Merge remote-tracking branch 'origin/main' into HEAD

[BugFix][Higgs-Audio-V3] Fix transcript and concurrent sampling tests (#6422 )
Signed-off-by: akshatvishu <akshatnayak197@gmail.com>
2026-08-30 03:36:08 -07:00 · 2026-08-30 08:52:15 +00:00 · 2026-08-30 06:51:24 +00:00 · 2026-08-30 04:14:21 +00:00 · 2026-08-30 03:25:43 +00:00 · 2026-08-29 13:05:24 -07:00
476 changed files with 40323 additions and 2910 deletions
@@ -1,4 +1,7 @@
 #!/bin/bash
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
 # vllm-omni customized version
 # Based on: https://github.com/vllm-project/ci-infra/blob/main/buildkite/bootstrap-amd.sh
 # Last synced: 2025-12-15
@@ -6,6 +9,9 @@

 set -euo pipefail

+# The bootstrap runs from the repository root; ShellCheck does not follow
+# sourced files unless invoked with -x.
+# shellcheck disable=SC1091
 source .buildkite/common/scripts/resolve_skip_ci.sh

 if [[ -z "${RUN_ALL:-}" ]]; then
@@ -63,6 +69,8 @@ upload_pipeline() {
    # Install minijinja
    ls .buildkite || buildkite-agent annotate --style error 'Please merge upstream main branch for buildkite CI'
    curl -sSfL https://github.com/mitsuhiko/minijinja/releases/download/2.3.1/minijinja-cli-installer.sh | sh
+    # Installed by the minijinja bootstrap above and only present on the CI agent.
+    # shellcheck disable=SC1091
    source /var/lib/buildkite-agent/.cargo/env

    if [[ $BUILDKITE_PIPELINE_SLUG == "fastcheck" ]]; then
@@ -90,22 +98,82 @@ upload_pipeline() {
    cd .buildkite/amd

    # Select test definition file: merge suite for main, ready suite for PRs.
-    # For debugging, DEBUG_TEST_YAML can override the selection — accepts
-    # "merge" or "ready" (case-insensitive).
+    # For debugging, DEBUG_TEST_YAML accepts a comma-separated list containing
+    # "merge" and/or "ready" (case-insensitive). Multiple suites are combined
+    # before rendering so they share one amd-build step.
    if [[ -n "${DEBUG_TEST_YAML:-}" ]]; then
-        case "${DEBUG_TEST_YAML,,}" in
-            merge)
-                TEST_YAML="test-amd-merge.yml"
-                ;;
-            ready)
-                TEST_YAML="test-amd-ready.yml"
-                ;;
-            *)
-                echo "ERROR: DEBUG_TEST_YAML must be 'merge' or 'ready', got '$DEBUG_TEST_YAML'" >&2
+        declare -a DEBUG_TEST_SPECS=()
+        declare -A SEEN_DEBUG_TESTS=()
+        IFS=',' read -ra REQUESTED_DEBUG_TESTS <<< "${DEBUG_TEST_YAML,,}"
+
+        for requested_test in "${REQUESTED_DEBUG_TESTS[@]}"; do
+            # Permit readable values such as "ready, merge," and ignore the
+            # empty item produced by a trailing comma.
+            requested_test="${requested_test//[[:space:]]/}"
+            [[ -z "$requested_test" ]] && continue
+
+            if [[ -n "${SEEN_DEBUG_TESTS[$requested_test]:-}" ]]; then
+                echo "ERROR: duplicate DEBUG_TEST_YAML suite '$requested_test'" >&2
                exit 1
-                ;;
-        esac
-        echo "DEBUG_TEST_YAML override: using $TEST_YAML"
+            fi
+            SEEN_DEBUG_TESTS[$requested_test]=1
+
+            case "$requested_test" in
+                ready)
+                    DEBUG_TEST_SPECS+=("READY_TESTS:test-amd-ready.yml")
+                    ;;
+                merge)
+                    DEBUG_TEST_SPECS+=("MERGE_TESTS:test-amd-merge.yml")
+                    ;;
+                *)
+                    echo "ERROR: DEBUG_TEST_YAML entries must be 'merge' or 'ready', got '$requested_test'" >&2
+                    exit 1
+                    ;;
+            esac
+        done
+
+        if [[ ${#DEBUG_TEST_SPECS[@]} -eq 0 ]]; then
+            echo "ERROR: DEBUG_TEST_YAML did not contain a test suite" >&2
+            exit 1
+        elif [[ ${#DEBUG_TEST_SPECS[@]} -eq 1 ]]; then
+            TEST_YAML="${DEBUG_TEST_SPECS[0]#*:}"
+        else
+            TEST_YAML=$(mktemp "${TMPDIR:-/tmp}/amd-debug-tests.XXXXXX.yml")
+            python - "$TEST_YAML" "${DEBUG_TEST_SPECS[@]}" <<'PY'
+import sys
+
+import yaml
+
+
+output_path, *suite_specs = sys.argv[1:]
+combined = {"env": {}, "steps": []}
+
+for suite_spec in suite_specs:
+    group_name, input_path = suite_spec.split(":", 1)
+    with open(input_path, encoding="utf-8") as test_file:
+        suite = yaml.safe_load(test_file)
+
+    for name, value in (suite.get("env") or {}).items():
+        previous = combined["env"].get(name, value)
+        if previous != value:
+            raise ValueError(
+                f"Conflicting environment value for {name}: {previous!r} != {value!r}"
+            )
+        combined["env"][name] = value
+
+    suite_steps = []
+    for entry in suite.get("steps") or []:
+        if "group" in entry:
+            suite_steps.extend(entry.get("steps") or [])
+        else:
+            suite_steps.append(entry)
+    combined["steps"].append({"group": group_name, "steps": suite_steps})
+
+with open(output_path, "w", encoding="utf-8") as output_file:
+    yaml.safe_dump(combined, output_file, sort_keys=False)
+PY
+        fi
+        echo "DEBUG_TEST_YAML override: using ${DEBUG_TEST_SPECS[*]}"
    elif [[ $BUILDKITE_BRANCH == "main" ]]; then
        TEST_YAML="test-amd-merge.yml"
    else
@@ -130,19 +198,20 @@ upload_pipeline() {
            > pipeline.yaml
    )
    cat pipeline.yaml
+    if [[ "$TEST_YAML" == "${TMPDIR:-/tmp}/amd-debug-tests."*.yml ]]; then
+        rm -f -- "$TEST_YAML"
+    fi
    buildkite-agent artifact upload pipeline.yaml
    buildkite-agent pipeline upload pipeline.yaml
    exit 0
 }

 get_diff() {
-    $(git add .)
-    echo $(git diff --name-only --diff-filter=ACMDR $(git merge-base origin/main HEAD))
+    git diff --name-only --diff-filter=ACMDR "$(git merge-base origin/main HEAD)"
 }

 get_diff_main() {
-    $(git add .)
-    echo $(git diff --name-only --diff-filter=ACMDR HEAD~1)
+    git diff --name-only --diff-filter=ACMDR HEAD~1
 }

 file_diff=$(get_diff)
@@ -181,7 +250,7 @@ for file in $file_diff; do
    # First check if file matches any pattern
    matches_pattern=0
    for pattern in "${patterns[@]}"; do
-        if [[ $file == $pattern* ]] || [[ $file == $pattern ]]; then
+        if [[ $file == "$pattern"* ]] || [[ $file == "$pattern" ]]; then
            matches_pattern=1
            break
        fi
@@ -191,7 +260,7 @@ for file in $file_diff; do
    if [[ $matches_pattern -eq 1 ]]; then
        matches_ignore=0
        for ignore in "${ignore_patterns[@]}"; do
-            if [[ $file == $ignore* ]] || [[ $file == $ignore ]]; then
+            if [[ $file == "$ignore"* ]] || [[ $file == "$ignore" ]]; then
                matches_ignore=1
                break
            fi
@@ -21,7 +21,7 @@ steps:

    - label: "Simple · Diffusion Test · Shard %N/%t"
      agent_pool: mi300_1
-      parallelism: 2
+      parallelism: 4
      depends_on: amd-build
      mirror_hardwares: [amdproduction]
      grade: Blocking
@@ -71,26 +71,30 @@ steps:
      commands:
        - export VLLM_LOGGING_LEVEL=DEBUG
        - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-        - |
-          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))
-          '
+        - pytest -s -v tests/diffusion/offloader/test_diffusion_cpu_offload.py
+        - pytest -s -v tests/diffusion/offloader/test_diffusion_layerwise_offload.py

-    - label: "Diffusion Sequence Parallelism Test (Need 4 GPUs)"
+    - label: "Diffusion Sequence Parallelism Test (Need 4 GPUs) · Shard 1/2"
      agent_pool: mi300_4
      depends_on: amd-build
      mirror_hardwares: [amdproduction]
+      timeout_in_minutes: 40
      grade: Blocking
      commands:
        - export VLLM_LOGGING_LEVEL=DEBUG
        - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-        - timeout 20m pytest -s -v tests/diffusion/distributed/test_sequence_parallel.py
-        - timeout 20m pytest -s -v tests/diffusion/distributed/test_ulysses_uaa_perf.py
+        - pytest -s -v tests/diffusion/distributed/test_sequence_parallel.py
+
+    - label: "Diffusion Sequence Parallelism Test (Need 4 GPUs) · Shard 2/2"
+      agent_pool: mi300_4
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      timeout_in_minutes: 40
+      grade: Blocking
+      commands:
+        - export VLLM_LOGGING_LEVEL=DEBUG
+        - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+        - pytest -s -v tests/diffusion/distributed/test_ulysses_uaa_perf.py

    # merge-only tests
    - label: "Diffusion Tensor Parallelism Test"
@@ -171,21 +175,45 @@ steps:
      grade: Blocking
      commands:
        - 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'
+        - pytest -s -v tests/e2e/offline_inference/test_qwen3_omni.py tests/e2e/online_serving/test_qwen3_omni.py tests/e2e/offline_inference/test_qwen3_omni_colocate_async.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
+      parallelism: 3
      depends_on: amd-build
      mirror_hardwares: [amdproduction]
      grade: Blocking
+      timeout_in_minutes: 80
      commands:
        - |
-          bash -c '
+          timeout --signal=TERM --kill-after=2m 80m 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
+            TESTS=(
+              "tests/e2e/online_serving/test_qwen3_tts_customvoice.py::test_default_cuda_graph_startup[async_chunk]"
+              "tests/e2e/online_serving/test_qwen3_tts_customvoice.py::test_text_to_audio_002[async_chunk]"
+              "tests/e2e/offline_inference/test_qwen3_tts_customvoice.py::test_text_to_audio_001[no_cuda_graph]"
+            )
+            TEST="$${TESTS[$$BUILDKITE_PARALLEL_JOB]}"
+            pytest -s -v "$$TEST" -m "advanced_model" --run-level "advanced_model"
+          '
+
+    # TODO: Restore this case to the blocking sharded step once its long-running
+    # Whisper validation on AMD CI is resolved.
+    - label: "Qwen3-TTS CustomVoice async_chunk Test (quarantined)"
+      agent_pool: mi300_1
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: NonBlocking
+      timeout_in_minutes: 80
+      commands:
+        - |
+          timeout --signal=TERM --kill-after=2m 75m 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::test_text_to_audio_001[async_chunk]" -m "advanced_model" --run-level "advanced_model"
          '

    - label: "Qwen3-TTS Base E2E Test"
@@ -191,10 +191,7 @@ steps:
      mirror_hardwares: [amdproduction]
      grade: Blocking
      commands:
-        - |
-          timeout 20m bash -c '
-            pytest -s -v tests/e2e/online_serving/test_cosyvoice3_tts_expansion.py -m "slow" --run-level "core_model"
-          '
+        - pytest -s -v tests/e2e/online_serving/test_cosyvoice3_tts_expansion.py -m "slow" --run-level "core_model"

 # TODO: Bagel test on ROCm is very unstable. @tjtanaa
 # Need to debug before reneable numerical changes across large PRs
@@ -122,6 +122,7 @@ steps:
      - label: "Omni · Qwen3-Omni Test"
        source_file_dependencies:
          - tests/e2e/offline_inference/test_qwen3_omni.py
+          - tests/e2e/offline_inference/test_qwen3_omni_colocate_async.py
          - tests/e2e/online_serving/test_qwen3_omni.py
          - tests/helpers/runtime.py
          - vllm_omni/entrypoints/openai/serving_chat.py
@@ -132,9 +133,10 @@ steps:
          - 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
+          - vllm_omni/deploy/qwen3_omni_moe_thinking.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'
+          - pytest -s -v tests/e2e/offline_inference/test_qwen3_omni.py tests/e2e/online_serving/test_qwen3_omni.py tests/e2e/offline_inference/test_qwen3_omni_colocate_async.py -m 'advanced_model and cuda' --run-level 'advanced_model'
        mirror_hardwares: h100_2

      - label: "Omni · MiniCPM-o 4.5 Test"
@@ -166,6 +168,7 @@ steps:
          - 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_server_vad.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
@@ -216,3 +219,15 @@ steps:
              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 · HunyuanVideo-1.5 Test"
+        source_file_dependencies:
+          - tests/e2e/online_serving/test_hunyuan_video_15_expansion.py
+          - vllm_omni/diffusion/models/hunyuan_video/
+        commands:
+          - |
+            timeout 40m bash -c "
+              export VLLM_IMAGE_FETCH_TIMEOUT=60
+              pytest -s -v tests/e2e/online_serving/test_hunyuan_video_15_expansion.py -m 'advanced_model and cuda and diffusion' --run-level 'advanced_model'
+            "
+        mirror_hardwares: h100_1
@@ -12,16 +12,22 @@ steps:
    if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test" || build.pull_request.labels includes "omni-test"
    steps:

-      - label: ":full_moon: Omni · Function Test with H100"
-        timeout_in_minutes: 120
+      - label: ":full_moon: Omni · Function Test with H100 · Single-GPU"
+        timeout_in_minutes: 90
        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 --run-level "full_model" -m "full_model and H100 and omni and cards_1" --ignore=tests/e2e/accuracy --ignore=tests/e2e/online_serving/test_minicpmo_4_5_duplex_expansion.py
+        mirror_hardwares: h100_1
+
+      - label: ":full_moon: Omni · Function Test with H100 · 2-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - pytest -sv tests/e2e --run-level "full_model" -m "full_model and H100 and omni and cards_2" --ignore=tests/e2e/accuracy --ignore=tests/e2e/online_serving/test_qwen3_omni_multi_replicas.py
        mirror_hardwares: h100_2

      - label: ":full_moon: Omni · MiniCPM-o 4.5 Duplex Test"
        timeout_in_minutes: 50
        commands:
-          - pytest -s -v tests/e2e/online_serving/test_minicpmo_4_5_duplex_expansion.py -m "full_model and cuda and H100 and omni" --run-level "full_model"
+          - pytest -s -v tests/e2e/online_serving/test_minicpmo_4_5_duplex_expansion.py -m "full_model and cuda and H100 and omni and cards_1" --run-level "full_model"
        mirror_hardwares: h100_1

      - label: ":full_moon: Omni · Doc Test with L4"
@@ -31,10 +37,10 @@ steps:
          - 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"
+      - label: ":full_moon: Omni · Doc Test with H100 · 2-GPU"
        timeout_in_minutes: 90
        commands:
-          - pytest -s -v tests/examples/ -m "full_model and omni and H100" --run-level "full_model"
+          - pytest -s -v tests/examples/ -m "full_model and omni and H100 and cards_2" --run-level "full_model"
        mirror_hardwares: h100_2

      - label: ":full_moon: Omni · Accuracy Test"
@@ -136,15 +142,29 @@ steps:
          - 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 · Perf Test"
-        key: nightly-tts-performance
+      - label: ":full_moon: TTS · Perf Test · Single-GPU"
+        key: nightly-tts-performance-single
        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
+            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_tts.json -m "H100 and cards_1"
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
+            exit $$EXIT
+        mirror_hardwares: h100_1
+
+      - label: ":full_moon: TTS · Perf Test · 2-GPU"
+        key: nightly-tts-performance-2gpu
+        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 -m "H100 and cards_2"
            EXIT=$$?
            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
            exit $$EXIT
@@ -179,7 +199,6 @@ steps:
      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: Diffusion X2I(&A&T) · Function Test with H100 · Single-GPU"
        timeout_in_minutes: 120
@@ -187,24 +206,24 @@ steps:
          - >-
            pytest -sv
            tests/e2e/online_serving/test_qwen_image_expansion.py
-            -m "full_model and diffusion and H100 and not distributed_cuda"
+            -m "full_model and diffusion and H100 and cards_1"
            --run-level "full_model"
        mirror_hardwares: h100_1

-      - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with H100 · Multi-GPU Qwen-Image"
+      - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with H100 · 2-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 distributed_cuda"
+            -m "full_model and diffusion and H100 and cards_2"
            --run-level "full_model"
        mirror_hardwares: h100_2

-      - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with L4"
+      - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with L4 · Multi-GPU"
        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
+          - pytest -sv tests/e2e/ -k "not test_wan and not hunyuan" -m "full_model and diffusion and L4 and not cards_1" --run-level "full_model" --ignore=tests/e2e/accuracy
        mirror_hardwares: l4_4

      - label: ":full_moon: Diffusion X2I(&A&T) · Doc Test"
@@ -235,8 +254,8 @@ steps:
          - pytest -s -v tests/e2e/accuracy/test_hidream_o1_image.py -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
+      - label: ":full_moon: Diffusion X2I(&A&T) · Perf Test · Qwen-Image · Single-GPU"
+        key: nightly-diffusion-x2iat-performance-qwen-image-single
        timeout_in_minutes: 180
        commands:
          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
@@ -244,7 +263,23 @@ steps:
          - 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
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_vllm_omni.json -m "H100 and cards_1"
+            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 X2I(&A&T) · Perf Test · Qwen-Image · 4-GPU"
+        key: nightly-diffusion-x2iat-performance-qwen-image-4gpu
+        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 -m "H100 and cards_4"
            EXIT=$$?
            buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
            buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
@@ -260,16 +295,34 @@ steps:
      build.pull_request.labels includes "diffusion-x2v-test"
    steps:

-      - label: ":full_moon: Diffusion X2V · Wan2.2 T2V Function Test"
+      - label: ":full_moon: Diffusion X2V · Wan2.2 T2V Function Test · Single-GPU"
        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"
+          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "t2v" -m "full_model and cuda and H100 and cards_1" --run-level "full_model"
+        mirror_hardwares: h100_1
+
+      - label: ":full_moon: Diffusion X2V · Wan2.2 T2V Function Test · 2-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "t2v" -m "full_model and cuda and H100 and cards_2" --run-level "full_model"
        mirror_hardwares: h100_2

-      - label: ":full_moon: Diffusion X2V · Wan2.2 I2V/TI2V Function Test"
+      - label: ":full_moon: Diffusion X2V · Wan2.2 I2V/TI2V Function Test · Single-GPU"
        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"
+          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "i2v" -m "full_model and cuda and H100 and cards_1" --run-level "full_model"
+        mirror_hardwares: h100_1
+
+      - label: ":full_moon: Diffusion X2V · Wan2.2 I2V/TI2V Function Test · 2-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "i2v" -m "full_model and cuda and H100 and cards_2" --run-level "full_model"
+        mirror_hardwares: h100_2
+
+      - label: ":full_moon: Diffusion X2V · HunyuanVideo-1.5 Function Test"
+        timeout_in_minutes: 90
+        commands:
+          - pytest -s -v tests/e2e/online_serving/test_hunyuan_video_15_expansion.py -m "full_model and cuda" --run-level "full_model"
        mirror_hardwares: h100_2

      - label: ":full_moon: Diffusion X2V · Doc Test"
@@ -304,17 +357,49 @@ steps:
          - 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 X2V · Perf Test"
-        key: nightly-diffusion-x2v-performance
+      - label: ":full_moon: Diffusion X2V · Perf Test · Single-GPU"
+        key: nightly-diffusion-x2v-performance-single
        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
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_wan22_i2v_vllm_omni.json -m "H100 and cards_1"
+            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 · 2-GPU"
+        key: nightly-diffusion-x2v-performance-2gpu
+        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 -m "H100 and cards_2"
            EXIT1=$$?
-            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_cosmos3_vllm_omni.json
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_cosmos3_vllm_omni.json -m "H100 and cards_2"
+            EXIT2=$$?
+            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 $$EXIT2
+        mirror_hardwares: h100_2
+
+      - label: ":full_moon: Diffusion X2V · Perf Test · HunyuanVideo-1.5"
+        key: nightly-diffusion-x2v-performance-hunyuanvideo15
+        timeout_in_minutes: 180
+        commands:
+          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_hunyuanvideo15_t2v_vllm_omni.json
+            EXIT1=$$?
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_hunyuanvideo15_i2v_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"
@@ -348,10 +433,16 @@ steps:
          - pytest -sv tests/diffusion/distributed/ -m 'full_model and cuda and L4' --run-level "full_model"
        mirror_hardwares: l4_4

-      - label: ":full_moon: Diffusion · Distributed Test with H100"
+      - label: ":full_moon: Diffusion · Distributed Test with H100 · 2-GPU"
        timeout_in_minutes: 60
        commands:
-          - pytest -sv tests/diffusion/distributed/ -m 'full_model and cuda and H100' --run-level "full_model"
+          - pytest -sv tests/diffusion/distributed/ -m 'full_model and cuda and H100 and cards_2' --run-level "full_model"
+        mirror_hardwares: h100_2
+
+      - label: ":full_moon: Diffusion · Distributed Test with H100 · 4-GPU"
+        timeout_in_minutes: 60
+        commands:
+          - pytest -sv tests/diffusion/distributed/ -m 'full_model and cuda and H100 and cards_4' --run-level "full_model"
        mirror_hardwares: h100_4

      - label: ":full_moon: Diffusion · Offloader Test"
@@ -386,19 +477,25 @@ steps:
    depends_on:
      - nightly-omni-performance-no-async-chunk
      - nightly-omni-performance-async-chunk
-      - nightly-tts-performance
-      - nightly-diffusion-x2iat-performance-qwen-image
-      - nightly-diffusion-x2v-performance
+      - nightly-tts-performance-single
+      - nightly-tts-performance-2gpu
+      - nightly-diffusion-x2iat-performance-qwen-image-single
+      - nightly-diffusion-x2iat-performance-qwen-image-4gpu
+      - nightly-diffusion-x2v-performance-single
+      - nightly-diffusion-x2v-performance-2gpu
    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-single
+      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-tts-performance-2gpu
      - 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-diffusion-x2iat-performance-qwen-image
-      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2v-performance
+      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2iat-performance-qwen-image-single
+      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2iat-performance-qwen-image-4gpu
+      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2v-performance-single
+      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2v-performance-2gpu
      - 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"
@@ -205,6 +205,7 @@ steps:
          - 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_server_vad.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
@@ -306,6 +307,24 @@ steps:
            "
        mirror_hardwares: h100_1

+      - label: "Diffusion · MiniMax-H3 DLO DP2 T2VA Test"
+        timeout_in_minutes: 90
+        source_file_dependencies:
+          - tests/e2e/online_serving/test_minimax_h3_dlo_dp2_t2va.py
+          - tests/helpers/assertions.py
+          - tests/helpers/runtime.py
+          - vllm_omni/diffusion/models/minimax_h3/
+          - vllm_omni/diffusion/offloader/
+          - vllm_omni/diffusion/distributed/
+          - vllm_omni/entrypoints/openai/
+        commands:
+          - |
+            timeout 80m bash -c "
+              export VLLM_IMAGE_FETCH_TIMEOUT=60
+              pytest -s -v tests/e2e/online_serving/test_minimax_h3_dlo_dp2_t2va.py -m 'core_model and cuda and diffusion' --run-level 'core_model'
+            "
+        mirror_hardwares: h100_2
+
      - label: "Diffusion · Qwen Image Test"
        source_file_dependencies:
          - tests/e2e/online_serving/test_qwen_image.py
@@ -319,3 +338,15 @@ steps:
              pytest -s -v tests/e2e/online_serving/test_qwen_image.py -m 'core_model' --run-level 'core_model'
            "
        mirror_hardwares: h100_1
+
+      - label: "Diffusion · HunyuanVideo-1.5 Test"
+        source_file_dependencies:
+          - tests/e2e/online_serving/test_hunyuan_video_15_expansion.py
+          - vllm_omni/diffusion/models/hunyuan_video/
+        commands:
+          - |
+            timeout 40m bash -c "
+              export VLLM_IMAGE_FETCH_TIMEOUT=60
+              pytest -s -v tests/e2e/online_serving/test_hunyuan_video_15_expansion.py -m 'core_model and diffusion' --run-level 'core_model'
+            "
+        mirror_hardwares: h100_1
@@ -44,10 +44,16 @@ steps:
          - pytest -s -v tests/dfx/reliability/test_reliability_hunyuan_image.py -m "slow"
        mirror_hardwares: h100_4

-      - label: "Reliability Test · Invalid parameters · H100"
-        timeout_in_minutes: 120
+      - label: "Reliability Test · Invalid parameters · H100 · Single-GPU"
+        timeout_in_minutes: 90
        commands:
-          - pytest -s -v tests/dfx/reliability/invalid_param_test/ -m "slow and H100"
+          - pytest -s -v tests/dfx/reliability/invalid_param_test/ -m "slow and H100 and cards_1"
+        mirror_hardwares: h100_1
+
+      - label: "Reliability Test · Invalid parameters · H100 · 2-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - pytest -s -v tests/dfx/reliability/invalid_param_test/ -m "slow and H100 and cards_2"
        mirror_hardwares: h100_2

      - label: "Reliability Test · Invalid parameters · L4"
@@ -105,27 +111,37 @@ steps:
    if: build.env("NON_CRITICAL") == "1"
    steps:

-      - label: "Omni · L4"
+      # Qwen2.5 expansion (CUDA 4-card / online 2-card) stays on this l4_4 job.
+      - label: "Omni · L4 · Multi-GPU"
        timeout_in_minutes: 90
        commands:
          - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
-          - pytest -s -v tests/e2e/*/test_qwen2_5_omni_expansion.py --run-level "core_model"
+          - pytest -s -v tests/e2e/*/test_qwen2_5_omni_expansion.py -m "slow and L4 and omni and not cards_1" --run-level "core_model"
          - >-
            pytest -s -v tests/e2e/
            --ignore=tests/e2e/online_serving/test_qwen2_5_omni_expansion.py
            --ignore=tests/e2e/offline_inference/test_qwen2_5_omni_expansion.py
-            -m "slow and L4 and omni"
+            -m "slow and L4 and omni and not cards_1"
            --run-level "full_model"
        mirror_hardwares: l4_4

-      - label: "Omni · H100"
+      - label: "Omni · H100 · Single-GPU"
        timeout_in_minutes: 90
        commands:
          - >-
            pytest -sv
-            tests/e2e/ -m "slow and H100 and omni"
+            tests/e2e/ -m "slow and H100 and omni and cards_1"
            --run-level "full_model"
-        mirror_hardwares: h100_2
+        mirror_hardwares: h100_1
+
+      - label: "Omni · H100 · 4-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/ -m "slow and H100 and omni and cards_4"
+            --run-level "full_model"
+        mirror_hardwares: h100_4

      - label: "TTS · L4"
        timeout_in_minutes: 120
@@ -169,12 +185,21 @@ steps:
            --run-level "full_model"
        mirror_hardwares: l4_1

-      - label: "Diffusion · L4"
+      - label: "Diffusion · L4 · Single-GPU"
        timeout_in_minutes: 60
        commands:
          - >-
            pytest -sv
-            tests/e2e/ -m "slow and diffusion and L4"
+            tests/e2e/ -m "slow and diffusion and L4 and cards_1"
+            --run-level "full_model"
+        mirror_hardwares: l4_1
+
+      - label: "Diffusion · L4 · Multi-GPU"
+        timeout_in_minutes: 60
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/ -m "slow and diffusion and L4 and not cards_1"
            --run-level "full_model"
        mirror_hardwares: l4_4

@@ -183,15 +208,24 @@ steps:
        commands:
          - >-
            pytest -sv
-            tests/e2e/ -m "slow and diffusion and H100 and not distributed_cuda"
+            tests/e2e/ -m "slow and diffusion and H100 and cards_1"
            --run-level "full_model"
        mirror_hardwares: h100_1

-      - label: "Diffusion · H100 · Multi-GPU"
+      - label: "Diffusion · H100 · 2-GPU"
        timeout_in_minutes: 90
        commands:
          - >-
            pytest -sv
-            tests/e2e/ -m "slow and diffusion and H100 and distributed_cuda"
+            tests/e2e/ -m "slow and diffusion and H100 and cards_2"
+            --run-level "full_model"
+        mirror_hardwares: h100_2
+
+      - label: "Diffusion · H100 · 4-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/ -m "slow and diffusion and H100 and cards_4"
            --run-level "full_model"
        mirror_hardwares: h100_4
@@ -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: "v0.28.0"
        priority: 100
        timeout_in_minutes: 60
        soft_fail: false
@@ -5,7 +5,12 @@ set -ex

 omni_source_dir=$(git rev-parse --show-toplevel)

-base_image_name="xpu/vllm-omni-ci-base:${VLLM_VERSION:?VLLM_VERSION must be set}"
+: "${VLLM_VERSION:?VLLM_VERSION must be set}"
+# Official upstream XPU image. It is published from vllm's own
+# docker/Dockerfile.xpu, which is exactly what build_vllm_base() clones and
+# builds below, so pulling it is equivalent to building the fallback base.
+upstream_base_image="vllm/vllm-openai-xpu:${VLLM_VERSION}"
+local_base_image="xpu/vllm-omni-ci-base:${VLLM_VERSION}"
 image_name="xpu/vllm-omni-ci:${BUILDKITE_COMMIT:?BUILDKITE_COMMIT must be set}"
 container_name="xpu_${BUILDKITE_COMMIT}_$(
    tr -dc A-Za-z0-9 </dev/urandom | head -c 10
@@ -49,9 +54,36 @@ build_vllm_base() (
        "${vllm_source_dir}"
 )

-if [ -z "$(docker images -q "${base_image_name}")" ]; then
-    build_vllm_base
-fi
+# Resolve the vLLM base image, in priority order:
+#   1. the published upstream image for VLLM_VERSION, pulled fresh (so moving
+#      tags like nightly are refreshed rather than served stale from disk);
+#   2. a local copy of that same image, when the registry is unreachable;
+#   3. VLLM_BASE, if that image is already on disk;
+#   4. otherwise build VLLM_BASE from the matching vLLM source tag (~30min).
+# VLLM_BASE names the fallback base and defaults to ${local_base_image}; set it
+# to reuse or produce a base under a different tag.
+resolve_vllm_base() {
+    if docker pull "${upstream_base_image}"; then
+        base_image_name="${upstream_base_image}"
+        return
+    fi
+
+    # Registry unreachable, but a previous run may have left the image behind.
+    if [ -n "$(docker images -q "${upstream_base_image}")" ]; then
+        echo "WARNING: could not pull ${upstream_base_image}; using local copy" >&2
+        base_image_name="${upstream_base_image}"
+        return
+    fi
+
+    echo "WARNING: ${upstream_base_image} unavailable remotely and locally" >&2
+    base_image_name="${VLLM_BASE:-${local_base_image}}"
+    if [ -z "$(docker images -q "${base_image_name}")" ]; then
+        echo "WARNING: building ${base_image_name} from vLLM ${VLLM_VERSION} source" >&2
+        build_vllm_base
+    fi
+}
+
+resolve_vllm_base

 # Try building the docker image
 docker_build "${image_name}" --build-arg "VLLM_BASE=${base_image_name}" --build-arg "VLLM_VERSION=${VLLM_VERSION}"
@@ -10,6 +10,18 @@ steps:
        -m 'core_model and npu and A3 and omni'
        --run-level 'core_model'

+  - label: "Diffusion NPU layer tests"
+    key: ready-diffusion-npu-layer-tests
+    depends_on: upload-ready-pipeline
+    mirror_hardwares: a3_npu_2
+    commands:
+      - >-
+        pytest -s -v
+        tests/diffusion/layers/test_activation.py
+        tests/diffusion/layers/test_fused_qk_norm_rope_npu.py
+        -m 'core_model and npu and A3'
+        --run-level 'core_model'
+
  - group: ":card_index_dividers: Omni Model Test"
    key: ready-omni-group
    depends_on: upload-ready-pipeline
@@ -338,7 +338,7 @@ Classify the model's **CI priority** first:

 **L2 & L3 online — same file, dual marks on the baseline smoke:** The **first / simplest** case in `test_{slug}.py` (default deploy, minimal steps, single prompt) should carry **both** `@pytest.mark.core_model` and `@pytest.mark.advanced_model` on the **same** function so L2 (`test-ready.yml`) and L3 (`test-merge.yml`) share one smoke test. Heavier deploy variants or API paths in the same file use **`advanced_model` only**. When L3 moves to nightly, migrate those heavier cases into `test_{slug}_expansion.py` with `full_model` and remove the dedicated `test-merge.yml` job (see `test_longcat_image_expansion.py`, `test_qwen_image_expansion.py`).

-**L4 design (high priority):** Combine multiple supported features (Cache-DiT, TP, USP, CFG, HSDP, CPU offload, quantization) into **few parametrized** `OmniServerParams` rows so each feature appears in at least one case without exploding GPU jobs. Shard single-GPU vs multi-GPU cases across the nightly X2I/X2V function steps (1-GPU vs `distributed_cuda`).
+**L4 design (high priority):** Combine multiple supported features (Cache-DiT, TP, USP, CFG, HSDP, CPU offload, quantization) into **few parametrized** `OmniServerParams` rows so each feature appears in at least one case without exploding GPU jobs. Shard single-GPU vs multi-GPU cases across the nightly X2I/X2V function steps (`cards_1` vs `not cards_1`).

 **L4 design (medium / low):** One or two parametrized rows covering the best quality/perf trade-off; skip perf JSON unless the model is high priority.

@@ -34,7 +34,7 @@ but the changes are correct — stage the modified files and re-commit.
 | `check-forbidden-imports` | Stdlib `re`/`base64`, pickle, Hugging Face Hub API, or direct Triton/TileLang | `import regex as re` and `pybase64`; use `vllm.transformers_utils.repo_utils`. Do **not** add the file to `allowed_files` without review |
 | `check-torch-cuda-call` | New `torch.cuda.*` call site | Use `current_omni_platform` / `OmniPlatform`; do not grow `ALLOWED_FILES` without review |
 | `check-tts-adapter-migration` | New `self._tts_model_type` branch in `serving_speech.py` | Put per-model logic in `tts_adapters/`. Lower `MAX_MODEL_TYPE_BRANCHES` when removing branches; do not raise it without review |
-| `check-test-ci-coverage` | New `tests/**/test_*.py` missing level or hardware mark | Add `core_model`/`advanced_model`/… plus `cpu`/`cuda`/`hardware_test(` |
+| `check-mark` | New `tests/**/test_*.py` missing level/hardware mark, or direct `pytest.mark.H100` | Add `core_model`/`advanced_model`/… plus `cpu`/`cuda`/`hardware_test(`; SKU marks only via helpers |
 | `mypy-3.10` | Type error on changed `vllm_omni/` files | Fix the types; model trees are excluded. Extra versions: `pre-commit run --hook-stage manual mypy-3.12` |
 | `markdownlint-cli2` | Docs/README markdown lint | Accept auto-fix under `docs/` / `recipes/` / root README |
 | `check-buildkite` | Invalid `.buildkite/*.yml` | Fix the YAML; do not grow `SKIP_FILES` without review |
@@ -52,8 +52,8 @@ Always attach markers deliberately:
  - `tts` — speech synthesis / TTS-only models (`/v1/audio/speech`, voice clone, etc.)
  - `diffusion` — generative diffusion models (image / audio / text / video from noise)
 - **Cross-cutting area** (when relevant): `parallel`, `cache`, `example`, `benchmark`
- **Hardware**: `cpu`, `gpu`, `cuda`, `rocm`, `npu`, `L4`, `H100`, `distributed_cuda`, …
- Optional: `slow`, distributed markers when multi-card is required
+- **Hardware**: `cpu`, `gpu`, `cuda`, `rocm`, `npu`, …; SKU (`H100`, `L4`, …) and `cards_{n}` via `@hardware_test(...)` / `hardware_marks(...)` only — never `pytest.mark.H100` (`check-mark` rejects it)
+- Optional: `slow`. Multi-card: `num_cards=N` on the helper (filter with `cards_N` / `not cards_1`)

 **Baseline smoke (L2 + L3):** The simplest e2e case per model — default deploy, minimal request — should usually carry **both** `@pytest.mark.core_model` and `@pytest.mark.advanced_model` on the **same** test function so `test-ready.yml` and `test-merge.yml` share one test. `send_*_request` picks validation depth from `--run-level`. References: `test_voxcpm2_tts.py::test_text_to_audio_001`, `test_qwen3_tts_customvoice.py::test_text_to_audio_001`. Heavier scenarios use **`advanced_model` only**; L4 expansion uses **`full_model`**.

