hardwares: l4_1

-  - group: ":card_index_dividers: Diffusion Test"
-    depends_on: upload-ready-pipeline
-    steps:
+  # - 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

-      - label: "Diffusion · Offloader Test"
-        commands:
-          - timeout 40m pytest -s -v tests/diffusion/offloader -m "core_model and cuda" --run-level "core_model"
-        mirror_hardwares: l4_1
+  # - group: ":card_index_dividers: Diffusion Test"
+  #   depends_on: upload-ready-pipeline
+  #   steps:

-      - label: "Diffusion · Cache Test"
-        commands:
-          - timeout 15m pytest -sv tests/diffusion/cache -m 'core_model and cuda' --run-level "core_model"
-        mirror_hardwares: l4_1
+  #     - label: "Diffusion · Offloader Test"
+  #       commands:
+  #         - timeout 40m pytest -s -v tests/diffusion/offloader -m "core_model and cuda" --run-level "core_model"
+  #       mirror_hardwares: l4_1

-      - label: "Diffusion · Batch Test"
-        commands:
-          - timeout 15m pytest -sv tests/diffusion/batching -m 'core_model and cuda' --run-level "core_model"
-        mirror_hardwares: l4_1
+  #     - label: "Diffusion · Cache Test"
+  #       commands:
+  #         - timeout 15m pytest -sv tests/diffusion/cache -m 'core_model and cuda' --run-level "core_model"
+  #       mirror_hardwares: l4_1

-      - 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/
-          - 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 · Batch Test"
+  #       commands:
+  #         - timeout 15m pytest -sv tests/diffusion/batching -m 'core_model and cuda' --run-level "core_model"
+  #       mirror_hardwares: l4_1

-      - label: "Diffusion · Model Test"
-        commands:
-          - timeout 15m pytest -sv tests/diffusion/models/ -m 'core_model and cuda' --run-level "core_model"
-        mirror_hardwares: l4_1
+  #     - label: "Diffusion · Distributed Test"
+  #       source_file_dependencies:
+  #         - tests/diffusion/distributed/
+  #         - vllm_omni/diffusion/distributed/
+  #         - vllm_omni/diffusion/attention/parallel/
+  #         - 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"
+  #       mirror_hardwares: l4_4

-      - label: "Diffusion · Other Test"
-        commands:
-          - timeout 15m pytest -sv tests/diffusion/*.py -m 'core_model and cuda' --run-level "core_model"
-        mirror_hardwares: l4_1
+  #     - label: "Diffusion · Model Test"
+  #       commands:
+  #         - timeout 15m pytest -sv tests/diffusion/models/ -m 'core_model and cuda' --run-level "core_model"
+  #       mirror_hardwares: l4_1

-  - group: ":card_index_dividers: Tiny Model Tests [Single GPU]"
-    depends_on: upload-ready-pipeline
-    steps:
+  #     - label: "Diffusion · Other Test"
+  #       commands:
+  #         - timeout 15m pytest -sv tests/diffusion/*.py -m 'core_model and cuda' --run-level "core_model"
+  #       mirror_hardwares: l4_1

-      - label: "Tiny Model Tests · Diffusion (base)"
-        commands:
-          - timeout 15m pytest -sv tests/model_tests/diffusion -m 'core_model and cuda' --run-level "core_model"
-        mirror_hardwares: l4_1
+  # - group: ":card_index_dividers: Tiny Model Tests [Single GPU]"
+  #   depends_on: upload-ready-pipeline
+  #   steps:

-  - label: "Engine&Model Executor Test"
-    depends_on: upload-ready-pipeline
-    commands:
-      - |
-        timeout 15m bash -c "
-                  pytest -sv tests/engine/ tests/model_executor/ -m 'core_model and cuda' --run-level 'core_model'
-        "
-    mirror_hardwares: l4_1
+  #     - label: "Tiny Model Tests · Diffusion (base)"
+  #       commands:
+  #         - timeout 15m pytest -sv tests/model_tests/diffusion -m 'core_model and cuda' --run-level "core_model"
+  #       mirror_hardwares: l4_1

-  - label: "Distributed Test"
-    depends_on: upload-ready-pipeline
-    commands:
-      - |
-        timeout 20m bash -c "
-          pytest -sv tests/distributed/ -m 'core_model and cuda and L4' --run-level 'core_model'
-        "
-    mirror_hardwares: l4_1
+  # - label: "Engine&Model Executor Test"
+  #   depends_on: upload-ready-pipeline
+  #   commands:
+  #     - |
+  #       timeout 15m bash -c "
+  #                 pytest -sv tests/engine/ tests/model_executor/ -m 'core_model and cuda' --run-level 'core_model'
+  #       "
+  #   mirror_hardwares: l4_1

-  - label: "Entrypoints Test"
-    depends_on: upload-ready-pipeline
-    commands:
-      - |
-        timeout 30m bash -c "
-          export VLLM_WORKER_MULTIPROC_METHOD=spawn
-          pytest -sv tests/entrypoints/ -m 'core_model and cuda' --run-level 'core_model'
-        "
-    mirror_hardwares: l4_1
+  # - label: "Distributed Test"
+  #   depends_on: upload-ready-pipeline
+  #   commands:
+  #     - |
+  #       timeout 20m bash -c "
+  #         pytest -sv tests/distributed/ -m 'core_model and cuda and L4' --run-level 'core_model'
+  #       "
+  #   mirror_hardwares: l4_1
+
+  # - label: "Entrypoints Test"
+  #   depends_on: upload-ready-pipeline
+  #   commands:
+  #     - |
+  #       timeout 30m bash -c "
+  #         export VLLM_WORKER_MULTIPROC_METHOD=spawn
+  #         pytest -sv tests/entrypoints/ -m 'core_model and cuda' --run-level 'core_model'
+  #       "
+  #   mirror_hardwares: l4_1

  - 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/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/
-          - vllm_omni/inputs/
-          - vllm_omni/outputs/
-        commands:
-          - |
-            timeout 20m bash -c "
-              pytest -s -v tests/e2e/features/rlhf_test/test_verl_omni_e2e.py
-            "
-        mirror_hardwares: l4_1
+      # - 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/
+      #     - vllm_omni/diffusion/
+      #     - vllm_omni/engine/
+      #     - vllm_omni/entrypoints/
+      #     - vllm_omni/inputs/
+      #     - vllm_omni/outputs/
+      #   commands:
+      #     - |
+      #       timeout 20m bash -c "
+      #         pytest -s -v tests/e2e/offline_inference/rlhf_test/test_verl_omni_e2e.py
+      #       "
+      #   mirror_hardwares: l4_1

-      - label: "Omni · Qwen3-Omni Test"
-        source_file_dependencies:
-          - tests/e2e/online_serving/test_qwen3_omni.py
-          - vllm_omni/model_executor/models/qwen3_omni/
-          - vllm_omni/model_executor/models/common/qwen3_code_predictor.py
-          - vllm_omni/model_executor/models/common/snake_activation.py
-          - vllm_omni/model_executor/models/qwen2_5_omni/qwen2_5_omni_thinker.py
-          - vllm_omni/model_executor/models/qwen3_tts/cuda_graph_decoder_wrapper.py
-          - vllm_omni/model_executor/stage_input_processors/qwen3_omni.py
-          - vllm_omni/deploy/qwen3_omni_moe.yaml
-        commands:
-          - |
-            timeout 20m bash -c "
-              pytest -s -v tests/e2e/online_serving/test_qwen3_omni.py -m 'core_model' --run-level 'core_model'
-            "
-        mirror_hardwares: h100_2
+      # - label: "Omni · Qwen3-Omni Test"
+      #   source_file_dependencies:
+      #     - tests/e2e/online_serving/test_qwen3_omni.py
+      #     - vllm_omni/model_executor/models/qwen3_omni/
+      #     - vllm_omni/model_executor/stage_input_processors/qwen3_omni.py
+      #     - vllm_omni/deploy/qwen3_omni_moe.yaml
+      #   commands:
+      #     - |
+      #       timeout 20m bash -c "
+      #         pytest -s -v tests/e2e/online_serving/test_qwen3_omni.py -m 'core_model' --run-level 'core_model'
+      #       "
+      #   mirror_hardwares: h100_2

      - label: "Omni · MiniCPM-o 4.5 Offline Test"
-        source_file_dependencies:
-          - tests/e2e/offline_inference/test_minicpmo_4_5.py
-          - vllm_omni/model_executor/models/minicpmo_4_5/
-          - vllm_omni/model_executor/models/step_audio2/
-          - vllm_omni/model_executor/models/cosyvoice3/code2wav_core/
-          - vllm_omni/model_executor/models/cosyvoice3/utils.py
-          - vllm_omni/model_executor/stage_input_processors/minicpmo_4_5_omni.py
-          - vllm_omni/deploy/minicpmo_4_5.yaml
        commands:
          - |
            timeout 20m bash -c "
@@ -199,14 +143,6 @@ steps:
        mirror_hardwares: h100_1