@@ -274,7 +274,7 @@ def test_text_to_video_001(omni_server, online_client) -> None:

 **3. Cross-cutting rules**

- New `.py` files need the Omni SPDX header (`Copyright contributors to the vLLM-Omni project`, not `vLLM project`). `tests/` may keep stdlib `re`/`base64`; pickle is still banned unless the file is already on the pickle allowlist. Every collected `tests/**/test_*.py` needs a CI **level** mark and a **hardware** mark/helper (`check-test-ci-coverage` runs locally; GHA skips it). Do not grow `allowed_files` to land a test. Policy: [docs/contributing/README.md](../../../docs/contributing/README.md#linting).
+- New `.py` files need the Omni SPDX header (`Copyright contributors to the vLLM-Omni project`, not `vLLM project`). `tests/` may keep stdlib `re`/`base64`; pickle is still banned unless the file is already on the pickle allowlist. Every collected `tests/**/test_*.py` needs a CI **level** mark and a **hardware** platform mark/helper (`check-mark` runs locally; GHA skips it). Do not write `pytest.mark.H100` / `pytest.mark.L4`; use the helpers so `cards_{n}` is attached. Do not grow `allowed_files` to land a test. Policy: [docs/contributing/README.md](../../../docs/contributing/README.md#linting).
 - Reuse existing fixtures for the chosen scenario; do not mix “online client” assumptions into offline `OmniRunner` tests without a clear reason.
 - Avoid external network dependency in assertions unless the scenario is explicitly “online serving” or doc examples that require a model hub (then align with CI secrets/cache).
 - Keep **one test function = one intent** (one modality combo, one endpoint contract, or one acceleration combo).
@@ -79,7 +79,7 @@ E2E (L2+) should not rely on mocks unless documenting a rare exception; prefer r
   - `full_model` — L4 nightly (`test-nightly.yml`); some expansion tests still carry both `advanced_model` and `full_model` during migration
 2. **Model type** (pick one): `omni`, `tts`, or `diffusion`
 3. **Cross-cutting** when relevant: `parallel`, `cache`, `example`, `benchmark`
-4. **Hardware**: `cpu`, `cuda`, `rocm`, `npu`, `L4`, `H100`, `distributed_cuda`, …
+4. **Hardware**: `cpu`, `cuda`, `rocm`, `npu`, `L4`, `H100`, `cards_1`, …
 5. Multi-card: `@hardware_test(...)` in `tests/helpers/mark.py`

 ## Command Templates
@@ -244,7 +244,7 @@ pytest -s -v dfx/reliability/invalid_param_test/test_invalid_image_generation.py

 ```bash
 cd tests
-pytest -s -v -m "core_model and distributed_cuda and L4" --run-level=core_model
+pytest -s -v -m "core_model and L4 and not cards_1" --run-level=core_model
 ```

 ### Concrete e2e paths (common in-tree)
@@ -27,4 +27,4 @@ jobs:
        # Don't run historical-debt hooks against the whole tree in CI yet.
        # They still run locally on changed files.
        env:
-          SKIP: check-test-ci-coverage,markdownlint-cli2,shellcheck,check-spdx-header,mypy-3.10
+          SKIP: check-mark,markdownlint-cli2,shellcheck,check-spdx-header,mypy-3.10
@@ -105,8 +105,8 @@ repos:
      <<: *mypy_common
      stages: [manual]

-    - id: check-test-ci-coverage
-      name: Ensure test files have pytestmarks
+    - id: check-mark
+      name: Ensure test files have CI marks (no direct SKU)
      entry: python tools/pre_commit/check_test_marks.py
      language: python
      types: [python]
@@ -1,4 +1,4 @@
-# Target vllm version: v0.27.0 (releases/v0.27.0 branch).
+# Target vllm version: v0.28.0 (tag v0.28.0 @ 2cf0a6915ce5).
 # 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
@@ -14,11 +14,14 @@
 # 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.
+# needed. The 0.28 rebase repeated the cycle: the v0.28.0rc / final-tag wheels
+# rode on the v0.27.0 base image with the full reinstall block until the
+# official v0.28.0 image shipped, at which point this file dropped back to the
+# form you are reading. 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=v0.28.0

 # 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:v0.28.0
 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=v0.28.0
 FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}

 # WORKDIR /vllm-workspace/vllm
@@ -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=v0.28.0-a3
 FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}

 # WORKDIR /vllm-workspace/vllm
@@ -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=v0.28.0
 FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}

 ARG APP_DIR=/vllm-workspace/vllm-omni
@@ -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=v0.28.0-a3
 FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}

 ARG APP_DIR=/vllm-workspace/vllm-omni
@@ -1,4 +1,4 @@
-ARG BASE_IMAGE=vllm/vllm-openai-rocm:v0.27.0
+ARG BASE_IMAGE=vllm/vllm-openai-rocm:v0.28.0
 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 ${BASE_IMAGE} AS base
 #    we should swap over to use stable release ASAP.
 #    We should avoid relying on custom commits.
 ARG USE_NIGHTLY_BUILD=0
-ARG VLLM_VERSION_OR_COMMIT_HASH=v0.27.0
+ARG VLLM_VERSION_OR_COMMIT_HASH=v0.28.0
 ARG ARG_PYTORCH_ROCM_ARCH
 ENV PYTORCH_ROCM_ARCH=${ARG_PYTORCH_ROCM_ARCH:-${PYTORCH_ROCM_ARCH}}

@@ -1,140 +1,33 @@
-# Argument to configure vllm base image if pre-built
-ARG VLLM_BASE=vllm-base
-
-FROM intel/deep-learning-essentials:2025.3.2-0-devel-ubuntu24.04 AS vllm-base
-
-WORKDIR /workspace/
-
-ARG PYTHON_VERSION=3.12
-ARG PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/xpu"
-
-RUN wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB | gpg --dearmor | tee /usr/share/keyrings/oneapi-archive-keyring.gpg > /dev/null && \
-    echo "deb [signed-by=/usr/share/keyrings/oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main" | tee /etc/apt/sources.list.d/oneAPI.list
-
-RUN apt clean && apt-get update -y && \
-    apt-get install -y --no-install-recommends --fix-missing \
-    curl \
-    ffmpeg \
-    git \
-    libsndfile1 \
-    libsm6 \
-    libxext6 \
-    libgl1 \
-    lsb-release \
-    libaio-dev \
-    numactl \
-    wget \
-    vim \
-    python3.12 \
-    python3.12-dev \
-    python3-pip
-
-# Add oneAPI repo, pin oneAPI to 2025.3, then install pinned packages in one layer.
-RUN wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB | gpg --dearmor | tee /usr/share/keyrings/oneapi-archive-keyring.gpg > /dev/null && \
-    echo "deb [signed-by=/usr/share/keyrings/oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main" | tee /etc/apt/sources.list.d/oneAPI.list && \
-    printf '%s\n' \
-    'Package: intel-oneapi-* intel-deep-learning-essentials* intel-pti*' \
-    'Pin: version 2025.3*' \
-    'Pin-Priority: 1001' \
-    > /etc/apt/preferences.d/oneapi-2025.3.pref && \
-    apt-get update -y && \
-    apt-get install -y --no-install-recommends \
-    intel-oneapi-compiler-dpcpp-cpp-2025.3 \
-    intel-oneapi-mkl-devel-2025.3 \
-    intel-oneapi-dnnl-devel-2025.3 && \
-    rm -rf /var/lib/apt/lists/*
-
-# Install UMD with fixed version
-RUN mkdir neo && \
-    cd neo && \
-    wget https://github.com/intel/intel-graphics-compiler/releases/download/v2.24.8/intel-igc-core-2_2.24.8+20344_amd64.deb && \
-    wget https://github.com/intel/intel-graphics-compiler/releases/download/v2.24.8/intel-igc-opencl-2_2.24.8+20344_amd64.deb && \
-    wget https://github.com/intel/compute-runtime/releases/download/25.48.36300.8/intel-ocloc_25.48.36300.8-0_amd64.deb && \
-    wget https://github.com/intel/compute-runtime/releases/download/25.48.36300.8/intel-opencl-icd_25.48.36300.8-0_amd64.deb && \
-    wget https://github.com/intel/compute-runtime/releases/download/25.48.36300.8/libigdgmm12_22.8.2_amd64.deb && \
-    wget https://github.com/intel/compute-runtime/releases/download/25.48.36300.8/libze-intel-gpu1_25.48.36300.8-0_amd64.deb && \
-    wget https://github.com/oneapi-src/level-zero/releases/download/v1.26.0/level-zero_1.26.0+u24.04_amd64.deb && \
-    dpkg -i *.deb && \
-    cd .. && \
-    rm -rf neo
-
-ENV PATH="/root/.local/bin:$PATH"
-ENV VIRTUAL_ENV="/opt/venv"
-ENV UV_PYTHON_INSTALL_DIR=/opt/uv/python
-RUN curl -LsSf https://astral.sh/uv/install.sh | sh
-RUN uv venv --python ${PYTHON_VERSION} --seed ${VIRTUAL_ENV}
-ENV PATH="$VIRTUAL_ENV/bin:$PATH"
-
-# This oneccl contains the BMG support which is not the case for default version of oneapi 2025.3.
-ARG ONECCL_INSTALLER="intel-oneccl-2021.15.9.14_offline.sh"
-RUN wget "https://github.com/uxlfoundation/oneCCL/releases/download/2021.15.9/${ONECCL_INSTALLER}" && \
-    bash "${ONECCL_INSTALLER}" -a --silent --eula accept && \
-    rm "${ONECCL_INSTALLER}" && \
-    echo "source /opt/intel/oneapi/setvars.sh --force" >> /root/.bashrc && \
-    echo "source /opt/intel/oneapi/ccl/2021.15/env/vars.sh --force" >> /root/.bashrc
-RUN rm -f /opt/intel/oneapi/ccl/latest && \
-    ln -s /opt/intel/oneapi/ccl/2021.15 /opt/intel/oneapi/ccl/latest
-
-SHELL ["bash", "-c"]
-CMD ["bash", "-c", "source /root/.bashrc && exec bash"]
-
-WORKDIR /workspace/
-
-ENV UV_HTTP_TIMEOUT=500
-
-# Configure package index for XPU
-ENV PIP_EXTRA_INDEX_URL=${PIP_EXTRA_INDEX_URL}
-ENV UV_EXTRA_INDEX_URL=${PIP_EXTRA_INDEX_URL}
-ENV UV_INDEX_STRATEGY="unsafe-best-match"
-ENV UV_LINK_MODE="copy"
-
-ARG VLLM_VERSION=v0.27.0
-RUN git clone -b ${VLLM_VERSION} https://github.com/vllm-project/vllm
-WORKDIR /workspace/vllm
-
-RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip install --upgrade pip && \
-    uv pip install -r requirements/xpu.txt && \
-    uv pip install setuptools_scm &&\
-    uv pip install grpcio-tools protobuf nanobind && \
-    source /opt/intel/oneapi/setvars.sh --force && \
-    source /opt/intel/oneapi/ccl/2021.15/env/vars.sh --force && \
-    export CMAKE_PREFIX_PATH="$(python3 -c 'import site; print(site.getsitepackages()[0])'):${CMAKE_PREFIX_PATH}"
-
-ENV LD_LIBRARY_PATH="/opt/intel/oneapi/ccl/2021.15/lib:$LD_LIBRARY_PATH:/usr/local/lib/"
-
-ENV VLLM_TARGET_DEVICE=xpu
-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 --no-build-isolation --no-deps .
-
-CMD ["/bin/bash"]
-
-FROM vllm-base AS vllm-openai
-
-# install nixl from source code
-RUN uv pip install nixl
-
-# install additional dependencies for openai api server
-RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip install accelerate hf_transfer pytest pytest_asyncio lm_eval[api] modelscope
-
-# install development dependencies (for testing)
-RUN uv pip install -e tests/vllm_test_utils
-
-# ensure vllm is properly installed
-RUN python -c "import vllm, inspect; print(vllm.__file__)"
-RUN uv pip show vllm
-
-CMD ["/bin/bash"]
-
-ENTRYPOINT []
+# Target vLLM version: v0.28.0. Keep VLLM_VERSION aligned with the target vLLM
+# release on every rebase.
+#
+# This file layers vLLM-Omni on top of the published upstream XPU image, which
+# already ships a self-consistent XPU runtime (torch-xpu, oneCCL/UCX+NIXL, the
+# pinned UMD/compute-runtime stack, and vLLM itself) built from
+# https://github.com/vllm-project/vllm/blob/main/docker/Dockerfile.xpu. Docker
+# pulls that base automatically, so there is nothing to build here beyond the
+# Omni layers. Mirrors how docker/Dockerfile.ci consumes vllm/vllm-openai.
+#
+# If the tag does not exist yet (targeting an unreleased vLLM commit), build the
+# base from upstream's own Dockerfile and point VLLM_BASE at the result:
+#
+#   git clone --depth 1 --branch $VLLM_VERSION https://github.com/vllm-project/vllm /tmp/vllm
+#   docker build -t vllm-openai-xpu:local --target vllm-openai \
+#       -f /tmp/vllm/docker/Dockerfile.xpu /tmp/vllm
+#   docker build -f docker/Dockerfile.xpu --build-arg VLLM_BASE=vllm-openai-xpu:local .
+#
+# .buildkite/intel/scripts/run-xpu-test.sh automates that pull-then-build fallback.
+ARG VLLM_BASE_IMAGE=vllm/vllm-openai-xpu
+ARG VLLM_VERSION=v0.28.0
+ARG VLLM_BASE=${VLLM_BASE_IMAGE}:${VLLM_VERSION}

 FROM ${VLLM_BASE} AS vllm-omni

+# The base image ships ENTRYPOINT ["vllm", "serve"]; clear it so this stage stays
+# usable as a plain shell/test container regardless of which VLLM_BASE was used.
+ENTRYPOINT []
+CMD ["/bin/bash"]
+
 RUN apt-get update && \
    apt-get install -y espeak-ng ffmpeg git jq && \
    apt-get clean && \
@@ -53,6 +53,7 @@ nav:
      - BitsAndBytes W4: user_guide/quantization/bitsandbytes.md
      - MXFP8 W8A8: user_guide/quantization/mxfp8.md
      - MXFP4 W4A4: user_guide/quantization/mxfp4.md
+      - SVDQuant W4A4: user_guide/quantization/svdquant.md
      - ModelOpt: user_guide/quantization/modelopt.md
      - AutoRound: user_guide/quantization/autoround.md
      - msModelSlim: user_guide/quantization/msmodelslim.md
@@ -114,6 +114,10 @@ Configuration classes.
 - [vllm_omni.model_executor.models.qwen3_tts.tokenizer_25hz.configuration_qwen3_tts_tokenizer_v1.Qwen3TTSTokenizerV1DecoderDiTConfig][]
 - [vllm_omni.model_executor.models.qwen3_tts.tokenizer_25hz.configuration_qwen3_tts_tokenizer_v1.Qwen3TTSTokenizerV1EncoderConfig][]
 - [vllm_omni.transformers_utils.configs.cosyvoice3.CosyVoice3Config][]
+- [vllm_omni.transformers_utils.configs.mammoth_moda2.Mammothmoda2Config][]
+- [vllm_omni.transformers_utils.configs.mammoth_moda2.Mammothmoda2Qwen2_5_VLConfig][]
+- [vllm_omni.transformers_utils.configs.mammoth_moda2.Mammothmoda2Qwen2_5_VLTextConfig][]
+- [vllm_omni.transformers_utils.configs.mammoth_moda2.Mammothmoda2Qwen2_5_VLVisionConfig][]
 - [vllm_omni.transformers_utils.configs.omnivoice.OmniVoiceConfig][]
 - [vllm_omni.transformers_utils.configs.voxcpm2.VoxCPM2Config][]

@@ -83,6 +83,7 @@ depends on the installed kernels and model path.
 | `SPEAKER_SAMPLES_DIR` | Filesystem path; default `~/.cache/vllm-omni/speakers` | Speech server; read when speaker storage initializes | Environment-only setting. The directory is created; filesystem errors propagate. | Stable |
 | `SPEAKER_MAX_UPLOADED` | Integer; default `1000` | Speech server; read when speaker storage initializes | Environment-only setting. A non-integer logs a warning and uses `1000`; range is not otherwise validated. | Stable |
 | `VLLM_OMNI_ASYNC_OUTPUT_TIMEOUT` | Float seconds; default `600` | Diffusion engine async-output wait in `step_streaming`; resolved per call on the request path, not at import | Environment-only setting. A non-float or `<=0` value warns once and uses the default. | Experimental |
+| `VLLM_OMNI_EVENT_DRIVEN_ORCH` | `1`, `true`, `yes` or `on` enables; default `0` (off) | Orchestration loop and the serving-side final-output drain; read once when the `Orchestrator` is constructed | Environment-only setting. Values are stripped and case-normalized; any unrecognized value leaves the legacy poll loop selected. | Experimental |
 | `VLLM_OMNI_INPUT_WAIT_TIMEOUT_S` | Float seconds; default `600`; `<=0` disables | Full-payload input coordinator, not async-chunk transfer; read when the scheduler module imports in each worker | Environment-only setting. A non-float logs a warning and uses `600`. | Stable operational control |
 | `VLLM_OMNI_ORCH_MONITOR_PATH` | Filesystem path; default `<current-working-directory>/vllm_omni_orch_monitor_<timestamp>.json` | Orchestrator monitor enabled by `--enable-orch-monitor`; read when the monitor is created | Environment-only path override. Parent directories are created; write errors are logged. | Diagnostic |
 | `VLLM_OMNI_VIDEO_SYNC_TIMEOUT` | Float seconds; default `600` | Synchronous Videos API; read when the API server module imports | Environment-only setting. A non-float raises `ValueError` during import. | Experimental |
@@ -59,7 +59,7 @@ The new deploy schema lives under `vllm_omni/deploy/` and is paired with a froze
 | `edges` | list | optional | `null` | Explicit edge list for the KV transfer graph. Auto-derived from stage inputs if omitted. |
 | `stages` | list | optional | `[]` | Per-stage runtime overrides matched by `stage_id`. Pipeline stages are still created from `PipelineConfig` when this list is empty. |
 | `platforms` | dict | optional | `null` | Keyed by `npu` / `rocm` / `xpu`, each contains a `stages:` list with per-platform overrides applied on top of the CUDA defaults. |
-| `pipeline` | str | optional | `null` | Override the auto-detected pipeline registry key (used for structural variants like `qwen2_5_omni_thinker_only`). |
+| `pipeline` | str | optional | `null` | Override the auto-detected pipeline registry key (used for structural variants like `qwen2_5_omni_thinker_only` / `qwen3_omni_moe_thinker_only`). |
 | `trust_remote_code` | bool \| null | optional | `null` | **Pipeline-wide.** Trust HF remote code on model load; applies to every stage when specified. |
 | `distributed_executor_backend` | str \| null | optional | `null` | **Pipeline-wide.** Distributed executor backend forwarded to vLLM (`"mp"`, `"ray"`, `"external_launcher"`). If omitted, vLLM auto-selects backend from runtime topology. |
 | `dtype` | str \| null | optional | `null` | **Pipeline-wide.** Model dtype for every stage. |
@@ -94,12 +94,14 @@ Each entry under `stages:` accepts any `StageDeployConfig` field directly (no ne
 | `max_num_batched_tokens` | int \| null | optional | `null` | Per-stage prefill/token budget; also contributes to the native maximum in-flight token limit. |
 | `max_model_len` | int \| null | optional | `null` | Per-sequence context or KV length; `-1` enables native cache-capacity auto-fitting, while values above the HF default auto-set `VLLM_ALLOW_LONG_MAX_MODEL_LEN=1`. |
 | `async_scheduling` | bool \| null | optional | `null` | Per-stage async scheduling toggle. |
-| `devices` | str \| null | optional | `null` | Device list assigned to this stage. |
+| `devices` | str \| null | optional | `null` | Device list assigned to this stage. The number of device ids must equal this stage's local world size (`tensor_parallel_size` × local data-parallel size × `pipeline_parallel_size`, or `num_replicas` × that product for a replica pool); a mismatch fails early — see the note below. |
 | `output_connectors` | dict \| null | optional | `null` | Keyed by `to_stage_<n>`; values are names registered under top-level `connectors:`. |
 | `input_connectors` | dict \| null | optional | `null` | Keyed by `from_stage_<n>`; values are names registered under top-level `connectors:`. |
 | `default_sampling_params` | dict \| null | optional | `null` | Baseline sampling params. Deep-merged with pipeline `sampling_constraints` (pipeline wins). |
 | `engine_extras` | dict | optional | `{}` | Catch-all for engine fields not listed above; deep-merged across overlays and forwarded to the stage engine. |

+**Note:** a stage's `devices` count must equal its local world size (`tensor_parallel_size` × `data_parallel_size_local` × `pipeline_parallel_size`, falling back to global `data_parallel_size` when the local size is unset), or `num_replicas` × that product for a replica pool. A mismatch fails early and names the offending stage. A top-level `--tensor-parallel-size` is broadcast to every stage, so it can make a single-GPU stage violate this contract ([issue #5003](gh-issue:5003)); fix that case with `--stage-overrides` (set `tensor_parallel_size` and `devices` together per stage) or set TP only on the multi-GPU stage.
+
 ### Connector schema

 Each entry under top-level `connectors:` follows this shape:
@@ -49,7 +49,7 @@ vLLM-Omni's `pre-commit` hooks will now run automatically every time you commit.
    `--all-files` run stays green while historical debt is cleaned up. The
    current `SKIP` list in
    [`.github/workflows/pre-commit.yml`](https://github.com/vllm-project/vllm-omni/blob/main/.github/workflows/pre-commit.yml)
-    is `check-test-ci-coverage`, `markdownlint-cli2`, `shellcheck`,
+    is `check-mark`, `markdownlint-cli2`, `shellcheck`,
    `check-spdx-header`, and `mypy-3.10`. Those hooks still run on **your
    commit** for changed files. A passing GitHub pre-commit check does not mean
    they passed locally. Hooks **not** on that list (forbidden imports,
@@ -66,7 +66,7 @@ already there). `check-pickle-imports` is gone: pickle is now one rule inside
 | `markdownlint-cli2` | Markdown in `docs/`, `recipes/`, `README.md`, `CONTRIBUTING.md` (not `.claude/` / `.cursor/` / `CLAUDE.md`) | skipped |
 | `mypy-3.10` | Type-check changed `vllm_omni/` files (model trees excluded). `tests/` uses `--follow-imports skip` | skipped |
 | `mypy-3.11` / `3.12` / `3.13` | Same checker, extra Python versions | not installed; `pre-commit run --hook-stage manual mypy-3.12` |
-| `check-test-ci-coverage` | Every `tests/**/test_*.py` has a CI level mark and a hardware mark/helper | skipped |
+| `check-mark` | Every `tests/**/test_*.py` has a CI level mark and a hardware platform mark or helper; no direct `pytest.mark.H100` / SKU | skipped |
 | `check-tts-adapter-migration` | `self._tts_model_type` branches in `serving_speech.py` must not increase | runs |
 | `shellcheck` | `*.sh` quoting / undefined vars | skipped |
 | `check-spdx-header` | Omni SPDX header on `.py` / `.pyi` / `.sh` / `.rs` / `.proto` | skipped |
@@ -144,11 +144,13 @@ pre-commit run --hook-stage manual mypy-3.12

 #### Test CI marks

-`check-test-ci-coverage` requires each collected test module under `tests/` to
+`check-mark` requires each collected test module under `tests/` to
 have at least one CI **level** mark (`core_model`, `advanced_model`,
-`full_model`, `local_model`, or `slow`) and a **hardware** mark (`cpu`, `cuda`,
-`H100`, …) or helper (`hardware_test(` / `hardware_marks(`). GitHub Actions
-skips this hook; local commit does not. See the
+`full_model`, `local_model`, or `slow`) and a **hardware** platform mark
+(`cpu`, `cuda`, …) or helper (`hardware_test(` / `hardware_marks(`). SKU marks
+(`H100`, `L4`, …) must come from those helpers so `cards_{n}` is attached;
+direct `pytest.mark.H100` is rejected. GitHub Actions skips this hook; local
+commit does not. See the
 [test writing guide](./ci/test_writing_guide.md).

 #### TTS adapter ratchet
@@ -402,7 +402,7 @@ loads the model once per mode.
 Before splitting, confirm each half actually collects at least one test under the job's forwarded filters — pytest exits with code 5 ("no tests collected") if a half is empty, which the script reports as a failure, red-ing a job that used to pass as one combined invocation.

 Also check the tests' `num_cards` against the job's `mirror_hardwares` preset.
-`cuda_marks` turns `num_cards > 1` into `skipif(device_count() < num_cards)`, so a
+`_cuda_marks` turns `num_cards > 1` into `skipif(device_count() < num_cards)`, so a
 test asking for more GPUs than the preset provides is silently skipped and its
 coverage artifact comes back empty. Splitting such a job produces a file that
 looks fine and measures nothing — "Diffusion · Z Image Test" is the current
@@ -81,8 +81,8 @@ Supported form:

 | Form | Example | Effect |
 | ---- | ------- | ------ |
-| Array (single platform) | `"mark": [{"hardware_marks": {"res": {"cuda": "H100"}, "num_cards": 2}}, "full_model", "diffusion"]` | Hardware marks from `hardware_marks(...)`; `num_cards` > 1 adds `distributed_*` (+ CUDA `skipif_cuda` when applicable). String entries become extra pytest marks. |
-| Array (multi-platform) | `"mark": [{"hardware_marks": {"res": {"cuda": "H100", "rocm": "MI325", "npu": "A2"}, "num_cards": {"cuda": 2, "rocm": 2, "npu": 1}}}, "full_model", "omni"]` | Same as above, but declares **multiple platforms** in one case. Filter examples: `-m "full_model and H100 and cuda"`, `-m "full_model and MI325 and rocm"`. |
+| Array (single platform) | `"mark": [{"hardware_marks": {"res": {"cuda": "H100"}, "num_cards": 2}}, "full_model", "diffusion"]` | Hardware marks from `hardware_marks(...)`; `num_cards` adds `cards_{n}` (including `cards_1` when omitted). String entries become extra pytest marks. |
+| Array (multi-platform) | `"mark": [{"hardware_marks": {"res": {"cuda": "H100", "rocm": "MI325"}, "num_cards": 2}}, "full_model", "omni"]` | Same as above, but declares **multiple platforms** on one item. All platforms must share the same `num_cards` (int or identical dict values). Different counts need `@hardware_test` or one `hardware_marks()` per `pytest.param`. Filter examples: `-m "full_model and H100 and cards_2 and cuda"`, `-m "full_model and MI325 and cards_2 and rocm"`. |

 Recommended for L4 perf cases:

@@ -157,7 +157,7 @@ pytest -s -v tests/dfx/perf/scripts/run_benchmark.py \
 ```

 <!-- markdownlint-disable-next-line MD051 -->
-See also [Markers for Tests](#markers-for-tests) for registered hardware markers (`H100`, `L4`, `cuda`, `distributed_cuda`, …).
+See also [Markers for Tests](#markers-for-tests) for registered hardware markers (`H100`, `L4`, `cuda`, `cards_1`, …).

 #### `server_params` Configuration

@@ -88,7 +88,7 @@ Failed jobs: 1/2
    ```bash
    cd tests
    pytest -s -v test_xxxx.py --run-level=core_model
-    pytest -s -v -m "core_model and distributed_cuda and L4" --run-level=core_model
+    pytest -s -v -m "core_model and L4 and not cards_1" --run-level=core_model
    ```

 === "L3 level"
@@ -119,7 +119,7 @@ Failed jobs: 1/2

    ```bash
    pytest -s -v test_xxxx.py --run-level=advanced_model
-    pytest -s -v -m "advanced_model and distributed_cuda and L4" --run-level=advanced_model
+    pytest -s -v -m "advanced_model and L4 and not cards_1" --run-level=advanced_model
    ```

 === "L4 level"
@@ -153,12 +153,12 @@ Failed jobs: 1/2
    ```bash
    cd tests
    pytest -s -v test_xxxx.py --run-level=full_model
-    pytest -s -v -m "full_model and distributed_cuda and L4" --run-level=full_model
+    pytest -s -v -m "full_model and L4 and not cards_1" --run-level=full_model
    pytest -s -v -m "full_model and (omni or tts) and H100" --run-level=full_model
    ```
    If you only want to run specific test cases on a particular platform, you can use:
    ```bash
-    pytest -s -v -m "full_model and distributed_cuda and L4"  --run-level=full_model
+    pytest -s -v -m "full_model and L4 and not cards_1"  --run-level=full_model
    ```
    Note: ``run_benchmark.py`` and ``run_diffusion_benchmark.py`` accept an optional ``--test-config-file``. If omitted, each loads every ``*.json`` under ``tests/dfx/perf/tests/`` (omni/tts vs diffusion split by ``is_diffusion_perf_config``) and pytest ``-m`` filters by each case's JSON ``mark``:
    ```bash
@@ -48,17 +48,12 @@ Defined in `pyproject.toml`:
 | `MI325`            | Tests that require MI325 GPU (AMD/ROCm) *                                                               |
 | `A2`               | Tests that require A2 NPU *                                                                             |
 | `A3`               | Tests that require A3 NPU *                                                                             |
-| `distributed_cuda` | Tests that require multi cards on CUDA platform *                                                       |
-| `distributed_rocm` | Tests that require multi cards on ROCm platform  *                                                      |
-| `distributed_npu`  | Tests that require multi cards on NPU platform  *                                                       |
-| `skipif_cuda`      | Skip if the num of CUDA cards is less than the required *                                               |
-| `skipif_rocm`      | Skip if the num of ROCm cards is less than the required *                                               |
-| `skipif_npu`       | Skip if the num of NPU cards is less than the required *                                                |
+| `cards_{n}`        | Tests that require *n* accelerator cards (`cards_1` … `cards_8`); auto-added from `num_cards` *         |
 | `slow`             | Slow tests (may skip in quick CI)                                                                       |
 | `benchmark`        | Benchmark tests (decorator on runner test functions; perf JSON uses `full_model` + type marker instead) |
 | `local_model`      | Tests requiring local / non-HF-hub model weights                                                        |

-\* Means those markers are auto-added by `@hardware_test` (parametrization decorator) or `hardware_marks` (only returning the list of marks for flexibility).
+\* Means those markers are auto-added by `@hardware_test` (parametrization decorator) or `hardware_marks` (only returning the list of marks for flexibility). Do **not** write `pytest.mark.H100` / `pytest.mark.L4` (or other SKU names) by hand: `check-mark` rejects that so `cards_{n}` cannot be skipped.

 #### Example usage for markers

@@ -81,25 +76,23 @@ def test_video_to_audio()
 This decorator is intended to make hardware-aware, cross-platform test authoring easier and more robust for CI/CD environments. The `hardware_test` decorator in `vllm-omni/tests/helpers/mark.py` performs the following actions:

 1. **Applies platform and resource markers**  
-   Adds the appropriate pytest markers for each specified hardware platform (e.g., `cuda`, `rocm`, `xpu`, `npu`) and resource type (e.g., `L4`, `H100`, `MI325`, `B60`, `A2`, `A3`).
+   Adds the appropriate pytest markers for each specified hardware platform (e.g., `cuda`, `rocm`, `xpu`, `npu`) and resource type (e.g., `L4`, `H100`, `H200`, `B200`, `MI325`, `B60`, `A2`, `A3`).
   ```python
   @pytest.mark.cuda
   @pytest.mark.L4
   ```
-2. **Handles multi-card (distributed) scenarios**  
-   For tests requiring multiple cards, it automatically adds distributed markers such as `distributed_cuda`, `distributed_rocm`, or `distributed_npu`.
-   ```python
-   @pytest.mark.distributed_cuda(num_cards=num_cards)
-   ```
+2. **Handles card-count filtering**  
+   Adds a filterable marker `cards_{n}` (including `cards_1` when `num_cards` is omitted / 1). Use this with the SKU and platform, e.g. `-m "H100 and cards_4 and cuda"` vs `-m "H100 and cards_2 and cuda"`. Any multi-card CUDA test is `-m "cuda and not cards_1"`.
+
+   When `res` lists more than one platform **and** `num_cards` differs by platform, `@hardware_test` collects **one variant per platform** so those `cards_{n}` marks do not stack. `-m "H100 and cards_4 and cuda"` then selects only the CUDA/H100 4-card variant, not a ROCm 2-card sibling. The same count on every platform stays a single item.
 3. **Supports flexible card requirements**  
   Accepts `num_cards` as either a single integer for all platforms or as a dictionary with per-platform values. If not specified, defaults to 1 card per platform.

 4. **Integrates resource validation**  
-   On CUDA, adds a skip marker (`skipif_cuda`) if the system does not have the required number of devices.
-   Support for `skipif_rocm` and `skipif_npu` will be implemented later.
+   On CUDA and XPU, when `num_cards > 1`, attaches pytest's built-in `skipif` if the machine has fewer devices than requested. This is not a named `skipif_*` marker and is not used for `-m` filtering.

 5. **Works with pytest filtering**  
-   Allows tests to be filtered and selected at runtime using standard pytest marker expressions (e.g., `-m "distributed_cuda and L4"`).
+   Allows tests to be filtered and selected at runtime using standard pytest marker expressions (e.g., `-m "H100 and cards_2"`, `-m "L4 and not cards_1"`).

 ##### Example usage for decorator

@@ -117,13 +110,14 @@ This decorator is intended to make hardware-aware, cross-platform test authoring
        num_cards=2,
    )
    ```
- `res` must be a dict; supported resources: CUDA (L4/H100), ROCm (MI325), XPU (B60), MUSA (S5000), NPU (A2/A3)
+- `res` must be a dict. SKU values come from `pyproject.toml` markers tagged `[hardware-resource]` plus the platform key (`[cuda]`, `[rocm]`, `[xpu]`, `[npu]`, `[musa]`).
 - `num_cards` can be int (all platforms) or dict (per platform); defaults to 1 when missing
- Distributed markers (`distributed_cuda`, `distributed_rocm`, `distributed_npu`) are auto-added for multi-card cases
 - Filtering examples:
-    - CUDA only: `pytest -m "distributed_cuda and L4"`
-    - ROCm only: `pytest -m "distributed_rocm and MI325"`
-    - NPU only: `pytest -m "distributed_npu"`
+    - CUDA H100 4-card: `pytest -m "H100 and cards_4 and cuda"`
+    - CUDA H100 2-card: `pytest -m "H100 and cards_2 and cuda"`
+    - CUDA multi-card (any count): `pytest -m "L4 and not cards_1"`
+    - ROCm multi-card: `pytest -m "MI325 and not cards_1"`
+    - NPU multi-card: `pytest -m "npu and not cards_1"`

 #### Function: `hardware_marks`

@@ -179,37 +173,30 @@ If you want to add support for a new platform (e.g., "tpu" for a new accelerator
   [tool.pytest.ini_options]
   markers = [
       # ... existing markers ...
-       "tpu: Tests that require TPU device",
-       "TPU_V3: Tests that require TPU v3 hardware",
-       "distributed_tpu: Tests that require multiple TPU devices",
+       "tpu: [hardware-platform] Tests that require TPU device",
+       # Add [gpu] next to [hardware-platform] if this platform should also get pytest.mark.gpu.
+       "TPU_V3: [hardware-resource] [tpu] Tests that require TPU v3 hardware",
+       "cards_1: [hardware-cards] Tests that require 1 accelerator card",
+       "cards_2: [hardware-cards] Tests that require 2 accelerator cards",
   ]
   ```
+   `check-mark` treats `[hardware-platform]` names (including `cpu`/`gpu`) as allowed `pytest.mark.<name>` values. `hardware_test(res=...)` still rejects `cpu`/`gpu` as `res` keys.
 2. **Implement a marker construction function for your platform** in `vllm-omni/tests/helpers/mark.py`:
   ```python
   # In vllm-omni/tests/helpers/mark.py

-   def tpu_marks(*, res: str, num_cards: int):
-       test_platform = pytest.mark.tpu
-       if res == "TPU_V3":
-           test_resource = pytest.mark.TPU_V3
-       else:
-           raise ValueError(
-               f"Invalid TPU resource type: {res}. Supported: TPU_V3")
-
-       if num_cards == 1:
-           return [test_platform, test_resource]
-       else:
-           test_distributed = pytest.mark.distributed_tpu(num_cards=num_cards)
-           # Optionally: add skipif_tpu when implemented
-           return [test_platform, test_resource, test_distributed]
+   def _tpu_marks(*, res: str, num_cards: int):
+       _require_sku("tpu", res)
+       return [pytest.mark.tpu, getattr(pytest.mark, res), _cards_mark(num_cards)]
   ```
-3. **Update `hardware_marks` to recognize your new platform**:
-    In the relevant place (see the `hardware_marks` implementation), add:
+3. **Register the builder** in `_marks_for_platform`:
    ```python
-    if platform == "tpu":
-        marks = tpu_marks(res=resource, num_cards=cards)
+    builders = {
+        # ... existing platforms ...
+        "tpu": _tpu_marks,
+    }
    ```
-    (`hardware_test` calls `hardware_marks` internally, so both will pick up the change.)
+    (`hardware_test` / `hardware_marks` both go through `_marks_for_platform`.)
 4. **(Recommended) Add a test using your new markers**:
   ```python
   @hardware_test(
@@ -223,11 +210,11 @@ If you want to add support for a new platform (e.g., "tpu" for a new accelerator
 **Summary**:  

 - Add pytest markers for your new platform/resources  
- Implement a marker function (`xxx_marks`)  
- Plug into `hardware_marks`  
- You're done: tests using `@hardware_test` or `hardware_marks` with your platform now automatically get the correct markers, distribution, and isolation!
+- Implement a private marker function (`_xxx_marks`)
+- Register it in `_marks_for_platform`
+- You're done: tests using `@hardware_test` or `hardware_marks` with your platform now automatically get the correct markers and `cards_{n}` filtering.

-See code in `vllm-omni/tests/helpers/mark.py` for existing examples (`cuda_marks`, `rocm_marks`, `npu_marks`).
+See code in `vllm-omni/tests/helpers/mark.py` for existing examples (`_cuda_marks`, `_rocm_marks`, `_npu_marks`).

 ## Test case style

@@ -438,7 +438,7 @@ selects the scheduler and worker family:
 Key topology fields belong to `StagePipelineConfig`:

 | Field | Description |
-|-------|-------------|
+| ------- | ------------- |
 | `model_stage` | Logical stage name (`ar_stage`, `decoder`, etc.). |
 | `execution_type` | Runtime family for this stage. |
 | `input_sources` | Upstream stage IDs that provide input. |
@@ -960,7 +960,7 @@ Common failures and fixes:
 | `check-forbidden-imports` | Stdlib `re`/`base64`, pickle, Hugging Face Hub API, or direct Triton/TileLang | `import regex as re` and `pybase64`; do not grow the allowlist without review |
 | `check-torch-cuda-call` | New `torch.cuda.*` call site | Use `current_omni_platform`; do not grow `ALLOWED_FILES` without review |
 | `check-tts-adapter-migration` | New `_tts_model_type` branch in `serving_speech.py` | Put logic in `tts_adapters/`; lower `MAX_MODEL_TYPE_BRANCHES` when removing branches |
-| `check-test-ci-coverage` | Test file missing level or hardware mark | Add `core_model`/… plus `cpu`/`cuda`/`hardware_test(` |
+| `check-mark` | Test file missing level/hardware mark, or direct `pytest.mark.H100` | Add `core_model`/… plus `cpu`/`cuda`/`hardware_test(`; SKU marks only via helpers |
 | `shellcheck` | Native `shellcheck` missing, or script warning | Install via apt/dnf/brew (or `shellcheck.exe` on Windows). See [Linting](../README.md#linting) |
 | `mypy-3.10` / `markdownlint-cli2` / `check-buildkite` | Types, docs markdown, or Buildkite YAML | See [Linting](../README.md#linting) |

@@ -322,6 +322,15 @@ Each 1-second window records:

 On shutdown the server also logs a short summary (`loop_active_pct`, per-replica queue averages/maxima).

+`loop_idle` and `loop_active` count loop iterations, so their absolute scale is
+tied to which orchestration loop is running. Under the default poll loop an idle
+orchestrator records roughly one iteration per millisecond. Under the
+event-driven loop (`VLLM_OMNI_EVENT_DRIVEN_ORCH=1`, see
+[Speech API](../serving/speech_api.md#orchestration-loop-experimental)) an idle
+orchestrator wakes only once per 0.5 s reconcile timeout, while a busy one still
+records one iteration per routed output. Window counts and the `loop_active_pct`
+summary are therefore not comparable across the two modes.
+
 ### Relationship to other diagnostics

 This monitor is intentionally separate from the existing profiling tools:
@@ -14,12 +14,12 @@ specified in the [Host Weight Runtime module design](../module/host_weight_runti

 ## Status

-The first implementation provides contracts and a CPU local-filesystem store.
-The initial diffusion consumer contract additionally defines typed,
+The implementation provides contracts and a CPU local-filesystem store. The
+diffusion consumer additionally defines typed,
 representation-independent final-layout identity/restoration mechanics plus a
-concrete BF16-with-preserved-FP32 policy for MiniMax H3. It remains
-library-only: no loader or DLO path selects, publishes, restores, or transports
-that artifact yet.
+concrete BF16-with-preserved-FP32 policy for MiniMax H3 and
+`black-forest-labs/FLUX.2-klein-4B`. The opt-in no-AllGather DLO integration
+selects, publishes, restores, and transfers these artifacts.

 V1 includes:

@@ -33,13 +33,12 @@ V1 includes:

 V1 does not include:

- a public CLI, loader activation, or default-on model integration;
- lease handoff to DLO or another transport consumer;
- FP8, quantized, merged-adaptation, or additional model producers;
- CUDA registration, pinned staging, H2D scheduling, or GPU kernels;
+- default-on activation or consumers outside no-AllGather DLO;
+- online FP8, quantized, merged-adaptation, or additional model producers;
+- HWR interaction with DLO AllGather;
 - a remote artifact provider or cross-node coordination;
 - automatic eviction; or
- a change to DLO AllGather or no-AllGather behavior.
+- a change to DLO collective or execution behavior.

 ## Motivation and use cases

@@ -255,7 +254,8 @@ The contract is intentionally separate from loader activation:
 - `FinalLayoutBF16Producer` accepts only the matching identity context and a
  finalized CPU model. It is `POST_LOAD_ONLY` and `SINGLE_PROCESS` per exact TP
  coordinate. Its BF16 policy preserves model-declared FP32 parameters and
-  buffers and revalidates MiniMax H3 mixed-precision invariants.
+  buffers, revalidates MiniMax H3 mixed-precision invariants, and revalidates
+  FLUX.2-klein's two block stacks, packed QKV mapping, and BF16 base layout.

 Other representations reuse source identity, typed parallel identity, tensor
 ownership, and exact restoration only when their policy proves those semantics.
@@ -26,6 +26,8 @@ Legend: ✅ supported, ⚠️ compatibility path or limited validation, ❌ unsu
 | **TP > 1** | ⚠️ Ordinary TP-aware loader only; no direct checkpoint mmap. | ⚠️ Ordinary TP-aware loader only; no direct checkpoint mmap. |
 | **HSDP** | ❌ Rejected to avoid double-sharding parameters. | ⚠️ Limited end-to-end coverage. |
 | **Per-tensor online FP8 linears** | ✅ Ordinary loader finalizes weights and scales before DLO sharding. | ✅ Ordinary loader retains complete rank-local tensors. |
+| **Online INT8 linears** | ✅ Ordinary loader finalizes the int8 weight (contiguous or transposed view) and fp32 scale before DLO sharding. | ✅ Ordinary loader retains complete rank-local tensors. |
+| **Online MXFP8 linears** | ✅ Ordinary loader finalizes the fp8 weight (contiguous) and e8m0 block scale (contiguous or transposed view) before DLO sharding. | ✅ Ordinary loader retains complete rank-local tensors. |
 | **Other online quantization methods** | ❌ Rejected until runtime packing and scale layouts are validated. | ⚠️ Allowed through the ordinary loader; validation is method-specific. |
 | **Model-level or standard layerwise CPU offload** | ❌ Disabled because DLO takes priority. | ❌ Disabled because DLO takes priority. |
 | **Resident leading layers** | ❌ Rejected. | ✅ Requires eligible resident paths in the model's `OffloadPlan`. |
@@ -220,9 +222,15 @@ model revision and parallel layout with `required`. TP coordinates have distinct
 identities, while equivalent DP replicas share them.

 The current producer/consumer boundary is the model-declared final-layout BF16
-contract (MiniMax H3 today). It supports ordinary-loader final layouts for TP1
-and TP2 rank identities plus SP layout identities; online quantization, HSDP,
-LoRA/adapted weights, and non-default load formats remain ineligible. HWR mode
+contract (MiniMax H3 and `black-forest-labs/FLUX.2-klein-4B`). The FLUX.2-klein
+contract covers both transformer block stacks, constructor-stable packed QKV
+mapping state, BF16 parameters, and any persistent loader-owned `beta`/`eps`
+buffers. The shared HWR machinery supports ordinary-loader final layouts for
+TP1 and TP2 rank identities plus SP layout identities. FLUX.2-klein evidence
+in this change is TP1-only; its TP2/SP layouts rely on the existing
+per-coordinate identity mechanics and remain unmeasured. Online quantization,
+HSDP, LoRA/adapted weights, and non-default load formats remain ineligible.
+HWR mode
 `disabled`, DLO-disabled, and DLO AllGather configurations stop before source
 identity or store construction and retain the existing checkpoint-mmap or
 ordinary-loader path.
@@ -432,11 +440,15 @@ requirements.

 Direct checkpoint mmap can back either transfer path. It is currently limited
 to proven TP1, non-HSDP, non-online-quantized layouts. Other layouts use the
-ordinary loader. Per-tensor online FP8 linears can use DLO AllGather after the
-ordinary loader finalizes their runtime weights and scales; DLO then shards and
-reconstructs those tensors with their recorded layouts. Other online methods
-must use `--dlo-no-use-allgather` or disable online quantization until their
-runtime layouts are validated.
+ordinary loader. Per-tensor online FP8, online INT8, and online MXFP8 linears
+can use DLO AllGather after the ordinary loader finalizes their runtime
+weights and scales; DLO then shards and reconstructs those tensors with their
+recorded layouts (they all keep plain 1-byte dtypes over ordinary strided
+views: online INT8 has an int8 weight plus fp32 scale, online MXFP8 has an
+fp8 weight plus e8m0 block scale, each with a contiguous or transposed-view
+finalized tensor, all covered by the same physical-order packing as online
+FP8). Other online methods must use `--dlo-no-use-allgather` or disable
+online quantization until their runtime layouts are validated.

 The Host Weight Runtime representation, publication, and no-AllGather consumer
 contracts are merged; see
@@ -453,8 +465,8 @@ Current source-level validation includes:
 - HSDP + DLO without AllGather acceptance at configuration level;
 - loader preflight fallback for TP, HSDP, online quantization, unknown custom
  loaders, missing keys, and shape/dtype mismatches;
- ordinary-loader fallback for per-tensor online FP8 linears followed by DLO
-  sharding of finalized weights and scales;
+- ordinary-loader fallback for per-tensor online FP8, online INT8, and online
+  MXFP8 linears followed by DLO sharding of finalized weights and scales;
 - exact loader-to-backend plan transfer and ordinary-loader fallback;
 - ordered publication hashing, overlapped durability, unordered parallel
  checksum fallback, and node-local source-digest reuse/invalidation;
@@ -22,59 +22,60 @@ We use **Qwen-Image** as the reference implementation for decode parallel, and *

 **VAE Patch Parallelism** is an acceleration technique for both **encoding** and **decoding**. Instead of processing the entire tensor at once, the tensor is:

-+ Split into multiple spatial tiles
+- Split into multiple spatial tiles

-+ Distributed across multiple ranks
+- Distributed across multiple ranks

-+ Encoded/Decoded in parallel
+- Encoded/Decoded in parallel

-+ Merged to reconstruct the final output
+- Merged to reconstruct the final output

 This approach:

-+ Distributes computation across multiple devices
+- Distributes computation across multiple devices

-+ Reduces peak memory usage per device
+- Reduces peak memory usage per device

-+ Accelerates encoding/decoding latency
+- Accelerates encoding/decoding latency

 ### When to Use Encode vs Decode Parallel

-| Operation | Use Case | Example |
-|-----------|----------|---------|
-| **Decode Parallel** | Text-to-Image, Text-to-Video | Latent → Image/Video |
-| **Encode Parallel** | Image-to-Video (I2V) | Image → Latent (for conditioning) |
+| Operation           | Use Case                     | Example                           |
+| ------------------- | ---------------------------- | --------------------------------- |
+| **Decode Parallel** | Text-to-Image, Text-to-Video | Latent → Image/Video              |
+| **Encode Parallel** | Image-to-Video (I2V)         | Image → Latent (for conditioning) |

 ### Architecture
+
 We introduce **DistributedVaeExecutor** as the core component responsible for distributed VAE encoding/decoding.

 The executor is model-agnostic and accepts three function parameters:

-+ split – Partition the latent into tiles
+- split – Partition the latent into tiles

-+ exec – Decode a single tile
+- exec – Decode a single tile

-+ merge – Combine decoded tiles into the final output
+- merge – Combine decoded tiles into the final output

 #### Execution Flow

-+ Call split(z) to generate a list of TileTask and a GridSpec
+- Call split(z) to generate a list of TileTask and a GridSpec

-+ Dispatch tasks across ranks using workload-based balancing
+- Dispatch tasks across ranks using workload-based balancing

-+ Each rank executes exec(task) on its assigned tiles
+- Each rank executes exec(task) on its assigned tiles

-+ Gather decoded tile results to rank 0
+- Gather decoded tile results to rank 0

-+ Rank 0 performs merge(...)
+- Rank 0 performs merge(...)

-+ (Optional) Broadcast final result to all ranks
+- (Optional) Broadcast final result to all ranks

 This design separates:

-+ Distributed execution logic
+- Distributed execution logic

-+ Model-specific tiling and merging logic
+- Model-specific tiling and merging logic

 #### Why split / exec / merge is necessary?

@@ -82,23 +83,23 @@ The latent tensor cannot be arbitrarily partitioned.

 During decoding:

-+ Each output pixel may depend on neighboring pixels
+- Each output pixel may depend on neighboring pixels

-+ The receptive field is model-dependent
+- The receptive field is model-dependent

 Therefore:

-+ Tiles must include overlap
+- Tiles must include overlap

-+ Merge must perform blending to avoid seams
+- Merge must perform blending to avoid seams

 ## Step-by-Step Implementation (Decode)

 ### Step 1: Implement DistributedAutoencoderKLQwenImage
+
 `QwenImagePipeline` use `AutoencoderKLQwenImage` for vae, so implement a distributed version:

-
-```
+```python
 class DistributedAutoencoderKLQwenImage(AutoencoderKLQwenImage, DistributedVaeMixin):
    @classmethod
    def from_pretrained(cls, *args: Any, **kwargs: Any):
@@ -106,28 +107,35 @@ class DistributedAutoencoderKLQwenImage(AutoencoderKLQwenImage, DistributedVaeMi
        model.init_distributed()
        return model
 ```
+
 **Key points**:
-+ Inherit both AutoencoderKLQwenImage and DistributedVaeMixin
-+ Call init_distributed() after loading weights
+
+- Inherit both AutoencoderKLQwenImage and DistributedVaeMixin
+- Call init_distributed() after loading weights

 ### Step 2: Implement split/exec/merge
+
 Reuse `AutoencoderKLQwenImage.tiled_decode` logic and divide it into three stages. And we need return tiles with `GridSpec` and `TileTask`:
-```
+
+```python
 class GridSpec:
    split_dims: tuple[int, ...]  # Tensor dimensions being split (e.g., (2, 3) for (B, C, H, W))
    grid_shape: tuple[int, ...]  # Tile grid layout (num_rows, num_cols)
    tile_spec: dict = field(default_factory=dict) # Metadata required for merging
    output_dtype: torch.dtype | None = None # Final output dtype
 ```
-```
+
+```python
 class TileTask:
    tile_id: int # task id
    grid_coord: tuple[int, ...]  # Tile position in grid
    tensor: torch.Tensor | list[torch.Tensor]  # The tile tensor
    workload: int | float = 1 # Used for load balancing (e.g., tile area)
 ```
+
 And tiled base split/exec/merge as follow:
-```
+
+```python
 def tile_split(self, z: torch.Tensor) -> tuple[list[TileTask], GridSpec]:
    # mostly copy from AutoencoderKL
    _, _, num_frames, height, width = z.shape
@@ -210,11 +218,14 @@ def tile_merge(self, coord_tensor_map: dict[tuple[int, ...], torch.Tensor], grid
 ```

 ### Step 3: Override tiled_decode
+
 We need to override tiled_decode, the main logic is:
-+ check distributed is enabled
-+ select split/exec/merge
-+ Invoke self.distributed_executor.execute to decode
-```
+
+- check distributed is enabled
+- select split/exec/merge
+- Invoke self.distributed_executor.execute to decode
+
+```python
 def tiled_decode(self, z: torch.Tensor, return_dict: bool = True):
    if not self.is_distributed_enabled():
        return super().tiled_decode(z, return_dict=return_dict)
@@ -230,11 +241,14 @@ def tiled_decode(self, z: torch.Tensor, return_dict: bool = True):

    return DecoderOutput(sample=result)
 ```
+
 `broadcast_result` is set to True or False depending on the model; when enabled, the result will be used even on ranks other than 0.

 ### Step 4: Modify Pipeline
+
 Change vae model from AutoencoderKLQwenImage to DistributedAutoencoderKLQwenImage
-```
+
+```diff
 class YourModelPipeline(nn.Module):
    def __init__(
        self,
@@ -259,8 +273,8 @@ For models that require VAE encoding (e.g., Image-to-Video), you can also parall

 Similar to decode, split the input tensor into tiles. Key considerations:

-+ **Patchify handling**: If the model uses `patch_size`, scale tile parameters accordingly
-+ **Temporal chunking**: Video VAEs may have temporal compression (e.g., 4x)
+- **Patchify handling**: If the model uses `patch_size`, scale tile parameters accordingly
+- **Temporal chunking**: Video VAEs may have temporal compression (e.g., 4x)

 ```python
 def encode_tile_split(self, x: torch.Tensor) -> tuple[list[TileTask], GridSpec]:
@@ -406,23 +420,28 @@ def tiled_encode(self, x: torch.Tensor) -> torch.Tensor:
 **Key differences from decode parallel:**

 | Aspect | Decode Parallel | Encode Parallel |
-|--------|-----------------|-----------------|
+| -------- | ----------------- | ----------------- |
 | `broadcast_result` | Often `False` (only rank 0 needs output) | `True` (all ranks need latents for diffusion) |
 | Patchify | Applied in merge (unpatchify) | Handled by parent `_encode()` before `tiled_encode()` |
 | Temporal chunking | Frame-by-frame | Chunk-based (e.g., 1 + 4n frames) |

 ## Testing
-Verify numerical consistency between:
-+ vae_patch_parallel_size = 1

-+ vae_patch_parallel_size = N
+Verify numerical consistency between:
+
+- vae_patch_parallel_size = 1
+- vae_patch_parallel_size = N

 Example:
+
+```python
 torch.allclose(output_1, output_n, atol=1e-5)
+```

 Testing requirements:
-+ Fix random seed
-+ Use identical tiling strategy
+
+- Fix random seed
+- Use identical tiling strategy

 ```python
 m = Omni(
@@ -434,6 +453,7 @@ m = Omni(
        ),
    )
 ```
+
 When vae_patch_parallel_size is larger than the DiT world size, it will automatically fall back to using the DiT world size instead.

 ## Reference Implementations
@@ -441,7 +461,7 @@ When vae_patch_parallel_size is larger than the DiT world size, it will automati
 Complete examples in the codebase:

 | Model | Path | Decode Parallel | Encode Parallel |
-|-------|------|-----------------|-----------------|
+| ------- | ------ | ----------------- | ----------------- |
 | **Z-Image** | `vllm_omni/diffusion/distributed/autoencoders/autoencoder_kl.py` | ✅ | ❌ |
 | **Wan2.2** | `vllm_omni/diffusion/distributed/autoencoders/autoencoder_kl_wan.py` | ✅ | ✅ |
 | **Qwen-Image** | `vllm_omni/diffusion/distributed/autoencoders/autoencoder_kl_qwenimage.py` | ✅ | ❌ |
@@ -456,7 +476,7 @@ The tile-parallel executor above assigns independent spatial **tiles** to ranks.
 ### How it differs from tile parallel

 | Aspect | Tile parallel (`"tile"`) | Spatially-sharded (`"spatial_shard_height"`/`"spatial_shard_width"`) |
-|--------|--------------------------|-----------------------------------------------|
+| -------- | -------------------------- | ----------------------------------------------- |
 | Unit of work | Independent overlapping tiles | A single global feature map sharded along H or W |
 | Cross-rank communication | Gather tiles to rank 0, stitch + blend | Per-conv **halo exchange** of boundary rows/cols (P2P) |
 | Output assembly | Blend overlapping tiles | All-gather shards on rank 0, trim padding (matches `broadcast_result=False`) |
@@ -481,7 +501,7 @@ serve.py (--vae-parallel-mode) / OmniEngineArgs
 ### Notes

 - The decoder is patched **in place** the first time spatial-shard decode runs and is bound to a single split dimension for the lifetime of the VAE instance.
- Numerical correctness vs. single-GPU decode is covered by `tests/diffusion/distributed/test_wan_spatial_shard.py::test_spatial_shard_decode_matches_reference` (multi-GPU, nightly `full_model` + `distributed_cuda`).
+- Numerical correctness vs. single-GPU decode is covered by `tests/diffusion/distributed/test_wan_spatial_shard.py::test_spatial_shard_decode_matches_reference` (multi-GPU, nightly `full_model` + `cards_2`).

 ---

@@ -37,6 +37,7 @@ validation_paths:
  - tests/engine/test_async_omni_engine_input.py
  - tests/engine/test_async_omni_engine_outputs.py
  - tests/engine/test_async_omni_engine_abort.py
+  - tests/e2e/offline_inference/test_qwen3_omni.py
  - tests/engine/test_async_omni_engine_stage_init.py
  - tests/engine/test_orchestrator.py
  - tests/engine/test_orchestrator_error_handling.py
@@ -66,6 +67,12 @@ the boundary affected by the in-flight stage client/process refactor in
 [#5441](https://github.com/vllm-project/vllm-omni/pull/5441). Names and
 responsibilities proposed only by that PR are not current contracts.

+The orchestration loop also has an opt-in event-driven mode
+(`VLLM_OMNI_EVENT_DRIVEN_ORCH=1`, default off) proposed in
+[#5221](https://github.com/vllm-project/vllm-omni/pull/5221). It changes poll
+cadence only: the routing, ordering, and terminal-state contracts below hold
+identically on both loops.
+
 ## Ownership boundary

 This document owns `AsyncOmniEngine`, `Orchestrator`, request-state creation,
@@ -107,6 +114,15 @@ Numbers become append-only after normative promotion.

 Test routing, output ordering, queue correlation, cancellation, failure
 propagation, shutdown ordering, and representative multi-stage execution.
+AR abort of a final-stage LLM request must deliver a terminal output with
+`finish_reason="abort"` and the generated prefix so collocated training can
+resume; diffusion abort remains whole-sample retry. Aborting a parallel-sampling
+child must drop `parent_requests` once no children remain. EngineCore control
+RPCs (`sleep` / `wake_up` / `pause_scheduler` / `resume_scheduler`) must
+propagate worker exceptions rather than returning `{"supported": False}`.
+When a request has multiple final output stages, only the last abort message
+is request-terminal. Output-processor abort state is committed only after the
+physical EngineCore abort succeeds.
 Changes that cross into stage runtime or public error behavior require review
 from that contract's owners.

@@ -43,6 +43,7 @@ depends_on:
 validation_paths:
  - tests/entrypoints/test_omni_entrypoints.py
  - tests/entrypoints/test_async_omni.py
+  - tests/entrypoints/test_async_omni_pause_sleep_routing.py
  - tests/entrypoints/test_async_omni_duplex.py
  - tests/entrypoints/test_serve.py
  - tests/entrypoints/test_stream_finish_reason.py
@@ -119,7 +120,13 @@ promotion.

 Test request validation, protocol conversion, model-adapter routing,
 streaming, session identity, disconnect/cancellation, and error mapping for
-each affected offline or serving entrypoint.
+each affected offline or serving entrypoint. Sleep must wait for in-flight
+`generate()` admission before EngineCore offload; `wake_up` does not resume
+admission — callers must `resume_generation()`. Sleeping tags are tracked per
+stage so `wake_up(stage_ids=[0])` does not skip a later `wake_up(stage_ids=[1])`.
+Streaming input pumps take an admission slot immediately before each EngineCore
+ADD or update, not while waiting for the next client chunk. Frontend abort
+keeps `request_states` until `generate()` consumes the terminal output.

 ## Promotion gate

@@ -257,6 +257,53 @@ Lookup checks the marker both before validation and after constructing the
 lease so a weaker overlapping lookup cannot return a lease after a stronger
 validation has denied the artifact.

+If publishing the external deny marker fails, lookup returns a retryable
+storage failure and marks the artifact directory with a persistent invalidation
+mode before releasing the shared lock. Cooperating readers check that mode both
+while opening and immediately before returning a lease. Lookup then makes a
+bounded attempt to take the exclusive artifact lock and quarantine the entry;
+the quarantine transition removes the internal mode marker. If a cooperating
+builder replaces the marked inode while the locks are exchanged, containment
+preserves that new publication instead of quarantining it. If neither the
+external marker nor the directory-mode marker can be persisted, cross-process
+exclusion cannot be guaranteed on an unavailable filesystem and the domain
+must remain failed rather than treating the artifact as an ordinary invalid
+cache entry.
+
+New publications carry the internal invalidation mode through the staging
+rename. Hardening clears it and is the logical authority transition for a
+process crash; the temporary- and artifact-parent syncs then make the rename
+durable, with an artifact-inode sync first making the hardened mode durable. A
+process that dies before hardening leaves a persistently denied
+entry, while one that dies after successful hardening leaves an authoritative
+ready entry. Any synchronous failure after rename is marked invalid and
+contained in quarantine before `PUBLICATION_FAILED` is returned, so that
+nonretryable result cannot later resolve as a ready hit. If the filesystem
+refuses both the inode marker and the quarantine move, the store instead returns
+a retryable storage failure with `outcome_uncertain` details; as with deny-marker
+double failure, cross-process exclusion cannot be guaranteed until storage
+recovers. Once the artifact-parent sync succeeds, a later artifact- or
+build-lock release error is logged and lookup resolves the committed entry
+instead of reporting a contradictory publication failure. The same authority
+rule applies when a competing publisher already installed byte-identical
+semantic content; staging-cleanup errors are retried without downgrading that
+validated ready entry to publication failure.
+
+Before widening a hardened artifact for a lifecycle move, the store durably
+sets the same invalidation mode. A process crash before rename therefore leaves
+the writable source denied. After rename, the store re-hardens the destination
+but retains the marker until both parent-directory syncs make the move durable;
+only then does it durably clear the marker. An interrupted move therefore leaves
+a marked transition that the next exact-key lifecycle operation re-hardens.
+Explicit cleanup uses the locked move and retries exact-key
+`.cleanup.*` tombstones left by an interrupted removal; a later build performs
+the same tombstone reconciliation before producing replacement content.
+
+An operational failure while inspecting a noncooperative competing publication
+is a retryable storage failure. The competing entry remains authoritative and
+is not mislabeled as corrupt or quarantined merely because its manifest could
+not be read transiently.
+
 `inspect_domain()` and `inspect_artifact()` return typed, read-only snapshots.
 Inspection validation does not create a deny marker. Reported lock activity is
 point-in-time evidence only; kernel locks are not a persistent owner registry.
@@ -1,7 +1,7 @@
 # Installation

 !!! important
-    vLLM-Omni is released against the matching upstream vLLM major/minor version. vLLM-Omni 0.26.x requires vLLM 0.26.x; the stable v0.26.0 instructions pin vLLM 0.26.0.
+    vLLM-Omni is released against the matching upstream vLLM major/minor version. vLLM-Omni 0.28.x requires vLLM 0.28.x; the stable v0.28.0 instructions pin vLLM 0.28.0.

 vLLM-Omni supports the following hardware platforms:

@@ -34,7 +34,7 @@ vLLM-Omni is a Python library that supports the following GPU variants. The libr

 ### Pre-built wheels

-Note: Pre-built wheels are currently available for vLLM-Omni 0.11.0rc1, 0.12.0rc1, 0.14.0rc1, 0.14.0, 0.16.0, 0.18.0, 0.20.0, 0.21.0, 0.22.0, 0.23.0, 0.24.0, 0.25.0, and 0.26.0. If you need a newer unreleased revision, please [build from source](https://docs.vllm.ai/projects/vllm-omni/en/latest/getting_started/installation/gpu/#build-wheel-from-source).
+Note: Pre-built wheels are currently available for vLLM-Omni 0.11.0rc1, 0.12.0rc1, 0.14.0rc1, 0.14.0, 0.16.0, 0.18.0, 0.20.0, 0.21.0, 0.22.0, 0.23.0, 0.24.0, 0.25.0, and 0.26.0; the 0.28.0 wheels are published together with the v0.28.0 release this branch produces. If you need a newer unreleased revision, please [build from source](https://docs.vllm.ai/projects/vllm-omni/en/latest/getting_started/installation/gpu/#build-wheel-from-source).

 === "NVIDIA CUDA"

@@ -20,7 +20,7 @@ Therefore, it is recommended to install vLLM and vLLM-Omni with a **fresh new**

 vLLM-Omni is built based on vLLM. Please install it with command below.
 ```bash
-uv pip install vllm==0.26.0 --torch-backend=auto
+uv pip install vllm==0.28.0 --torch-backend=auto
 ```

 #### Installation of vLLM-Omni
@@ -39,13 +39,13 @@ uv pip install 'vllm-omni[demo]'
 # --8<-- [start:build-wheel-from-source]

 #### Installation of vLLM
-If you do not need to modify source code of vLLM, you can directly install the stable 0.26.0 release version of the library
+If you do not need to modify source code of vLLM, you can directly install the stable 0.28.0 release version of the library

 ```bash
-uv pip install vllm==0.26.0 --torch-backend=auto
+uv pip install vllm==0.28.0 --torch-backend=auto
 ```

-The 0.26.0 release of vLLM ships CUDA 13.0-compatible binaries by default. If you need a different CUDA variant or want to reuse an existing PyTorch installation, build vLLM from source instead.
+The 0.28.0 release of vLLM ships CUDA 13.0-compatible binaries by default. If you need a different CUDA variant or want to reuse an existing PyTorch installation, build vLLM from source instead.

 #### Installation of vLLM-Omni
 Since vllm-omni is rapidly evolving, it's recommended to install it from source
@@ -66,12 +66,12 @@ If you want to check, modify or debug with source code of vLLM, install the libr
 ```bash
 git clone https://github.com/vllm-project/vllm.git
 cd vllm
-git checkout v0.26.0
+git checkout v0.28.0
 ```
 Set up environment variables to get pre-built wheels. If there are internet problems, just download the whl file manually. And set `VLLM_PRECOMPILED_WHEEL_LOCATION` as your local absolute path of whl file.
 ```bash
-#For CUDA 13.0 (the default for v0.26.0; the wheel filename has no `+cu130` suffix)
-export VLLM_PRECOMPILED_WHEEL_LOCATION=https://github.com/vllm-project/vllm/releases/download/v0.26.0/vllm-0.26.0-cp38-abi3-manylinux_2_28_x86_64.whl
+#For CUDA 13.0 (the default for v0.28.0; the wheel filename has no `+cu130` suffix)
+export VLLM_PRECOMPILED_WHEEL_LOCATION=https://github.com/vllm-project/vllm/releases/download/v0.28.0/vllm-0.28.0-cp38-abi3-manylinux_2_28_x86_64.whl
 ```
 Install vllm with command below (If you have no existing PyTorch).
 ```bash
@@ -102,7 +102,7 @@ docker run --runtime nvidia --gpus 2 \
    --env "HF_TOKEN=$HF_TOKEN" \
    -p 8091:8091 \
    --ipc=host \
-    vllm/vllm-omni:v0.26.0 \
+    vllm/vllm-omni:v0.28.0 \
    vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091
 ```

@@ -124,7 +124,7 @@ If you want to specify the base vLLM version:
 ```bash
 DOCKER_BUILDKIT=1 docker build \
  -f docker/Dockerfile.cuda \
-  --build-arg BASE_IMAGE=vllm/vllm-openai:v0.26.0 \
+  --build-arg BASE_IMAGE=vllm/vllm-openai:v0.28.0 \
  -t vllm-omni-cuda .
 ```

@@ -13,7 +13,7 @@ vLLM-Omni current recommends the steps in under setup through Docker Images.

 vLLM-Omni is built based on vLLM. Please install it with command below.
 ```bash
-uv pip install vllm==0.26.0+rocm723 --extra-index-url https://wheels.vllm.ai/rocm/0.26.0/rocm723
+uv pip install vllm==0.28.0+rocm723 --extra-index-url https://wheels.vllm.ai/rocm/0.28.0/rocm723
 ```

 #### Installation of vLLM-Omni
@@ -34,13 +34,13 @@ uv pip install onnxruntime-rocm
 # --8<-- [start:build-wheel-from-source]

 #### Installation of vLLM
-If you do not need to modify source code of vLLM, you can directly install the stable 0.26.0 release version of the library
+If you do not need to modify source code of vLLM, you can directly install the stable 0.28.0 release version of the library

 ```bash
-uv pip install vllm==0.26.0+rocm723 --extra-index-url https://wheels.vllm.ai/rocm/0.26.0/rocm723
+uv pip install vllm==0.28.0+rocm723 --extra-index-url https://wheels.vllm.ai/rocm/0.28.0/rocm723
 ```

-The pre-built 0.26.0 vLLM wheel targets ROCm 7.2.3. If you need a different ROCm stack or want to reuse an existing PyTorch installation, build vLLM from source instead.
+The pre-built 0.28.0 vLLM wheel targets ROCm 7.2.3. If you need a different ROCm stack or want to reuse an existing PyTorch installation, build vLLM from source instead.

 #### Installation of vLLM-Omni
 Since vllm-omni is rapidly evolving, it's recommended to install it from source
@@ -58,7 +58,7 @@ If you want to check, modify or debug with source code of vLLM, install the libr
 ```bash
 git clone https://github.com/vllm-project/vllm.git
 cd vllm
-git checkout v0.26.0
+git checkout v0.28.0
 python3 -m pip install -r requirements/rocm.txt
 python3 setup.py develop
 ```
@@ -130,7 +130,7 @@ docker run --rm \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  --env "HF_TOKEN=$HF_TOKEN" \
  -p 8091:8091 \
-  vllm/vllm-omni-rocm:v0.26.0 \
+  vllm/vllm-omni-rocm:v0.28.0 \
  --model Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091
 ```

@@ -149,7 +149,7 @@ docker run --rm -it \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  --env "HF_TOKEN=$HF_TOKEN" \
  --entrypoint bash \
-  vllm/vllm-omni-rocm:v0.26.0
+  vllm/vllm-omni-rocm:v0.28.0
 ```

 # --8<-- [end:pre-built-images]
@@ -23,6 +23,19 @@ vLLM-Omni currently recommends using the Docker image setup steps below.
 DOCKER_BUILDKIT=1 docker build -f docker/Dockerfile.xpu -t vllm-omni-xpu --shm-size=4g .
 ```

+This layers vLLM-Omni on top of the published `vllm/vllm-openai-xpu:<VLLM_VERSION>`
+base image, which Docker pulls automatically. To target a different vLLM release,
+pass `--build-arg VLLM_VERSION=<tag>`. If that tag has not been published yet, build
+the base from upstream's own Dockerfile first and point the build at it:
+
+```bash
+git clone --depth 1 --branch "$VLLM_VERSION" https://github.com/vllm-project/vllm /tmp/vllm
+DOCKER_BUILDKIT=1 docker build -t vllm-openai-xpu:local --target vllm-openai \
+  -f /tmp/vllm/docker/Dockerfile.xpu /tmp/vllm
+DOCKER_BUILDKIT=1 docker build -f docker/Dockerfile.xpu -t vllm-omni-xpu --shm-size=4g \
+  --build-arg VLLM_BASE=vllm-openai-xpu:local .
+```
+
 #### Launch the docker image

 ##### Launch with OpenAI API Server
@@ -19,10 +19,10 @@ uv venv --python 3.12 --seed
 source .venv/bin/activate

 # On CUDA
-uv pip install vllm==0.26.0 --torch-backend=auto
+uv pip install vllm==0.28.0 --torch-backend=auto

 # On ROCm
-uv pip install vllm==0.26.0+rocm723 --extra-index-url https://wheels.vllm.ai/rocm/0.26.0/rocm723
+uv pip install vllm==0.28.0+rocm723 --extra-index-url https://wheels.vllm.ai/rocm/0.28.0/rocm723

 git clone https://github.com/vllm-project/vllm-omni.git
 cd vllm-omni
@@ -34,7 +34,7 @@ For additional installation methods — please see the [installation guide](inst
 !!! note
    It is important to install the same major & minor version of vLLM and vLLM Omni, otherwise things may not work as expected. If the versions are misaligned, you will see a warning when you import vLLM Omni.

-    If you are seeing strange behavior with the `vllm` command not handling the `--omni` flag correctly, you most likely have a version mismatch with vLLM < `0.26.0` and vLLM Omni `0.26.0`, as vLLM Omni no longer hijacks the vLLM entrypoint. Updating vLLM should resolve this issue.
+    If you are seeing strange behavior with the `vllm` command not handling the `--omni` flag correctly, you most likely have a version mismatch with vLLM < `0.28.0` and vLLM Omni `0.28.0`, as vLLM Omni no longer hijacks the vLLM entrypoint. Updating vLLM should resolve this issue.