      - label: "Omni · MiniCPM-o 4.5 Online Test"
-        source_file_dependencies:
-          - tests/e2e/online_serving/test_minicpmo_4_5.py
-          - vllm_omni/model_executor/models/minicpmo_4_5/
-          - vllm_omni/model_executor/models/step_audio2/
-          - vllm_omni/model_executor/models/cosyvoice3/code2wav_core/
-          - vllm_omni/model_executor/models/cosyvoice3/utils.py
-          - vllm_omni/model_executor/stage_input_processors/minicpmo_4_5_omni.py
-          - vllm_omni/deploy/minicpmo_4_5.yaml
        commands:
          - |
            timeout 20m bash -c "
@@ -215,33 +151,6 @@ steps:
        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
-          - vllm_omni/deploy/minicpmo_4_5.yaml
-          - 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/models/step_audio2/
-          - vllm_omni/model_executor/models/cosyvoice3/code2wav_core/
-          - vllm_omni/model_executor/models/cosyvoice3/utils.py
-          - 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/engine/orchestrator.py
-          - vllm_omni/entrypoints/cli/serve.py
-          - vllm_omni/entrypoints/openai/api_server.py
-          - vllm_omni/entrypoints/openai/duplex_capability.py
-          - vllm_omni/config/stage_config.py
        commands:
          - |
            timeout 20m bash -c "
@@ -255,86 +164,156 @@ steps:
          - 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/models/step_audio2/
+          - vllm_omni/model_executor/models/cosyvoice3/code2wav_core/
+          - vllm_omni/model_executor/models/cosyvoice3/utils.py
          - 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 · Qwen3-TTS CustomVoice Test"
-        source_file_dependencies:
-          - tests/e2e/online_serving/test_qwen3_tts_customvoice.py
-          - vllm_omni/model_executor/models/qwen3_tts/
-          - vllm_omni/model_executor/models/common/qwen3_code_predictor.py
-          - vllm_omni/model_executor/models/common/snake_activation.py
-          - vllm_omni/model_executor/models/common/alias_free_activation.py
-          - vllm_omni/model_executor/stage_input_processors/qwen3_tts.py
-          - vllm_omni/deploy/qwen3_tts.yaml
-        commands:
-          - |
-            timeout 20m bash -c "
-              export VLLM_ALLOW_LONG_MAX_MODEL_LEN=1
-              pytest -s -v tests/e2e/online_serving/test_qwen3_tts_customvoice.py -m 'core_model' --run-level 'core_model'
-            "
-        mirror_hardwares: l4_1
+      # - label: "TTS · VoxCPM2 Test"
+      #   source_file_dependencies:
+      #     - tests/e2e/online_serving/test_voxcpm2_tts.py
+      #     - vllm_omni/model_executor/models/voxcpm2/
+      #     - vllm_omni/deploy/voxcpm2.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/online_serving/test_voxcpm2_tts.py -m 'core_model' --run-level 'core_model'
+      #       "
+      #   mirror_hardwares: l4_1

-      - label: "TTS · Qwen3-TTS Base Test"
-        source_file_dependencies:
-          - tests/e2e/online_serving/test_qwen3_tts_base.py
-          - vllm_omni/model_executor/models/qwen3_tts/
-          - vllm_omni/model_executor/models/common/qwen3_code_predictor.py
-          - vllm_omni/model_executor/models/common/snake_activation.py
-          - vllm_omni/model_executor/models/common/alias_free_activation.py
-          - vllm_omni/model_executor/stage_input_processors/qwen3_tts.py
-          - vllm_omni/deploy/qwen3_tts.yaml
-        commands:
-          - |
-            timeout 20m bash -c "
-              export VLLM_WORKER_MULTIPROC_METHOD=spawn
-              export VLLM_ALLOW_LONG_MAX_MODEL_LEN=1
-              export VLLM_OMNI_USE_V2_RUNNER=1
-              pytest -s -v tests/e2e/online_serving/test_qwen3_tts_base.py -m 'core_model' --run-level 'core_model'
-            "
-        mirror_hardwares: l4_1
+      # - label: "TTS · Qwen3-TTS CustomVoice Test"
+      #   source_file_dependencies:
+      #     - tests/e2e/online_serving/test_qwen3_tts_customvoice.py
+      #     - vllm_omni/model_executor/models/qwen3_tts/
+      #     - vllm_omni/model_executor/stage_input_processors/qwen3_tts.py
+      #     - vllm_omni/deploy/qwen3_tts.yaml
+      #   commands:
+      #     - |
+      #       timeout 20m bash -c "
+      #         export VLLM_ALLOW_LONG_MAX_MODEL_LEN=1
+      #         pytest -s -v tests/e2e/online_serving/test_qwen3_tts_customvoice.py -m 'core_model' --run-level 'core_model'
+      #       "
+      #   mirror_hardwares: l4_1

-      - label: "Diffusion · Wan22 Test"
-        source_file_dependencies:
-          - tests/e2e/online_serving/test_wan22_t2v.py
-          - vllm_omni/diffusion/models/wan2_2/
-          - vllm_omni/diffusion/models/dmd2/
-        commands:
-          - |
-            timeout 40m bash -c "
-              export VLLM_IMAGE_FETCH_TIMEOUT=60
-              pytest -s -v tests/e2e/online_serving/test_wan22_t2v.py -m 'core_model' --run-level 'core_model'
-            "
-        mirror_hardwares: h100_1
+      # - label: "TTS · Qwen3-TTS Base Test"
+      #   source_file_dependencies:
+      #     - tests/e2e/online_serving/test_qwen3_tts_base.py
+      #     - vllm_omni/model_executor/models/qwen3_tts/
+      #     - vllm_omni/model_executor/stage_input_processors/qwen3_tts.py
+      #     - vllm_omni/deploy/qwen3_tts.yaml
+      #   commands:
+      #     - |
+      #       timeout 20m bash -c "
+      #         export VLLM_WORKER_MULTIPROC_METHOD=spawn
+      #         export VLLM_ALLOW_LONG_MAX_MODEL_LEN=1
+      #         export VLLM_OMNI_USE_V2_RUNNER=1
+      #         pytest -s -v tests/e2e/online_serving/test_qwen3_tts_base.py -m 'core_model' --run-level 'core_model'
+      #       "
+      #   mirror_hardwares: l4_1

-      - label: "Diffusion · Cosmos3 Test"
-        source_file_dependencies:
-          - tests/e2e/online_serving/test_cosmos3.py
-          - vllm_omni/diffusion/models/cosmos3/
-          - vllm_omni/diffusion/distributed/autoencoders/autoencoder_kl_wan.py
-          - vllm_omni/experimental/world_models/
-          - vllm_omni/model_extras/cosmos3.py
-        commands:
-          - |
-            timeout 40m bash -c "
-              export VLLM_IMAGE_FETCH_TIMEOUT=60
-              pytest -s -v tests/e2e/online_serving/test_cosmos3.py -m 'core_model' --run-level 'core_model'
-            "
-        mirror_hardwares: h100_1
+      # - label: "TTS · Higgs-Audio-V3 Test"
+      #   source_file_dependencies:
+      #     - tests/e2e/online_serving/test_higgs_audio_v3.py
+      #     - vllm_omni/model_executor/models/higgs_audio_v3/
+      #     - vllm_omni/model_executor/stage_input_processors/higgs_audio_v3.py
+      #     - vllm_omni/deploy/higgs_multimodal_qwen3.yaml
+      #   commands:
+      #     - |
+      #       timeout 20m bash -c "
+      #         export VLLM_WORKER_MULTIPROC_METHOD=spawn
+      #         export VLLM_USE_DEEP_GEMM=0
+      #         export VLLM_MOE_USE_DEEP_GEMM=0
+      #         pytest -s -v tests/e2e/online_serving/test_higgs_audio_v3.py -m 'core_model' --run-level 'core_model'
+      #       "
+      #   mirror_hardwares: l4_1

-      - label: "Diffusion · Qwen Image Test"
-        source_file_dependencies:
-          - tests/e2e/online_serving/test_qwen_image.py
-          - vllm_omni/diffusion/models/qwen_image/
-          - vllm_omni/diffusion/models/dmd2/
-          - vllm_omni/diffusion/distributed/autoencoders/autoencoder_kl_qwenimage.py
-        commands:
-          - |
-            timeout 40m bash -c "
-              export VLLM_IMAGE_FETCH_TIMEOUT=60
-              pytest -s -v tests/e2e/online_serving/test_qwen_image.py -m 'core_model' --run-level 'core_model'
-            "
-        mirror_hardwares: h100_1
+      # - label: "Diffusion · Qwen Image Edit Test"
+      #   source_file_dependencies:
+      #     - tests/e2e/online_serving/test_qwen_image_edit.py
+      #     - vllm_omni/diffusion/models/qwen_image/
+      #   commands:
+      #     - |
+      #       timeout 20m bash -c "
+      #         pytest -s -v tests/e2e/online_serving/test_qwen_image_edit.py -m 'core_model' --run-level 'core_model'
+      #       "
+      #   mirror_hardwares: h100_1
+
+      # - label: "Diffusion · Bagel Test"
+      #   source_file_dependencies:
+      #     - tests/e2e/online_serving/test_bagel.py
+      #     - vllm_omni/model_executor/models/bagel/
+      #     - vllm_omni/diffusion/models/bagel/
+      #     - vllm_omni/model_executor/stage_input_processors/bagel.py
+      #     - vllm_omni/deploy/bagel.yaml
+      #   commands:
+      #     - |
+      #       timeout 40m bash -c "
+      #         export VLLM_IMAGE_FETCH_TIMEOUT=60
+      #         pytest -s -v tests/e2e/online_serving/test_bagel.py -m 'core_model' --run-level 'core_model'
+      #       "
+      #   mirror_hardwares: h100_1
+
+      # - label: "Diffusion · Wan22 Test"
+      #   source_file_dependencies:
+      #     - tests/e2e/online_serving/test_wan22_t2v.py
+      #     - vllm_omni/diffusion/models/wan2_2/
+      #   commands:
+      #     - |
+      #       timeout 40m bash -c "
+      #         export VLLM_IMAGE_FETCH_TIMEOUT=60
+      #         pytest -s -v tests/e2e/online_serving/test_wan22_t2v.py -m 'core_model' --run-level 'core_model'
+      #       "
+      #   mirror_hardwares: h100_1
+
+      # - label: "Diffusion · Cosmos3 Test"
+      #   source_file_dependencies:
+      #     - tests/e2e/online_serving/test_cosmos3.py
+      #     - vllm_omni/diffusion/models/cosmos3/
+      #   commands:
+      #     - |
+      #       timeout 40m bash -c "
+      #         export VLLM_IMAGE_FETCH_TIMEOUT=60
+      #         pytest -s -v tests/e2e/online_serving/test_cosmos3.py -m 'core_model' --run-level 'core_model'
+      #       "
+      #   mirror_hardwares: h100_1
+
+      # - label: "Diffusion · Qwen Image Test"
+      #   source_file_dependencies:
+      #     - tests/e2e/online_serving/test_qwen_image.py
+      #     - vllm_omni/diffusion/models/qwen_image/
+      #   commands:
+      #     - |
+      #       timeout 40m bash -c "
+      #         export VLLM_IMAGE_FETCH_TIMEOUT=60
+      #         pytest -s -v tests/e2e/online_serving/test_qwen_image.py -m 'core_model' --run-level 'core_model'
+      #       "
+      #   mirror_hardwares: h100_1
+
+      # - label: "Diffusion · Qwen Image Layered Test"
+      #   source_file_dependencies:
+      #     - tests/e2e/online_serving/test_qwen_image_layered.py
+      #     - vllm_omni/diffusion/models/qwen_image/
+      #   commands:
+      #     - |
+      #       timeout 40m bash -c "
+      #         export VLLM_IMAGE_FETCH_TIMEOUT=60
+      #         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
@@ -30,6 +30,14 @@ steps:
      - pytest -s -v tests/dfx/reliability/test_reliability_hunyuan_image.py -m "slow"
    mirror_hardwares: h100_4

+  - label: "Reliability Test - voxcpm2"
+    timeout_in_minutes: 180
+    depends_on: upload-weekly-pipeline
+    if: build.env("WEEKLY") == "1" || build.pull_request.labels includes "weekly-test"
+    commands:
+      - pytest -s -v tests/dfx/reliability/test_reliability_voxcpm2.py -m "slow"
+    mirror_hardwares: l4_1
+
  - group: "Reliability Test - Invalid parameters Test"
    depends_on: upload-weekly-pipeline
    if: build.env("WEEKLY") == "1" || build.pull_request.labels includes "weekly-test"
@@ -100,22 +108,6 @@ steps:
            --run-level "full_model"
        mirror_hardwares: h100_1

-      # Own step: the image has no NanoCodec, so this file import-skips out of
-      # the mark-selected TTS steps above. The overrides keep nemo_toolkit[tts]
-      # from downgrading transformers; see
-      # examples/offline_inference/text_to_speech/README.md.
-      - label: "TTS · Gepard-1.0 · L4"
-        timeout_in_minutes: 60
-        commands:
-          - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
-          - printf '%s\n' "transformers>=5.5.3" "huggingface-hub>=1.0" > /tmp/gepard-overrides.txt
-          - uv pip install --python "$(python3 -c 'import sys; print(sys.executable)')" "nemo_toolkit[tts]==2.7.3" --overrides /tmp/gepard-overrides.txt
-          - >-
-            pytest -s -v tests/e2e/offline_inference/test_gepard_tts.py
-            -m "tts and cuda"
-            --run-level "full_model"
-        mirror_hardwares: l4_1
-
      - label: "Diffusion · L4"
        timeout_in_minutes: 60
        commands:
@@ -141,4 +133,4 @@ steps:
            pytest -sv
            tests/e2e/ -m "slow and diffusion and H100 and distributed_cuda"
            --run-level "full_model"
-        mirror_hardwares: h100_4
+        mirror_hardwares: h100_2
@@ -10,7 +10,7 @@ steps:
          DOCKER_BUILDKIT: "1"
          # Buildkite will automatically replace this with the actual commit hash
          VLLM_IMAGE_TAG: "${BUILDKITE_COMMIT}"
-          VLLM_VERSION: "v0.27.0"
+          VLLM_VERSION: "v0.25.0"
        priority: 100
        timeout_in_minutes: 60
        soft_fail: false
@@ -4,6 +4,10 @@

 set -euo pipefail

+# minicpm-challenge branch: skip entire Intel/XPU CI (no pipeline upload => no image build / tests).
+echo "--- Skipping Intel/XPU CI on this branch (including image build)"
+exit 0
+
 source .buildkite/common/scripts/resolve_skip_ci.sh

 upload_pipeline() {
@@ -34,23 +34,8 @@ 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 "${base_image_name}")" ]; then
-    build_vllm_base
+    docker_build "${base_image_name}" --target vllm-base --build-arg "VLLM_VERSION=${VLLM_VERSION}"
 fi