 ## Offline Inference

@@ -26,15 +26,15 @@ th {
 | `InternVLAA1Pipeline` | InternVLA-A1 | `InternRobotics/InternVLA-A1-3B` | ✅︎ | ✅︎ | | | — |
 | `Gr00tN1d7Pipeline` | GR00T N1.7 | `nvidia/GR00T-N1.7-3B` | ✅︎ | | | | — |
 | `HunyuanImage3ForCausalMM` | HunyuanImage3.0 (DiT-only) | `tencent/HunyuanImage-3.0`, `tencent/HunyuanImage-3.0-Instruct` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
-| `QwenImagePipeline` | Qwen-Image | `Qwen/Qwen-Image` | ✅︎ | | | | [Published](https://recipes.vllm.ai/Qwen/Qwen-Image) |
-| `QwenImagePipeline` | Qwen-Image-2512 | `Qwen/Qwen-Image-2512` | ✅︎ | | | | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/Qwen/Qwen-Image-2512.md) |
+| `QwenImagePipeline` | Qwen-Image | `Qwen/Qwen-Image` | ✅︎ | | | ✅︎ | [Published](https://recipes.vllm.ai/Qwen/Qwen-Image) |
+| `QwenImagePipeline` | Qwen-Image-2512 | `Qwen/Qwen-Image-2512` | ✅︎ | | | ✅︎ | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/Qwen/Qwen-Image-2512.md) |
 | `QwenImageEditPipeline` | Qwen-Image-Edit | `Qwen/Qwen-Image-Edit` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
 | `QwenImageEditPlusPipeline` | Qwen-Image-Edit-2509 | `Qwen/Qwen-Image-Edit-2509` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
 | `QwenImageLayeredPipeline` | Qwen-Image-Layered | `Qwen/Qwen-Image-Layered` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
 | `QwenImageEditPlusPipeline` | Qwen-Image-Edit-2511 | `Qwen/Qwen-Image-Edit-2511` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
 | `GlmImagePipeline` | GLM-Image | `zai-org/GLM-Image` | ✅︎ | ✅︎ | | | — |
 | `ZImagePipeline` | Z-Image | `Tongyi-MAI/Z-Image-Turbo` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
-| `Krea2Pipeline` | Krea 2 (Raw + Turbo) | `krea/Krea-2-Raw`, `krea/Krea-2-Turbo` | ✅︎ | | | | — |
+| `Krea2Pipeline` | Krea 2 (Raw + Turbo) | `krea/Krea-2-Raw`, `krea/Krea-2-Turbo` | ✅︎ | | | ✅︎ | — |
 | `WanPipeline` | Wan2.1-T2V, Wan2.2-T2V, Wan2.2-TI2V | `Wan-AI/Wan2.1-T2V-1.3B-Diffusers`, `Wan-AI/Wan2.1-T2V-14B-Diffusers`, `Wan-AI/Wan2.2-T2V-A14B-Diffusers`, `Wan-AI/Wan2.2-TI2V-5B-Diffusers` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
 | `WanImageToVideoPipeline` | Wan2.2-I2V | `Wan-AI/Wan2.2-I2V-A14B-Diffusers` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
 | `LingBotWorldCausalDMDPipeline` | LingBot-World 2.0 v1 (experimental) | `robbyant/lingbot-world-v2-14b-causal-fast-diffusers` | Experimental | | | | — |
@@ -42,7 +42,7 @@ th {
 | `SanaWmPipeline` | SANA-WM | `BBBBruce/SANA-WM_bidirectional-stage1-diffusers` | ✅︎ | | | | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/NVIDIA/SANA-WM.md) |
 | `Wan22S2VPipeline` | Wan2.2-S2V | `Wan-AI/Wan2.2-S2V-14B` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
 | `Wan22VACEPipeline` | Wan2.1-VACE | `Wan-AI/Wan2.1-VACE-1.3B-diffusers`, `Wan-AI/Wan2.1-VACE-14B-diffusers` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
-| `Wan22VACEPipeline` | Wan2.2-VACE | `Pyros13/Wan2.2-VACE-Fun-A14B-Diffusers` | ✅︎ |   |   |   | — |
+| `Wan22VACEPipeline` | Wan2.2-VACE | `Pyros13/Wan2.2-VACE-Fun-A14B-Diffusers` | ✅︎ |   |   | ✅︎ | — |
 | `LTX2Pipeline` | LTX-2 / LTX-2.3 one-stage T2V and I2V | `Lightricks/LTX-2`, `diffusers/LTX-2.3-Diffusers` | ✅︎ | ✅︎ | | | — |
 | `LTX2TwoStagePipeline` | LTX-2 / LTX-2.3 ordinary two-stage T2V and I2V | `Lightricks/LTX-2`, `diffusers/LTX-2.3-Diffusers` + matching Lightricks LoRA and upsampler | ✅︎ | ✅︎ | | | — |
 | `LTX2DistilledOneStagePipeline` | LTX-2 / LTX-2.3 merged-distilled one-stage T2V and I2V | `rootonchair/LTX-2-19b-distilled`, `diffusers/LTX-2.3-Distilled-Diffusers` | ✅︎ | ✅︎ | | | — |
@@ -62,13 +62,15 @@ th {
 | `LongcatImagePipeline` | LongCat-Image | `meituan-longcat/LongCat-Image` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
 | `LongCatImageEditPipeline` | LongCat-Image-Edit | `meituan-longcat/LongCat-Image-Edit` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
 | `LongCatVideoAvatarPipeline` | LongCat-Video-Avatar-1.5 A2V/AI2V (native single-speaker and multi-speaker AI2V/AVC) | `meituan-longcat/LongCat-Video-Avatar-1.5` | ✅︎ | | | | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/meituan-longcat/LongCat-Video-Avatar-1.5.md) |
-| `BooguImagePipeline` | Boogu-Image | `Boogu/Boogu-Image-0.1-Base`, `Boogu/Boogu-Image-0.1-Edit` | ✅︎ | | | | — |
+| `BooguImagePipeline` | Boogu-Image | `Boogu/Boogu-Image-0.1-Base`, `Boogu/Boogu-Image-0.1-Edit` | ✅︎ | | | ✅︎ | — |
 | `StableDiffusionXLPipeline` | Stable-Diffusion-XL | `stabilityai/stable-diffusion-xl-base-1.0` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
 | `StableDiffusion3Pipeline` | Stable-Diffusion-3 | `stabilityai/stable-diffusion-3.5-medium` | ✅︎ | ✅︎ | | ✅︎ | — |
 | `CosyVoice3Model` | CosyVoice3 | `FunAudioLLM/Fun-CosyVoice3-0.5B-2512` | ✅︎ | ✅︎ | | ✅︎ | — |
-| `OmniVoiceModel` | OmniVoice | `k2-fsa/OmniVoice` | ✅︎ | | | | — |
-| `VoxCPM2TalkerForConditionalGeneration` | VoxCPM2 | `openbmb/VoxCPM2` | ✅︎ | | | | — |
+| `OmniVoiceModel` | OmniVoice | `k2-fsa/OmniVoice` | ✅︎ | | | ✅︎ | — |
+| `VoxCPM2TalkerForConditionalGeneration` | VoxCPM2 | `openbmb/VoxCPM2` | ✅︎ | | | ✅︎ | — |
 | `DotsTTSForConditionalGeneration` | dots.tts | `rednote-hilab/dots.tts-soar` | ✅︎ | | | | — |
+| `MammothModa2ForConditionalGeneration` | MammothModa2-Preview | `bytedance-research/MammothModa2-Preview` | ✅︎ | ✅︎ | | | — |
+| `MammothModa2ForConditionalGeneration` | MammothModa2-Dev (AR-only image understanding) | `bytedance-research/MammothModa2-Dev` | ✅︎ | | | | — |
 | `Flux2KleinPipeline` | FLUX.2-klein | `black-forest-labs/FLUX.2-klein-4B`, `black-forest-labs/FLUX.2-klein-9B` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
 | `FluxKontextPipeline` | FLUX.1-Kontext-dev | `black-forest-labs/FLUX.1-Kontext-dev` | ✅︎ | ✅︎ | | | — |
 | `FluxPipeline` | FLUX.1-dev | `black-forest-labs/FLUX.1-dev` | ✅︎ | ✅︎ | | ✅︎ | — |
@@ -76,10 +78,10 @@ th {
 | `OmniGen2Pipeline` | OmniGen2 | `OmniGen2/OmniGen2` | ✅︎ | ✅︎ | | ✅︎ | — |
 | `StableAudioPipeline` | Stable-Audio-Open | `stabilityai/stable-audio-open-1.0` | ✅︎ | ✅︎ | | ✅︎ | — |
 | `MiniMaxMusic3ForConditionalGeneration` | MiniMax Music 3 (text-to-music) | `MiniMaxAI/MiniMax-Music3` | ✅︎ | | | | — |
-| `Qwen3TTSForConditionalGeneration` | Qwen3-TTS-12Hz-1.7B-CustomVoice | `Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice` | ✅︎ | | | | [Published](https://recipes.vllm.ai/Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice) |
+| `Qwen3TTSForConditionalGeneration` | Qwen3-TTS-12Hz-1.7B-CustomVoice | `Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice` | ✅︎ | | | ✅︎ | [Published](https://recipes.vllm.ai/Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice) |
 | `Qwen3TTSForConditionalGeneration` | Qwen3-TTS-12Hz-1.7B-VoiceDesign | `Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
 | `Qwen3TTSForConditionalGeneration` | Qwen3-TTS-12Hz-1.7B-Base | `Qwen/Qwen3-TTS-12Hz-1.7B-Base` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
-| `MingTTSForConditionalGeneration` | Ming-omni-tts dense 0.5B | `inclusionAI/Ming-omni-tts-0.5B` | ✅︎ | | | | — |
+| `MingTTSForConditionalGeneration` | Ming-omni-tts dense 0.5B | `inclusionAI/Ming-omni-tts-0.5B` | ✅︎ | | | ✅︎ | — |
 | `GLMTTSForConditionalGeneration` | GLM-TTS | `zai-org/GLM-TTS` | ✅︎ | | | | — |
 | `MossTTSNanoForCausalLM` | MOSS-TTS-Nano | `OpenMOSS-Team/MOSS-TTS-Nano` | ✅︎ | | | | — |
 | `MossTTSDelayModel` | MOSS-TTS, MOSS-TTSD, MOSS-SoundEffect, MOSS-VoiceGenerator | `OpenMOSS-Team/MOSS-VoiceGenerator` | ✅︎ | | | | — |
@@ -99,10 +101,10 @@ th {
 | `HunyuanVideo15ImageToVideoPipeline` | HunyuanVideo-1.5-I2V | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_i2v`, `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_i2v` | ✅︎ | ✅︎ | | | — |
 | `VoxtralTTSForConditionalGeneration` | Voxtral TTS | `mistralai/Voxtral-4B-TTS-2603` | ✅︎ | ✅︎ | | | — |
 | `CovoAudioForConditionalGeneration` | Covo-Audio-Chat | `tencent/Covo-Audio-Chat` | ✅︎ | | | | — |
-|`DyninOmniForConditionalGeneration` | Dynin-Omni | `snu-aidas/Dynin-Omni` | ✅︎ | | | | — |
+|`DyninOmniForConditionalGeneration` | Dynin-Omni | `snu-aidas/Dynin-Omni` | ✅︎ | | | ✅︎ | — |
 | `MiniCPMO45OmniForConditionalGeneration` | MiniCPM-o 4.5 | `openbmb/MiniCPM-o-4_5` | ✅︎ | | ✅︎ | | — |
 | `ErnieImagePipeline` | ERNIE-Image | `baidu/ERNIE-Image`, `baidu/ERNIE-Image-Turbo` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
-| `GepardTalkerForConditionalGeneration` | Gepard-1.0 | `nineninesix/gepard-1.0` | ✅︎ | | | | — |
+| `GepardTalkerForConditionalGeneration` | Gepard-1.0 | `nineninesix/gepard-1.0` | ✅︎ | | | ✅︎ | — |
 |`HiDreamImagePipeline` | HiDream-I1-Full | `HiDream-ai/HiDream-I1-Full` | ✅︎ | ✅︎ | | | — |
 | `HiDreamO1ImagePipeline` | HiDream-O1-Image | `HiDream-ai/HiDream-O1-Image` | ✅︎ | | | | — |

@@ -811,6 +811,51 @@ If you encounter OOM errors:

 Use `/v1/audio/voices` to list available voices for the loaded model.

+## Orchestration Loop (experimental)
+
+Multi-stage omni deployments route stage outputs through a single orchestrator
+loop. By default that loop polls every stage replica on a 1 ms cadence. An
+opt-in event-driven mode replaces the poll with one reader task per live stage
+replica awaiting its client directly, and switches the serving-side
+final-output drain to a condition-variable wakeup at the same time.
+
+**Configuration (environment variables):**
+
+| Variable | Default | Description |
+|----------|---------|-------------|
+| `VLLM_OMNI_EVENT_DRIVEN_ORCH` | `0` (off) | Switches the orchestration loop and the final-output drain from the legacy 1 ms poll to event-driven wakeups. Enabled by `1`, `true`, `yes`, or `on`, matched case-insensitively after surrounding whitespace is stripped; any other value leaves it off. |
+
+Set it on the process that runs the orchestrator (stage 0 of an omni
+deployment) before starting the server:
+
+```bash
+export VLLM_OMNI_EVENT_DRIVEN_ORCH=1
+vllm serve Qwen/Qwen3-TTS-12Hz-1.7B-Base \
+    --omni \
+    --port 8091
+```
+
+The server logs the selected loop mode and its reader/poller counts once at
+startup, so you can confirm which loop is live.
+
+Routing, output ordering, and terminal-state behavior are identical on both
+loops; only the poll cadence changes. Leaving the variable unset keeps the
+legacy poll loop, which is the supported default.
+
+**Known limitations:**
+
+- The measured serving A/B (idle CPU 2.43% to 0.07%; TTFP p99 -32% at
+  concurrency 8) predates the rebuild on the per-replica fault-isolation work
+  in [#4285](https://github.com/vllm-project/vllm-omni/pull/4285). That work
+  changed dead-replica handling and reader/poller lifecycle rather than the
+  steady-state output path, and the parity suite covers it, but the serving
+  A/B has not been re-run on the current head.
+- The diffusion-poller branch is covered by unit tests only. Deployments whose
+  stages all run as standard engine cores never exercise it, including GLM-TTS,
+  which deploys its DiT without `stage_type: diffusion`.
+- Concurrency 1 and 32 measured at parity with the legacy loop. At 32 the
+  latency is admission-bound, which this mode does not address.
+
 ## Development

 Enable debug logging:
@@ -17,6 +17,8 @@ globally.
 | --- | --- | --- |
 | `sparsity` | finite, `[0, 1]` | Nominal dropped-key-block fraction; default `0.8`; `0` disables sparsity |
 | `start_step` | integer, `>= 0` | Number of early denoise steps kept dense |
+| `end_step` | integer, `>= 0` | Number of final denoise steps kept dense (tail fallback) |
+| `precision` | `"bf16"`, `"fp8"`, `"mix"` | Kernel precision mode; default `"bf16"`. Requires MindIE-SD with `sparse_attention(precision=...)` |
 | `skip_layers` | selector such as `"0-3,38"` | DiT blocks kept dense |

 ```bash
@@ -52,6 +54,12 @@ platform raises. It is incompatible with ring sequence parallelism because
 the kernel needs the complete key sequence for ranking; use Ulysses sequence
 parallelism with `ring_degree=1`.

+The `precision` knob requires a MindIE-SD release whose `sparse_attention`
+accepts `precision=`; older releases accept it through `**kwargs` but
+silently ignore it and run the BF16 path. `RAINFUSION_ATTN` raises at runtime
+when a non-`bf16` precision is requested without that support, so pin a
+compatible MindIE-SD release when using `precision="mix"` or `"fp8"`.
+
 ## Geometry handling

 RainFusion handles arbitrary video grids by rearranging video spatially and,
@@ -12,15 +12,17 @@ LoRA adapters are lightweight, model-specific fine-tuning weights that can be ap
 ## LoRA Adapter Format

 ### PEFT (Parameter-Efficient Fine-Tuning) format (default)
+
 A typical PEFT-format LoRA adapter directory structure:

-```
+```text
 lora_adapter/
 ├── adapter_config.json
 └── adapter_model.safetensors
 ```

 The `adapter_config.json` file contains metadata about the LoRA adapter, including:
+
 - `r`: LoRA rank
 - `lora_alpha`: LoRA alpha scaling factor
 - `target_modules`: List of module names to apply LoRA to
@@ -67,7 +69,7 @@ For distilled few-step LoRAs, pass `lora_backend="distill"` together with one or
 For Qwen-Image and Wan pipelines, the distill backend calls `pipeline.load_lora_weights(...)` during worker initialization.

 | Pipeline | Supported distilled LoRA repo | Notes |
-|----------|--------------------------------|-------|
+| ---------- | -------------------------------- | ------- |
 | `QwenImagePipeline` | `lightx2v/Qwen-Image-2512-Lightning` | Used with Qwen-Image-2512 Lightning-style few-step inference. |
 | `Wan22Pipeline` | `lightx2v/Wan2.1-Distill-Loras`, `lightx2v/Wan2.2-Distill-Loras` | Wan2.1 uses one LoRA file. For dual-transformer Wan2.2 MoE, pass high-noise then low-noise LoRA files. |
 | `Wan22I2VPipeline` | `lightx2v/Wan2.2-Distill-Loras` | For dual-transformer Wan2.2 MoE, pass high-noise then low-noise LoRA files. |
@@ -240,6 +242,37 @@ Notes:
 - Output quality and speed depend on the replacement checkpoints and sampling params you choose.
 - If you only need to fuse distilled LoRAs into a Wan2.2 checkpoint at load time (without the full LightX2V convert + assemble pipeline), you can instead pass them directly via `--lora-backend distill --lora-path <high>.safetensors <low>.safetensors`. See the [Distill backend](#distill-backend-fuse-distilled-lora-at-init) section above.

+## MiniMax-H3 adapter-declared schedules
+
+MiniMax-H3 supports two few-step mechanisms that must not be conflated:
+
+- **Checkpoint-pinned schedule**: a merged release writes `base_schedule` into
+  `model_index.json`. Requests must pass `num_inference_steps` as the interval
+  count (for example `4` for `[1.0, 0.7, 0.4, 0.15, 0.0]`).
+- **Runtime Turbo LoRA**: LightX2V Turbo artifacts request five sigma points and
+  enforce `flow_shift=6`, `audio_flow_shift=3`.
+- **Runtime native LoRA**: FlashGen-style artifacts declare
+  `key_format=minimax-h3-native` and embed `base_schedule` in safetensors
+  metadata. When active, the adapter schedule overrides the base checkpoint and
+  requests must use the interval-count contract (`num_inference_steps=4`).
+  Request-mode generation may omit the field and take the count from the adapter
+  schedule; step execution requires it explicitly, because the step scheduler
+  derives the total step count from the request before the adapter schedule is
+  known.
+
+Native artifacts also declare `qkv_layout=grouped`. The H3 loader reorders fused
+`qkv_proj` LoRA rows with the same `_reorder_grouped_qkv_to_qkv` helper used for
+base-weight loading, then binds the packed Q/K/V slices through the legacy PEFT
+manager without modifying `DiffusionLoRAManager`.
+
+Both runtime adapters run with distributed layerwise offload in request-mode
+generation, where the manager keeps the LoRA A/B buffers resident on the compute
+device while DLO streams the base blocks. MiniMax-H3 step execution still
+rejects DLO, so `--step-execution` cannot be combined with
+`--enable-distributed-layerwise-offload`. Model-level CPU offload and standard
+layerwise offload are rejected in every mode, because the dynamic LoRA tensors
+are neither parameters nor registered buffers and therefore do not participate
+in those weight lifecycles.

 ## See Also

@@ -112,6 +112,12 @@ perform a collective and uses the same rank-local transfer behavior.
 HWR is an opt-in startup optimization for models that declare the final-layout
 BF16 restore contract. Use it only with no-AllGather DLO:

+The validated BF16 model contracts currently cover MiniMax H3 and
+`black-forest-labs/FLUX.2-klein-4B`. FLUX.2-klein-9B shares the same model
+class but has not been validated against this contract. FLUX.2-dev, online
+FP8, HSDP, LoRA/adapted weights, and non-default load formats remain outside
+the validated scope.
+
 ```bash
 vllm serve /path/to/model --omni \
  --enable-distributed-layerwise-offload \
@@ -129,6 +129,7 @@ The following tables show which models support each feature:
 | **Krea 2**               |     ❌     |     ✅      |           ❌           |       ❌        |         ❌         |          ❌          |   ✅    |             ✅             |      ✅ (decode)      |       ❌        |        ❌         |
 | **LongCat-Image**        |     ✅     |     ✅      |           ✅           |       ✅        |         ✅         |          ❌          |   ❌    |             ✅             |          ❌           |       ❌        |        ❌         |
 | **LongCat-Image-Edit**   |     ✅     |     ✅      |           ✅           |       ✅        |         ✅         |          ❌          |   ❌    |             ✅             |          ❌           |       ❌        |        ❌         |
+| **MammothModa2(T2I)**    |     ❌     |     ❌      |           ❌           |       ❌        |         ❌         |          ❌          |   ❌    |             ❌             |          ❌           |       ❌        |        ❌         |
 | **Nextstep_1(T2I)**      |     ❓     |     ❓      |           ❌           |       ✅        |         ✅         |          ❌          |   ❌    |             ✅             |          ❌           |       ❌        |        ❌         |
 | **OmniGen2**             |     ❌     |     ✅      |           ✅           |       ❌        |         ✅         |          ❌          |   ❌    |             ❌             |          ❌           |       ❌        |        ❌         |
 | **Ovis-Image**           |     ❌     |     ✅      |           ❌           |       ✅        |         ❌         |          ❌          |   ❌    |             ✅             |          ❌           |       ❌        |        ❌         |
@@ -28,6 +28,18 @@ vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091 \
    --deploy-config /path/to/deploy_config_file
 ```

+To serve **thinker-only** (text output, no talker / code2wav loaded) with Instruct
+weights, pass the bundled thinker-only deploy YAML. The `pipeline:` key
+`qwen3_omni_moe_thinker_only` overrides the HF `enable_audio_output` resolver:
+
+```bash
+vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091 \
+    --deploy-config vllm_omni/deploy/qwen3_omni_moe_thinking.yaml
+```
+
+Captioner / Thinking checkpoints (`enable_audio_output=false`) still auto-select
+the same single-stage pipeline without `--deploy-config`.
+
 ### Launch individual stages (stage-based CLI)

 Adopt the stage-based CLI architecture to independently instantiate execution processes per functional stage.
@@ -0,0 +1,50 @@
+# SVDQuant W4A4
+
+## Overview
+
+[SVDQuant](https://arxiv.org/abs/2411.05007) combines four-bit weights
+and activations with a small low-rank branch that corrects part of the
+quantization error. vLLM-Omni consumes an offline-quantized checkpoint;
+it does not calibrate the model while loading.
+
+## Checkpoint contract
+
+Place the following entry in the diffusion transformer's `config.json`:
+
+```json
+{
+  "quantization_config": {
+    "quant_method": "svdquant",
+    "rank": 32,
+    "precision": "nvfp4",
+    "act_unsigned": false,
+    "modules_to_not_convert": []
+  }
+}
+```
+
+For a quantized linear with input size `K`, output size `N`, and correction
+rank `R`, the checkpoint stores:
+
+| Suffix | Shape | dtype |
+| --- | --- | --- |
+| `qweight` | `(N, K / 2)` | `int8` (two packed FP4 values per byte) |
+| `wscales` | `(K / 16, N)` | `float8_e4m3fn` |
+| `proj_down` | `(K, R)` | `bfloat16` |
+| `proj_up` | `(N, R)` | `bfloat16` |
+| `smooth_factor` | `(K,)` | `bfloat16` |
+| `wcscales` | `(N,)` | `bfloat16` |
+| `wtscale` | `(1,)` | `bfloat16` |
+
+`K` must be divisible by 16 on every tensor-parallel rank. Set `wcscales` to
+ones when no per-output correction is needed. Modules listed in
+`modules_to_not_convert` keep their checkpoint precision.
+
+## Runtime support
+
+The compatibility path accepts BF16 inputs and executes an NVFP4 GEMM followed
+by the BF16 rank correction. It supports vLLM's FlashInfer, CUTLASS, and FBGEMM
+NVFP4 tensor layouts; incompatible forced backends fail during model loading.
+SM103 is the currently validated and enabled hardware target. Native fusion of
+the NVFP4 GEMM and rank correction is separate from this checkpoint-loading
+contract.
@@ -0,0 +1,9 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
+"""vLLM-Omni example scripts, importable as a package.
+
+Keep this ``__init__.py``: with the upstream vLLM installed editable (which also
+places an ``examples`` package on ``sys.path``), a regular package at this repo
+root wins over the upstream ``examples`` namespace during ``import examples``.
+"""
@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project

 """
 Image-to-Video generation example using Wan2.2 I2V/TI2V models, LTX2/LTX-2.3,
@@ -111,6 +111,13 @@ def build_image_to_video_prompt(
    return result


+def _validate_video_output_type(output_type: str) -> None:
+    if output_type not in {"image", "video"}:
+        raise ValueError(
+            f"Unexpected output type '{output_type}', expected 'video' or legacy 'image' for video generation."
+        )
+
+
 def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser(
        description=(
@@ -665,10 +672,7 @@ def main():
        frames = frames[0] if frames else None

    if isinstance(frames, OmniRequestOutput):
-        if frames.final_output_type != "image":
-            raise ValueError(
-                f"Unexpected output type '{frames.final_output_type}', expected 'image' for video generation."
-            )
+        _validate_video_output_type(frames.final_output_type)
        if frames.multimodal_output and "audio" in frames.multimodal_output:
            audio = frames.multimodal_output["audio"]
            audio_sample_rate = frames.multimodal_output.get("audio_sample_rate", audio_sample_rate)
@@ -0,0 +1,117 @@
+"""
+Offline inference example: MammothModa2 image summarization (single AR stage).
+
+Example:
+  uv run python examples/offline_inference/mammothmodal2_preview/run_mammothmoda2_image_summarize.py \
+    --model path/to/MammothModa2-Preview \
+    --deploy-config vllm_omni/deploy/mammoth_moda2_ar.yaml \
+    --image /path/to/input.jpg \
+    --question "Please summarize the content of this image."
+"""
+
+from __future__ import annotations
+
+import argparse
+import os
+
+from PIL import Image
+from vllm import SamplingParams
+from vllm.multimodal.image import convert_image_mode
+
+from vllm_omni import Omni
+
+DEFAULT_SYSTEM = "You are a helpful assistant."
+DEFAULT_QUESTION = "Please summarize the content of this image."
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="MammothModa2 image summarization (offline, AR only).")
+    parser.add_argument("--model", type=str, required=True, help="Path to model directory or model id.")
+    parser.add_argument("--deploy-config", type=str, required=True, help="Path to deploy config yaml.")
+    parser.add_argument("--image", type=str, required=True, help="Path to input image.")
+    parser.add_argument("--question", type=str, default=DEFAULT_QUESTION, help="Question/instruction for the model.")
+    parser.add_argument("--system", type=str, default=DEFAULT_SYSTEM, help="System prompt.")
+    parser.add_argument(
+        "--max-tokens",
+        type=int,
+        default=512,
+        help="Max new tokens to generate.",
+    )
+    parser.add_argument("--temperature", type=float, default=0.2)
+    parser.add_argument("--top-p", type=float, default=0.9)
+    parser.add_argument("--seed", type=int, default=42)
+    parser.add_argument("--trust-remote-code", action="store_true")
+    parser.add_argument(
+        "--enable-diffusion-pipeline-profiler",
+        action="store_true",
+        help="Enable diffusion pipeline profiler to display stage durations.",
+    )
+    return parser.parse_args()
+
+
+def build_prompt(system: str, question: str) -> str:
+    return (
+        f"<|im_start|>system\n{system}<|im_end|>\n"
+        "<|im_start|>user\n"
+        "<|vision_start|><|image_pad|><|vision_end|>"
+        f"{question}<|im_end|>\n"
+        "<|im_start|>assistant\n"
+    )
+
+
+def main() -> None:
+    args = parse_args()
+
+    if not os.path.exists(args.image):
+        raise FileNotFoundError(f"Image file not found: {args.image}")
+
+    pil_image = Image.open(args.image)
+    image_data = convert_image_mode(pil_image, "RGB")
+    prompt = build_prompt(args.system, args.question)
+
+    omni = Omni(
+        model=args.model,
+        deploy_config=args.deploy_config,
+        trust_remote_code=args.trust_remote_code,
+        enable_diffusion_pipeline_profiler=args.enable_diffusion_pipeline_profiler,
+    )
+    try:
+        sp = SamplingParams(
+            temperature=float(args.temperature),
+            top_p=float(args.top_p),
+            top_k=-1,
+            max_tokens=int(args.max_tokens),
+            seed=int(args.seed),
+            detokenize=True,
+        )
+        # NOTE: omni.generate() returns a Generator[OmniRequestOutput, None, None].
+        # Consume it inside the try block so the worker isn't closed early.
+        outputs = list(
+            omni.generate(
+                [
+                    {
+                        "prompt": prompt,
+                        "multi_modal_data": {"image": image_data},
+                        "additional_information": {"omni_task": ["chat"]},
+                    }
+                ],
+                [sp],
+            )
+        )
+    finally:
+        omni.close()
+
+    lines: list[str] = []
+    for stage_outputs in outputs:
+        ro = stage_outputs
+        text = ro.outputs[0].text if getattr(ro, "outputs", None) else str(ro)
+        lines.append(f"request_id: {getattr(ro, 'request_id', 'unknown')}\n")
+        lines.append("answer:\n")
+        lines.append(text.strip() + "\n")
+        lines.append("\n")
+
+    print("\n".join(lines))
+
+
+if __name__ == "__main__":
+    main()
@@ -599,6 +599,17 @@ def main():
            if args.seed is not None and hasattr(params, "seed"):
                params.seed = args.seed

+            # MammothModa2's AR stage emits one visual token per grid cell,
+            # one EOL token per row, and one final look-ahead token whose hidden
+            # state is unavailable. Size the first stage from the prompt metadata
+            # instead of SamplingParams' default of 16 tokens.
+            prompt_info = prompt_dict.get("additional_information", {})
+            if idx == 0 and prompt_info.get("omni_task") == ["t2i"]:
+                ar_width = int(prompt_info.get("ar_width", [0])[0])
+                ar_height = int(prompt_info.get("ar_height", [0])[0])
+                if ar_width > 0 and ar_height > 0:
+                    params.max_tokens = ar_height * (ar_width + 1) + 1
+
    if not diffusion_replaced and len(sampling_params_list) == 1:
        sampling_params_list = [diffusion_params]

@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project

 import argparse
 import json
@@ -174,6 +174,13 @@ def build_text_to_video_prompt(prompt: str, negative_prompt: str | None) -> dict
    return result


+def _validate_video_output_type(output_type: str) -> None:
+    if output_type not in {"image", "video"}:
+        raise ValueError(
+            f"Unexpected output type '{output_type}', expected 'video' or legacy 'image' for video generation."
+        )
+
+
 def _normalize_float_tensor(tensor: torch.Tensor, source_range: str) -> torch.Tensor:
    """Normalize decoded frames according to the pipeline's output contract."""
    if not tensor.is_floating_point():
@@ -620,10 +627,7 @@ def main():
        frames = frames[0] if frames else None

    if isinstance(frames, OmniRequestOutput):
-        if frames.final_output_type != "image":
-            raise ValueError(
-                f"Unexpected output type '{frames.final_output_type}', expected 'image' for video generation."
-            )
+        _validate_video_output_type(frames.final_output_type)
        if frames.multimodal_output and "audio" in frames.multimodal_output:
            audio = frames.multimodal_output["audio"]
            audio_sample_rate = frames.multimodal_output.get("audio_sample_rate", audio_sample_rate)
@@ -10,10 +10,12 @@ Generate text from text or image inputs with vLLM-Omni's shared offline entrypoi
 | --- | --- | --- | --- |
 | `ByteDance-Seed/BAGEL-7B-MoT` | Yes | Yes | `vllm_omni/deploy/bagel.yaml` |
 | `tencent/HunyuanImage-3.0-Instruct` | Yes | Yes | `vllm_omni/deploy/hunyuan_image3_ar.yaml` |
+| `bytedance-research/MammothModa2-Preview` | Yes | Yes | `vllm_omni/deploy/mammoth_moda2_ar.yaml` |
+| `bytedance-research/MammothModa2-Dev` | Yes | Yes | `vllm_omni/deploy/mammoth_moda2_ar.yaml` |

 The script recognizes these model families from `config.json`, applies the
-model-specific prompt format, and selects an AR-only deploy for HunyuanImage-3.
-BAGEL uses its registered default deploy.
+model-specific prompt format, and selects an AR-only deploy for HunyuanImage-3
+and MammothModa2. BAGEL uses its registered default deploy.

 ## Text-To-Text

@@ -33,6 +35,22 @@ python examples/offline_inference/x_to_text/x_to_text.py \
  --prompt "Explain multimodal inference in three concise sentences."
 ```

+### MammothModa2
+
+```bash
+python examples/offline_inference/x_to_text/x_to_text.py \
+  --model bytedance-research/MammothModa2-Preview \
+  --prompt "Explain multimodal inference in three concise sentences."
+```
+
+The same command supports the Dev checkpoint:
+
+```bash
+python examples/offline_inference/x_to_text/x_to_text.py \
+  --model bytedance-research/MammothModa2-Dev \
+  --prompt "Explain multimodal inference in three concise sentences."
+```
+
 ## Image-To-Text

 Pass one image with `--image`. The default question can be replaced with any
@@ -54,6 +72,16 @@ python examples/offline_inference/x_to_text/x_to_text.py \
  --prompt "Describe this image in detail."
 ```

+```bash
+python examples/offline_inference/x_to_text/x_to_text.py \
+  --model bytedance-research/MammothModa2-Preview \
+  --image image.png \
+  --prompt "Describe this image in detail."
+```
+
+Use `bytedance-research/MammothModa2-Dev` in the same command to run I2T with
+the Dev checkpoint.
+
 ## Key Arguments

 | Argument | Default | Description |
@@ -74,4 +102,4 @@ python examples/offline_inference/x_to_text/x_to_text.py \

 The committed HunyuanImage-3 AR-only default uses four GPUs with TP=4. To use a
 different topology, pass a custom AR-only YAML through `--deploy-config`.
-BAGEL uses its repository deploy default unless overridden.
+BAGEL and MammothModa2 use their repository deploy defaults unless overridden.
@@ -15,6 +15,7 @@ from vllm_omni.model_extras import build_x_to_text_prompt, get_x_to_text_model_f