 # Try building the docker image
@@ -87,9 +72,8 @@ time timeout -k 30 30m docker run \
    echo $ZE_AFFINITY_MASK
    pip install tblib==3.1.0
    cd /workspace/vllm-omni
-    XPU_TEST_PATHS="tests/diffusion tests/dfx tests/e2e"
-    pytest -v -s $XPU_TEST_PATHS -m "core_model and xpu and B60"
+    pytest -v -s -m "core_model and xpu and B60"
    pytest -v -s tests/diffusion/quantization/test_mxfp8_config.py
-    pytest -v -s $XPU_TEST_PATHS -m "advanced_model and xpu and B60"
-    pytest -v -s $XPU_TEST_PATHS -m "omni and xpu and B60"
+    pytest -v -s -m "advanced_model and xpu and B60"
+    pytest -v -s -m "omni and xpu and B60"
 '
@@ -1,52 +1,60 @@
+env:
+  VLLM_WORKER_MULTIPROC_METHOD: spawn
+  VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
+  HF_HUB_DOWNLOAD_TIMEOUT: 300
+  HF_HUB_ETAG_TIMEOUT: 60
+
 steps:
+  # - group: ":card_index_dividers: Diffusion X2V Model Test"
+  #   key: nightly-diffusion-x2v-group
+  #   depends_on: upload-nightly-pipeline
+  #   if: >-
+  #     build.env("NIGHTLY") == "1" ||
+  #     build.pull_request.labels includes "nightly-test" ||
+  #     build.pull_request.labels includes "diffusion-x2v-test"
+  #   steps:
+  #     - label: ":full_moon: Diffusion X2V · Function Test"
+  #       mirror_hardwares: a2b3_npu_4
+  #       env:
+  #         VLLM_WORKER_MULTIPROC_METHOD: spawn
+  #         HF_TOKEN: "${HF_TOKEN}"
+  #       commands:
+  #         - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k npu_i2v --run-level "full_model"
+  # - 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"
+  #       mirror_hardwares: a3_npu_2
+  #       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
+  #         - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
+  #         - |
+  #           set +e
+  #           pytest -s -v tests/dfx/perf/scripts/run_benchmark.py -m npu --test-config-file tests/dfx/perf/tests/test_tts.json
+  #           EXIT=$$?
+  #           buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
+  #           exit $$EXIT
+
  - group: ":card_index_dividers: Omni Model Test"
-    key: nightly-omni-test-group
+    key: nightly-omni-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 "omni-test"
    steps:
-      - 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:
-          - 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"
+        timeout_in_minutes: 180
        mirror_hardwares: a3_npu_2
        env:
          VLLM_WORKER_MULTIPROC_METHOD: spawn
          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
          HF_TOKEN: "${HF_TOKEN}"
        commands:
-          - pytest -s -v tests/e2e/offline_inference/test_minicpmo_4_5.py tests/e2e/online_serving/test_minicpmo_4_5.py tests/e2e/online_serving/test_minicpmo_4_5_expansion.py -m 'full_model' --run-level 'full_model'
+          - pytest -s -v tests/e2e/offline_inference/test_minicpmo_4_5.py tests/e2e/online_serving/test_minicpmo_4_5.py tests/e2e/online_serving/test_minicpmo_4_5_expansion.py -m 'full_model and npu and A3 and omni' --run-level 'full_model'
      - label: ":full_moon: Omni · MiniCPM-o 4.5 · Duplex Test"
        timeout_in_minutes: 180
        mirror_hardwares: a3_npu_2
@@ -55,11 +63,17 @@ steps:
          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
          HF_TOKEN: "${HF_TOKEN}"
        commands:
+          # NPU has no merge-level pipeline, so advanced_model duplex coverage
+          # rides on nightly alongside full_model.
+          #
+          # test_duplex_soft_interrupt is CUDA-only and carries no npu mark, so
+          # the marker filter below drops it: it needs real-time duplex
+          # throughput that the current NPU stack does not reach.
          - >-
            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"
+            -m "(full_model or advanced_model) and npu and A3 and omni"
            --run-level full_model
      - label: ":full_moon: Omni · MiniCPM-o 4.5 · Accuracy Test"
        timeout_in_minutes: 180
@@ -69,24 +83,35 @@ steps:
          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
          HF_TOKEN: "${HF_TOKEN}"
        commands:
-          # Pin the Seed-TTS scorer to the second card: _get_eval_device() otherwise
-          # auto-picks npu:0, which the omni server under test already fills with its
-          # KV cache, so Whisper OOMs on `.to(device)` and every clip reports
-          # "ASR / WER failed".
          - export SEED_TTS_WER_EVAL=1
+          - export SEED_TTS_SIM_EVAL=1
          - export SEED_TTS_EVAL_DEVICE=npu:1
          - |
            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
+            pytest -s -v tests/e2e/accuracy/minicpmo_4_5/test_minicpmo_4_5.py -k "not videomme" -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"
+      - label: ":full_moon: Omni · MiniCPM-o 4.5 · Video-MME Accuracy Test"
+        key: nightly-omni-accuracy-minicpmo-4-5-videomme-npu
+        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:
+          # Video-MME needs no repo override here: the NPU runners pre-stage the dataset under
+          # the lmms-eval mirror, which is now VIDEOMME_DEFAULT_HF_REPO, so snapshot_download
+          # resolves it straight out of the hub cache.
+          - |
+            set +e
+            pytest -s -v tests/e2e/accuracy/minicpmo_4_5/test_minicpmo_4_5.py -k videomme -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
+      - label: ":full_moon: Omni · MiniCPM-o 4.5 · Perf Test"
+        key: nightly-omni-performance-minicpmo-4-5-npu
        timeout_in_minutes: 180
        mirror_hardwares: a3_npu_2
        env:
@@ -95,118 +120,42 @@ steps:
          HF_TOKEN: "${HF_TOKEN}"
        commands:
          - export BENCHMARK_DIR=tests/dfx/perf/results
-          - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
          - |
            set +e
-            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py -m npu --test-config-file tests/dfx/perf/tests/test_tts.json
+            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py -m "npu and A3" --test-config-file tests/dfx/perf/tests/test_minicpmo_4_5.json
            EXIT=$$?
            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
            exit $$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"
+      - label: ":full_moon: Omni · MiniCPM-o 4.5 · Perf Test (A2)"
+        key: nightly-omni-performance-minicpmo-4-5-npu-a2
        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
+        mirror_hardwares: a2b3_npu_1
        env:
          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
          HF_TOKEN: "${HF_TOKEN}"
-          DIFFUSION_ATTENTION_BACKEND: TORCH_SDPA
+          HF_HUB_OFFLINE: "1"
        commands:
-          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
+          - export 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
+            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py -m "npu and A2" --test-config-file tests/dfx/perf/tests/test_minicpmo_4_5.json
            EXIT=$$?
-            buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
-            buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
+            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
            exit $$EXIT
-      - label: ":full_moon: Diffusion Perf · HunyuanImage3 TP=2 SP=2"
+      - label: ":full_moon: Omni · MiniCPM-o 4.5 · Duplex Seed-TTS Perf Test"
+        key: nightly-omni-performance-minicpmo-4-5-duplex-seed-tts-npu
        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
+        mirror_hardwares: a3_npu_2
        env:
          VLLM_WORKER_MULTIPROC_METHOD: spawn
          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
          HF_TOKEN: "${HF_TOKEN}"
        commands:
-          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k npu_i2v --run-level "full_model"
-      - label: ":full_moon: Diffusion X2V · Perf Test"
-        key: nightly-diffusion-x2v-performance
-        timeout_in_minutes: 180
-        mirror_hardwares: a3_npu_4
-        env:
-          VLLM_WORKER_MULTIPROC_METHOD: spawn
-          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
-          HF_TOKEN: "${HF_TOKEN}"
-          DIFFUSION_ATTENTION_BACKEND: TORCH_SDPA
-        commands:
-          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
+          - export 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
+            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py -m "npu and A3" --test-config-file tests/dfx/perf/tests/test_minicpmo_4_5_duplex_seed_tts.json
            EXIT=$$?
-            buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
-            buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
+            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
            exit $$EXIT
@@ -1,15 +1,4 @@
 steps:
-  - label: "Platforms Test"
-    key: ready-platforms-test
-    depends_on: upload-ready-pipeline
-    mirror_hardwares: a3_npu_2
-    commands:
-      - >-
-        pytest -s -v
-        tests/platforms/npu/test_minicpmo_code2wav_npugraph.py
-        -m 'core_model and npu and A3 and omni'
-        --run-level 'core_model'
-
  - group: ":card_index_dividers: Omni Model Test"
    key: ready-omni-group
    depends_on: upload-ready-pipeline
@@ -19,6 +8,7 @@ steps:
        env:
          VLLM_WORKER_MULTIPROC_METHOD: spawn
          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
+          HF_TOKEN: "${HF_TOKEN}"
        commands:
          - pytest -s -v tests/e2e/offline_inference/test_minicpmo_4_5.py tests/e2e/online_serving/test_minicpmo_4_5.py -m 'core_model and npu and A3 and omni' --run-level 'core_model'
      - label: "Omni · MiniCPM-o 4.5 Duplex Test"
@@ -26,6 +16,7 @@ steps:
        env:
          VLLM_WORKER_MULTIPROC_METHOD: spawn
          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
+          HF_TOKEN: "${HF_TOKEN}"
        commands:
          - pytest -s -v tests/e2e/online_serving/test_minicpmo_4_5_duplex.py -m 'core_model and npu and A3 and omni' --run-level 'core_model'
      - label: "Omni · MiniCPM-o 4.5 Perf Test"
@@ -38,15 +29,15 @@ steps:
          - 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
-    depends_on: upload-ready-pipeline
-    steps:
-      - label: ":sound: TTS · Qwen3-TTS CustomVoice Test"
-        mirror_hardwares: a2b3_npu_1
-        env:
-          VLLM_WORKER_MULTIPROC_METHOD: spawn
-          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
-          HF_TOKEN: "${HF_TOKEN}"
-        commands:
-          - pytest -s -v tests/e2e/online_serving/test_qwen3_tts_customvoice.py -m 'core_model and npu' --run-level 'core_model'
+  # - group: ":card_index_dividers: TTS Model Test"
+  #   key: ready-tts-group
+  #   depends_on: upload-ready-pipeline
+  #   steps:
+  #     - label: ":sound: TTS · Qwen3-TTS CustomVoice Test"
+  #       mirror_hardwares: a2b3_npu_1
+  #       env:
+  #         VLLM_WORKER_MULTIPROC_METHOD: spawn
+  #         VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
+  #         HF_TOKEN: "${HF_TOKEN}"
+  #       commands:
+  #         - pytest -s -v tests/e2e/online_serving/test_qwen3_tts_customvoice.py -m 'core_model' --run-level 'core_model'
@@ -40,11 +40,9 @@ 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"
@@ -65,7 +63,7 @@ steps:
              --progress plain \
              -f docker/Dockerfile.cuda .
          - "docker push public.ecr.aws/q9t5s3a7/vllm-omni-release-repo:$BUILDKITE_COMMIT-$(uname -m)"
-          # Fallback default tag (no arch suffix) if arm64 build fails.
+          # re-tag to default image tag and push, just in case 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:
@@ -88,26 +86,6 @@ 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:
@@ -1,147 +0,0 @@
-#!/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"
@@ -1,87 +1,50 @@
 #!/bin/bash
 # SPDX-License-Identifier: Apache-2.0
 #
-# 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>
+# 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.

 set -euo pipefail

-MODE="${1:-release}"
+RELEASE_VERSION=$(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

+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 ${MODE} images to ${DOCKERHUB_REPO}"
+echo "Publishing release images v${RELEASE_VERSION}"
 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

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

-docker pull "${SRC_X86}"
-docker pull "${SRC_ARM}"
+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"

-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}:${tag}"
-}
+docker tag "${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"

-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}" "${ARCH_BASE}"
-done
+docker manifest rm "${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}"

 echo ""
-echo "Successfully published ${MODE} images"
+echo "Successfully published release images for v${RELEASE_VERSION}"
@@ -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.md` — if CPU offload supported (add to supported models table)
+2. `docs/user_guide/diffusion/cpu_offload_diffusion.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`)
@@ -220,70 +220,65 @@ See `plan/voxcpm2_native_ar_design.md`.

 ### Steps

-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.
+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.

-   Create `vllm_omni/entrypoints/openai/tts_adapters/your_model.py`:
+   **Point 1** — stage constant (near the top, alongside the other `_*_TTS_MODEL_STAGES` sets):
   ```python
-   @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,
-           )
+   _YOUR_MODEL_TTS_MODEL_STAGES = {"your_stage_key"}
   ```

-   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 2** — union into `_TTS_MODEL_STAGES`:
+   ```python
+   _TTS_MODEL_STAGES: set[str] = (
+       ...
+       | _YOUR_MODEL_TTS_MODEL_STAGES
+   )
+   ```

-   > **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 3** — model type detection in `_detect_tts_model_type()`:
+   ```python
+   if model_stage in _YOUR_MODEL_TTS_MODEL_STAGES:
+       return "your_model"
+   ```

-   **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 4** — validation dispatch in `_validate_tts_request()`:
+   ```python
+   if self._tts_model_type == "your_model":
+       return self._validate_your_model_request(request)
+   ```

-   **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).
+   **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

-   > **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.
+   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.

-   > **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.
+   > **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.

 2. **Handle model-specific parameters**:
   - Voice cloning: `ref_audio` encoding and prompt injection
@@ -300,7 +295,7 @@ 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, called from the adapter."""
+    """Build prompt with reference audio for voice cloning in serving_speech.py."""
    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()]
@@ -326,7 +321,7 @@ 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, adapter 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, `serving_speech` 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` |
@@ -371,7 +366,7 @@ See [vllm-omni-test skill](../vllm-omni-test/SKILL.md) § **Runtime send helpers

 ### Deliverables

- One adapter file under `tts_adapters/` plus its line in the package import block
+- Updated `serving_speech.py` with all 5 integration points (single commit)
 - 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)
@@ -519,8 +514,8 @@ Use this checklist when integrating a new TTS model:
 - [ ] README.md written

 ### Phase 3: Online Serving
- [ ] 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)
+- [ ] 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)
 - [ ] 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, 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.
+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.
 ---