 _REPO_ROOT = Path(__file__).resolve().parents[3]
 _HUNYUAN_AR_DEPLOY_CONFIG = _REPO_ROOT / "vllm_omni" / "deploy" / "hunyuan_image3_ar.yaml"
+_MAMMOTH_AR_DEPLOY_CONFIG = _REPO_ROOT / "vllm_omni" / "deploy" / "mammoth_moda2_ar.yaml"


 def parse_args() -> argparse.Namespace:
@@ -61,7 +62,7 @@ def main() -> None:
    omni_kwargs: dict[str, Any] = {
        "model": args.model,
        "mode": "image-to-text" if image is not None else "text-to-text",
-        "trust_remote_code": args.trust_remote_code or family == "hunyuan_image3",
+        "trust_remote_code": args.trust_remote_code or family in {"hunyuan_image3", "mammoth_moda2"},
        "log_stats": args.log_stats,
    }
    if args.enforce_eager is not None:
@@ -70,6 +71,8 @@ def main() -> None:
        omni_kwargs["deploy_config"] = args.deploy_config
    elif family == "hunyuan_image3":
        omni_kwargs["deploy_config"] = str(_HUNYUAN_AR_DEPLOY_CONFIG)
+    elif family == "mammoth_moda2":
+        omni_kwargs["deploy_config"] = str(_MAMMOTH_AR_DEPLOY_CONFIG)

    omni = Omni(**omni_kwargs)
    try:
@@ -159,6 +159,33 @@ python examples/online_serving/minicpmo/realtime_duplex_demo.py \
    --output-dir /tmp/minicpmo_realtime_duplex_demo
 ```

+Video input uses the same session. PyAV demuxes JPEG frames at `--video-fps`
+(default 1.0) and vLLM `load_audio` extracts a 16 kHz mono WAV unless
+`--input-wav` overrides the soundtrack. Frames keep their capture size
+(`--frame-max-side 0`); the server `process_image` normalizes at 448.
+
+`--stack-frames N` raises the visual refresh rate the way official duplex does:
+each 1 s unit also samples the `N-1` sub-frames captured inside it and tiles
+them into a single composite image sent next to that unit's base frame. The
+audio cadence never changes — a unit is always one second — and the wire always
+carries 2 images per unit however large `N` is, because the sub-frames share one
+composite. Official uses 5 for high refresh rate mode:
+
+```bash
+python examples/online_serving/minicpmo/realtime_duplex_demo.py \
+    --url ws://localhost:8099/v1/realtime?duplex=1 \
+    --model openbmb/MiniCPM-o-4_5 \
+    --input-video /path/to/clip.mp4 \
+    --stack-frames 5 \
+    --ref-audio /path/to/MiniCPM-o-Demo/assets/ref_audio/ref_minicpm_signature.wav \
+    --output-dir /tmp/minicpmo_realtime_duplex_video_demo
+```
+
+Detail inside a composite is capped by `scale_resolution=448` at
+`max_slice_nums=1`: official suggests HD slicing (`max_slice_nums=[2, 1]`) for
+stacked frames, which the duplex adapter does not implement yet. Reading small
+text or digits out of a wide scene is limited by that, not by frame timing.
+
 ## Open the experimental browser client

 The browser UI serves the page and proxies the same-origin Realtime WebSocket to
@@ -192,6 +219,64 @@ stronger `response-required` mode is diagnostic: it requires a purpose-built
 two-response WAV, its `--input-sha256`, and an
 `--expect-second-response-substring` value.

+## Run Omni-DuplexEval
+
+Omni-DuplexEval generation uses the vLLM-Omni native MiniCPM-o duplex
+endpoint. Evaluation uses a separate OpenAI-compatible multimodal judge
+served from the same vLLM-Omni environment. This validates the vLLM-Omni
+duplex client and local judge integration; it does not reproduce the paper's
+original MiniCPM-o inference implementation.
+
+Start the duplex generation server:
+
+```bash
+vllm serve openbmb/MiniCPM-o-4_5 --omni --trust-remote-code \
+    --deploy-config vllm_omni/deploy/minicpmo_4_5_duplex.yaml \
+    --served-model-name openbmb/MiniCPM-o-4_5 \
+    --host 0.0.0.0 --port 8099
+```
+
+Start a separate multimodal judge. The allowed path must contain any local
+videos passed with `--judge-video-mode video_url`:
+
+```bash
+vllm serve Qwen/Qwen2.5-VL-7B-Instruct \
+    --served-model-name Qwen/Qwen2.5-VL-7B-Instruct \
+    --allowed-local-media-path /data/omni-duplex-eval \
+    --host 0.0.0.0 --port 8000
+```
+
+Generate, evaluate, and summarize:
+
+```bash
+vllm bench omni-duplex-eval --omni generate \
+    --url ws://127.0.0.1:8099/v1/realtime?duplex=1 \
+    --model openbmb/MiniCPM-o-4_5 \
+    --ref-audio /data/ref.wav \
+    --dataset Hothan/Omni-DuplexEval \
+    --concurrency 2 \
+    --response-root /data/omni-duplex-eval/responses
+
+vllm bench omni-duplex-eval --omni evaluate \
+    --dataset Hothan/Omni-DuplexEval \
+    --response-root /data/omni-duplex-eval/responses \
+    --score-root /data/omni-duplex-eval/scores \
+    --judge-base-url http://127.0.0.1:8000 \
+    --judge-model Qwen/Qwen2.5-VL-7B-Instruct \
+    --judge-video-mode video_url \
+    --eval-workers 4
+
+vllm bench omni-duplex-eval --omni summarize \
+    --score-root /data/omni-duplex-eval/scores
+```
+
+The dataset names such as `RTD_OCR` and `PR_correction` are Hugging Face
+splits, not dataset configurations. Pass one with `--split`, or omit it to
+run all nine splits. `--limit 1` is useful for a smoke test. Realtime
+generation records `clock=media`; artifacts generated with
+`--pace as-fast-as-possible` record `clock=invalid` and evaluation rejects
+them unless `--allow-invalid-clock` is explicit.
+
 ## Related examples

 - [Offline MiniCPM-o inference](../../offline_inference/minicpmo/)
@@ -12,7 +12,9 @@ from __future__ import annotations
 import argparse
 import asyncio
 import base64
+import io
 import json
+import math
 import sys
 import time
 from pathlib import Path
@@ -22,8 +24,8 @@ if str(REPO_ROOT) not in sys.path:
    sys.path.insert(0, str(REPO_ROOT))

 from vllm_omni.experimental.fullduplex.client import (  # noqa: E402
-    PCM16_BYTES_PER_SAMPLE,  # noqa: F401 - compatibility export
-    PCM16_SAMPLE_RATE,  # noqa: F401 - compatibility export
+    PCM16_BYTES_PER_SAMPLE,
+    PCM16_SAMPLE_RATE,
    RealtimeDuplexClient,
    RealtimeEventCollector,
    build_realtime_url,
@@ -38,6 +40,10 @@ from vllm_omni.experimental.fullduplex.client import (  # noqa: E402
 from vllm_omni.experimental.fullduplex.client import (  # noqa: E402
    reference_audio_data_url as _ref_audio_data_url,
 )
+from vllm_omni.experimental.fullduplex.video_stacking import (  # noqa: E402
+    concat_frames_b64,
+    unit_subframe_offsets,
+)


 class _StreamingOutputWriter:
@@ -159,14 +165,258 @@ def _event_count_after(
    return sum(event.get("type") == event_type for event in events[index + 1 :])


-async def run_demo(args: argparse.Namespace) -> dict[str, object]:
-    input_pcm16 = read_pcm16_wav(Path(args.input_wav))
-    if not input_pcm16:
-        raise ValueError("input WAV has no audio")
+def _probe_video_frames_and_fps(video_path: Path) -> tuple[int, float]:
+    """Frame count and average FPS via PyAV (same approach as Daily-Omni)."""
+    import av

+    with av.open(str(video_path)) as container:
+        stream = container.streams.video[0]
+        avg_fps = float(stream.average_rate) if stream.average_rate else 0.0
+        num_frames = stream.frames or 0
+        if num_frames <= 0:
+            duration: float | None = None
+            if stream.duration is not None and stream.time_base is not None:
+                duration = float(stream.duration * stream.time_base)
+            elif container.duration is not None:
+                duration = container.duration / av.time_base
+            if duration and avg_fps:
+                num_frames = int(math.floor(duration * avg_fps))
+        if num_frames <= 0:
+            stream.thread_type = "AUTO"
+            num_frames = sum(1 for _ in container.decode(stream))
+    if num_frames <= 0:
+        raise ValueError(f"No decodable video frames in {video_path}")
+    return num_frames, avg_fps or 1.0
+
+
+def _upright(image, rotation: int):
+    """Apply a container display-matrix rotation, in degrees, to ``image``.
+
+    PyAV hands back the stored raster and leaves the display matrix for the
+    caller, unlike the ffmpeg CLI which autorotates. Phone clips routinely
+    carry +/-90, so skipping this feeds the model a sideways frame: arrows and
+    digits stop being readable and the model mirrors the motion it reports.
+    """
+    from PIL import Image
+
+    quarter_turns = {
+        90: Image.Transpose.ROTATE_90,
+        180: Image.Transpose.ROTATE_180,
+        270: Image.Transpose.ROTATE_270,
+    }
+    turn = quarter_turns.get(int(rotation) % 360)
+    return image.transpose(turn) if turn is not None else image
+
+
+def _decode_video_frames_rgb(video_path: Path, frame_idx: list[int]) -> list:
+    """Decode presentation-order ``frame_idx`` into upright RGB ``PIL.Image``s."""
+    import av
+
+    wanted = sorted({int(i) for i in frame_idx})
+    if not wanted:
+        return []
+
+    decoded: dict[int, object] = {}
+    with av.open(str(video_path)) as container:
+        stream = container.streams.video[0]
+        stream.thread_type = "AUTO"
+        cursor = 0
+        for position, frame in enumerate(container.decode(stream)):
+            if wanted[cursor] > position:
+                continue
+            image = _upright(frame.to_image(), getattr(frame, "rotation", 0) or 0)
+            while cursor < len(wanted) and wanted[cursor] <= position:
+                decoded[wanted[cursor]] = image
+                cursor += 1
+            if cursor >= len(wanted):
+                break
+
+    if not decoded:
+        raise ValueError(f"Decoded no frames from {video_path} for indices {wanted[:8]}")
+    last_image = decoded[max(decoded)]
+    return [decoded.get(int(i), last_image) for i in frame_idx]
+
+
+def _resize_max_side(image, max_side: int):
+    width, height = image.size
+    longest = max(width, height)
+    if longest <= max_side or max_side <= 0:
+        return image
+    scale = max_side / float(longest)
+    new_size = (max(1, int(round(width * scale))), max(1, int(round(height * scale))))
+    return image.resize(new_size)
+
+
+def _jpeg_bytes(image, *, quality: int = 95) -> bytes:
+    buf = io.BytesIO()
+    image.convert("RGB").save(buf, format="JPEG", quality=quality)
+    return buf.getvalue()
+
+
+def _to_pcm16_bytes(audio_np) -> bytes:
+    import numpy as np
+
+    arr = np.asarray(audio_np).reshape(-1)
+    if arr.dtype != np.int16:
+        arr = (np.clip(arr, -1.0, 1.0) * 32767.0).astype(np.int16)
+    return arr.tobytes()
+
+
+def _pcm16_wav_duration_s(path: Path) -> float:
+    pcm16 = read_pcm16_wav(path)
+    return len(pcm16) / float(PCM16_SAMPLE_RATE * PCM16_BYTES_PER_SAMPLE)
+
+
+def _extract_video_frames(
+    video_path: Path,
+    work_dir: Path,
+    *,
+    fps: float,
+    max_side: int,
+    duration_s: float,
+    stack_frames: int = 1,
+) -> tuple[list[str], list[str | None]]:
+    """Demux JPEG frames only. Frame count follows ``duration_s``, not soundtrack.
+
+    Returns the base frames (one per model unit) and the parallel stacked track.
+    With ``stack_frames=N`` each unit also samples its ``N-1`` interior
+    sub-frames and tiles them into one composite, which is how official duplex
+    raises the visual refresh rate while keeping one second of audio per unit.
+    A unit whose interior falls past the end of the clip stacks nothing.
+    """
+    num_frames, avg_fps = _probe_video_frames_and_fps(video_path)
+    sample_fps = max(float(fps), 1e-6)
+    unit_s = 1.0 / sample_fps
+    timeline_s = max(float(duration_s), unit_s)
+    num_samples = max(1, int(math.ceil(timeline_s * sample_fps - 1e-9)))
+    offsets = unit_subframe_offsets(stack_frames, unit_s=unit_s)
+
+    def frame_at(seconds: float) -> int:
+        # Official get_video_frame_audio_segments uses truncating int(), not
+        # round(), so 29.97 fps lands on the same presentation index.
+        return min(int(seconds * avg_fps), num_frames - 1)
+
+    base_idx = [frame_at(unit * unit_s) for unit in range(num_samples)]
+    sub_times = [
+        [unit * unit_s + offset for offset in offsets if unit * unit_s + offset < timeline_s]
+        for unit in range(num_samples)
+    ]
+    sub_idx = [frame_at(seconds) for times in sub_times for seconds in times]
+    images = _decode_video_frames_rgb(video_path, base_idx + sub_idx)
+    sub_images = iter(images[len(base_idx) :])
+
+    frame_dir = work_dir / "frames"
+    frame_dir.mkdir(parents=True, exist_ok=True)
+    for stale in frame_dir.glob("*.jpg"):
+        stale.unlink()
+    frames_b64: list[str] = []
+    stacked_b64: list[str | None] = []
+    for index, (image, times) in enumerate(zip(images[: len(base_idx)], sub_times), start=1):
+        jpeg = _jpeg_bytes(_resize_max_side(image, max_side))
+        (frame_dir / f"frame_{index:05d}.jpg").write_bytes(jpeg)
+        frames_b64.append(base64.b64encode(jpeg).decode("ascii"))
+        interior = [_resize_max_side(next(sub_images), max_side) for _ in times]
+        if not interior:
+            stacked_b64.append(None)
+            continue
+        composite = concat_frames_b64(interior)
+        (frame_dir / f"stack_{index:05d}.jpg").write_bytes(base64.b64decode(composite))
+        stacked_b64.append(composite)
+    return frames_b64, stacked_b64
+
+
+def _extract_video_soundtrack(video_path: Path, work_dir: Path) -> Path:
+    """Write a 16 kHz mono WAV from the video soundtrack."""
+    try:
+        from vllm.multimodal.media.audio import load_audio
+    except ImportError as exc:  # pragma: no cover
+        raise SystemExit("--input-video needs vLLM audio helpers (load_audio)") from exc
+
+    audio_np, sr = load_audio(str(video_path), sr=PCM16_SAMPLE_RATE, mono=True)
+    work_dir.mkdir(parents=True, exist_ok=True)
+    audio_path = work_dir / "input_16k.wav"
+    write_pcm16_wav(audio_path, _to_pcm16_bytes(audio_np), sample_rate_hz=int(sr))
+    return audio_path
+
+
+def _extract_video_input(
+    video_path: Path,
+    work_dir: Path,
+    *,
+    fps: float,
+    max_side: int,
+    stack_frames: int,
+) -> tuple[Path, list[str], list[str | None]]:
+    """Demux a video into duplex inputs: 16 kHz mono WAV plus JPEG frames.
+
+    Use this only when the video soundtrack is the audio source. When the
+    caller already has ``--input-wav``, extract frames against that WAV
+    duration instead so a silent clip cannot fail ``load_audio``.
+    """
+    audio_path = _extract_video_soundtrack(video_path, work_dir)
+    frames_b64, stacked_b64 = _extract_video_frames(
+        video_path,
+        work_dir,
+        fps=fps,
+        max_side=max_side,
+        duration_s=_pcm16_wav_duration_s(audio_path),
+        stack_frames=stack_frames,
+    )
+    return audio_path, frames_b64, stacked_b64
+
+
+def _resolve_duplex_av_inputs(
+    *,
+    input_wav: str | None,
+    input_video: str | None,
+    work_dir: Path,
+    fps: float,
+    max_side: int,
+    stack_frames: int = 1,
+) -> tuple[str, list[str], list[str | None]]:
+    """Return ``(wav_path, frames_b64, stacked_b64)``. External WAV wins over video audio."""
+    if input_video:
+        video_path = Path(input_video)
+        if input_wav:
+            frames, stacked = _extract_video_frames(
+                video_path,
+                work_dir,
+                fps=fps,
+                max_side=max_side,
+                duration_s=_pcm16_wav_duration_s(Path(input_wav)),
+                stack_frames=stack_frames,
+            )
+            return input_wav, frames, stacked
+        extracted_wav, frames, stacked = _extract_video_input(
+            video_path,
+            work_dir,
+            fps=fps,
+            max_side=max_side,
+            stack_frames=stack_frames,
+        )
+        return str(extracted_wav), frames, stacked
+    if not input_wav:
+        raise SystemExit("provide --input-wav, --input-video, or both")
+    return input_wav, [], []
+
+
+async def run_demo(args: argparse.Namespace) -> dict[str, object]:
    output_dir = Path(args.output_dir)
    stream_writer = _StreamingOutputWriter(output_dir)

+    input_wav, video_frames, stacked_video_frames = _resolve_duplex_av_inputs(
+        input_wav=args.input_wav,
+        input_video=args.input_video,
+        work_dir=output_dir / "video_input",
+        fps=args.video_fps,
+        max_side=args.frame_max_side,
+        stack_frames=args.stack_frames,
+    )
+
+    input_pcm16 = read_pcm16_wav(Path(input_wav))
+    if not input_pcm16:
+        raise ValueError("input WAV has no audio")
+
    url = build_realtime_url(
        args.url,
        args.model,
@@ -184,10 +434,12 @@ async def run_demo(args: argparse.Namespace) -> dict[str, object]:
            timeout_s=args.timeout_s,
        )
        stream_event_cursor = len(client.events.events)
-        await client.stream_pcm16(
+        frames_sent = await client.stream_pcm16(
            input_pcm16,
            chunk_ms=args.chunk_ms,
            realtime=not args.no_realtime_pacing,
+            video_frames=video_frames,
+            stacked_video_frames=stacked_video_frames,
        )
        commit_event_cursor = len(client.events.events)
        stream_decision = _latest_model_decision(client.events.events, stream_event_cursor)
@@ -310,6 +562,10 @@ async def run_demo(args: argparse.Namespace) -> dict[str, object]:
                    committed_index,
                ),
            },
+            "input_video": args.input_video,
+            "video_frames_extracted": len(video_frames),
+            "video_frames_sent": frames_sent,
+            "video_stacked_frames_extracted": sum(frame is not None for frame in stacked_video_frames),
            "audio_bytes": len(audio),
            "audio_chunk_count": len(stream_writer.audio_chunk_paths),
            "audio_chunk_files": [str(path) for path in stream_writer.audio_chunk_paths],
@@ -356,7 +612,48 @@ def parse_args() -> argparse.Namespace:
    parser.add_argument("--url", default="ws://localhost:8099/v1/realtime?duplex=1")
    parser.add_argument("--model", default="openbmb/MiniCPM-o-4_5")
    parser.add_argument("--session-id")
-    parser.add_argument("--input-wav", required=True)
+    parser.add_argument(
+        "--input-wav",
+        help="Mono 16 kHz PCM16 clip to stream. Optional when --input-video carries the audio track.",
+    )
+    parser.add_argument(
+        "--input-video",
+        help=(
+            "Video to stream as omni duplex input. PyAV demuxes frames and "
+            "vLLM load_audio extracts a 16 kHz mono WAV (unless --input-wav overrides the audio)."
+        ),
+    )
+    parser.add_argument(
+        "--video-fps",
+        type=float,
+        default=1.0,
+        help="Frame extraction rate. The wire contract carries ~1 frame per second of audio.",
+    )
+    parser.add_argument(
+        "--frame-max-side",
+        type=int,
+        default=0,
+        help=(
+            "Optional client-side downscale of the longest side, in pixels. "
+            "0 (default) keeps the capture resolution so the server "
+            "process_image can normalize at scale_resolution=448 from a "
+            "higher-quality source. The web demo never resizes on the client."
+        ),
+    )
+    parser.add_argument(
+        "--stack-frames",
+        type=int,
+        default=1,
+        help=(
+            "Frames sampled per 1 s audio unit. 1 (default) sends the unit's "
+            "frame alone. N > 1 also samples the N-1 sub-frames inside the unit "
+            "and tiles them into one composite sent next to the base frame, so "
+            "motion (arrows, doors, digits changing) is visible at N fps while "
+            "the audio stays 1 s per unit. Official uses 5 for high refresh "
+            "rate mode; the wire carries 2 images per unit at any N because the "
+            "sub-frames share one composite."
+        ),
+    )
    parser.add_argument(
        "--ref-audio",
        required=True,
@@ -372,7 +669,7 @@ def parse_args() -> argparse.Namespace:
        "--temperature",
        type=float,
        default=None,
-        help="Stage0 sampling temperature; response-required soft-interrupt uses 0.0.",
+        help="Stage 0 sampling temperature; omit to preserve the model default.",
    )
    parser.add_argument("--no-realtime-pacing", action="store_true")
    parser.add_argument("--require-audio", action="store_true")
@@ -24,6 +24,18 @@ vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091 \
    --deploy-config /path/to/your_deploy_config.yaml
 ```

+To serve **thinker-only** (text output, no talker / code2wav loaded) with Instruct
+weights, pass the bundled thinker-only deploy YAML. The `pipeline:` key
+`qwen3_omni_moe_thinker_only` overrides the HF `enable_audio_output` resolver:
+
+```bash
+vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091 \
+    --deploy-config vllm_omni/deploy/qwen3_omni_moe_thinking.yaml
+```
+
+Captioner / Thinking checkpoints (`enable_audio_output=false`) still auto-select
+the same single-stage pipeline without `--deploy-config`.
+
 For a 3x-GPU multi-replica layout (talker/code2wav scale-out on cuda:1,2),
 use `--stage-overrides` on top of the default config:

@@ -71,11 +71,11 @@ dev = [
    "voxcpm==2.0.3", # for VoxCPM2 path-locating in tests
    "torchdiffeq==0.2.5", # for covo_audio Test
    "FlagEmbedding==1.4.0", # for hunyuan_image3 tests
-    "kernels==0.14.1", # for accuracy tests comparison with diffusers output (0.15.0 incompatible with diffusers 0.38.0)
    "s3tokenizer==0.3.0", # for tts test
    # Provides top-level cosyvoice2; do not co-install stepaudio2-minicpmo,
    # whose top-level stepaudio2 package conflicts with this distribution.
    "step-audio2==1.0.0", # Step-Audio2 / MiniCPM-o 4.5 Token2Wav (flashcosyvoice / hyperpyyaml)
+    "silero-vad==6.1", # Realtime server-VAD duplex E2E
 ]

 demo = [
@@ -85,6 +85,10 @@ demo = [
    "cn2an>=0.5.22",  # Chinese number normalization for TTS (e.g. "1次" -> "一次")
 ]

+server-vad = [
+    "silero-vad==6.1",
+]
+
 forced-aligner = [
    "qwen-asr>=0.0.6",  # faithful Japanese/Korean word segmentation for the TTS forced aligner
 ]
@@ -244,11 +248,11 @@ addopts = [
    "--dist=loadgroup"
 ]
 markers = [
-    # ci/cd required
-    "core_model: L1&L2 tests (run in each PR)",
-    "advanced_model: L3 level tests (run on each merge)",
-    "full_model: L4 level tests (run nightly)",
-    "local_model: tests (run locally)",
+    # ci/cd required — tag with [ci-level] for tests.helpers.mark.get_level_markers().
+    "core_model: [ci-level] L1&L2 tests (run in each PR)",
+    "advanced_model: [ci-level] L3 level tests (run on each merge)",
+    "full_model: [ci-level] L4 level tests (run nightly)",
+    "local_model: [ci-level] tests (run locally)",
    # function module markers
    "diffusion: Diffusion model tests",
    "omni: Omni model tests",
@@ -258,38 +262,41 @@ markers = [
    "sp: Sequence parallelism tests",
    "example: Doc example code tests",
    # platform markers
-    "cpu: Tests that run on CPU",
-    "gpu: Tests that run on GPU (auto-added)",
-    "cuda: Tests that run on CUDA (auto-added)",
-    "rocm: Tests that run on AMD/ROCm (auto-added)",
-    "xpu: Tests that run on XPU (auto-added)",
-    "npu: Tests that run on NPU/Ascend (auto-added)",
-    "musa: Tests that run on MUSA/Moore Threads (auto-added)",
+    # Tag with [hardware-platform] for tests.helpers.mark.get_supported_platforms().
+    # Add [gpu] on accelerator platforms that should also get pytest.mark.gpu.
+    # cpu/gpu are pytest marks; hardware_test(res=...) rejects them as keys.
+    "cpu: [hardware-platform] Tests that run on CPU",
+    "gpu: [hardware-platform] Tests that run on GPU (auto-added)",
+    "cuda: [hardware-platform] [gpu] Tests that run on CUDA (auto-added)",
+    "rocm: [hardware-platform] [gpu] Tests that run on AMD/ROCm (auto-added)",
+    "xpu: [hardware-platform] [gpu] Tests that run on XPU (auto-added)",
+    "npu: [hardware-platform] Tests that run on NPU/Ascend (auto-added)",
+    "musa: [hardware-platform] [gpu] Tests that run on MUSA/Moore Threads (auto-added)",
    # specified computation resources marks (auto-added)
-    # Tag with [hardware-resource] for tests.helpers.mark.get_hardware_mark_list().
-    "H100: [hardware-resource] Tests that require H100 GPU",
-    "H800: [hardware-resource] Tests that require H800 GPU",
-    "H200: [hardware-resource] Tests that require H200 GPU",
-    "L4: [hardware-resource] Tests that require L4 GPU",
-    "B200: [hardware-resource] Tests that require B200 GPU",
-    "MI325: [hardware-resource] Tests that require MI325 GPU (AMD/ROCm)",
-    "B60: [hardware-resource] Tests that require Intel Arc Pro B60 XPU",
-    "S5000: [hardware-resource] Tests that require S5000 GPU (Moore Threads/MUSA)",
-    "A2: [hardware-resource] Tests that require A2 NPU",
-    "A3: [hardware-resource] Tests that require A3 NPU",
-    "distributed_cuda: Tests that require multi cards on CUDA platform",
-    "distributed_rocm: Tests that require multi cards on ROCm platform",
-    "distributed_xpu: Tests that require multi cards on XPU platform",
-    "distributed_npu: Tests that require multi cards on NPU platform",
-    "distributed_musa: Tests that require multi cards on MUSA platform",
-    "skipif_cuda: Skip if the num of CUDA cards is less than the required",
-    "skipif_rocm: Skip if the num of ROCm cards is less than the required",
-    "skipif_xpu: Skip if the num of XPU cards is less than the required",
-    "skipif_npu: Skip if the num of NPU cards is less than the required",
-    "skipif_musa: Skip if the num of MUSA cards is less than the required",
+    # [hardware-resource] + [cuda]/[rocm]/[xpu]/[npu]/[musa] for hardware_test(res=...).
+    # Do not put [gpu] on SKU lines: that would make gpu a res key.
+    "H100: [hardware-resource] [cuda] Tests that require H100 GPU",
+    "H800: [hardware-resource] [cuda] Tests that require H800 GPU",
+    "H200: [hardware-resource] [cuda] Tests that require H200 GPU",
+    "L4: [hardware-resource] [cuda] Tests that require L4 GPU",
+    "B200: [hardware-resource] [cuda] Tests that require B200 GPU",
+    "MI325: [hardware-resource] [rocm] Tests that require MI325 GPU (AMD/ROCm)",
+    "B60: [hardware-resource] [xpu] Tests that require Intel Arc Pro B60 XPU",
+    "S5000: [hardware-resource] [musa] Tests that require S5000 GPU (Moore Threads/MUSA)",
+    "A2: [hardware-resource] [npu] Tests that require A2 NPU",
+    "A3: [hardware-resource] [npu] Tests that require A3 NPU",
+    # Tag with [hardware-cards] for tests.helpers.mark.get_supported_card_counts().
+    "cards_1: [hardware-cards] Tests that require 1 accelerator card",
+    "cards_2: [hardware-cards] Tests that require 2 accelerator cards",
+    "cards_3: [hardware-cards] Tests that require 3 accelerator cards",
+    "cards_4: [hardware-cards] Tests that require 4 accelerator cards",
+    "cards_5: [hardware-cards] Tests that require 5 accelerator cards",
+    "cards_6: [hardware-cards] Tests that require 6 accelerator cards",
+    "cards_7: [hardware-cards] Tests that require 7 accelerator cards",
+    "cards_8: [hardware-cards] Tests that require 8 accelerator cards",
    # more detailed markers
    "sp: Sequence parallelism tests (multi-GPU)",
-    "slow: Slow tests (may skip in quick CI)",
+    "slow: [ci-level] Slow tests (may skip in quick CI)",
    "benchmark: Benchmark tests",
    "xdist: xdist-parallelizable tests",
 ]
@@ -10,14 +10,22 @@ checkpoint for T2V and first-frame I2V. Every output includes synchronized
 48 kHz stereo audio.

 The raw [`Lightricks/LTX-2.5`](https://huggingface.co/Lightricks/LTX-2.5)
-repository is not directly loadable with `--model`. It supplies the official
-upsampler and LoRA sidecars used by the Full/SFT two-stage pipeline, so accept
-both model licenses and authenticate before first use.
+repository is not directly loadable with `--model`. It supplies the canonical
+Diffusion VAE checkpoint and the official upsampler and LoRA sidecars used by
+the Full/SFT two-stage pipeline, so accept both model licenses and authenticate
+before first use.
+
+The vLLM-Omni integration code is provided under the project's
+[Apache-2.0 license](../../LICENSE). The LTX-2.5 model weights are separate
+artifacts covered by the
+[LTX-2.x Community License](https://github.com/Lightricks/LTX-2/blob/main/LICENSE.md),
+not the vLLM-Omni license. Users are responsible for reviewing and complying
+with the model license before downloading or using the weights.

 ## Choose a pipeline

 | Pipeline | Mode | Default size | Schedule |
-|---|---|---:|---:|
+| --- | --- | ---: | ---: |
 | `LTX2Pipeline` | Full/SFT one-stage | 960x544 | 30 steps |
 | `LTX2TwoStagePipeline` | Full/SFT two-stage | 1920x1088 | 30 + 3 steps |
 | `LTX2DistilledOneStagePipeline` | Distilled one-stage | 960x544 | 8 steps |
@@ -28,6 +36,40 @@ latent upsampler, and run a three-step refinement stage. Select the class with
 `--model-class-name`; no `--task-type` flag is required. Supplying one initial
 image selects I2V, while omitting it selects T2V.

+## Video decoder
+
+LTX-2.5 uses the canonical Native Diffusion VAE decoder by default. To opt in
+to the legacy convolutional VAE, set the startup-only stage-0 model extra
+`ltx2_use_conv_vae: true`. The choice applies to all requests and all four
+pipeline classes above. DiffVAE loads its NATTEN kernel from Hugging Face Hub
+during startup. `decode_timestep` and `decode_noise_scale` condition only the
+legacy ConvVAE and have no effect when DiffVAE is selected.
+
+For offline Python usage:
+
+```python
+omni = Omni(
+    model="Lightricks/LTX-2.5-Diffusers",
+    model_class_name="LTX2Pipeline",
+    stage_overrides='{"0":{"extras":{"ltx2_use_conv_vae":true}}}',
+)
+```
+
+For online serving:
+
+```bash
+vllm serve Lightricks/LTX-2.5-Diffusers \
+  --omni \
+  --model-class-name LTX2Pipeline \
+  --stage-overrides '{"0":{"extras":{"ltx2_use_conv_vae":true}}}'
+```
+
+Both decoders are untiled by default. Set `vae_use_tiling` for memory-saving
+serial tiling; DiffVAE tiles only above 80 frames or 768 pixels in either spatial
+dimension. DiffVAE also supports distributed VAE decode; see the
+[VAE Parallelism Guide](../../docs/user_guide/diffusion/parallelism/vae_parallelism.md).
+DiffVAE is decoder-only, so I2V still uses the convolutional VAE encoder.
+
 ## Prerequisites

 ```bash
@@ -42,7 +84,7 @@ Install matching vLLM and vLLM-Omni versions, and ensure `ffmpeg` and
 ## Hardware

 | GPU | Status | Recommended scope |
-|---|---|---|
+| --- | --- | --- |
 | NVIDIA B300 | Verified | All four canonical pipelines |
 | NVIDIA B200 or H200 | Capacity-based recommendation; not yet verified | All four canonical pipelines |
 | NVIDIA GB200 or GB300 | Capacity-based recommendation; not yet verified | All four canonical pipelines |
@@ -0,0 +1,212 @@
+# MammothModa2
+
+> MammothModa2-Preview and MammothModa2-Dev unified understanding and generation
+
+## Summary
+
+- Vendor: ByteDance Research
+- Models: `bytedance-research/MammothModa2-Preview`, `bytedance-research/MammothModa2-Dev`
+- Tasks: Preview and Dev text-to-image (AR → DiT); Dev text/image understanding
+- Mode: Offline inference
+- Maintainer: Community
+
+## When to use this recipe
+
+Use this recipe to run MammothModa2-Preview through shared task-oriented
+offline examples. Text-to-image uses the shared image example (`text_to_image.py`) instead of a model-specific script.
+The generic example formats the AR prompt, drives the AR → DiT stage pipeline,
+and forwards MammothModa2-specific generation parameters through the
+pipeline-declared `extra_body` contract.
+
+MammothModa2's DiT stage consumes its inputs through the multi-stage kwargs
+interface (not `OmniDiffusionRequest`), so its generation knobs
+(`text_guidance_scale`, `cfg_range`, `num_inference_steps`) are passed via
+`--extra-body` rather than the standard `--num-inference-steps` / `--cfg-scale`
+flags. Image size uses the standard `--height` / `--width` flags.
+
+## References
+
+- Upstream model:
+  [`bytedance-research/MammothModa2-Preview`](https://huggingface.co/bytedance-research/MammothModa2-Preview)
+- Dev model:
+  [`bytedance-research/MammothModa2-Dev`](https://huggingface.co/bytedance-research/MammothModa2-Dev)
+- Related offline example:
+  [`examples/offline_inference/text_to_image/text_to_image.py`](../../examples/offline_inference/text_to_image/text_to_image.py)
+- Related T2T/I2T example:
+  [`examples/offline_inference/x_to_text/x_to_text.py`](../../examples/offline_inference/x_to_text/x_to_text.py)
+- Declared parameters:
+  [`vllm_omni/model_extras/mammothmodal2_preview.py`](../../vllm_omni/model_extras/mammothmodal2_preview.py)
+- Deploy config:
+  [`vllm_omni/deploy/mammoth_moda2.yaml`](../../vllm_omni/deploy/mammoth_moda2.yaml)
+
+## Hardware Support
+
+The default deploy config runs both the AR and DiT stages on a single GPU
+(`devices: "0"`). The committed `gpu_memory_utilization` split (stage-0 AR `0.5`,
+stage-1 DiT `0.3`) is sized for an ~80 GB GPU. The model also fits on a 48 GB GPU
+after rebalancing the split so the AR weights (~23 GB) leave room for the KV
+cache — see the note under *1x L40S 48GB*.
+
+## GPU
+
+### 1x L40S 48GB
+
+> **48 GB config adjustment:** the committed
+> `vllm_omni/deploy/mammoth_moda2.yaml` uses
+> `gpu_memory_utilization` 0.5 / 0.3 (sized for ~80 GB). To fit on a 48 GB L40S,
+> set the stage-0 (AR) value to `0.8` and the stage-1 (DiT) value to `0.16`
+> before running. (On an ~80 GB GPU, leave the defaults unchanged.)
+
+#### Environment
+
+- OS: Linux
+- Python: Match the repository requirements for your checkout
+- Driver / runtime: NVIDIA CUDA environment with one L40S 48 GB (verified) or an
+  ~80 GB GPU for the default config
+- vLLM version: Match the repository requirements for your checkout
+- vLLM-Omni version or commit: Use the commit you are deploying from
+
+#### Offline Commands
+
+Download the model:
+
+```bash
+hf download bytedance-research/MammothModa2-Preview --local-dir ./MammothModa2-Preview
+```
+
+Run text-to-image with the shared offline example from the repository root. The
+deploy config sets `trust_remote_code`, so no extra flag is needed. Forward the
+MammothModa2 generation parameters as a JSON object through `--extra-body`:
+
+```bash
+python examples/offline_inference/text_to_image/text_to_image.py \
+  --model ./MammothModa2-Preview \
+  --deploy-config vllm_omni/deploy/mammoth_moda2.yaml \
+  --prompt "A stylish woman riding a motorcycle in NYC, movie poster style" \
+  --height 1024 \
+  --width 1024 \
+  --extra-body '{"text_guidance_scale": 4.0, "cfg_range": [0.0, 1.0], "num_inference_steps": 50}' \
+  --output mammoth_t2i.png
+```
+
+The `--extra-body` JSON forwards MammothModa2-specific parameters into
+`OmniDiffusionSamplingParams.extra_args`. Keys are filtered against the model's
+declared `extra_body_params` (see
+[`vllm_omni/model_extras/mammothmodal2_preview.py`](../../vllm_omni/model_extras/mammothmodal2_preview.py)),
+so unknown keys for MammothModa2 are silently dropped:
+
+- `text_guidance_scale` — classifier-free guidance scale for the DiT stage
+  (default `9.0`; CFG is active only when `> 1.0`).
+- `cfg_range` — relative step range `[start, end]` over which CFG is applied
+  (default `[0.0, 1.0]`).
+- `num_inference_steps` — number of DiT denoising steps (default `50`).
+
+`--height` and `--width` must be multiples of 16.
+
+Run text-to-text through the shared understanding example. It recognizes the
+MammothModa2 checkpoint and automatically selects `mammoth_moda2_ar.yaml`:
+
+```bash
+python examples/offline_inference/x_to_text/x_to_text.py \
+  --model ./MammothModa2-Preview \
+  --prompt "Explain multimodal generation in three sentences."
+```
+
+Add an image for image-to-text or image summarization. The shared example
+uses MammothModa2's chat and vision-token template:
+
+```bash
+python examples/offline_inference/x_to_text/x_to_text.py \
+  --model ./MammothModa2-Preview \
+  --image /path/to/input.jpg \
+  --prompt "Please summarize the content of this image."
+```
+
+#### Verification
+
+The example writes the generated image to the `--output` path. Confirm the file
+exists and is a valid image:
+
+```bash
+ls -lh mammoth_t2i.png
+python -c "from PIL import Image; print(Image.open('mammoth_t2i.png').size)"
+```
+
+### 1x AMD MI300X, MammothModa2 Preview
+
+#### Environment
+
+- OS: Linux 6.8.0-134-generic, x86_64
+- Container: official ROCm image built from `docker/Dockerfile.rocm`
+- Python: 3.12.13
+- PyTorch: 2.11.0+gitd0c8b1f
+- Driver / runtime: AMD 6.19.14.31400000 / ROCm 7.2.53211
+- GPU: one AMD Instinct MI300X, `gfx942:sramecc+:xnack-`, 191.69 GiB visible HBM
+- vLLM version: 0.27.0+rocm723
+- vLLM Omni version or commit: `73e1368c7bb940efe1a025859c9d6c8eeeb2e3f0`
+- Installed vLLM Omni package metadata: `0.27.0rc2.dev44+g55abdade9.rocm`
+
+#### Offline Commands
+
+The checked run used the committed stage split, with `gpu_memory_utilization` set to 0.5 for AR and 0.3 for DiT:
+
+```bash
+python3 examples/offline_inference/text_to_image/text_to_image.py \
+    --model bytedance-research/MammothModa2-Preview \
+    --deploy-config vllm_omni/deploy/mammoth_moda2.yaml \
+    --prompt "A stylish woman riding a motorcycle in NYC, movie poster style" \
+    --height 1024 \
+    --width 1024 \
+    --seed 42 \
+    --extra-body '{"text_guidance_scale": 4.0, "cfg_range": [0.0, 1.0], "num_inference_steps": 50}' \
+    --enable-diffusion-pipeline-profiler \
+    --log-stats \
+    --output mammoth_t2i.png
+```
+
+#### Verification
+
+The first request took 85.224 seconds. The AR stage generated 4,161 visual tokens in 72.996 seconds, and the DiT stage took 12.163 seconds. AR weight loading used 21.4 GiB and took 8.250 seconds. DiT weight loading used 5.49 GiB and took 1.824 seconds. The largest one second whole device memory sample was 106.57 GiB, including the AR KV cache reserved by the 0.5 memory setting.
+
+The output was a valid 1024 by 1024 RGB PNG.
+
+## MammothModa2-Dev unified inference
+
+MammothModa2-Dev uses a Qwen3-VL AR backbone, while MammothModa2-Preview uses
+Qwen2.5-VL. vLLM-Omni selects the matching implementation from the nested
+`llm_config.model_type`; no checkpoint edits or `trust_remote_code` flag are
+required.
+
+Text-to-text and image-to-text use the AR-only deploy. Text-to-image loads the
+Qwen3 generation experts (`gen_mlp`), extra visual vocabulary and image head,
+then sends the generated visual tokens and hidden states to the DiT stage.
+
+Download the checkpoint:
+
+```bash
+hf download bytedance-research/MammothModa2-Dev --local-dir ./MammothModa2-Dev
+```
+
+Run text-to-text through the shared understanding example. It recognizes the
+Dev checkpoint as MammothModa2 and automatically selects
+`mammoth_moda2_ar.yaml`:
+
+```bash
+python examples/offline_inference/x_to_text/x_to_text.py \
+  --model ./MammothModa2-Dev \
+  --prompt "Explain multimodal generation in three sentences."
+```
+
+Add an image for image-to-text or image summarization:
+
+```bash
+python examples/offline_inference/x_to_text/x_to_text.py \
+  --model ./MammothModa2-Dev \
+  --image ./image.png \
+  --prompt "Please summarize the content of this image."
+```
+
+The Dev checkpoint is approximately 47.55 GiB on disk. In the verified AR-only
+run, loaded model weights used approximately 16.97 GiB of GPU memory before KV
+and encoder caches. Allow additional GPU memory for those caches and the input
+image.
@@ -0,0 +1,82 @@
+# MiniMax H3 disaggregated text encoder
+
+This opt-in topology runs the Qwen3-VL text encoder as a vLLM stage and sends
+its hidden states and token-role metadata to an encoder-free diffusion stage.
+The standard MiniMax H3 recipes remain single-stage and continue to load the
+text encoder inside the diffusion pipeline.
+
+## Start the server
+
+Choose the topology explicitly and load its deployment defaults:
+
+```bash
+vllm-omni serve MiniMaxAI/MiniMax-H3 \
+  --omni \
+  --deploy-config vllm_omni/deploy/minimax_h3_disaggregated.yaml
+```
+
+The default deployment assigns stage 0 to GPUs 0-1 with tensor parallel size
+2 and `max_num_seqs: 1`. Stage 1 uses GPUs 2-5 with diffusion tensor parallel
+size 1, Ulysses degree 4, and VAE patch parallel size 4. Adjust the
+`devices`, `tensor_parallel_size`, and stage 1 `parallel_config` values in a
+deployment override for the available hardware. Diffusion quantization,
+layerwise offload, distributed layerwise offload, VAE parallelism, and USP
+settings use the same stage 1 options documented in [MiniMax-H3.md](MiniMax-H3.md).
+
+For example, this five-GPU topology assigns one GPU to the encoder and four
+to the diffusion stage. `--stage-overrides` keeps placement and parallelism
+scoped to the owning stage rather than broadcasting an override to both:
+
+```bash
+CUDA_VISIBLE_DEVICES=0,1,2,3,4 \
+vllm-omni serve MiniMaxAI/MiniMax-H3 \
+  --omni \
+  --deploy-config vllm_omni/deploy/minimax_h3_disaggregated.yaml \
+  --stage-overrides '{"0":{"devices":"0","tensor_parallel_size":1},"1":{"devices":"1,2,3,4","tensor_parallel_size":1,"ulysses_degree":4,"vae_patch_parallel_size":4}}'
+```
+
+For memory-constrained deployments, start from the CPU-offload or distributed
+layerwise-offload profiles in [MiniMax-H3.md](MiniMax-H3.md). Apply memory and
+quantization options only to Stage 1 with `--stage-overrides`; retain the
+encoder's BF16 configuration and the video/audio VAEs' FP32 precision. Select
+one offload strategy per deployment:
+
+```bash
+# Stage 1 model-level CPU offload.
+--stage-overrides '{"1":{"enable_cpu_offload":true}}'
+
+# Stage 1 distributed layerwise offload. Tune resident layers for available RAM.
+--stage-overrides '{"1":{"enable_distributed_layerwise_offload":true,"dlo_use_allgather":false,"dlo_resident_layers":20}}'
+
+# Stage 1 online FP8 quantization of the DiT only.
+--stage-overrides '{"1":{"diffusion_quantization_config":"{\"transformer\":{\"method\":\"fp8\"}}"}}'
+```
+
+The Stage 1 VAE patch-parallel options remain independent of offload and
+quantization. See [MiniMax-H3.md](MiniMax-H3.md) for memory requirements and
+hardware-qualified profiles before combining these options.
+
+Stage 1 sets `model_loaded.text_encoder: false`; it must not load or download
+text-encoder weights. This H3 topology explicitly keeps its single-replica
+diffusion stage inline, avoiding serialization of decoded video through a
+subprocess. It expects both stages to run in one deployment; cross-node payload
+transport is outside this configuration.
+
+The `/v1/videos` request schema and `extra_params.task` values (`t2va`,
+`fl2va`, and `ref2va`) are unchanged from the single-stage recipe.
+
+## Turbo LoRA
+
+MiniMax-H3 Turbo uses five sigma points, `flow_shift=6`, and
+`audio_flow_shift=3`. Start Turbo deployments with the dedicated defaults so
+requests that omit sampling controls do not inherit the 50-step base schedule:
+
+```bash
+vllm-omni serve MiniMaxAI/MiniMax-H3 \
+  --omni \
+  --lora-path /path/to/MiniMax-H3-Turbo \
+  --deploy-config vllm_omni/deploy/minimax_h3_disaggregated_turbo.yaml
+```
+
+The base deployment intentionally retains 50 inference steps for non-LoRA
+quality. Turbo LoRA supports T2VA and FL2VA requests, not Ref2VA.
@@ -860,6 +860,63 @@ fixed HBM usage. Model-level and standard layerwise offload remain unsupported.
 The five requested sigma points produce the four denoiser evaluations expected
 by the Turbo artifact.

+### FlashGen native LoRA
+
+The FlashGen 4-step T2VA artifact uses the native MiniMax-H3 module layout and
+declares its distilled sigma schedule in safetensors metadata. It is published on
+[ModelScope](https://modelscope.cn/models/FlashGen/Minimax-H3-4step-lora-flashgen):
+
+```text
+FlashGen/Minimax-H3-4step-lora-flashgen/minimax_h3_t2va_flashgen_4step_v1.0_768p_bf16.safetensors
+```
+
+Download only that file:
+
+```bash
+python -m pip install modelscope
+export FLASHGEN_DIR=/path/to/minimax-h3-flashgen-lora
+export FLASHGEN_FILE=minimax_h3_t2va_flashgen_4step_v1.0_768p_bf16.safetensors
+modelscope download FlashGen/Minimax-H3-4step-lora-flashgen \
+  --local_dir "${FLASHGEN_DIR}" \
+  --include "${FLASHGEN_FILE}"
+export FLASHGEN_LORA="${FLASHGEN_DIR}/${FLASHGEN_FILE}"
+```
+
+Start from a non-offloaded or DLO FL2VA server command and add
+`--task-type fl2va --lora-backend peft --lora-path "${FLASHGEN_LORA}"`.
+Each request must use T2VA and the distilled interval-count contract:
+
+```bash
+-F 'num_inference_steps=4' \
+-F 'extra_params={"task":"t2va","duration":5.2}' \
+-F "lora={\"name\":\"h3-flashgen-v1.0\",\"path\":\"${FLASHGEN_LORA}\",\"scale\":1.0}"
+```
+
+This path rejects Ref2VA and checkpoints that already pin `base_schedule` in
+`model_index.json`. The adapter metadata carries
+`base_schedule=1.0,0.7,0.4,0.15,0.0`, so `num_inference_steps=4` means four
+denoiser evaluations, not five sigma points. Request-mode generation may omit
+the field and take the count from the adapter schedule; `--step-execution`
+requires it explicitly, because the step scheduler reads the total step count
+off the request at admission, before the adapter schedule is known.
+
+DLO is supported in request-mode generation on the same terms as the Turbo
+adapter: the request-switchable LoRA A/B buffers stay resident on the
+accelerator while DLO streams only the base blocks, so budget for that
+additional fixed HBM usage. The native artifact is rank 64 over 259 target
+modules, and its packed `qkv_proj` and `fc1` layers reuse the full-input A
+tensor per slice while B carries slice-local output rows, so the resident
+footprint exceeds the on-disk payload; measure it for your parallel layout
+rather than assuming the checkpoint size. Pure Ulysses replicates the adapter
+on every rank, while DiT tensor parallelism shards the B buffers. Model-level
+and standard layerwise offload remain unsupported, and `--step-execution`
+cannot be combined with `--enable-distributed-layerwise-offload`.
+
+To validate a deployment, post the same fixed-seed T2VA request twice with the
+adapter and twice without it, then compare the four output digests. The adapter
+is bound and deterministic when each pair matches internally and the two pairs
+differ from each other.
+
 ## Key parameters

 | Parameter | Recommended value | Notes |
@@ -1,7 +1,7 @@
 # NemotronLabs VoiceChat 11B

-> Offline speech-to-speech: a 16 kHz user utterance in, the agent's spoken reply
-> (text + 22.05 kHz WAV) out, on a 3-stage vLLM-Omni pipeline.
+> Speech-to-speech on a 3-stage vLLM-Omni pipeline: offline single-turn
+> inference and experimental model-native, frame-locked Realtime serving.

 ## Summary

@@ -11,8 +11,8 @@
  timeline: a Conformer + NemotronH hybrid-Mamba thinker emits one text token
  per acoustic frame, a Gemma3-1B EAR-TTS talker turns the text timeline into
  31-quantizer RVQ code stacks, and an RVQ-VAE codec decodes them to audio.
- Mode: Offline single-turn inference (batch=1). Online/duplex serving,
-  function calling, and batch>1 are documented follow-ups.
+- Mode: Offline single-turn inference (batch=1), plus experimental native
+  duplex Realtime serving at one 80 ms microphone frame per scheduler wake.
 - Maintainer: [`@yuekaizhang`](https://github.com/yuekaizhang)

 ## When to use this recipe
@@ -33,6 +33,11 @@ NeMo modules (`nemo_vendored/`), so no `nemo_toolkit` install is needed.
  [`vllm_omni/model_executor/models/nemotron_voicechat/`](../../vllm_omni/model_executor/models/nemotron_voicechat/)
 - Staged pipeline config:
  [`vllm_omni/deploy/nemotron_labs_voicechat.yaml`](../../vllm_omni/deploy/nemotron_labs_voicechat.yaml)
+- Native duplex config and E2E probe:
+  [`vllm_omni/deploy/nemotron_labs_voicechat_duplex.yaml`](../../vllm_omni/deploy/nemotron_labs_voicechat_duplex.yaml),
+  [`tests/e2e/online_serving/nemotron_voicechat_realtime_duplex.py`](../../tests/e2e/online_serving/nemotron_voicechat_realtime_duplex.py)
+- Nightly model-level gate:
+  [`tests/e2e/online_serving/test_nemotron_voicechat_duplex.py`](../../tests/e2e/online_serving/test_nemotron_voicechat_duplex.py)
 - Upstream model card:
  [`nvidia/NVIDIA-NemotronLabs-VoiceChat-11B`](https://huggingface.co/nvidia/NVIDIA-NemotronLabs-VoiceChat-11B)
 - Reference implementation: NVIDIA-NeMo/Speech, branch `nemotron-labs-voicechat`
@@ -60,7 +65,7 @@ output stayed within one word of the reference in testing.

 - OS: Linux
 - Python: 3.12
- vLLM version: 0.26.0
+- vLLM version: 0.27.0
 - vLLM-Omni version or commit: this PR / current `main`

 #### Command
@@ -88,6 +93,55 @@ The reply text should read as a coherent spoken-style answer to the question in
 the input WAV, and the WAV should transcribe to (approximately) the same text
 with any ASR model.

+#### Native duplex serving
+
+The checkpoint does not contain the underlying Nemotron text tokenizer. Set
+`NEMOTRON_VOICECHAT_LLM_PATH` to a local snapshot of
+`nvidia/NVIDIA-Nemotron-Nano-9B-v2` before starting an offline deployment.
+
+```bash
+export HF_HUB_OFFLINE=1
+export TRANSFORMERS_OFFLINE=1
+export NEMOTRON_VOICECHAT_LLM_PATH=/path/to/NVIDIA-Nemotron-Nano-9B-v2
+
+vllm-omni serve /path/to/NVIDIA-NemotronLabs-VoiceChat-11B \
+  --omni \
+  --served-model-name nemotron-voicechat \
+  --deploy-config vllm_omni/deploy/nemotron_labs_voicechat_duplex.yaml
+```
+
+The single-GPU profile runs three eager fp32 engine processes on one device.
+
+In another shell, stream only the user channel of one of NVIDIA's bundled
+stereo conversation fixtures:
+
+```bash
+python tests/e2e/online_serving/nemotron_voicechat_realtime_duplex.py \
+  --model nemotron-voicechat \
+  --input-wav /path/to/NVIDIA-NemotronLabs-VoiceChat-11B/turn_taking.wav \
+  --input-channel 0 \
+  --output-dir results/nemotron_voicechat_duplex \
+  --timeout-s 600
+```
+
+The probe requires a completed response, non-silent 22.05 kHz output, and the
+advertised native 80 ms append capabilities. Add `--expect-function-call` and
+use `tool_call.wav` to validate the function-call channel. To validate the
+Realtime tool round trip, also provide the expected arguments and return a
+tool result:
+
+```bash
+python tests/e2e/online_serving/nemotron_voicechat_realtime_duplex.py \
+  --model nemotron-voicechat \
+  --input-wav /path/to/NVIDIA-NemotronLabs-VoiceChat-11B/tool_call.wav \
+  --output-dir results/nemotron_voicechat_tool_call \
+  --expect-function-call \
+  --expected-function-name generate_random_number \
+  --expected-function-arguments '{"min":1,"max":50}' \
+  --function-output 20 \
+  --expected-post-tool-text "random number"
+```
+
 #### Notes

 - Memory usage: the shipped yaml runs all three stages on one GPU
@@ -109,5 +163,14 @@ with any ASR model.
  instead of pausing.
 - The talker's `max_tokens` is 16383 (its stage prompt is one placeholder
  token, and the stage context is 16384).
- Known limitations: batch=1 only; offline single turn (no online/duplex
-  serving yet); the function-call channel is decoded but not yet acted on.
+- The 80 ms frame period is a model/protocol contract, not a throughput
+  guarantee. In an eager fp32 H200 profile, Stage 2 emitted 80 ms packets at
+  roughly 0.87--0.88 s intervals (RTF about 10.8--10.9); placing the three
+  stages on separate GPUs did not remove the serial 8-step EAR-TTS bottleneck.
+  The current native duplex path is functionally streaming but not wall-clock
+  realtime.
+- Known limitations: batch=1 only. The native duplex deployment allows one
+  active session and does not support barge-in. Tool execution remains
+  client-owned: the server emits function-call events, accepts a validated
+  `function_call_output`, and resumes the live model with the returned result;
+  it does not execute arbitrary tools itself.
@@ -18,11 +18,11 @@ examples in this repository.

 Qwen3-TTS supports three task types, each backed by a dedicated model checkpoint:

-| Task Type     | Model                                    | Description                                                  |
-| ------------- | ---------------------------------------- | ------------------------------------------------------------ |
-| `CustomVoice` | `Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice`  | Predefined speaker voices with optional style/emotion control |
-| `VoiceDesign` | `Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign`  | Generate speech from a natural language voice description     |
-| `Base`        | `Qwen/Qwen3-TTS-12Hz-1.7B-Base`         | Voice cloning from reference audio + transcript              |
+| Task Type     | Model                                    | Description                                                   |
+| ------------- | ---------------------------------------- | ------------------------------------------------------------- |
+| `CustomVoice` | `Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice`   | Predefined speaker voices with optional style/emotion control |
+| `VoiceDesign` | `Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign`   | Generate speech from a natural language voice description     |
+| `Base`        | `Qwen/Qwen3-TTS-12Hz-1.7B-Base`          | Voice cloning from reference audio + transcript               |

 Smaller 0.6B variants are also available for `CustomVoice` and `Base`.

@@ -166,6 +166,15 @@ python examples/offline_inference/text_to_speech/qwen3_tts/end2end.py --query-ty
 - Key flags: `--omni` is required. `--deploy-config` points to the bundled two-stage pipeline config.
 - Async chunking: Enabled by default in `qwen3_tts.yaml` for streaming-friendly first-audio latency. Raw audio streaming requires `stream=true`, `stream_format="audio"`, and `response_format="pcm"`.
 - Task/model matching: Each task type requires its matching model checkpoint. Using a CustomVoice model for a Base (voice clone) request will fail.
+- Base codec termination: Base requests without an explicit `max_new_tokens`
+  use a text-scaled safety ceiling (at least 192 codec frames and no more than
+  the configured model limit). If the Talker reaches that ceiling without
+  codec EOS, non-streaming serving discards the incomplete audio and retries
+  once with a fresh seed; an explicit `seed` or `max_new_tokens` disables the
+  retry. SSE and WebSocket clients receive structured errors and must discard
+  previously emitted audio when the error contains `"action":"discard"`;
+  raw PCM streams terminate with a connection error after any partial bytes
+  already sent.
 - Known limitations: The server serves one model variant at a time. To switch task types (e.g., CustomVoice to Base), restart the server with the corresponding model.

 ## Hardware Support
@@ -87,7 +87,7 @@ recipes/
 | [`Wan-AI/Wan2.1-VACE.md`](./Wan-AI/Wan2.1-VACE.md) | Unified T2V, I2V, V2LF, FLF2V, inpaint, and R2V | 1x RTX 5090 (1.3B) / 1x L40S 48GB with layerwise offload (14B) |
 | [`XiaomiMiMo/MiMo-Audio.md`](./XiaomiMiMo/MiMo-Audio.md) | Offline + online omni audio (TTS, ASR, dialogue) | 1x RTX 5090 / 5090D 32GB |
 | [`StabilityAI/Stable-Diffusion-3.5.md`](./StabilityAI/Stable-Diffusion-3.5.md) | Text-to-image serving (SD 3.5-medium and SD 3.5-large) | 1x RTX A6000 48GB |
-| [`zai-org/GLM-TTS.md`](./zai-org/GLM-TTS.md) | Online serving for Chinese/English zero-shot voice-cloned TTS | 1x A40 48GB |
+| [`zai-org/GLM-TTS.md`](./zai-org/GLM-TTS.md) | Online serving for Chinese/English zero-shot voice-cloned TTS | 1x A40 48GB / 2x non-standard RTX 4090 48GB (49,140 MiB each) |
 | [`GLM/GLM-Image.md`](./GLM/GLM-Image.md) | Online serving for image generation | 1x A800 80GB / 2x A800 80GB |
 | [`JD/JoyAI-VL-Interaction.md`](./JD/JoyAI-VL-Interaction.md) | Real-time streaming video-language interaction (proactive speak/silence/delegate) | 1x GPU 24GB+ |

@@ -1,4 +1,4 @@
-# GLM-TTS for Chinese/English TTS on 1x GPU
+# GLM-TTS for Chinese/English TTS

 ## Summary

@@ -103,3 +103,137 @@ python examples/online_serving/text_to_speech/glm_tts/openai_speech_client.py \
 - Key flags: `--omni` is required; `--trust-remote-code` is needed for the GLM-TTS phoneme tokenizer; the DiT stage enables bucketed model-internal CUDA graphs with eager fallback.
 - Voice cloning: requires `ref_audio` + `ref_text` together. Reference audio should be 3-10 seconds. Feature extraction (WhisperVQ tokenizer, CampPlus ONNX, mel) runs on the model side.
 - Known limitations: First request may be slow due to lazy model loading (WhisperVQ, CampPlus ONNX). Warm-cache RTF is approximately 0.6-0.7x on A40.
+
+### 2x non-standard RTX 4090 48GB
+
+This configuration was personally validated on two non-standard RTX 4090 cards
+reporting 49,140 MiB each in `nvidia-smi`. Stock 24GB RTX 4090 cards were not
+validated.
+
+#### Environment
+
+- OS: Ubuntu 22.04.1 LTS
+- NVIDIA driver: 580.76.05
+- Python: 3.12.13
+- PyTorch: 2.11.0+cu130
+- CUDA runtime and JIT toolkit: 13.0
+- vLLM: 0.26.0
+- vLLM-Omni: commit `66a9b0c8`, version
+  `0.26.0rc2.dev5+g66a9b0c84`
+- FlashInfer: 0.6.14
+
+The host's default `nvcc` was 11.8. The working CUDA toolkit came from the
+Python environment, so `CUDA_HOME` was set to
+`$VIRTUAL_ENV/lib/python3.12/site-packages/nvidia/cu13`.
+
+#### Command
+
+From the repository root, start the async-chunk server with:
+
+```bash
+export CUDA_HOME="$VIRTUAL_ENV/lib/python3.12/site-packages/nvidia/cu13"
+export PATH="$CUDA_HOME/bin:$PATH"
+export LD_LIBRARY_PATH="$CUDA_HOME/lib:${LD_LIBRARY_PATH:-}"
+
+CUDA_VISIBLE_DEVICES=0,1 vllm-omni serve zai-org/GLM-TTS \
+  --omni \
+  --trust-remote-code \
+  --port 8091 \
+  --deploy-config vllm_omni/deploy/glm_tts.yaml \
+  --stage-overrides '{"1":{"devices":"1"}}' \
+  --allowed-local-media-path "${PWD}/tests/assets"
+```
+
+Stage 0 runs on GPU 0, while the override places stage 1 on GPU 1. For the sync
+variant, add `--no-async-chunk` to the same command.
+
+#### Verification
+
+Run the Chinese voice-cloning check:
+
+```bash
+python examples/online_serving/text_to_speech/glm_tts/openai_speech_client.py \
+  --model zai-org/GLM-TTS \
+  --text "今天天气很好，我们一起学习语音模型推理。" \
+  --ref-audio "file://${PWD}/tests/assets/glm_tts/jiayan_zh.wav" \
+  --ref-text "他当时还跟线下其他的站姐吵架，然后，打架进局子了。" \
+  --response-format wav \
+  --output glm_tts_zh.wav
+```
+
+Run the English voice-cloning check with the same Chinese reference:
+
+```bash
+python examples/online_serving/text_to_speech/glm_tts/openai_speech_client.py \
+  --model zai-org/GLM-TTS \
+  --text "Hello, this is a voice cloning test with GLM-TTS." \
+  --ref-audio "file://${PWD}/tests/assets/glm_tts/jiayan_zh.wav" \
+  --ref-text "他当时还跟线下其他的站姐吵架，然后，打架进局子了。" \
+  --response-format wav \
+  --output glm_tts_en.wav
+```
+
+Inspect either output:
+
+```bash
+ffprobe -v error -select_streams a:0 \
+  -show_entries stream=codec_name,sample_rate,channels \
+  -of default=noprint_wrappers=1 glm_tts_zh.wav
+```
+
+Expected output:
+
+```text
+codec_name=pcm_s16le
+sample_rate=24000
+channels=1
+```
+
+The English smoke test produced a valid WAV with an actual duration of 5.020
+seconds.
+
+#### Observed warm-cache results
+
+These observations are from one controlled run, not a hardware or performance
+guarantee. Cold-start and JIT costs were excluded. Values are mean +/- standard
+deviation.
+
+##### Measurement procedure
+
+- For the single-GPU server, use the documented command with
+  `CUDA_VISIBLE_DEVICES=0` and omit `--stage-overrides`; both bundled stages
+  remain on GPU 0. For the dual-GPU server, use the command as shown.
+- Async chunking is the default. Add `--no-async-chunk` for sync mode.
+- For non-streaming measurements, use the exact Chinese client command above.
+  For streaming, add `--stream` to that command and change the output filename
+  to `glm_tts_zh.pcm`; streaming output is raw PCM, not WAV.
+- The fixed Chinese prompt and reference were exactly those shown in
+  Verification and yielded 3.900 seconds of audio. Each case used one warm-up
+  followed by three measured requests at concurrency 1.
+- Wall time starts immediately before the HTTP POST and stops after the complete
+  response body. TTFA runs from immediately before the POST to the first
+  non-empty audio bytes. RTF is wall time divided by audio duration.
+- Peak total device memory used was sampled every 0.2 seconds and reported by
+  NVML via `nvmlDeviceGetMemoryInfo.used`. The readings include background and
+  baseline use and are not baseline-subtracted.
+
+| GPUs | Mode | Response | Wall time (s) | TTFA (s) | RTF |
+|---:|---|---|---:|---:|---:|
+| 1 | Async | Non-streaming | 2.144 +/- 0.076 | -- | 0.550 +/- 0.019 |
+| 2 | Async | Non-streaming | 1.969 +/- 0.130 | -- | 0.505 +/- 0.033 |
+| 1 | Async | Streaming | 2.135 +/- 0.093 | 1.245 +/- 0.178 | 0.548 +/- 0.024 |
+| 2 | Async | Streaming | 1.891 +/- 0.006 | 1.170 +/- 0.042 | 0.485 +/- 0.002 |
+| 1 | Sync | Non-streaming | 1.822 +/- 0.016 | -- | 0.467 +/- 0.004 |
+| 2 | Sync | Non-streaming | 1.792 +/- 0.023 | -- | 0.459 +/- 0.006 |
+
+Moving stage 1 to GPU 1 reduced async non-streaming wall time by 8.2%. It
+reduced async streaming wall time by 11.4% and TTFA by 6.0%, and reduced sync
+wall time by 1.6%. Single- and dual-GPU outputs were bitwise identical only
+within matching execution and response modes.
+
+| Configuration | GPU 0 | GPU 1 |
+|---|---:|---:|
+| Single-GPU async | 31.52 GiB | -- |
+| Dual-GPU async | 29.23 GiB | 2.91 GiB |
+| Single-GPU sync | 31.47 GiB | -- |
+| Dual-GPU sync | 29.23 GiB | 2.91 GiB |
@@ -13,6 +13,8 @@ transformers >= 5.10.1, < 5.15
 av>=14.0.0
 omegaconf>=2.3.0
 diffusers==0.40.0
+# Hub kernels, including LTX-2.5 NATTEN
+kernels==0.14.1
 safetensors>=0.8.0
 accelerate==1.12.0
 soundfile>=0.13.1
@@ -0,0 +1,143 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
+from __future__ import annotations
+
+import hashlib
+import json
+import sys
+from types import SimpleNamespace
+
+import pytest
+
+from vllm_omni.benchmarks.duplex import omni_duplex_eval_eval as eval_module
+from vllm_omni.benchmarks.duplex.omni_duplex_eval_clock import (
+    extract_timed_sentences,
+    normalize_response_items,
+    split_text,
+    validate_clock,
+)
+from vllm_omni.benchmarks.duplex.omni_duplex_eval_dataset import (
+    DuplexSample,
+    canonical_task_type,
+    family_for_split,
+    load_samples,
+    task_type_for_split,
+)
+from vllm_omni.benchmarks.duplex.omni_duplex_eval_metrics import (
+    PROTOCOL_PIN,
+    build_content_prompt,
+    build_reminder_prompt,
+    build_temporal_prompt,
+    parse_judge_json,
+    reminder_window,
+    summarize_pr_results,
+    temporal_window,
+)
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
+
+
+def test_dataset_mapping_and_manifest(tmp_path):
+    manifest = tmp_path / "samples.json"
+    manifest.write_text(json.dumps([{"id": "a", "split": "RTD_OCR", "video": "clip.mp4"}]), encoding="utf-8")
+    sample = load_samples(manifest, media_root=tmp_path)[0]
+    assert sample.family == "rtd"
+    assert sample.video == str(tmp_path / "clip.mp4")
+    assert family_for_split("PR_event_reminder") == "pr"
+    assert canonical_task_type("post-event-reminder") == "post_event_reminder"
+    assert task_type_for_split("PR_correction") == "correction"
+    assert load_samples([{"id": "pr", "split": "PR_correction"}])[0].task_type == "correction"
+
+
+def test_hugging_face_subset_names_are_splits(monkeypatch):
+    calls = []
+
+    def fake_load_dataset(name, **kwargs):
+        calls.append((name, kwargs))
+        return [{"id": "pr", "question_text": "Correct this."}]
+
+    monkeypatch.setitem(sys.modules, "datasets", SimpleNamespace(load_dataset=fake_load_dataset))
+    sample = load_samples("Hothan/Omni-DuplexEval", split="PR_correction")[0]
+    assert calls == [("Hothan/Omni-DuplexEval", {"split": "PR_correction"})]
+    assert sample.split == "PR_correction"
+    assert sample.task_type == "correction"
+
+
+def test_response_aliases_and_clock_guard():
+    assert split_text("One. Two!") == ["One.", "Two!"]
+    assert normalize_response_items({"chunks": [{"text": "x", "current_time": 800}]}) == [
+        {"sentence": "x", "start": 800.0, "end": 800.0}
+    ]
+    with pytest.raises(ValueError, match="clock=invalid"):
+        validate_clock({"clock": "invalid"})
+    validate_clock({"clock": "invalid"}, allow_invalid=True)
+    timed = extract_timed_sentences([{"type": "response.output_text.delta", "delta": "Done.", "_media_clock_ms": 800}])
+    assert [item.as_dict() for item in timed] == [{"sentence": "Done.", "start": 0.8, "end": 0.8}]
+    with pytest.raises(ValueError, match="only clock=media"):
+        extract_timed_sentences([], clock="wall")
+
+
+def test_protocol_windows_and_parsing():
+    assert PROTOCOL_PIN == "ca3c122b4d4bf67afd6b18ea5e724b4561bdde48"
+    assert temporal_window(4, 5, 10) == (2.0, 3.0)
+    assert temporal_window(1, 1.2, 10) == (0.0, 0.5)
+    assert reminder_window(5) == (5.0, 15.0)
+    assert parse_judge_json('noise {"content_score": 2.345, "is_relevant": 1}') == {
+        "content_score": 2.35,
+        "is_relevant": 1,
+    }
+    assert parse_judge_json('noise "success_score": 1') == {"success_score": 1}
+    assert summarize_pr_results([{"task_type": "correction", "all_success": 1}])["mean_all_success"] == 1.0
+
+
+def test_protocol_prompt_wording_is_pinned():
+    prompts = [
+        build_temporal_prompt(1, 2, "response", "question"),
+        build_content_prompt("response", "question", ["one", "two"]),
+        build_reminder_prompt("instruction", "response", "proactive_reminder"),
+        build_reminder_prompt("instruction", "response", "post_event_reminder"),
+        build_reminder_prompt("instruction", "response", "correction", "answer"),
+    ]
+    assert [hashlib.sha256(prompt.encode()).hexdigest() for prompt in prompts] == [
+        "5f7f5fcb4a37e4d2eea8ba23beabf74630e6fc2fb5b2661ceb94403254bb04d5",
+        "1fccbed6c1ed8ea1c76d5564d7289ed6adca3678f6a0da2cc735942123d64457",
+        "d0a0e3a9d873d2f368c5c99b9fe46681f683cc36a9fe0e09b99d6cbca1d6dd0c",
+        "08f9951243c859d271f82ee53da22cd0bdab1fe93c6ca04f4870d3b959c0803a",
+        "3de131a7e36cb4b8ac369bb05e87ede518be20221e214df3644211c179131a73",
+    ]
+
+
+def test_frame_sample_content_passes_frames(tmp_path, monkeypatch):
+    response = tmp_path / "response.json"
+    response.write_text(json.dumps([{"sentence": "A person moves.", "start": 1, "end": 2}]), encoding="utf-8")
+    response.with_name("response.meta.json").write_text(json.dumps({"clock": "media"}), encoding="utf-8")
+    video = tmp_path / "video.mp4"
+    video.write_bytes(b"video")
+    monkeypatch.setattr(eval_module, "extract_jpeg", lambda *args, **kwargs: b"\xff\xd8jpeg")
+
+    class Judge:
+        def temporal(self, *args, **kwargs):
+            return '{"temporal_score": 3, "is_relevant": 1}'
+
+        def content(self, prompt, video, frames=None, *, mode="video_url"):
+            assert mode == "frame-sample"
+            assert frames == [b"\xff\xd8jpeg", b"\xff\xd8jpeg"]
+            return '{"content_score": 3}'
+
+    sample = DuplexSample(
+        id="rtd",
+        split="RTD_OCR",
+        family="rtd",
+        task_type=None,
+        video=video,
+        video_duration=4,
+    )
+    score = eval_module.evaluate_sample(
+        sample,
+        response,
+        tmp_path / "score.json",
+        Judge(),
+        judge_video_mode="frame-sample",
+    )
+    assert score["content"]["frame_count"] == 2
@@ -279,17 +279,79 @@ def test_unmeasured_tpot_stays_missing_after_tokenizer_fallback():
    assert metrics.request_goodput == 0.0


-def test_measured_zero_itl_is_not_treated_as_missing():
+def test_zero_itl_does_not_create_zero_tpot():
    output = _make_output(100, output_tokens=3)
    output.itl = [0.0, 0.0]
    output.duplex_session_metrics = {"mean_ttft_ms": 100.0}

    metrics = _calculate_test_metrics([output], {"tpot": 1.0})

-    assert (metrics.num_tpot_samples, metrics.num_itl_samples) == (1, 2)
-    assert metrics.mean_tpot_ms == 0.0
+    assert (metrics.num_tpot_samples, metrics.num_itl_samples) == (0, 2)
+    assert math.isnan(metrics.mean_tpot_ms)
    assert metrics.mean_itl_ms == 0.0
-    assert metrics.request_goodput == 1.0