 # PR Pre-Check
@@ -65,8 +65,6 @@ 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

 ```
@@ -79,7 +77,6 @@ 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
@@ -47,19 +47,6 @@ 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:
@@ -1,68 +0,0 @@
-# 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 @@
-# Repository Skills for vLLM-Omni
+# Claude Skills for vLLM-Omni

-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.
+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.

 ## Directory Structure

@@ -14,41 +14,31 @@ 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`](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
+- `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

 ## Maintenance Guidelines

@@ -1,239 +0,0 @@
---
-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.
@@ -1,4 +0,0 @@
-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."
@@ -1,121 +0,0 @@
-# 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.
@@ -1,51 +0,0 @@
-# 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.
@@ -1,39 +0,0 @@
-# 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.
@@ -1,48 +0,0 @@
-# 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.
@@ -1,36 +0,0 @@
-# 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.
@@ -1,105 +0,0 @@
-# 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.
@@ -1,99 +0,0 @@
-# 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.
@@ -1,24 +0,0 @@
-# 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.
@@ -1,26 +0,0 @@
-# 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.
@@ -1,22 +0,0 @@
-# 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.
@@ -1,24 +0,0 @@
-# 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.
@@ -1,22 +0,0 @@
-# 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.
@@ -1,24 +0,0 @@
-# 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.
@@ -1,22 +0,0 @@
-# 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.
@@ -1,24 +0,0 @@
-# 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.
@@ -1,33 +0,0 @@
-# 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.
@@ -1,23 +0,0 @@
-# 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.
@@ -1,25 +0,0 @@
-# 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.
@@ -1,24 +0,0 @@
-# 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.
@@ -1,23 +0,0 @@
-# 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.
@@ -1,23 +0,0 @@
-# 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.
@@ -1,25 +0,0 @@
-# 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.
@@ -1,21 +0,0 @@
-# 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.
@@ -1,24 +0,0 @@
-# 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.
@@ -1,21 +0,0 @@
-# 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.
@@ -1,22 +0,0 @@
-# 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.
@@ -1,23 +0,0 @@
-# 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.
@@ -1,34 +0,0 @@
-# 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.
@@ -1,129 +0,0 @@
-# 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.
@@ -1,191 +0,0 @@
-# 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.
@@ -1,76 +0,0 @@
-# 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.
@@ -1,11 +1,6 @@
 # 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
@@ -21,15 +16,30 @@
 /.claude/ @hsliuustc0106

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

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

-# ComfyUI / external apps
-/apps/ @fhfuih @alex-jw-brooks
+# 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

-# Benchmarks (out-of-tree)
+# 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/accuracy/ @david6666666
 /benchmarks/build_dataset/ @linyueqian @yuanheng-zhao
 /benchmarks/diffusion/ @Isotr0py @princepride @SamitHuang @wtomin @ZJY0516 @RuixiangMa @xuechendi
@@ -37,112 +47,20 @@
 /benchmarks/glm_image/ @princepride @JaredforReal
 /benchmarks/tts/ @linyueqian @ZeldaHuang @princepride @Sy0307 @gcanlin

-# =====================================================================
-# Module design ownership (docs/design/module/*.md)
-# Each section header cites the source design doc.
-# =====================================================================
-
-# Autoregressive runtime — docs/design/module/ar_runtime.md
-/vllm_omni/core/ @tzhouam @yinpeiqi @fake0fan @Sy0307 @Gaohan123
-/vllm_omni/worker/ @tzhouam @yinpeiqi @fake0fan @Sy0307 @Gaohan123
-
-# 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
-
-# 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
-
-# Observability — docs/design/module/observability.md
-/vllm_omni/metrics/ @lishunyang12 @vraiti
-
-# Profiling — docs/design/module/profiling.md
+# Usability and Observability
+/vllm_omni/deploy/ @tzhouam @lishunyang12
+/vllm_omni/metrics/ @lishunyang12
 /vllm_omni/profiler/ @gcanlin

-# Benchmarking (in-tree) — docs/design/module/benchmarking.md
-/vllm_omni/benchmarks/ @alex-jw-brooks @Bounty-hunter
+# ComfyUI
+/apps/ @fhfuih @alex-jw-brooks

-# LoRA (no dedicated design doc; ownership intentionally narrowed)
-/vllm_omni/lora/ @SamitHuang @wtomin
+# Large Scale Serving
+/vllm_omni/distributed/ @princepride @yuanheng-zhao @xuechendi @natureofnature @fake0fan

-# Transformers utils (related path only in design docs; preserves prior ownership)
-/vllm_omni/transformers_utils/ @tzhouam @yuanheng-zhao
+# Quantization and configuration
+/vllm_omni/config/ @lishunyang12 @alex-jw-brooks
+/vllm_omni/quantization/ @david6666666 @Isotr0py @lishunyang12

-# =====================================================================
-# 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
+# Hardware plugins and accelerators
+/vllm_omni/platforms/ @gcanlin @tjtanaa @xuechendi
@@ -46,8 +46,6 @@ htmlcov/
 .cache
 nosetests.xml
 coverage.xml
-coverage-*.xml
-coverage-*.xml.gz
 *.cover
 *.py,cover
 .hypothesis/
@@ -254,7 +252,6 @@ outputs/
 results/
 generated/
 output_*/
-/tests/e2e/accuracy/artifacts/

 # Configuration overrides
 configs/local.yaml
@@ -61,16 +61,6 @@ 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|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
@@ -11,15 +11,10 @@ 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. 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"
+      # 1 GB memory cap and time out the mkdocs build.
+      - python -m pip install --index-url https://download.pytorch.org/whl/cpu torch

 mkdocs:
  configuration: mkdocs.yml
@@ -9,18 +9,21 @@ 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/1aPj0OGl_-ZVoib-Qne5dGDAlrRFB-PdHl6E-EE99g8E/edit?usp=sharing"><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/111-L8zF7A1j_YI_cR8JsblofdScdRr2f/edit?usp=sharing&ouid=110473603432222024453&rtpof=true&sd=true"><b>Slides</b></a> |
 </p>


 ---

 *Latest News* 🔥
- [2026/08] [VeRL-Omni](https://github.com/verl-project/verl-omni) `v0.2.0` is released: faster diffusion RL powered by vLLM-Omni (request-level/step-wise batching with FA3), rebuilt Qwen3-Omni multimodal training (DPO & GSPO), plus LTX-2.3, Qwen-Image-Edit support and more. See the [release notes](https://github.com/verl-project/verl-omni/releases/tag/v0.2.0).
- [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] 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.
+- [2026/06] 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.
 - [2026/03] 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.

 ---
@@ -52,13 +55,12 @@ 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, MiniCPM-o 4.5, Cosmos3, HunyuanImage, BAGEL)
+- **Omni-modality models** (e.g. Qwen3-Omni, Cosmos3, HunyuanImage, BAGEL)
 - **TTS models** (e.g. Qwen3-TTS, VoxCPM2, Ming-Omni-TTS, CosyVoice3)
- **Diffusion models** — image, video, and audio generation (e.g. MiniMax H3, Qwen-Image, Wan2.2, FLUX)
+- **Diffusion models** — image, video, and audio generation (e.g. 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, first-frame/image-to-video, and reference-conditioned video
+- **Generate Video** for text-to-video and image-to-video tasks
 - **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, MiniMax-H3)
+### Text-to-video and image-to-video generation (e.g., Wan)

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

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

 > [!TIP]
-> 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.
+> The node automatically choose text-to-video or image-to-video API endpoints depending on whether you connect an image input or not.

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

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

 # A dictionary that contains the friendly/humanly readable titles for the nodes
@@ -53,7 +49,6 @@ 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",
@@ -61,7 +56,6 @@ NODE_DISPLAY_NAME_MAPPINGS = {
    "VLLMOmniRemoteLoRA": "LoRA",
    "VLLMOmniQwenTTSParams": "Qwen TTS Params",
    "VLLMOmniWanParams": "Wan Video Params",
-    "VLLMOmniMiniMaxH3Params": "MiniMax-H3 Video Params",
 }

 WEB_DIRECTORY = "./web"
@@ -10,9 +10,7 @@ from .utils.types import (
    AudioFormat,
    AutoregressionSamplingParams,
    DiffusionSamplingParams,
-    MiniMaxH3ModelSpecificParams,
    QwenTTSModelSpecificParams,
-    VideoReferences,
    WanModelSpecificParams,
 )
 from .utils.validators import (
@@ -165,8 +163,7 @@ class VLLMOmniGenerateVideo(_VLLMOmniGenerateBase):
                "num_frames": ("INT", {"default": 41, "min": 1}),
            },
            "optional": {
-                "frame": ("IMAGE",),
-                "references": ("VIDEO_REFERENCES",),
+                "image": ("IMAGE",),
                "sampling_params": ("SAMPLING_PARAMS",),
                "lora": ("REMOTE_LORA",),
                "model_params": ("VIDEO_PARAMS",),
@@ -177,15 +174,6 @@ 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,
@@ -196,8 +184,7 @@ class VLLMOmniGenerateVideo(_VLLMOmniGenerateBase):
        fps: int,
        num_frames: int,
        negative_prompt: str | None = None,
-        frame: torch.Tensor | None = None,
-        references: dict | None = None,
+        image: torch.Tensor | None = None,
        sampling_params: dict | list[dict] | None = None,
        model_params: dict | None = None,
        lora: dict | None = None,
@@ -220,16 +207,14 @@ class VLLMOmniGenerateVideo(_VLLMOmniGenerateBase):

        if sampling_params is not None:
            sampling_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
+        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,
-            frame=frame,  # frame present => fl2va / Wan I2V
-            references=references,
+            image=image,  # image present => i2v, absent => t2v
            width=width,
            height=height,
            num_frames=num_frames,
@@ -749,78 +734,3 @@ 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,)
@@ -24,7 +24,6 @@ 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
@@ -64,11 +63,7 @@ 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 * 30,
+        self, base_url: str, timeout: float | None = None, poll_interval: float = 5.0, max_poll_duration: float = 60 * 5
    ):
        self.base_url = base_url
        self.timeout = aiohttp.ClientTimeout(total=timeout)
@@ -266,17 +261,12 @@ class VLLMOmniClient:
        num_frames: int,
        fps: int,
        negative_prompt: str | None = None,
-        frame: torch.Tensor | None = None,
-        references: dict | None = None,
+        image: torch.Tensor | None = None,
        sampling_params: dict | None = None,
        model_params: dict | None = None,
        lora: dict | None = None,
-        **extra_params,
+        **extra_body,
    ) -> 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)
@@ -289,91 +279,23 @@ 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))

-        # === 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:
+        if image is not None:
            image_filename = "image.png"  # Required for multipart form
            form.add_field(
                "input_reference",
-                image_tensor_to_png_bytes(input_reference_image, image_filename),
+                image_tensor_to_png_bytes(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"
@@ -1,12 +1,7 @@
-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:
@@ -34,30 +29,6 @@ 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": [
@@ -100,21 +71,6 @@ _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] = {}
@@ -124,10 +80,12 @@ del _MODEL_PIPELINE_SPECS


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

@@ -143,9 +101,6 @@ 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"}]}
@@ -157,13 +112,7 @@ 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/params_builder")
+                print(f"✓ {path:<40} → No preprocessor")
        else:
            print(f"✗ {path:<40} → No match")
@@ -1,8 +1,12 @@
 from collections.abc import Callable
 from enum import Enum, auto
-from typing import Any, Literal, TypeAlias
-
-from typing_extensions import NotRequired, TypedDict
+from typing import (
+    Any,
+    Literal,
+    NotRequired,
+    TypeAlias,
+    TypedDict,
+)

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

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


-class MiniMaxH3ModelSpecificParams(dict):
-    pass
-
-
-class VideoReferences(dict):
-    pass
-
-
 class ModelMode(Enum):
    IMAGE_GENERATION = auto()
    VIDEO_GENERATION = auto()
@@ -51,11 +47,9 @@ 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 +1,513 @@
-{"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
+{
+    "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
+}
@@ -1,6 +1,5 @@
 import asyncio
 import base64
-import json
 import mimetypes
 import os
 import time
@@ -14,17 +13,6 @@ 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
@@ -367,21 +355,13 @@ async def async_request_v1_videos(
        )
    elif input.video_paths and len(input.video_paths) > 0:
        video_path = input.video_paths[0]
-        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),
-            )
+        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
@@ -858,12 +858,7 @@ 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)
-            # 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))
+            writer = cv2.VideoWriter(video_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):
@@ -876,10 +871,6 @@ 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(
@@ -887,40 +878,6 @@ 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.

@@ -158,6 +158,7 @@ def _build_omni_kwargs(args, quantization=None):
 def _generate_image(omni, args, prompt, seed):
    """Generate a single image and return (PIL.Image, time_seconds, memory_gib)."""
    from vllm_omni.inputs.data import OmniDiffusionSamplingParams
+    from vllm_omni.outputs import OmniRequestOutput
    from vllm_omni.platforms import current_omni_platform

    generator = torch.Generator(device=current_omni_platform.device_type).manual_seed(seed)
@@ -175,7 +176,7 @@ def _generate_image(omni, args, prompt, seed):
    elapsed = time.perf_counter() - start
    peak_mem = torch.accelerator.max_memory_allocated() / (1024**3)

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

-    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
+    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]
    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 = stage_outputs.request_output
                request_id = getattr(request_output, "request_id", "")

                images = getattr(request_output, "images", [])
@@ -167,11 +167,9 @@ def main() -> None:
    ]
    native_cmd = [
        sys.executable,
-        "examples/offline_inference/text_to_video/text_to_video.py",
+        "examples/offline_inference/text_to_video/text_to_video_lingbot.py",
        "--model",
        args.model,
-        "--model-class-name",
-        "LingBotVideoPipeline",
        "--prompt",
        args.prompt,
        "--output",
@@ -139,34 +139,6 @@ 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
@@ -84,17 +84,12 @@ 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] = dict((model_cfg.get("task_extra_body") or {}).get(task) or {})
-    if request_seed is not None:
-        task_extra_body["seed"] = request_seed
+    task_extra_body: dict[str, Any] = (model_cfg.get("task_extra_body") or {}).get(task) or {}

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

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


@@ -231,12 +225,6 @@ 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")
@@ -248,13 +236,6 @@ 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)"
    )
@@ -275,9 +256,6 @@ 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())
@@ -330,9 +308,6 @@ 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:
@@ -7,17 +7,6 @@
 # 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.
@@ -1,24 +1,7 @@
-# Target vllm version: v0.27.0 (releases/v0.27.0 branch).
+# Target vllm version: v0.25.0 (releases/v0.25.0 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=v0.27.0
+ARG VLLM_BASE_TAG=v0.25.0

 # We use registry-based layer caching for the dependencies,
 # where the tag is determined based on the hash of the contents
@@ -1,4 +1,4 @@
-ARG BASE_IMAGE=vllm/vllm-openai:v0.26.0
+ARG BASE_IMAGE=vllm/vllm-openai:v0.25.0
 FROM ${BASE_IMAGE}

 ARG COMMON_WORKDIR=/app
@@ -1,5 +1,5 @@
 ARG VLLM_ASCEND_IMAGE=quay.io/atlas-ci/vllm-ascend
-ARG VLLM_ASCEND_TAG=v0.27.0
+ARG VLLM_ASCEND_TAG=v0.25.0
 FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}

 # WORKDIR /vllm-workspace/vllm
@@ -26,10 +26,6 @@ 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=v0.27.0-a3
+ARG VLLM_ASCEND_TAG=v0.25.0-a3
 FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}

 # WORKDIR /vllm-workspace/vllm
@@ -26,10 +26,6 @@ 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=v0.27.0
+ARG VLLM_ASCEND_TAG=v0.25.0
 FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}

 ARG APP_DIR=/vllm-workspace/vllm-omni
@@ -32,19 +32,19 @@ RUN source /usr/local/Ascend/ascend-toolkit/set_env.sh && \
      "pytest-cov>=4.0.0" \
      "pytest-mock>=3.10.0" \
      "openpyxl>=3.0.0" \
+      "opencc==1.4.1" \
      "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" \
+      "jiwer>=4.0.0" \
+      "zhon>=2.1.1" \
+      "zhconv>=1.4.3" \
+      "funasr>=1.3.0" \
      "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 \
@@ -1,5 +1,5 @@
 ARG VLLM_ASCEND_IMAGE=quay.io/atlas-ci/vllm-ascend
-ARG VLLM_ASCEND_TAG=v0.27.0-a3
+ARG VLLM_ASCEND_TAG=v0.25.0-a3
 FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}

 ARG APP_DIR=/vllm-workspace/vllm-omni
@@ -32,20 +32,19 @@ RUN source /usr/local/Ascend/ascend-toolkit/set_env.sh && \
      "pytest-cov>=4.0.0" \
      "pytest-mock>=3.10.0" \
      "openpyxl>=3.0.0" \
+      "opencc==1.4.1" \
      "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" \
+      "jiwer>=4.0.0" \
+      "zhon>=2.1.1" \
+      "zhconv>=1.4.3" \
+      "funasr>=1.3.0" \
      "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 \
@@ -1,4 +1,4 @@
-ARG BASE_IMAGE=vllm/vllm-openai-rocm:v0.27.0
+ARG BASE_IMAGE=vllm/vllm-openai-rocm:v0.25.0
 FROM ${BASE_IMAGE} AS base

 # Declare a variable to know if we want to use the nightly build or the stable build.
@@ -10,7 +10,7 @@ FROM ${BASE_IMAGE} AS base
 #    we should swap over to use stable release ASAP.
 #    We should avoid relying on custom commits.
 ARG USE_NIGHTLY_BUILD=0
-ARG VLLM_VERSION_OR_COMMIT_HASH=v0.27.0
+ARG VLLM_VERSION_OR_COMMIT_HASH=89138b21cc246ae944c741d5c399c148e2b770ab
 ARG ARG_PYTORCH_ROCM_ARCH
 ENV PYTORCH_ROCM_ARCH=${ARG_PYTORCH_ROCM_ARCH:-${PYTORCH_ROCM_ARCH}}

@@ -79,16 +79,9 @@ RUN printf '%s\n' \
    '    os._exit(_exit_code)' \
    > ${COMMON_WORKDIR}/vllm-omni/conftest.py

-# Some optional dependencies pull in the CPU ONNX Runtime distribution. The
-# CPU and ROCm wheels own the same import path and cannot coexist, so remove
-# the CPU wheel and reinstall the ROCm wheel after dependency resolution.
 RUN cd ${COMMON_WORKDIR}/vllm-omni && \
    uv pip install --python "$(python3 -c 'import sys; print(sys.executable)')" \
        --no-cache-dir ".[dev]" --no-build-isolation && \
-    python3 -m pip uninstall -y onnxruntime && \
-    python3 -m pip install --no-cache-dir --force-reinstall --no-deps \
-        "onnxruntime-rocm>=1.22.2" && \
-    python3 -c "import importlib.metadata as m; import onnxruntime as ort; assert 'ROCMExecutionProvider' in ort.get_available_providers(), ort.get_available_providers(); assert m.version('onnxruntime-rocm'); assert not any(d.metadata['Name'] == 'onnxruntime' for d in m.distributions())" && \
    python3 -c "from torchcodec.decoders import VideoDecoder"

 RUN ln -sf /usr/bin/python3 /usr/bin/python
@@ -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=v0.27.0
+ARG VLLM_VERSION=v0.25.0
 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==3.7.2 && \
+    uv pip install triton-xpu==3.7.1 && \
    uv pip install --no-build-isolation --no-deps .

 CMD ["/bin/bash"]
@@ -144,13 +144,12 @@ 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 && \
-    uv pip install triton-xpu==3.7.2 --reinstall
+    uv pip uninstall triton oneccl oneccl-devel && \
+    uv pip install triton-xpu==3.7.1 --reinstall

 FROM vllm-omni AS vllm-omni-openai

@@ -16,21 +16,31 @@ 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
-      - Speech-To-Video: user_guide/examples/offline_inference/speech_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
      - 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
-      - Speech-To-Video: user_guide/examples/online_serving/speech_to_video.md
-      - Text-To-Image: user_guide/examples/online_serving/text_to_image.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
+      - Text-To-Image: user_guide/examples/online_serving/text_to_image.md
      - Text-To-Video: user_guide/examples/online_serving/text_to_video.md
  - General:
    - usage/*
@@ -40,11 +50,29 @@ nav:
  - Models:
    - models/supported_models.md
  - Features:
-    - Overview: features/README.md
-    - Runtime and Stage Execution:
-      - Execution Modes and Streaming: user_guide/diffusion/execution_modes.md
-      - Prefill-Decode Disaggregation (experimental): features/pd_disaggregation.md
-      - Sleep Mode: features/sleep_mode.md
+    - 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
    - Quantization:
      - Overview: user_guide/quantization/overview.md
      - Online Quantization: user_guide/quantization/online.md
@@ -55,43 +83,9 @@ 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:
-        - Overview: user_guide/diffusion/cpu_offload.md
-        - Model-Level: user_guide/diffusion/offloader/cpu_offload.md
-        - Layerwise: user_guide/diffusion/offloader/layerwise_offload.md
-        - Distributed Layerwise: user_guide/diffusion/offloader/distributed_layerwise_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:
-        - Overview: user_guide/diffusion/attention_backends.md
-        - Dense Backends: user_guide/diffusion/attention_backends/dense_backends.md
-        - TRTLLM: user_guide/diffusion/attention_backends/trtllm.md
-        - SageAttention: user_guide/diffusion/attention_backends/sage.md
-        - Hugging Face Hub: user_guide/diffusion/attention_backends/huggingface_hub.md
-        - RainFusion: user_guide/diffusion/attention_backends/rainfusion.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
@@ -108,71 +102,31 @@ nav:
  - CI: contributing/ci
  - Design Documents:
    - design/index.md
-    - Architecture Documents:
-      - Architecture Overview: design/architecture_overview.md
+    - design/architecture_overview.md
    - Feature Design:
-      - 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
-        - Realtime AR-Diffusion: design/feature/realtime_ar_diffusion.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:
-          - Backend Selection: design/feature/attention_backend_selection.md
-          - Skip-Softmax: design/feature/skip_softmax.md
-        - CPU Offloading:
-          - Overview: design/feature/offloader/README.md
-          - Model-Level: design/feature/offloader/cpu_offload.md
-          - Layerwise: design/feature/offloader/layerwise_offload.md
-          - Distributed Layerwise: design/feature/offloader/distributed_layerwise_offload.md
-        - Cache-DiT: design/feature/cache_dit.md
-        - TeaCache: design/feature/teacache.md
-        - Diffusion Continuous Batching: design/feature/diffusion_continuous_batching.md
-    - Infrastructure and Performance:
-      - Prometheus Metrics: design/metrics.md
-      - Speech Generation Performance Optimizations: design/qwen3_omni_tts_performance_optimization.md
+      - 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
    - Module Design:
-      - 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
+      - design/module/ar_module.md
+      - design/module/dit_module.md
+      - design/module/entrypoint_module.md
+      - design/module/async_omni_architecture.md
  - Docs Guide: contributing/DOCS_GUIDE.md
 - API Reference:
  - api/README.md
@@ -180,8 +134,7 @@ nav:
 - CLI Reference: cli
 - Community:
  - Governance: community/governance.md
-  - Contact Us: community/contact_us.md
-  - Community Meetings: community/meetings.md
+  - community/*
  - Slack: https://slack.vllm.ai
  - Blog: https://blog.vllm.ai
  - Forum: https://discuss.vllm.ai
@@ -52,16 +52,15 @@ 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, MiniCPM-o 4.5, Cosmos3, HunyuanImage, BAGEL)
+- Omni-modality models (e.g. Qwen3-Omni, Cosmos3, HunyuanImage, BAGEL)
 - TTS models (e.g. Qwen3-TTS, VoxCPM2, Ming-Omni-TTS, CosyVoice3)
- Diffusion models — image, video, and audio generation (e.g. MiniMax H3, Qwen-Image, Wan2.2, FLUX)
+- Diffusion models — image, video, and audio generation (e.g. 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/1aPj0OGl_-ZVoib-Qne5dGDAlrRFB-PdHl6E-EE99g8E/edit?usp=sharing)
+- [vllm-omni architecture design and recent roadmaps](https://docs.google.com/presentation/d/1XJWgv79lORl8rbaVvp2d5Sqs6ZEBgAgj/edit?slide=id.p1#slide=id.p1)
 - [vllm-omni announcement blogpost](https://blog.vllm.ai/2025/11/30/vllm-omni.html)
@@ -18,12 +18,18 @@ Specify the port:
 vllm serve Qwen/Qwen2.5-Omni-7B --omni --port 8091
 ```