+    assert metrics.request_goodput == 0.0
+
+
+def test_stage_output_tokens_without_client_timing_omit_tpot(capsys):
+    output = _make_output(100, output_tokens=5)
+    output.itl = []
+    output.text_latency = 0.0
+    output.ttft = 0.1
+
+    metrics, _ = calculate_metrics(
+        input_requests=[],
+        outputs=[output],
+        dur_s=1.0,
+        tokenizer=None,
+        selected_percentiles=[50.0, 99.0],
+        goodput_config_dict={},
+        task_type=TaskType.GENERATION,
+        selected_percentile_metrics=["tpot"],
+        max_concurrency=None,
+        request_rate=float("inf"),
+        benchmark_duration=1.0,
+    )
+
+    printed = capsys.readouterr().out
+    assert metrics.num_tpot_samples == 0
+    assert math.isnan(metrics.mean_tpot_ms)
+    assert "Time per Output Token" not in printed
+    assert "Mean TPOT" not in printed
+
+
+def test_consistent_client_latency_can_supply_tpot_fallback():
+    output = _make_output(100, output_tokens=10)
+    output.itl = []
+    output.ttft = 0.1
+    output.text_latency = 1.0
+
+    metrics = _calculate_test_metrics([output])
+
+    assert metrics.num_tpot_samples == 1
+    assert metrics.mean_tpot_ms == pytest.approx(100.0)
+
+
+def test_single_token_responses_do_not_report_zero_tpot(capsys):
+    output = _make_output(100, output_tokens=1)
+
+    metrics, _ = calculate_metrics(
+        input_requests=[],
+        outputs=[output],
+        dur_s=1.0,
+        tokenizer=None,
+        selected_percentiles=[50.0],
+        goodput_config_dict={},
+        task_type=TaskType.GENERATION,
+        selected_percentile_metrics=["tpot"],
+        max_concurrency=None,
+        request_rate=float("inf"),
+        benchmark_duration=1.0,
+    )
+
+    printed = capsys.readouterr().out
+    assert metrics.num_tpot_samples == 0
+    assert math.isnan(metrics.mean_tpot_ms)
+    assert "Time per Output Token" not in printed


 def test_duplex_goodput_does_not_pair_measurements_from_different_requests():
@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project

 """
 Unit tests for patch.py
@@ -17,6 +17,7 @@ from vllm.benchmarks.lib.endpoint_request_func import RequestFuncInput

 from vllm_omni.benchmarks.patch.patch import (
    MixRequestFuncOutput,
+    _apply_stage0_token_timings,
    async_request_openai_chat_omni_completions,
    async_request_openai_realtime_duplex,
 )
@@ -102,7 +103,24 @@ async def test_seed_tts_realtime_duplex_exports_per_request_metrics(monkeypatch)
                received_at_s=now + 0.03,
            )
            self.events.add(
-                {"type": "response.done", "response": {"id": response_id}},
+                {
+                    "type": "response.done",
+                    "response": {
+                        "id": response_id,
+                        "metadata": {
+                            "vllm_omni": {
+                                "stage_metrics": {
+                                    "0": {
+                                        "num_tokens_out": 3,
+                                        "vllm_ttft_ms": 20.0,
+                                        "vllm_tpot_ms": 10.0,
+                                        "vllm_itls_ms": [10.0, 10.0],
+                                    }
+                                }
+                            }
+                        },
+                    },
+                },
                received_at_s=now + 0.04,
            )

@@ -143,6 +161,7 @@ async def test_seed_tts_realtime_duplex_exports_per_request_metrics(monkeypatch)
    assert client.configure_kwargs["native_duplex"] is False
    assert client.configure_kwargs["extra_body"] == {
        "ref_audio": "data:audio/wav;base64,AAAA",
+        "return_stage_metrics": True,
    }
    assert client.sent == [
        event
@@ -167,6 +186,10 @@ async def test_seed_tts_realtime_duplex_exports_per_request_metrics(monkeypatch)
    assert output.audio_ttfp > output.ttft
    assert output.audio_rtf > 0
    assert output.latency > 0
+    assert output.output_tokens == 12
+    assert output.itl == [0.01, 0.01] * 4
+    assert output.text_latency == pytest.approx(output.ttft + 0.08)
+    assert output.tpot_measured is True
    assert output.tts_turn_pcm_bytes == [b"\x00\x00" * 2400] * 4
    assert output.tts_output_pcm_bytes == b"\x00\x00" * 9600
    session_id = output.duplex_request_metrics[0]["session_id"]
@@ -198,6 +221,54 @@ async def test_seed_tts_realtime_duplex_exports_per_request_metrics(monkeypatch)
    }


+def test_stage0_token_timings_use_weighted_tpot_when_itls_are_incomplete():
+    output = MixRequestFuncOutput(ttft=0.1)
+
+    measured = _apply_stage0_token_timings(
+        output,
+        [
+            {"output_token_count": 3, "itls_ms": [], "tpot_ms": 10.0},
+            {"output_token_count": 2, "itls_ms": [], "tpot_ms": 40.0},
+        ],
+        expected_output_tokens=5,
+    )
+
+    assert measured is True
+    assert output.itl == []
+    assert output.tpot_measured is True
+    assert (output.text_latency - output.ttft) / 4 == pytest.approx(0.02)
+
+
+def test_stage0_zero_itls_fall_back_to_positive_tpot():
+    output = MixRequestFuncOutput(ttft=0.1)
+
+    measured = _apply_stage0_token_timings(
+        output,
+        [{"output_token_count": 3, "itls_ms": [0.0, 0.0], "tpot_ms": 25.0}],
+        expected_output_tokens=3,
+    )
+
+    assert measured is True
+    assert output.itl == []
+    assert output.tpot_measured is True
+    assert (output.text_latency - output.ttft) / 2 == pytest.approx(0.025)
+
+
+def test_stage0_token_count_without_timing_is_not_measured():
+    output = MixRequestFuncOutput(ttft=0.1)
+
+    measured = _apply_stage0_token_timings(
+        output,
+        [{"output_token_count": 3, "itls_ms": [], "tpot_ms": None}],
+        expected_output_tokens=3,
+    )
+
+    assert measured is False
+    assert output.itl == []
+    assert output.text_latency == output.ttft
+    assert output.tpot_measured is False
+
+
 def create_sse_chunk(data_dict):
    """Helper to create SSE formatted chunk"""
    return f"data: {json.dumps(data_dict)}\n\n".encode()
@@ -1,3 +1,6 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
 """Tests for Daily-Omni ``--daily-omni-pack-mode`` packing recipes.

 The ``minicpm-interleave`` mode is an accuracy-critical protocol: MiniCPM-o only reaches
@@ -27,6 +30,8 @@ _MODULE_NAME = "vllm_omni.benchmarks.data_modules.daily_omni_dataset"

 if _MODULE_NAME not in sys.modules:
    _spec = importlib.util.spec_from_file_location(_MODULE_NAME, _MODULE_PATH)
+    if _spec is None or _spec.loader is None:
+        raise ImportError(f"Cannot load {_MODULE_NAME} from {_MODULE_PATH}")
    _mod = importlib.util.module_from_spec(_spec)
    sys.modules[_MODULE_NAME] = _mod
    _spec.loader.exec_module(_mod)
@@ -87,7 +92,9 @@ def _stub_extract(monkeypatch, dataset: DailyOmniDataset, counter: list[int]) ->
    def fake_extract(video_path, *, audio_path, include_audio, **_kw):
        counter.append(1)
        frames = [Image.new("RGB", (16, 16), color=(i, i, i)) for i in range(_NUM_FRAMES)]
-        segments = [np.zeros(1600, dtype=np.float32) for _ in range(_NUM_FRAMES)] if include_audio else []
+        segments: list[np.ndarray] = (
+            [np.zeros(1600, dtype=np.float32) for _ in range(_NUM_FRAMES)] if include_audio else []
+        )
        return frames, segments

    monkeypatch.setattr(dataset, "_extract_minicpm_frame_audio_segments", fake_extract)
@@ -585,7 +585,7 @@ async def test_adapter_requires_and_forwards_exact_prepared_payload(tmp_path: Pa

@pytest.mark.asyncio
@pytest.mark.parametrize(
-    ("request_metrics", "expected_itl", "expected_text_latency"),
+    ("request_metrics", "expected_itl", "expected_text_latency", "expected_tpot_measured"),
    [
        (
            [
@@ -594,6 +594,7 @@ async def test_adapter_requires_and_forwards_exact_prepared_payload(tmp_path: Pa
            ],
            [0.01, 0.02, 0.03],
            0.16,
+            True,
        ),
        (
            [
@@ -602,6 +603,7 @@ async def test_adapter_requires_and_forwards_exact_prepared_payload(tmp_path: Pa
            ],
            [],
            0.18,
+            True,
        ),
        (
            [
@@ -610,6 +612,7 @@ async def test_adapter_requires_and_forwards_exact_prepared_payload(tmp_path: Pa
            ],
            [],
            0.1,
+            False,
        ),
    ],
 )
@@ -619,6 +622,7 @@ async def test_adapter_reports_exact_or_weighted_token_timing(
    request_metrics: list[dict[str, object]],
    expected_itl: list[float],
    expected_text_latency: float,
+    expected_tpot_measured: bool,
 ):
    case, options = _case(tmp_path), _session_options(tmp_path)
    request = _request(case, options)
@@ -643,7 +647,7 @@ async def test_adapter_reports_exact_or_weighted_token_timing(
    assert output.success
    assert output.itl == expected_itl
    assert output.text_latency == pytest.approx(expected_text_latency)
-    assert output.tpot_measured is True
+    assert output.tpot_measured is expected_tpot_measured


 def test_batch_finalization_publishes_only_measured_results(tmp_path: Path, monkeypatch: pytest.MonkeyPatch):
@@ -34,6 +34,7 @@ from vllm_omni.config.stage_config import (
    build_stage_runtime_overrides,
    load_deploy_config,
    merge_pipeline_deploy,
+    normalize_pipeline_cli_overrides,
    pipeline_cfg_resolver,
 )
 from vllm_omni.engine.arg_utils import SHARED_FIELDS, internal_blacklist_keys
@@ -429,6 +430,20 @@ class TestStageConfigFactory:
        assert cfg["final_output"] is True
        assert cfg["final_output_type"] == "image"

+    @pytest.mark.parametrize(
+        "model_class_name",
+        [
+            "MiniMaxH3Pipeline",
+            "WanPipeline",
+            "LTX2Pipeline",
+            "Cosmos3OmniDiffusersPipeline",
+        ],
+    )
+    def test_default_video_diffusion_output_type(self, model_class_name):
+        configs = StageConfigFactory.create_default_diffusion({"model_class_name": model_class_name})
+
+        assert configs[0]["final_output_type"] == "video"
+
    def test_default_diffusion_with_parallel_config(self):
        """Test diffusion config calculates devices from parallel_config."""

@@ -534,6 +549,7 @@ class TestPipelineDiscovery:
        """Check that specific models are in OMNI_PIPELINES."""
        assert "qwen2_5_omni" in OMNI_PIPELINES
        assert "qwen3_omni_moe" in OMNI_PIPELINES
+        assert "qwen3_omni_moe_thinker_only" in OMNI_PIPELINES
        assert "qwen3_tts" in OMNI_PIPELINES

    def test_registry_resolver_qwen3_omni_all_stages(self):
@@ -558,6 +574,18 @@ class TestPipelineDiscovery:
        assert pipeline.default_deploy_config_name is None
        assert len(pipeline.stages) == 1  # thinker only

+    def test_registry_qwen3_omni_thinker_only_key_is_static(self):
+        """Explicit registry key must not re-enter the HF-config resolver."""
+        pipeline = resolve_pipeline_config(
+            "qwen3_omni_moe_thinker_only",
+            Q3_OMNI_ALL_STAGES_HF_CONFIG,
+        )
+        assert pipeline is OMNI_PIPELINES["qwen3_omni_moe_thinker_only"]
+        assert pipeline.model_type == "qwen3_omni_moe_thinker_only"
+        assert len(pipeline.stages) == 1
+        assert pipeline.stages[0].engine_output_type == "text"
+        assert pipeline.stages[0].final_output_type == "text"
+
    @pytest.mark.parametrize(
        "pipeline",
        [registered for registered in OMNI_PIPELINES.values() if isinstance(registered, PipelineConfig)],
@@ -1131,7 +1159,7 @@ class TestDeployConfigLoading:
        stages = merge_pipeline_deploy(pipeline, deploy)

        assert deploy.session_mode == "duplex"
-        assert deploy.active_stream_window == 1
+        assert deploy.active_stream_window == max_sessions
        assert deploy.duplex_session.max_sessions == max_sessions
        assert [stage.session_mode for stage in stages] == ["duplex", "duplex", "duplex"]
        assert [stage.to_omegaconf().session_mode for stage in stages] == ["duplex", "duplex", "duplex"]
@@ -1225,14 +1253,22 @@ stages:
        assert deploy.connectors is not None
        assert deploy.platforms is not None

-    def test_qwen3_omni_thinker_only_deploy_config_uses_resolved_pipeline(self):
+    @pytest.mark.parametrize(
+        ("hf_config", "model"),
+        [
+            (Q3_OMNI_THINKER_HF_CONFIG, "Qwen/Qwen3-Omni-30B-A3B-Thinking"),
+            (Q3_OMNI_ALL_STAGES_HF_CONFIG, "Qwen/Qwen3-Omni-30B-A3B-Instruct"),
+        ],
+        ids=["thinker_checkpoint", "instruct_forced_thinker_only"],
+    )
+    def test_qwen3_omni_thinker_only_deploy_config_uses_resolved_pipeline(self, hf_config, model):
        deploy_path = Path(get_deploy_config_path("qwen3_omni_moe_thinking.yaml"))
        with patch(
            "vllm_omni.config.config_factory.get_config",
-            return_value=Q3_OMNI_THINKER_HF_CONFIG,
+            return_value=hf_config,
        ):
            pipeline = StageConfigFactory.get_pipeline_config(
-                "Qwen/Qwen3-Omni-30B-A3B-Thinking",
+                model,
                trust_remote_code=False,
                deploy_config_path=str(deploy_path),
            )
@@ -1241,6 +1277,7 @@ stages:
        assert pipeline.model_type == "qwen3_omni_moe_thinker_only"

        deploy = load_deploy_config(deploy_path)
+        assert deploy.pipeline == "qwen3_omni_moe_thinker_only"
        stages = merge_pipeline_deploy(pipeline, deploy)

        assert len(stages) == 1
@@ -1292,6 +1329,8 @@ stages:
    @pytest.mark.parametrize(
        ("deploy_name", "pipeline_name", "stage_count", "final_output_type", "declared_pipeline"),
        [
+            ("mammoth_moda2.yaml", "mammoth_moda2", 2, "image", "mammoth_moda2"),
+            ("mammoth_moda2_ar.yaml", "mammoth_moda2_ar", 1, "text", "mammoth_moda2_ar"),
            ("omnivoice.yaml", "omnivoice", 1, "audio", "omnivoice"),
            ("mimo_audio.yaml", "mimo_audio", 2, "audio", None),
            ("step_audio_2.yaml", "step_audio_2", 2, "audio", None),
@@ -1300,6 +1339,13 @@ stages:
            ("step_audio2_ci.yaml", "step_audio_2_asr", 1, "text", "step_audio_2_asr"),
            ("hunyuan_video_15.yaml", "hunyuan_video_15", 1, "video", None),
            ("wan2_2_ti2v.yaml", "wan2_2_ti2v", 1, "video", None),
+            (
+                "minimax_h3_disaggregated.yaml",
+                "minimax_h3_disaggregated",
+                2,
+                "video",
+                "minimax_h3_disaggregated",
+            ),
        ],
    )
    def test_load_new_registry_backed_deploy_configs(
@@ -1320,9 +1366,91 @@ stages:
        assert len(stages) == stage_count
        assert stages[-1].final_output is True
        assert stages[-1].final_output_type == final_output_type
+        if pipeline_name == "minimax_h3_disaggregated":
+            assert stages[0].yaml_engine_args["model_path_resolver"].endswith(".resolve_minimax_h3_model_root")
+            assert (
+                stages[1].yaml_engine_args["model_path_resolver"].endswith(".resolve_minimax_h3_diffusion_model_path")
+            )
+            assert "model_path_resolver" not in stages[0].yaml_extras
+            assert "model_path_resolver" not in stages[1].yaml_extras
        if deploy.trust_remote_code is not None:
            assert {s.yaml_engine_args.get("trust_remote_code") for s in stages} == {deploy.trust_remote_code}

+    def test_minimax_h3_resolvers_reach_production_engine_args(self, tmp_path: Path, monkeypatch):
+        from vllm_omni.engine import stage_init_utils
+
+        model_root = tmp_path / "MiniMax-H3"
+        (model_root / "FL2VA" / "text_encoder").mkdir(parents=True)
+        (model_root / "Ref2VA" / "text_encoder").mkdir(parents=True)
+        monkeypatch.setattr(stage_init_utils, "resolve_worker_cls", lambda _engine_args: None)
+
+        deploy = load_deploy_config(get_deploy_config_path("minimax_h3_disaggregated.yaml"))
+        assert deploy.stages[1].engine_extras["model_loaded"] == {"text_encoder": False}
+        stages = merge_pipeline_deploy(OMNI_PIPELINES["minimax_h3_disaggregated"], deploy)
+        resolved = [stage_init_utils.build_engine_args_dict(stage.to_omegaconf(), str(model_root)) for stage in stages]
+
+        assert resolved[0]["model"] == str(model_root / "FL2VA" / "text_encoder")
+        assert resolved[1]["model"] == str(model_root / "FL2VA")
+        assert all("model_path_resolver" not in engine_args for engine_args in resolved)
+
+    def test_minimax_h3_text_encoder_tp_reaches_only_stage_zero(self):
+        pipeline = OMNI_PIPELINES["minimax_h3_disaggregated"]
+        stages, _ = StageConfigFactory._create_legacy_from_registry(
+            pipeline,
+            {"text_encoder_tp_size": 4},
+        )
+
+        assert stages[0].runtime_overrides["tensor_parallel_size"] == 4
+        assert "text_encoder_tp_size" not in stages[0].runtime_overrides
+        assert "text_encoder_tp_size" not in stages[1].runtime_overrides
+        assert stages[1].yaml_engine_args["parallel_config"]["tensor_parallel_size"] == 1
+
+    def test_minimax_h3_disaggregated_defaults_match_validated_topology(self):
+        pipeline = OMNI_PIPELINES["minimax_h3_disaggregated"]
+        deploy = load_deploy_config(Path(get_deploy_config_path("minimax_h3_disaggregated.yaml")))
+        stages = merge_pipeline_deploy(pipeline, deploy)
+
+        assert stages[0].yaml_engine_args["max_num_seqs"] == 1
+        assert stages[0].yaml_engine_args["model_path_resolver"].endswith(".resolve_minimax_h3_model_root")
+        assert stages[1].yaml_engine_args["model_path_resolver"].endswith(".resolve_minimax_h3_diffusion_model_path")
+        parallel = stages[1].yaml_engine_args["parallel_config"]
+        assert parallel["tensor_parallel_size"] == 1
+        assert parallel["ulysses_degree"] == 4
+        assert parallel["vae_patch_parallel_size"] == 4
+        assert stages[1].yaml_engine_args["inline_diffusion"] is True
+        assert stages[1].yaml_extras["default_sampling_params"]["num_inference_steps"] == 50
+
+        turbo = load_deploy_config(Path(get_deploy_config_path("minimax_h3_disaggregated_turbo.yaml")))
+        turbo_stages = merge_pipeline_deploy(pipeline, turbo)
+        turbo_sampling = turbo_stages[1].yaml_extras["default_sampling_params"]
+        assert turbo_sampling["num_inference_steps"] == 5
+        assert turbo_sampling["extra_args"] == {"flow_shift": 6.0, "audio_flow_shift": 3.0}
+
+    def test_minimax_h3_text_encoder_tp_alias_targets_stage_zero(self):
+        pipeline = OMNI_PIPELINES["minimax_h3_disaggregated"]
+
+        assert normalize_pipeline_cli_overrides(pipeline, {}) == {}
+        assert normalize_pipeline_cli_overrides(pipeline, {"text_encoder_tp_size": 4}) == {
+            "stage_0_tensor_parallel_size": 4
+        }
+
+    def test_minimax_h3_stage_zero_tp_override_wins_alias_conflict(self):
+        pipeline = OMNI_PIPELINES["minimax_h3_disaggregated"]
+
+        with pytest.warns(UserWarning, match="stage_0_tensor_parallel_size=2 takes precedence"):
+            normalized = normalize_pipeline_cli_overrides(
+                pipeline,
+                {"text_encoder_tp_size": 4, "stage_0_tensor_parallel_size": 2},
+            )
+
+        assert normalized == {"stage_0_tensor_parallel_size": 2}
+
+    def test_minimax_h3_rejects_text_encoder_tp_on_diffusion_stage(self):
+        pipeline = OMNI_PIPELINES["minimax_h3_disaggregated"]
+
+        with pytest.raises(ValueError, match="stage_1_text_encoder_tp_size cannot be set"):
+            normalize_pipeline_cli_overrides(pipeline, {"stage_1_text_encoder_tp_size": 4})
+
    @pytest.mark.parametrize(
        ("config_json", "model_index", "expected_pipeline"),
        [
@@ -1358,6 +1486,29 @@ stages:
        assert pipeline is not None
        assert pipeline.model_type == expected_pipeline

+    def test_minimax_h3_disaggregation_is_not_auto_discovered(self):
+        def get_model_file(filename, _model, revision=None):
+            del revision
+            if filename == "config.json":
+                return None
+            if filename == "model_index.json":
+                return {"_class_name": "MiniMaxH3Pipeline"}
+            return None
+
+        with (
+            patch.object(StageConfigFactory, "get_hf_config", return_value=None),
+            patch(
+                "vllm_omni.config.config_factory.get_hf_file_to_dict",
+                side_effect=get_model_file,
+            ),
+        ):
+            pipeline = StageConfigFactory.get_pipeline_config(
+                model="/models/unrelated-checkpoint-name",
+                trust_remote_code=False,
+            )
+
+        assert pipeline is None
+
    @pytest.mark.parametrize("deploy_name", ["step_audio_2.yaml", "step_audio_2_async_chunk.yaml"])
    def test_step_audio2_deploy_configs_fit_two_gpus(self, deploy_name):
        deploy = load_deploy_config(Path(get_deploy_config_path(deploy_name)))
@@ -2248,6 +2399,20 @@ class TestPlatformOverrides:
        assert rocm.stages[0].enforce_eager is None
        assert rocm.stages[1].enforce_eager is True

+    def test_higgs_audio_v3_rocm_uses_triton_attention(self):
+        deploy_path = Path(get_deploy_config_path("higgs_multimodal_qwen3.yaml"))
+
+        base = load_deploy_config(deploy_path)
+        assert base.stages[0].engine_extras["attention_backend"] == "FLASHINFER"
+
+        rocm = _apply_platform_overrides(base, platform="rocm")
+        assert rocm.stages[0].engine_extras["attention_backend"] == "TRITON_ATTN"
+
+        pipeline = resolve_pipeline_config("higgs_multimodal_qwen3")
+        assert isinstance(pipeline, PipelineConfig)
+        stages = merge_pipeline_deploy(pipeline, rocm)
+        assert stages[0].yaml_engine_args["attention_backend"] == "TRITON_ATTN"
+
    def test_qwen3_omni_cuda_uses_thinker_rotary_custom_op(self):
        deploy_path = Path(get_deploy_config_path("qwen3_omni_moe.yaml"))
        pipeline = resolve_pipeline_config(
@@ -164,7 +164,7 @@ def test_inventory_matches_reviewed_snapshot_counts():
    """Make an inventory expansion an explicit review decision."""
    category_counts = Counter(item.category for item in ENVIRONMENT_VARIABLE_INVENTORY.values())
    assert category_counts == {
-        EnvironmentVariableCategory.PUBLIC_OMNI: 23,
+        EnvironmentVariableCategory.PUBLIC_OMNI: 24,
        EnvironmentVariableCategory.INHERITED_VLLM: 20,
        EnvironmentVariableCategory.PLATFORM_EXTERNAL: 27,
        EnvironmentVariableCategory.MODEL_SPECIFIC: 56,
@@ -92,10 +92,23 @@ def _from_pipeline_key(
    )


+def test_minimax_h3_text_encoder_tp_targets_only_structured_stage_zero() -> None:
+    config = _from_pipeline_key(
+        "minimax_h3_disaggregated",
+        cli_overrides={"text_encoder_tp_size": 4},
+    )
+
+    assert config.stage_by_id(0).parallel_config.tensor_parallel_size == 4
+    diffusion = config.stage_by_id(1)
+    assert diffusion.parallel_config.tensor_parallel_size == 1
+    assert diffusion.parallel_config.text_encoder_tp_size == 1
+
+
 def test_duplex_session_capacity_propagates_to_every_structured_stage() -> None:
    omni_config = _from_pipeline_key("personaplex")

    assert [stage.model_config.duplex_max_sessions for stage in omni_config.stage_configs] == [2, 2]
+    assert [stage.model_config.session_mode for stage in omni_config.stage_configs] == ["duplex", "duplex"]


 def test_non_duplex_deploy_keeps_model_session_capacity_at_one(tmp_path: Path) -> None:
@@ -105,6 +118,7 @@ def test_non_duplex_deploy_keeps_model_session_capacity_at_one(tmp_path: Path) -
    omni_config = _from_pipeline_key("personaplex", deploy_config_path=str(deploy_path))

    assert [stage.model_config.duplex_max_sessions for stage in omni_config.stage_configs] == [1, 1]
+    assert [stage.model_config.session_mode for stage in omni_config.stage_configs] == ["turn", "turn"]


@pytest.mark.parametrize("model_type", sorted(OMNI_PIPELINES))
@@ -132,6 +146,7 @@ def test_vllm_omni_config_from_pipeline_config_matches_merge_pipeline_deploy(mod

        engine_args = legacy_stage.yaml_engine_args
        assert omni_stage.model_config.duplex_max_sessions == engine_args.get("duplex_max_sessions", 1)
+        assert omni_stage.model_config.session_mode == engine_args.get("session_mode", "turn")
        assert omni_stage.model_config.enforce_eager == engine_args.get("enforce_eager", False)
        assert omni_stage.load_config.load_format == engine_args.get("load_format", "auto")
        assert omni_stage.load_config.tokenizer_mode == engine_args.get("tokenizer_mode", "auto")
@@ -579,6 +594,7 @@ def test_sub_config_fields_match_structured_scopes():
        "interleave_mm_strings",
        "media_io_kwargs",
        "active_stream_window",
+        "session_mode",
        "duplex_max_sessions",
        "enable_sleep_mode",
        "default_sampling_params",
@@ -637,10 +653,11 @@ def test_sub_config_fields_match_structured_scopes():
        "ring_degree",
        "allgather_degree",
        "ulysses_mode",
+        "ulysses_a2a_permute",
        "cfg_parallel_size",
        "vae_patch_parallel_size",
-        "text_encoder_tp_size",
        "vae_parallel_mode",
+        "text_encoder_tp_size",
        "use_hsdp",
        "mask_sp_padding",
        "hsdp_shard_size",
@@ -842,6 +859,23 @@ def test_from_pipeline_config_derives_sequence_parallel_size_from_allgather_degr
    assert stage.parallel_config.world_size == 2


+def test_from_pipeline_config_preserves_deployed_ulysses_a2a_permute() -> None:
+    pipeline = _resolve_pipeline_or_skip("dreamzero")
+    deploy = DeployConfig(
+        stages=[
+            StageDeployConfig(
+                stage_id=0,
+                ulysses_a2a_permute=True,
+            )
+        ]
+    )
+
+    stage = VllmOmniConfig.from_pipeline_config(pipeline, user_deploy_config=deploy).stage_by_id(0)
+
+    assert isinstance(stage, VllmOmniDiffusionStageConfig)
+    assert stage.parallel_config.ulysses_a2a_permute is True
+
+
 def test_diffusion_parallel_config_accepts_four_way_guidance_parallelism():
    cfg = OmniStageDiffusionParallelConfig(cfg_parallel_size=4)

@@ -936,6 +970,35 @@ def test_structured_diffusion_config_rejects_non_boolean_compile_dynamic():
        omni_config_module._DiffusionConfigProjection(diffusion_compile_dynamic="false")


+def test_from_pipeline_config_routes_ltx2_conv_vae_extra(tmp_path):
+    deploy_path = tmp_path / "dreamzero_ltx2_extras.yaml"
+    deploy_path.write_text(
+        "\n".join(
+            [
+                "pipeline: dreamzero",
+                "async_chunk: false",
+                "stages:",
+                "  - stage_id: 0",
+                "    extras:",
+                "      ltx2_use_conv_vae: true",
+            ]
+        )
+    )
+
+    stage = _from_pipeline_key("dreamzero", deploy_config_path=str(deploy_path)).stage_by_id(0)
+
+    assert stage.diffusion_config.extras["ltx2_use_conv_vae"] is True
+
+
+def test_stage_override_routes_ltx2_conv_vae_extra():
+    stage = _from_pipeline_key(
+        "dreamzero",
+        cli_overrides={"stage_0_extras": {"ltx2_use_conv_vae": True}},
+    ).stage_by_id(0)
+
+    assert stage.diffusion_config.extras["ltx2_use_conv_vae"] is True
+
+
 def test_structured_diffusion_config_rejects_invalid_compile_granularity():
    with pytest.raises(ValidationError, match="diffusion_compile_granularity"):
        omni_config_module._DiffusionConfigProjection(diffusion_compile_granularity="block")
@@ -66,3 +66,10 @@ def test_register_resolver_requires_model_type(custom_resolver, clean_pipeline_r
    """Ensure that registering a custom resolver to OMNI_PIPELINES requires an explicit model_type."""
    with pytest.raises(ValueError):
        register_pipeline(custom_resolver)
+
+
+def test_minimax_h3_disaggregation_is_explicit_opt_in():
+    assert "minimax_h3" not in OMNI_PIPELINES
+    pipeline = OMNI_PIPELINES["minimax_h3_disaggregated"]
+    assert isinstance(pipeline, PipelineConfig)
+    assert pipeline.model_type == "minimax_h3_disaggregated"
@@ -25,9 +25,9 @@ pytest_plugins = (
    "tests.helpers.fixtures.clean",
    "tests.helpers.fixtures.log",
    "tests.helpers.fixtures.media",
-    "tests.helpers.fixtures.run_args",
+    "tests.helpers.fixtures.pytest_collection",
+    "tests.helpers.fixtures.pytest_run_args",
    "tests.helpers.fixtures.runtime",
-    "tests.helpers.fixtures.xdist",
 )


@@ -0,0 +1,89 @@
+"""Tests for ``OmniARScheduler._drop_aborted_queued_requests``.
+
+``schedule()`` sweeps ``FINISHED_ABORTED`` requests out of the queues before
+handing control to upstream, because upstream ``Scheduler.schedule()`` raises
+``RuntimeError: Invalid request status: FINISHED_ABORTED`` for any request it
+admits in a finished state -- which kills the stage's engine core, not just
+the request.
+
+Regression for the sweep missing ``skipped_waiting``, the third queue
+upstream admits from. An aborted duplex session parked there was re-selected
+by ``_select_waiting_queue_for_scheduling`` on a later tick and crashed the
+stage.
+"""
+
+from __future__ import annotations
+
+import pytest
+from vllm.v1.core.sched.request_queue import SchedulingPolicy, create_request_queue
+from vllm.v1.request import RequestStatus
+
+import vllm_omni.core.sched.omni_ar_scheduler as ar_sched_mod
+from vllm_omni.core.sched.omni_ar_scheduler import OmniARScheduler
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
+
+_POLICIES = [
+    pytest.param(SchedulingPolicy.FCFS, id="fcfs"),
+    pytest.param(SchedulingPolicy.PRIORITY, id="priority"),
+]
+
+
+class _StubRequest:
+    """Minimal ``Request`` stub with the surface the sweep exercises.
+
+    Carries the ``(priority, arrival_time)`` ordering ``PriorityRequestQueue``
+    relies on so the same stub works under both scheduling policies.
+    """
+
+    def __init__(self, request_id: str, status: RequestStatus, arrival_time: float = 0.0) -> None:
+        self.request_id = request_id
+        self.status = status
+        self.priority = 0
+        self.arrival_time = arrival_time
+
+    def __lt__(self, other: _StubRequest) -> bool:
+        return (self.priority, self.arrival_time) < (other.priority, other.arrival_time)
+
+
+def _make_scheduler(policy: SchedulingPolicy, *, waiting=(), skipped_waiting=(), running=()):
+    """Bare scheduler with just the surface the sweep reads."""
+    scheduler = OmniARScheduler.__new__(OmniARScheduler)
+    scheduler.waiting = create_request_queue(policy)
+    for req in waiting:
+        scheduler.waiting.add_request(req)
+    scheduler.skipped_waiting = create_request_queue(policy)
+    for req in skipped_waiting:
+        scheduler.skipped_waiting.add_request(req)
+    scheduler.running = list(running)
+    return scheduler
+
+
+@pytest.mark.parametrize("policy", _POLICIES)
+def test_schedule_sweeps_skipped_waiting_before_upstream_selection(
+    monkeypatch: pytest.MonkeyPatch,
+    policy: SchedulingPolicy,
+) -> None:
+    """The public schedule path must remove aborted skipped requests first."""
+    aborted = _StubRequest("req-aborted", RequestStatus.FINISHED_ABORTED)
+    scheduler = _make_scheduler(policy, skipped_waiting=[aborted])
+    scheduler.chunk_transfer_adapter = None
+    scheduler.input_coordinator = None
+    scheduler.num_waiting_for_streaming_input = 0
+    scheduler._process_pending_omni_inputs = lambda model_mode: None
+    scheduler._resync_streaming_input_counter = lambda: None
+    scheduler._should_defer_waiting_admission = lambda: False
+    scheduler._restore_omni_wait_queues = lambda: None
+    scheduler._postprocess_omni_schedule_output = lambda *args, **kwargs: None
+    scheduler.get_finished_requests_needing_kv_transfer = lambda: {}
+    scheduler._wrap_omni_scheduler_output = lambda output, **kwargs: output
+
+    sentinel = object()
+
+    def upstream_schedule(_self, _throttle_prefills: bool = False):
+        assert list(scheduler.skipped_waiting) == []
+        return sentinel
+
+    monkeypatch.setattr(ar_sched_mod.VLLMScheduler, "schedule", upstream_schedule)
+
+    assert OmniARScheduler.schedule(scheduler) is sentinel
@@ -100,6 +100,7 @@ def _make_drain_sched(session: Request) -> MagicMock:
    sched.pending_stop_after_extraction = set()
    sched.connector = None
    sched.kv_cache_manager.take_events.return_value = None
+    sched.kv_cache_manager.estimate_cached_tokens.return_value = 0
    sched.finished_req_ids_dict = {}
    sched.make_stats.return_value = None
    return sched
@@ -141,13 +142,16 @@ def test_exact_drain_delivers_new_segment_frame() -> None:
    session.num_computed_tokens = 6
    session.num_output_placeholders = 1
    session.num_in_flight_tokens = 1
+    session.async_tokens_to_discard = 1

    _replace_streaming_session(session)
    assert session.num_stale_output_tokens == 1
+    assert session.async_tokens_to_discard == 1

    sched = _make_drain_sched(session)
    assert _run_step(sched, session, num_scheduled=1, token=42) is False  # late frame dropped
    assert session.num_stale_output_tokens == 0
+    assert session.async_tokens_to_discard == 0
    assert _run_step(sched, session, num_scheduled=1, token=43) is True  # new segment survives


@@ -18,17 +18,19 @@ from vllm.sampling_params import SamplingParams
 from vllm.v1.core.sched.output import SchedulerOutput
 from vllm.v1.outputs import ModelRunnerOutput
 from vllm.v1.request import Request, RequestStatus, StreamingUpdate
-from vllm_omni.core.sched.omni_ar_scheduler import OmniARScheduler
+from vllm_omni.core.sched.omni_ar_scheduler import OmniARAsyncScheduler, OmniARScheduler

 # isort: on

 pytestmark = [pytest.mark.core_model, pytest.mark.cpu]


-def _make_scheduler(*, stage_id: int = 0) -> OmniARScheduler:
+def _make_scheduler(*, stage_id: int = 0, session_mode: str = "turn") -> OmniARScheduler:
    sched = OmniARScheduler.__new__(OmniARScheduler)
    sched._new_prompt_len_snapshot = {}
-    sched.vllm_config = SimpleNamespace(model_config=SimpleNamespace(stage_id=stage_id))
+    sched.vllm_config = SimpleNamespace(
+        model_config=SimpleNamespace(stage_id=stage_id, session_mode=session_mode),
+    )
    sched.num_waiting_for_streaming_input = 0
    sched.log_stats = False
    sched.chunk_transfer_adapter = None
@@ -93,6 +95,7 @@ def _run_resumable_segment_stop(
    sched.pending_stop_after_extraction = set()
    sched.connector = None
    sched.kv_cache_manager.take_events.return_value = None
+    sched.kv_cache_manager.estimate_cached_tokens.return_value = 0
    sched.finished_req_ids_dict = {}
    sched.make_stats.return_value = None

@@ -184,6 +187,7 @@ def test_resumable_segment_boundary_keeps_pre_transition_send_watermark() -> Non

    def replace_with_next_segment(request: Request) -> bool:
        request.num_computed_tokens = 0
+        request._omni_segment_generation = 1
        request.status = RequestStatus.WAITING
        return False

@@ -198,7 +202,9 @@ def test_resumable_segment_boundary_keeps_pre_transition_send_watermark() -> Non
        inter_stage_output,
        session,
        True,
+        new_token_ids=[42],
        confirmed_num_computed_tokens=26,
+        segment_generation=0,
    )


@@ -226,6 +232,7 @@ def test_running_decode_step_without_inter_stage_payload_does_not_raise() -> Non
    sched.pending_stop_after_extraction = set()
    sched.connector = None
    sched.kv_cache_manager.take_events.return_value = None
+    sched.kv_cache_manager.estimate_cached_tokens.return_value = 0
    sched.finished_req_ids_dict = {}
    sched.make_stats.return_value = None

@@ -283,6 +290,140 @@ def test_queued_streaming_update_on_async_stop_fences_in_flight_once() -> None:
    assert session.num_stale_output_tokens == session.num_in_flight_tokens == 1


+def test_stale_async_frame_is_dropped_before_output_processing() -> None:
+    session = _make_request()
+    session.status = RequestStatus.RUNNING
+    session.num_in_flight_tokens = 2
+    session.num_computed_tokens = session.num_tokens + 1
+    session.num_output_placeholders = 1
+    session.async_tokens_to_discard = 1
+    session.sampling_params = SimpleNamespace(num_logprobs=1)
+    num_computed_tokens = session.num_computed_tokens
+    num_output_placeholders = session.num_output_placeholders
+
+    sched = MagicMock()
+    sched.requests = {session.request_id: session}
+    sched.perf_metrics = None
+    sched.structured_output_manager.should_advance.return_value = False
+
+    def discard_stale_output(request: Request, token_ids: list[int]) -> tuple[list[int], bool]:
+        request.async_tokens_to_discard = 0
+        return token_ids, False
+
+    sched._update_request_with_output.side_effect = discard_stale_output
+    sched._process_kv_transfer_trigger.return_value = False
+    sched.chunk_transfer_adapter = MagicMock()
+    sched.running = [session]
+    sched.waiting_for_transfer_free = set()
+    sched.transfer_triggered_requests = set()
+    sched.active_kv_transfers = set()
+    sched.pending_stop_after_extraction = set()
+    sched.connector = None
+    sched.kv_cache_manager.take_events.return_value = None
+    sched.kv_cache_manager.estimate_cached_tokens.return_value = 0
+    sched.finished_req_ids_dict = {}
+    sched.make_stats.return_value = None
+
+    scheduler_output = MagicMock(spec=SchedulerOutput)
+    scheduler_output.num_scheduled_tokens = {session.request_id: 1}
+    scheduler_output.scheduled_spec_decode_tokens = {}
+    scheduler_output.num_invalid_spec_tokens = 0
+
+    model_runner_output = MagicMock(spec=ModelRunnerOutput)
+    model_runner_output.sampled_token_ids = [[42]]
+    model_runner_output.logprobs = None
+    model_runner_output.prompt_logprobs_dict = {}
+    model_runner_output.pooler_output = None
+    model_runner_output.multimodal_outputs = None
+    model_runner_output.inter_stage_outputs = [{"hidden": object()}]
+    model_runner_output.num_nans_in_logits = None
+    model_runner_output.kv_connector_output = None
+    model_runner_output.cudagraph_stats = None
+    model_runner_output.req_id_to_index = {session.request_id: 0}
+    model_runner_output.routed_experts = None
+
+    OmniARScheduler.update_from_output(sched, scheduler_output, model_runner_output)
+
+    assert session.async_tokens_to_discard == 0
+    assert session.status == RequestStatus.RUNNING
+    assert session.num_computed_tokens == num_computed_tokens
+    assert session.num_output_placeholders == num_output_placeholders
+    sched.chunk_transfer_adapter.save_async.assert_not_called()
+
+    session.sampling_params = SamplingParams(max_tokens=8)
+    next_payload = {"hidden": object()}
+    sched._update_request_with_output.side_effect = None
+    sched._update_request_with_output.return_value = ([43], False)
+    model_runner_output.sampled_token_ids = [[43]]
+    model_runner_output.inter_stage_outputs = [next_payload]
+
+    OmniARScheduler.update_from_output(sched, scheduler_output, model_runner_output)
+
+    sched.chunk_transfer_adapter.save_async.assert_called_once_with(
+        next_payload,
+        session,