-Load a custom deploy configuration with `--deploy-config`:
+For a migrated model, 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.
@@ -8,6 +8,7 @@ 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
@@ -31,7 +32,7 @@ CUDA_VISIBLE_DEVICES=1 vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni \
    --omni-master-port 26000
 ```

-When utilizing a custom deployment YAML, append `--deploy-config /path/to/override.yaml` to each command execution.
+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`.

 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.
@@ -1,9 +1,5 @@
 # Contact Us

- 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.
+- 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
@@ -40,7 +40,6 @@ 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
@@ -52,11 +51,31 @@ 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

-## Reviewer Routing
+## Diffusion Workload Division

-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).
+This section breaks down diffusion-related responsibilities. If you have PRs touching these areas, please ping the listed owners for review.

-`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.
+### 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.

 ## Committer Nomination Process

@@ -1,8 +0,0 @@
-# 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/).
@@ -0,0 +1 @@
+# Meetups
@@ -0,0 +1,12 @@
+# 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!
Author	SHA1	Message	Date
amy-why-3459	ecd9d99da0	[CI][MiniCPM-o] Add NPU Video-MME accuracy nightly as a standalone job (#6596 ) Signed-off-by: amy-why-3459 <wuhaiyan17@huawei.com>	2026-08-25 14:11:52 +08:00
amy-why-3459	a964efc55b	[CI][MiniCPM-o] Switch MiniCPM-o 4.5 perf Seed-TTS to Chinese and retune accuracy gates (#6447 ) Signed-off-by: amy-why-3459 <wuhaiyan17@huawei.com>	2026-08-21 18:56:49 +08:00
amy-why-3459	4105c717fe	[CI][MiniCPM-o] Disable Seed-TTS shuffle in MiniCPM-o 4.5 perf benches (#6140 ) Signed-off-by: amy-why-3459 <wuhaiyan17@huawei.com>	2026-08-13 14:30:46 +08:00
amy-why-3459	0e97310d03	[CI][MiniCPM-o] Switch Seed-TTS accuracy gate to Chinese split (2020) (#6108 ) Signed-off-by: amy-why-3459 <wuhaiyan17@huawei.com>	2026-08-13 09:40:35 +08:00
psv666 Claude Opus 5 amy-why-3459	bb0a513df0	[Bugfix][MiniCPM-o] Fix offline dataset loading and stale example sampling params (#6055 ) Signed-off-by: psv666 <2693925048@qq.com> Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com> Co-authored-by: amy-why-3459 <wuhaiyan17@huawei.com>	2026-08-12 20:36:09 +08:00
NATURE	49977ce7a3	[Perf] release unused Code2Wav stream caches (#6100 ) Signed-off-by: natureofnature <wzliu@connect.hku.hk>	2026-08-12 18:43:40 +08:00
amy-why-3459	b4cc0d6da6	[CI][MiniCPM-o] Add MiniCPM-o 4.5 perf coverage to the ready gate (#6092 ) Signed-off-by: amy-why-3459 <wuhaiyan17@huawei.com>	2026-08-12 15:21:50 +08:00
psv666andClaude Opus 5	009b80d686	[Bugfix][MiniCPM-o] Wire the duplex sampling hook into the NPU AR model runner (#5895 ) Signed-off-by: psv666 <2693925048@qq.com> Co-authored-by: Claude Opus 5 <noreply@anthropic.com>	2026-08-07 22:38:45 +08:00
wangyuandamy-why-3459	0a12ac52ee	[CI/Build] Resolve hardware-nested perf baselines per concurrency sweep (#5858 ) Signed-off-by: wangyu <410167048@qq.com> Co-authored-by: amy-why-3459 <wuhaiyan17@huawei.com>	2026-08-07 16:54:38 +08:00
psv666andamy-why-3459	1be47550d0	[CI/Build][MiniCPM-o] Fix NPU accuracy job timeout and silent Daily-O… (#5847 ) Signed-off-by: psv666 <2693925048@qq.com> Co-authored-by: amy-why-3459 <wuhaiyan17@huawei.com>	2026-08-07 12:35:03 +08:00
amy-why-3459	d7a22612db	[Benchmark][MiniCPM-o] Add Video-MME dataset support for Omni bench s… (#5868 ) Signed-off-by: amy-why-3459 <wuhaiyan17@huawei.com>	2026-08-07 01:10:29 +08:00
psv666	271d2f51e2	[Bugfix] npu omni runner alignment (#5846 ) Signed-off-by: psv666 <2693925048@qq.com>	2026-08-06 16:07:19 +08:00
ruirui(rein) yang	4a0a481778	[CI] MiniCPM-o4.5 CI concurrency=8 OOM bugfix (#5790 ) Signed-off-by: R2-Y <ruiruyang2@gmail.com>	2026-08-05 11:56:21 +08:00
psv666andamy-why-3459	4ce5fcb16c	[CI/Build][MiniCPM-o] Add MiniCPM-o 4.5 accuracy and performance nightly coverage (#5710 ) Signed-off-by: psv666 <2693925048@qq.com> Signed-off-by: psv666 <150513104+psv666@users.noreply.github.com> Signed-off-by: amy-why-3459 <wuhaiyan17@huawei.com> Co-authored-by: amy-why-3459 <wuhaiyan17@huawei.com>	2026-08-05 00:49:14 +08:00
ruirui(rein) yangandamy-why-3459	a5eb2a9a87	[CI][MiniCPM-o]MiniCPM-o ACC PERF CI (#5575 ) Signed-off-by: R2-Y <ruiruyang2@gmail.com> Co-authored-by: amy-why-3459 <wuhaiyan17@huawei.com>	2026-08-04 18:46:20 +08:00
ZacheryAU	434c220da1	[bugfix][CI][MiniCPM-o] fix CI failure of test_audio_to_text_audio_001 in Buildkit 13077 (#5705 )	2026-08-04 11:34:36 +08:00
wangyu	63fdc8e7f0	[CI/Build][MiniCPM-o] Narrow nightly H100 function tests to MiniCPM-o 4.5 (#5696 ) Signed-off-by: wangyu <410167048@qq.com>	2026-08-03 15:06:38 +08:00
ZacheryAU SYLAR lishunyang12	f05babab84	[Bugfix] Use MiniCPM-o speech template in long audio test (#5623 ) (#5651 ) Signed-off-by: lishunyang12 <lishunyang03@gmail.com> Co-authored-by: SYLAR <125541396+lishunyang12@users.noreply.github.com> Co-authored-by: lishunyang12 <lishunyang03@gmail.com>	2026-08-02 17:38:02 +08:00
ruirui(rein) yang	75b93e0c67	[Bugfix][MiniCPM-o]Fix implicit deploy connector resolution for registered pipelines (#5532 ) Signed-off-by: R2-Y <ruiruyang2@gmail.com>	2026-07-31 10:58:34 +08:00
amy-why-3459	ed32f4e988	[Bugfix][MiniCPM-o] Align Daily-Omni bench with MiniCPM interleaved AV recipe (#5606 ) Signed-off-by: amy-why-3459 <wuhaiyan17@huawei.com>	2026-07-31 10:58:04 +08:00
ruirui(rein) yang	c50408a808	[bugfix][MiniCPM-o]delete duplicate files (#5537 ) Signed-off-by: R2-Y <ruiruyang2@gmail.com>	2026-07-30 10:42:42 +08:00
amy-why-3459	a80d1e614d	[CI][MiniCPM-o] Prefer standard omni serve over stage CLI in e2e/reli… (#5553 ) Signed-off-by: amy-why-3459 <wuhaiyan17@huawei.com>	2026-07-29 21:04:52 +08:00
amy-why-3459	00d796edf9	[CI][MiniCPM-o] Run e2e tests on single-GPU deploy config (#5506 ) Signed-off-by: amy-why-3459 <wuhaiyan17@huawei.com>	2026-07-29 19:43:31 +08:00
wangyu	0b3898c884	[CI] Point merge/nightly Buildkite triggers at minicpm-challenge (#5497 ) Signed-off-by: wangyu <410167048@qq.com> Signed-off-by: [Your Name] <your.email@example.com> Signed-off-by: wangyu <53896905+yenuo26@users.noreply.github.com>	2026-07-28 18:47:20 +08:00
Sy03	ffa9a6af69	[WIP][Bugfix][MiniCPM-o] Fix Full duplex bugs for minicpm-challenge (#5496 ) Signed-off-by: Sy03 <1370724210@qq.com>	2026-07-28 15:59:26 +08:00