+        False,
+        new_token_ids=[43],
+        confirmed_num_computed_tokens=None,
+    )
+
+
+def test_legacy_stale_async_marker_bypasses_real_placeholder_accounting() -> None:
+    request = _make_request()
+    request.status = RequestStatus.RUNNING
+    request.num_in_flight_tokens = 1
+    request.num_output_placeholders = 0
+    request.async_tokens_to_discard = 1
+    request.num_stale_output_tokens = 0
+
+    sched = OmniARAsyncScheduler.__new__(OmniARAsyncScheduler)
+    sched.requests = {request.request_id: request}
+    sched.perf_metrics = None
+    sched.chunk_transfer_adapter = None
+    sched.connector = None
+    sched.ec_connector = None
+    sched.waiting_for_transfer_free = set()
+    sched.transfer_triggered_requests = set()
+    sched.active_kv_transfers = set()
+    sched.pending_stop_after_extraction = set()
+    sched.finished_req_ids_dict = {}
+    sched._new_prompt_len_snapshot = {}
+    sched.kv_cache_manager = MagicMock()
+    sched.kv_cache_manager.take_events.return_value = None
+    sched._remove_stopped_requests_from_queues = MagicMock()
+    sched._handle_failed_kv_load_outputs = MagicMock(return_value=[])
+    sched._cleanup_kv_tracking = MagicMock()
+    sched._aggregate_kv_connector_stats = MagicMock(return_value=None)
+    sched._publish_kv_cache_events = MagicMock()
+    sched._attach_finished_request_sets = MagicMock()
+    sched._attach_scheduler_stats = MagicMock()
+    sched._capture_omni_connector_output = MagicMock()
+
+    scheduler_output = MagicMock(spec=SchedulerOutput)
+    scheduler_output.num_scheduled_tokens = {request.request_id: 1}
+    scheduler_output.scheduled_spec_decode_tokens = {}
+    scheduler_output.num_invalid_spec_tokens = 0
+
+    model_runner_output = MagicMock(spec=ModelRunnerOutput)
+    model_runner_output.sampled_token_ids = [[42]]
+    model_runner_output.logprobs = None
+    model_runner_output.prompt_logprobs_dict = {}
+    model_runner_output.pooler_output = None
+    model_runner_output.multimodal_outputs = None
+    model_runner_output.inter_stage_outputs = None
+    model_runner_output.num_nans_in_logits = None
+    model_runner_output.kv_connector_output = None
+    model_runner_output.cudagraph_stats = None
+    model_runner_output.req_id_to_index = {request.request_id: 0}
+    model_runner_output.routed_experts = None
+
+    OmniARScheduler.update_from_output(sched, scheduler_output, model_runner_output)
+
+    assert request.async_tokens_to_discard == 0
+    assert request.num_output_placeholders == 0
+    assert request._output_token_ids == []
+
+
 def test_stage0_streaming_update_discards_outstanding_async_placeholder_token() -> None:
    sched = _make_scheduler(stage_id=0)
    session = _make_request()
@@ -427,3 +568,71 @@ def test_ready_async_chunk_prompt_replacement_releases_stale_kv_once() -> None:
    assert sched.chunk_transfer_adapter.replaced_streaming_prompt_ids == set()
    assert sched.chunk_transfer_adapter.requests_with_ready_chunks == {session.request_id}
    assert sched.chunk_transfer_adapter.requests_num_chunks_sent == {}
+
+
+def test_chunk_segment_cleanup_keeps_requeued_resumable_receiver() -> None:
+    """A WAITING_FOR_CHUNK stop must not delete its newly parked session."""
+    session = _make_request()
+    session.resumable = True
+    session.status = RequestStatus.WAITING_FOR_STREAMING_REQ
+
+    def queue(*requests):
+        result = MagicMock()
+        result.requests = set(requests)
+        result.add_request.side_effect = result.requests.add
+        result.remove_requests.side_effect = result.requests.difference_update
+        return result
+
+    sched = OmniARScheduler.__new__(OmniARScheduler)
+    sched.vllm_config = SimpleNamespace(model_config=SimpleNamespace(session_mode="duplex"))
+    sched.running = []
+    sched.waiting = queue()
+    sched.skipped_waiting = queue(session)
+    sched.num_waiting_for_streaming_input = 1
+    sched.chunk_transfer_adapter = SimpleNamespace(
+        receives_chunks=True,
+        segment_finished_requests={session.request_id},
+    )
+
+    sched._resume_downstream_chunk_receiver(session)
+    sched._remove_stopped_requests_from_queues(set(), {session})
+
+    assert session.status == RequestStatus.WAITING
+    assert session in sched.waiting.requests
+    assert session not in sched.skipped_waiting.requests
+    assert sched.num_waiting_for_streaming_input == 0
+    assert session.request_id not in sched.chunk_transfer_adapter.segment_finished_requests
+
+
+@pytest.mark.parametrize(
+    ("receives_chunks", "session_mode"),
+    [
+        (False, "duplex"),
+        (True, "turn"),
+    ],
+)
+def test_chunk_segment_cleanup_keeps_explicit_update_stage_parked(
+    receives_chunks: bool,
+    session_mode: str,
+) -> None:
+    """Only duplex connector-driven receivers resume without an update."""
+    session = _make_request()
+    session.status = RequestStatus.WAITING_FOR_STREAMING_REQ
+
+    sched = OmniARScheduler.__new__(OmniARScheduler)
+    sched.vllm_config = SimpleNamespace(model_config=SimpleNamespace(session_mode=session_mode))
+    sched.num_waiting_for_streaming_input = 1
+    sched.chunk_transfer_adapter = SimpleNamespace(
+        receives_chunks=receives_chunks,
+        segment_finished_requests={session.request_id},
+    )
+    sched.skipped_waiting = MagicMock()
+    sched._enqueue_waiting_request = MagicMock()
+
+    sched._resume_downstream_chunk_receiver(session)
+
+    assert session.status == RequestStatus.WAITING_FOR_STREAMING_REQ
+    assert sched.num_waiting_for_streaming_input == 1
+    assert session.request_id not in sched.chunk_transfer_adapter.segment_finished_requests
+    sched.skipped_waiting.remove_requests.assert_not_called()
+    sched._enqueue_waiting_request.assert_not_called()
@@ -76,6 +76,9 @@ def _make_scheduler(
        else:
            hf_config.voxcpm2_runtime_config = runtime_config
    sched.vllm_config = SimpleNamespace(model_config=model_config)
+    # Upstream ``Scheduler.__init__`` always creates ``skipped_waiting``; the
+    # abort sweep at the top of ``schedule()`` reads it.
+    sched.skipped_waiting = _MockQueue()
    return sched


@@ -138,6 +138,7 @@ def test_resumable_generation_stop_marks_segment_boundary() -> None:
    sched.recompute_kv_load_failures = False
    sched.connector = None
    sched.kv_cache_manager.take_events.return_value = None
+    sched.kv_cache_manager.estimate_cached_tokens.return_value = 0
    sched.finished_req_ids_dict = {}
    sched.make_stats.return_value = None

@@ -0,0 +1,180 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
+"""v0.28 deferred KV-block free fence in the Omni schedulers (#6606 review).
+
+Upstream v0.28 (defer_block_free) advances ``sched_step_seq`` for every
+non-empty scheduled step and, at the top of ``update_from_output()``,
+advances ``processed_step_seq`` and drains the deferred frees. The Omni
+schedulers each carried only half of that contract:
+
+- ``OmniARScheduler.schedule()`` delegates to ``super().schedule()`` (fence
+  advances) but its reimplemented ``update_from_output()`` never advanced
+  ``processed_step_seq`` nor drained -> deferred blocks accumulate without
+  bound under async/PP KV-consumer configurations.
+- ``OmniGenerationScheduler`` reimplements both: the fast-path
+  ``schedule()`` never advanced ``sched_step_seq`` (so finished requests
+  freed blocks an in-flight GPU step could still be writing) and its
+  ``update_from_output()`` never drained.
+
+These tests pin the restored contract at the call boundary; the drain and
+free logic itself is upstream-tested.
+"""
+
+from __future__ import annotations
+
+from unittest.mock import MagicMock
+
+import pytest
+
+# Imports must run in this order: vllm_omni applies patches to vllm.v1.request
+# before Request / RequestStatus are bound in this module.
+# isort: off
+import vllm_omni  # noqa: F401 - import for side effects (patch vLLM)
+from vllm.sampling_params import SamplingParams
+from vllm.v1.core.sched.interface import PauseState
+from vllm.v1.core.sched.output import SchedulerOutput
+from vllm.v1.core.sched.request_queue import SchedulingPolicy
+from vllm.v1.outputs import ModelRunnerOutput
+from vllm.v1.request import Request, RequestStatus
+from vllm_omni.core.sched.omni_ar_scheduler import OmniARScheduler
+from vllm_omni.core.sched.omni_generation_scheduler import OmniGenerationScheduler
+
+# isort: on
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
+
+
+def _make_update_sched(scheduler_cls) -> MagicMock:
+    """MagicMock scheduler driving the real ``update_from_output`` with an
+    empty batch; only the fence state carries real values."""
+    sched = MagicMock()
+    sched.requests = {}
+    sched.perf_metrics = None
+    sched.chunk_transfer_adapter = None
+    sched.defer_block_free = True
+    sched.processed_step_seq = 0
+    return sched
+
+
+def _empty_frame(*, total_num_scheduled_tokens: int):
+    scheduler_output = MagicMock(spec=SchedulerOutput)
+    scheduler_output.num_scheduled_tokens = {}
+    scheduler_output.total_num_scheduled_tokens = total_num_scheduled_tokens
+    model_runner_output = MagicMock(spec=ModelRunnerOutput)
+    model_runner_output.sampled_token_ids = []
+    model_runner_output.logprobs = None
+    model_runner_output.prompt_logprobs_dict = {}
+    model_runner_output.pooler_output = None
+    model_runner_output.num_nans_in_logits = None
+    model_runner_output.kv_connector_output = None
+    model_runner_output.cudagraph_stats = None
+    return scheduler_output, model_runner_output
+
+
+@pytest.mark.parametrize("scheduler_cls", [OmniARScheduler, OmniGenerationScheduler])
+def test_update_from_output_advances_fence_and_drains(scheduler_cls) -> None:
+    sched = _make_update_sched(scheduler_cls)
+    scheduler_output, model_runner_output = _empty_frame(total_num_scheduled_tokens=3)
+
+    scheduler_cls.update_from_output(sched, scheduler_output, model_runner_output)
+
+    assert sched.processed_step_seq == 1
+    sched._drain_deferred_frees.assert_called_once_with()
+
+
+@pytest.mark.parametrize("scheduler_cls", [OmniARScheduler, OmniGenerationScheduler])
+def test_update_from_output_zero_token_step_leaves_fence(scheduler_cls) -> None:
+    """0-token steps do not advance the fence upstream (their seq was never
+    advanced in schedule()); draining on them would run ahead of the GPU."""
+    sched = _make_update_sched(scheduler_cls)
+    scheduler_output, model_runner_output = _empty_frame(total_num_scheduled_tokens=0)
+
+    scheduler_cls.update_from_output(sched, scheduler_output, model_runner_output)
+
+    assert sched.processed_step_seq == 0
+    sched._drain_deferred_frees.assert_not_called()
+
+
+@pytest.mark.parametrize("scheduler_cls", [OmniARScheduler, OmniGenerationScheduler])
+def test_update_from_output_defer_disabled_leaves_fence(scheduler_cls) -> None:
+    sched = _make_update_sched(scheduler_cls)
+    sched.defer_block_free = False
+    scheduler_output, model_runner_output = _empty_frame(total_num_scheduled_tokens=3)
+
+    scheduler_cls.update_from_output(sched, scheduler_output, model_runner_output)
+
+    assert sched.processed_step_seq == 0
+    sched._drain_deferred_frees.assert_not_called()
+
+
+def _make_fast_path_sched(*, defer_block_free: bool) -> tuple[MagicMock, Request]:
+    """MagicMock scheduler that drives the real generation fast-path
+    ``schedule()`` over one running one-shot request."""
+    request = Request(
+        request_id="req-fence",
+        prompt_token_ids=[1, 2, 3],
+        sampling_params=SamplingParams(max_tokens=4),
+        pooling_params=None,
+        arrival_time=100.0,
+        block_hasher=None,
+    )
+    request.status = RequestStatus.RUNNING
+
+    sched = MagicMock()
+    sched.max_num_scheduled_tokens = 8
+    sched._pause_state = PauseState.UNPAUSED
+    sched.requests = {request.request_id: request}
+    sched.running = [request]
+    sched.waiting = []  # falsy: the waiting loop never runs
+    sched.policy = SchedulingPolicy.FCFS
+    sched.chunk_transfer_adapter = None
+    sched.scheduler_config = MagicMock(enable_chunked_prefill=True)
+    sched.num_lookahead_tokens = 0
+    sched.log_stats = False
+    sched.kv_cache_config = MagicMock(kv_cache_groups=[])
+    sched.use_v2_model_runner = False
+    sched.prev_step_scheduled_req_ids = set()
+    sched.needs_kv_cache_zeroing = False
+    sched.connector = None
+    sched.ec_connector = None
+    sched.defer_block_free = defer_block_free
+    sched.sched_step_seq = 0
+    # last_sched_seq stamping order: _update_after_schedule must observe the
+    # already-advanced fence value, as upstream schedule() guarantees.
+    sched.seq_at_update_after_schedule = None
+
+    def _capture(_scheduler_output):
+        sched.seq_at_update_after_schedule = sched.sched_step_seq
+
+    sched._update_after_schedule.side_effect = _capture
+    return sched, request
+
+
+def test_fast_path_schedule_advances_fence_before_update_after_schedule() -> None:
+    sched, request = _make_fast_path_sched(defer_block_free=True)
+
+    OmniGenerationScheduler.schedule(sched)
+
+    assert sched.sched_step_seq == 1
+    assert sched.seq_at_update_after_schedule == 1
+
+
+def test_fast_path_schedule_defer_disabled_leaves_fence() -> None:
+    sched, request = _make_fast_path_sched(defer_block_free=False)
+
+    OmniGenerationScheduler.schedule(sched)
+
+    assert sched.sched_step_seq == 0
+
+
+def test_generation_schedule_sweeps_and_resyncs_around_pending_inputs() -> None:
+    sched, request = _make_fast_path_sched(defer_block_free=False)
+    calls: list[str] = []
+    sched._drop_aborted_queued_requests.side_effect = lambda: calls.append("sweep")
+    sched._process_pending_omni_inputs.side_effect = lambda model_mode: calls.append(model_mode)
+    sched._resync_streaming_input_counter.side_effect = lambda: calls.append("resync")
+
+    OmniGenerationScheduler.schedule(sched)
+
+    assert calls == ["sweep", "generation", "sweep", "resync"]
@@ -0,0 +1,180 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
+"""v0.28 EC request-finish contract in the Omni schedulers (#6606 review).
+
+Upstream v0.28 ``_free_request()`` fires ``ec_connector.request_finished()``
+BEFORE the encoder cache is freed (so the connector can inspect per-request
+state), honors its delayed-free result, and returns the generated
+``ec_transfer_params``; ``update_from_output()`` then carries them into the
+``EngineCoreOutput``. ``OmniARScheduler`` previously skipped the hook and
+always returned ``None``; both Omni schedulers discarded the second tuple
+item, so a remote encoder-cache handle could never reach the frontend.
+"""
+
+from __future__ import annotations
+
+from unittest.mock import MagicMock
+
+import pytest
+
+# Imports must run in this order: vllm_omni applies patches to vllm.v1.request
+# before Request / RequestStatus are bound in this module.
+# isort: off
+import vllm_omni  # noqa: F401 - import for side effects (patch vLLM)
+from vllm.sampling_params import SamplingParams
+from vllm.v1.core.sched.output import SchedulerOutput
+from vllm.v1.outputs import ModelRunnerOutput
+from vllm.v1.request import Request, RequestStatus
+from vllm_omni.core.sched.omni_ar_scheduler import OmniARScheduler
+from vllm_omni.core.sched.omni_generation_scheduler import OmniGenerationScheduler
+
+# isort: on
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
+
+EC_PARAMS = {"remote_encoder_cache_handle": "ec-1"}
+
+
+def _make_free_request_sched(*, ec_connector) -> tuple[OmniARScheduler, MagicMock]:
+    """Minimal real-`_free_request` harness; `order` records the EC hook
+    firing relative to the encoder-cache free."""
+    order = MagicMock()
+    sched = OmniARScheduler.__new__(OmniARScheduler)
+    sched._omits_kv_transfer_cache = {}
+    sched._connector_finished = lambda request: (False, None)
+    sched.ec_connector = ec_connector
+    if ec_connector is not None:
+        order.attach_mock(ec_connector.request_finished, "request_finished")
+    sched.encoder_cache_manager = MagicMock()
+    order.attach_mock(sched.encoder_cache_manager.free, "encoder_cache_free")
+    sched.finished_req_ids = set()
+    sched._new_prompt_len_snapshot = {}
+    sched.finished_req_ids_dict = None
+    sched._should_transfer_kv_for_request = lambda req_id: False
+    sched._free_blocks = MagicMock()
+    sched._free_input_coordinator_request = MagicMock()
+    sched.chunk_transfer_adapter = None
+    return sched, order
+
+
+class _FakeFinishedRequest:
+    def __init__(self, request_id: str) -> None:
+        self.request_id = request_id
+
+    def is_finished(self) -> bool:
+        return True
+
+
+def test_free_request_fires_ec_hook_before_encoder_cache_free() -> None:
+    ec_connector = MagicMock()
+    ec_connector.request_finished.return_value = (False, EC_PARAMS)
+    sched, order = _make_free_request_sched(ec_connector=ec_connector)
+    request = _FakeFinishedRequest("req-ec")
+
+    kv_params, ec_params = sched._free_request(request)
+
+    assert ec_params == EC_PARAMS
+    ec_connector.request_finished.assert_called_once_with(request)
+    hook_calls = [name for name, *_ in order.mock_calls]
+    assert hook_calls.index("request_finished") < hook_calls.index("encoder_cache_free")
+    sched._free_blocks.assert_called_once_with(request)
+
+
+def test_free_request_honors_ec_delayed_free() -> None:
+    ec_connector = MagicMock()
+    ec_connector.request_finished.return_value = (True, EC_PARAMS)
+    sched, _ = _make_free_request_sched(ec_connector=ec_connector)
+    request = _FakeFinishedRequest("req-ec-delay")
+
+    kv_params, ec_params = sched._free_request(request)
+
+    assert ec_params == EC_PARAMS
+    sched._free_blocks.assert_not_called()
+
+
+def test_free_request_without_ec_connector_returns_none() -> None:
+    sched, _ = _make_free_request_sched(ec_connector=None)
+    request = _FakeFinishedRequest("req-no-ec")
+
+    kv_params, ec_params = sched._free_request(request)
+
+    assert ec_params is None
+    sched._free_blocks.assert_called_once_with(request)
+
+
+def _make_finishing_request() -> Request:
+    request = Request(
+        request_id="req-ec-finish",
+        prompt_token_ids=[1, 2, 3],
+        sampling_params=SamplingParams(max_tokens=4),
+        pooling_params=None,
+        arrival_time=100.0,
+        block_hasher=None,
+    )
+    request.status = RequestStatus.RUNNING
+    return request
+
+
+def _make_finish_sched(request: Request) -> MagicMock:
+    """MagicMock scheduler driving the real ``update_from_output`` through a
+    finishing request; ``_free_request`` reports EC transfer params."""
+    sched = MagicMock()
+    sched.requests = {request.request_id: request}
+    sched.perf_metrics = None
+    sched.defer_block_free = False
+    sched.structured_output_manager.should_advance.return_value = False
+    sched._process_kv_transfer_trigger.return_value = False
+    sched._maybe_decode_pooling_output.return_value = None
+    sched._handle_stopped_request.return_value = True
+    sched._free_request.return_value = (None, EC_PARAMS)
+    sched.kv_cache_manager.estimate_cached_tokens.return_value = 0
+    sched.chunk_transfer_adapter = None
+    sched.finished_req_ids_dict = None
+    sched._new_prompt_len_snapshot = {}
+    return sched
+
+
+def test_ar_update_from_output_carries_ec_params_to_engine_output() -> None:
+    request = _make_finishing_request()
+    sched = _make_finish_sched(request)
+    sched._update_request_with_output.return_value = ([42], True)
+
+    engine_core_outputs = OmniARScheduler.update_from_output(sched, *_finish_frame_parts(request))
+
+    (eco,) = engine_core_outputs[request.client_index].outputs
+    assert eco.ec_transfer_params == EC_PARAMS
+    sched._free_request.assert_called_once_with(request)
+
+
+def _finish_frame_parts(request: Request):
+    scheduler_output = MagicMock(spec=SchedulerOutput)
+    scheduler_output.num_scheduled_tokens = {request.request_id: 1}
+    scheduler_output.total_num_scheduled_tokens = 1
+    scheduler_output.scheduled_spec_decode_tokens = {}
+    scheduler_output.num_invalid_spec_tokens = 0
+
+    model_runner_output = MagicMock(spec=ModelRunnerOutput)
+    model_runner_output.sampled_token_ids = [[42]]
+    model_runner_output.logprobs = None
+    model_runner_output.prompt_logprobs_dict = {}
+    model_runner_output.pooler_output = None
+    model_runner_output.num_nans_in_logits = None
+    model_runner_output.kv_connector_output = None
+    model_runner_output.cudagraph_stats = None
+    model_runner_output.req_id_to_index = {request.request_id: 0}
+    model_runner_output.routed_experts = None
+    return scheduler_output, model_runner_output
+
+
+def test_generation_update_from_output_carries_ec_params_to_engine_output() -> None:
+    request = _make_finishing_request()
+    # One-shot generation finish: the whole prompt is computed.
+    request.num_computed_tokens = len(request.prompt_token_ids)
+    sched = _make_finish_sched(request)
+
+    engine_core_outputs = OmniGenerationScheduler.update_from_output(sched, *_finish_frame_parts(request))
+
+    (eco,) = engine_core_outputs[request.client_index].outputs
+    assert eco.ec_transfer_params == EC_PARAMS
+    sched._free_request.assert_called_once_with(request)
@@ -0,0 +1,126 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
+"""v0.28 prefill-stats finalization in the Omni schedulers (#6606 review).
+
+Upstream v0.28 finalizes prefill statistics with
+``PrefillStats.finalize(kv_cache_manager.estimate_cached_tokens(request))``
+before emitting them, which computes ``num_cache_creation_tokens`` (tokens
+computed and written to the prefix cache). Both Omni schedulers previously
+emitted ``take_prefill_stats()`` raw, so creation usage was always 0.
+"""
+
+from __future__ import annotations
+
+from unittest.mock import MagicMock
+
+import pytest
+
+# Imports must run in this order: vllm_omni applies patches to vllm.v1.request
+# before Request / RequestStatus are bound in this module.
+# isort: off
+import vllm_omni  # noqa: F401 - import for side effects (patch vLLM)
+from vllm.sampling_params import SamplingParams
+from vllm.v1.core.sched.output import SchedulerOutput
+from vllm.v1.outputs import ModelRunnerOutput
+from vllm.v1.request import Request, RequestStatus
+from vllm_omni.core.sched.omni_ar_scheduler import OmniARScheduler
+from vllm_omni.core.sched.omni_generation_scheduler import OmniGenerationScheduler
+
+# isort: on
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
+
+
+def _make_request() -> Request:
+    request = Request(
+        request_id="req-prefill-stats",
+        prompt_token_ids=[1, 2, 3],
+        sampling_params=SamplingParams(max_tokens=4),
+        pooling_params=None,
+        arrival_time=100.0,
+        block_hasher=None,
+    )
+    request.status = RequestStatus.RUNNING
+    # A scheduled prefill: 3 prompt tokens, 1 from the local prefix cache.
+    request.prefill_stats.set(
+        num_prompt_tokens=3,
+        num_local_cached_tokens=1,
+        num_external_cached_tokens=0,
+    )
+    return request
+
+
+def _make_sched(request: Request) -> MagicMock:
+    sched = MagicMock()
+    sched.requests = {request.request_id: request}
+    sched.perf_metrics = None
+    sched.defer_block_free = False
+    sched.structured_output_manager.should_advance.return_value = False
+    sched._process_kv_transfer_trigger.return_value = False
+    sched._maybe_decode_pooling_output.return_value = None
+    sched._handle_stopped_request.return_value = True
+    # The whole 3-token prompt ends up cached: 3 - 1 pre-cached = 2 created.
+    sched.kv_cache_manager.estimate_cached_tokens.return_value = 3
+
+    def _free_and_invalidate(req):
+        # Lifecycle guard: freeing releases the KV blocks, after which the
+        # manager reports 0 cached tokens. If finalize ran after
+        # _free_request (the reviewed bug), creation count would compute
+        # against 0 and the assertions below would see 0, not 2.
+        sched.kv_cache_manager.estimate_cached_tokens.return_value = 0
+        return (None, None)
+
+    sched._free_request.side_effect = _free_and_invalidate
+    sched.chunk_transfer_adapter = None
+    sched.finished_req_ids_dict = None
+    sched._new_prompt_len_snapshot = {}
+    return sched
+
+
+def _frame(request: Request):
+    scheduler_output = MagicMock(spec=SchedulerOutput)
+    scheduler_output.num_scheduled_tokens = {request.request_id: 1}
+    scheduler_output.total_num_scheduled_tokens = 1
+    scheduler_output.scheduled_spec_decode_tokens = {}
+    scheduler_output.num_invalid_spec_tokens = 0
+
+    model_runner_output = MagicMock(spec=ModelRunnerOutput)
+    model_runner_output.sampled_token_ids = [[42]]
+    model_runner_output.logprobs = None
+    model_runner_output.prompt_logprobs_dict = {}
+    model_runner_output.pooler_output = None
+    model_runner_output.num_nans_in_logits = None
+    model_runner_output.kv_connector_output = None
+    model_runner_output.cudagraph_stats = None
+    model_runner_output.req_id_to_index = {request.request_id: 0}
+    model_runner_output.routed_experts = None
+    return scheduler_output, model_runner_output
+
+
+def test_ar_scheduler_finalizes_prefill_stats_before_emitting() -> None:
+    request = _make_request()
+    sched = _make_sched(request)
+    sched._update_request_with_output.return_value = ([42], True)
+
+    engine_core_outputs = OmniARScheduler.update_from_output(sched, *_frame(request))
+
+    (eco,) = engine_core_outputs[request.client_index].outputs
+    assert eco.prefill_stats is not None
+    assert eco.prefill_stats.num_cached_tokens == 1
+    assert eco.prefill_stats.num_cache_creation_tokens == 2
+    sched.kv_cache_manager.estimate_cached_tokens.assert_called_once_with(request)
+
+
+def test_generation_scheduler_finalizes_prefill_stats_before_emitting() -> None:
+    request = _make_request()
+    request.num_computed_tokens = len(request.prompt_token_ids)
+    sched = _make_sched(request)
+
+    engine_core_outputs = OmniGenerationScheduler.update_from_output(sched, *_frame(request))
+
+    (eco,) = engine_core_outputs[request.client_index].outputs
+    assert eco.prefill_stats is not None
+    assert eco.prefill_stats.num_cached_tokens == 1
+    assert eco.prefill_stats.num_cache_creation_tokens == 2
+    sched.kv_cache_manager.estimate_cached_tokens.assert_called_once_with(request)
@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 """Unit coverage for the two stage-input timeout hooks.

 ``_process_pending_input_timeouts`` covers the full-payload transport;
@@ -253,6 +253,38 @@ def test_log_failed_chunk_sends_noop_without_adapter():
    scheduler._log_failed_chunk_sends()


+class _FakeReceiveFailureAdapter:
+    def __init__(self, failures):
+        self._failures = dict(failures)
+
+    def collect_failed_receive_request_ids(self):
+        failures, self._failures = self._failures, {}
+        return failures
+
+
+class _FakeReceiveFailureScheduler(OmniSchedulerMixin):
+    def __init__(self, requests, adapter):
+        self.requests = requests
+        self.chunk_transfer_adapter = adapter
+        self.finish_calls = []
+
+    def finish_requests(self, req_ids, status):
+        self.finish_calls.append((set(req_ids), status))
+
+
+def test_process_chunk_receive_failures_delegates_to_finish_requests():
+    request_id = "invalid-chunk"
+    adapter = _FakeReceiveFailureAdapter({request_id: "prompt exceeds limit"})
+    scheduler = _FakeReceiveFailureScheduler({request_id: SimpleNamespace(request_id=request_id)}, adapter)
+
+    scheduler._process_chunk_receive_failures()
+
+    assert len(scheduler.finish_calls) == 1
+    finished_ids, status = scheduler.finish_calls[0]
+    assert finished_ids == {request_id}
+    assert getattr(status, "name", str(status)).endswith("FINISHED_ERROR")
+
+
 # --- R1.4: VLLM_OMNI_INPUT_WAIT_TIMEOUT_S is parsed at import time, so each of
 # these reloads the module under a different environment. One knob now gates the
 # deadline on both transports, which is why a silent 0 matters more than it did
@@ -0,0 +1,168 @@
+"""Tests for the ``num_waiting_for_streaming_input`` resync placed in
+``OmniARScheduler.finish_requests`` / ``OmniGenerationScheduler.finish_requests``
+and in ``OmniARScheduler.schedule``.
+
+Upstream keeps that counter incrementally (``+1`` when a resumable request
+parks in a waiting queue as ``WAITING_FOR_STREAMING_REQ``, ``-1`` when the
+next update arrives or the request is finished) and
+``Scheduler.get_num_unfinished_requests`` *subtracts* it from the waiting
+queues. Omni rewrites ``request.status`` outside both hooks -- the
+chunk-transfer adapter's park/restore and
+``_realign_request_status_to_queues`` -- so a counted request can leave
+``WAITING_FOR_STREAMING_REQ`` without the matching decrement and inflate the
+counter for the lifetime of the process.
+
+An inflated counter does not degrade throughput, it hangs the stage:
+``EngineCore.has_work()`` reads false while a live request sits in
+``waiting``, so the engine blocks in ``input_queue.get()`` and never calls
+``schedule()`` again. Regression for the Qwen3-Omni duplex symptom where the
+server answered the first conversation after a boot and was silent from the
+second on while still reporting healthy.
+"""
+
+from __future__ import annotations
+
+import pytest
+from vllm.v1.core.sched.request_queue import SchedulingPolicy, create_request_queue
+from vllm.v1.request import RequestStatus
+
+import vllm_omni.core.sched.omni_ar_scheduler as ar_sched_mod
+import vllm_omni.core.sched.omni_generation_scheduler as gen_sched_mod
+from vllm_omni.core.sched.omni_ar_scheduler import OmniARScheduler
+from vllm_omni.core.sched.omni_generation_scheduler import OmniGenerationScheduler
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
+
+
+class _StubRequest:
+    def __init__(self, request_id: str, status: RequestStatus) -> None:
+        self.request_id = request_id
+        self.status = status
+
+    def is_finished(self) -> bool:
+        return RequestStatus.is_finished(self.status)
+
+
+_SCHEDULER_PARAMS = [
+    pytest.param(OmniARScheduler, ar_sched_mod, id="ar"),
+    pytest.param(OmniGenerationScheduler, gen_sched_mod, id="generation"),
+]
+
+
+def _make_scheduler(scheduler_cls, *, requests, running, waiting, counter, skipped=None):
+    scheduler = scheduler_cls.__new__(scheduler_cls)
+    scheduler.chunk_transfer_adapter = None
+    scheduler.input_coordinator = None
+    scheduler.requests = requests
+    scheduler.running = running
+    scheduler.waiting = waiting
+    scheduler.skipped_waiting = skipped if skipped is not None else []
+    scheduler.num_waiting_for_streaming_input = counter
+    return scheduler
+
+
+@pytest.mark.parametrize(("scheduler_cls", "scheduler_mod"), _SCHEDULER_PARAMS)
+def test_finish_requests_clears_counter_left_by_a_stomped_status(
+    monkeypatch: pytest.MonkeyPatch,
+    scheduler_cls,
+    scheduler_mod,
+) -> None:
+    """The abort case that wedged the talker.
+
+    The request was counted while parked for streaming input, then had its
+    status rewritten to ``RUNNING`` by omni's queue surgery, so upstream's
+    ``finish_requests`` took the running branch and never decremented. With
+    no parked requests left, the counter must land at 0.
+    """
+    aborted = _StubRequest("req-aborted", RequestStatus.RUNNING)
+    scheduler = _make_scheduler(
+        scheduler_cls,
+        requests={},  # upstream already dropped it
+        running=[],
+        waiting=[],
+        counter=1,
+    )
+
+    monkeypatch.setattr(
+        scheduler_mod.VLLMScheduler,
+        "finish_requests",
+        lambda self, request_ids, finished_status: [aborted],
+    )
+
+    scheduler_cls.finish_requests(scheduler, [aborted.request_id], RequestStatus.FINISHED_ABORTED)
+
+    assert scheduler.num_waiting_for_streaming_input == 0
+
+
+def test_resync_counts_parked_requests_in_skipped_waiting() -> None:
+    """``get_num_unfinished_requests`` sums ``waiting`` and ``skipped_waiting``,
+    so the resync has to look at both or it under-counts and reintroduces the
+    spin the counter exists to prevent."""
+    parked = _StubRequest("req-skipped", RequestStatus.WAITING_FOR_STREAMING_REQ)
+    scheduler = _make_scheduler(
+        OmniARScheduler,
+        requests={parked.request_id: parked},
+        running=[],
+        waiting=[],
+        counter=0,
+        skipped=[parked],
+    )
+
+    scheduler._resync_streaming_input_counter()
+
+    assert scheduler.num_waiting_for_streaming_input == 1
+
+
+def test_ar_schedule_resyncs_before_delegating_upstream(monkeypatch: pytest.MonkeyPatch) -> None:
+    """Pins placement in ``schedule``: the chunk-transfer adapter rewrites
+    status during ``process_pending_chunks``, so the resync has to run after
+    that and before upstream schedules, while ``self.waiting`` is still the
+    real queue."""
+    parked = _StubRequest("req-resumed", RequestStatus.RUNNING)
+    scheduler = OmniARScheduler.__new__(OmniARScheduler)
+    scheduler.chunk_transfer_adapter = None
+    scheduler.input_coordinator = None
+    scheduler.requests = {parked.request_id: parked}
+    scheduler.running = [parked]
+    scheduler.waiting = []
+    scheduler.skipped_waiting = []
+    scheduler.num_waiting_for_streaming_input = 1  # leaked by the status rewrite
+    scheduler.policy = "fcfs"
+
+    seen: dict[str, int] = {}
+
+    monkeypatch.setattr(
+        ar_sched_mod.OmniSchedulerMixin, "_consume_pending_connector_output", lambda self, model_mode: None
+    )
+    monkeypatch.setattr(ar_sched_mod.OmniSchedulerMixin, "_process_pending_input_timeouts", lambda self: None)
+    monkeypatch.setattr(OmniARScheduler, "_should_defer_waiting_admission", lambda self: False)
+
+    def fake_schedule(self, throttle_prefills=False):
+        seen["counter"] = self.num_waiting_for_streaming_input
+        raise _StopScheduleError
+
+    class _StopScheduleError(Exception):
+        pass
+
+    monkeypatch.setattr(ar_sched_mod.VLLMScheduler, "schedule", fake_schedule)
+
+    with pytest.raises(_StopScheduleError):
+        OmniARScheduler.schedule(scheduler)
+
+    assert seen["counter"] == 0
+
+
+def test_generation_scheduler_sweep_removes_aborted_requests_from_all_queues() -> None:
+    aborted = _StubRequest("req-generation-aborted", RequestStatus.FINISHED_ABORTED)
+    scheduler = OmniGenerationScheduler.__new__(OmniGenerationScheduler)
+    scheduler.waiting = create_request_queue(SchedulingPolicy.FCFS)
+    scheduler.waiting.add_request(aborted)
+    scheduler.skipped_waiting = create_request_queue(SchedulingPolicy.FCFS)
+    scheduler.skipped_waiting.add_request(aborted)
+    scheduler.running = [aborted]
+
+    scheduler._drop_aborted_queued_requests()
+
+    assert list(scheduler.waiting) == []
+    assert list(scheduler.skipped_waiting) == []
+    assert scheduler.running == []
@@ -1,4 +1,8 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
 import json
+import math
 import os
 import threading
 from collections.abc import Callable
@@ -159,6 +163,18 @@ def benchmark_params(request):

 def assert_result(result, params, num_prompt) -> None:
    assert result["completed"] == num_prompt, "Request failures exist"
+    baseline = params.get("baseline")
+    hardware = result.get("Hardware")
+    hardware_baseline = baseline.get(hardware) if isinstance(baseline, dict) and isinstance(hardware, str) else None
+    if isinstance(hardware_baseline, dict) and "mean_tpot_ms" in hardware_baseline:
+        num_tpot_samples = result.get("num_tpot_samples")
+        mean_tpot_ms = result.get("mean_tpot_ms")
+        assert isinstance(num_tpot_samples, int) and not isinstance(num_tpot_samples, bool) and num_tpot_samples > 0, (
+            "TPOT baseline is configured, but no measurable TPOT samples were produced"
+        )
+        assert (
+            isinstance(mean_tpot_ms, int | float) and not isinstance(mean_tpot_ms, bool) and math.isfinite(mean_tpot_ms)
+        ), "TPOT baseline is configured, but mean_tpot_ms is not finite"
    expected_audio_turns = params.get("expected_duplex_audio_turns_per_session")
    if expected_audio_turns is not None:
        session_metrics = result.get("duplex_session_metrics")
@@ -0,0 +1,106 @@
+[
+  {
+    "test_name": "test_hunyuanvideo15_i2v_single_device",
+    "mark": [
+      {
+        "hardware_marks": {
+          "res": {
+            "cuda": "H100"
+          },
+          "num_cards": 1
+        }
+      },
+      "full_model",
+      "diffusion"
+    ],
+    "description": "Single-device baseline",
+    "server_type": "vllm-omni",
+    "server_params": {
+      "model": "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_i2v",
+      "serve_args": {
+        "flow-shift": 5.0,
+        "enable-diffusion-pipeline-profiler": true
+      }
+    },
+    "benchmark_params": [
+      {
+        "name": "832x480_frames33_steps4",
+        "dataset": "random",
+        "task": "i2v",
+        "num-prompts": 10,
+        "max-concurrency": 1,
+        "num-input-images": 1,
+        "seed": 42,
+        "enable-negative-prompt": true,
+        "random-request-config": [
+          {
+            "width": 832,
+            "height": 480,
+            "num_inference_steps": 4,
+            "num_frames": 33,
+            "fps": 24,
+            "weight": 1
+          }
+        ],
+        "extra-body": {
+          "guidance_scale": 6.0,
+          "flow_shift": 5.0
+        }
+      }
+    ]
+  },
+  {
+    "test_name": "test_hunyuanvideo15_i2v_cachedit_tp2_vae_patch2",
+    "mark": [
+      {
+        "hardware_marks": {
+          "res": {
+            "cuda": "H100"
+          },
+          "num_cards": 2
+        }
+      },
+      "full_model",
+      "diffusion"
+    ],
+    "description": "CacheDiT + TP=2 + VAE patch parallel=2 + VAE tiling",
+    "server_type": "vllm-omni",
+    "server_params": {
+      "model": "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_i2v",
+      "serve_args": {
+        "flow-shift": 5.0,
+        "cache-backend": "cache_dit",
+        "tensor-parallel-size": 2,
+        "vae-patch-parallel-size": 2,
+        "vae-use-tiling": true,
+        "enable-diffusion-pipeline-profiler": true
+      }
+    },
+    "benchmark_params": [
+      {
+        "name": "832x480_frames33_steps4",
+        "dataset": "random",
+        "task": "i2v",
+        "num-prompts": 10,
+        "max-concurrency": 1,
+        "num-input-images": 1,
+        "seed": 42,
+        "enable-negative-prompt": true,
+        "random-request-config": [
+          {
+            "width": 832,
+            "height": 480,
+            "num_inference_steps": 4,
+            "num_frames": 33,
+            "fps": 24,
+            "weight": 1
+          }
+        ],
+        "extra-body": {
+          "guidance_scale": 6.0,
+          "flow_shift": 5.0
+        }
+      }
+    ]
+  }
+]
@@ -0,0 +1,104 @@
+[
+  {
+    "test_name": "test_hunyuanvideo15_t2v_single_device",
+    "mark": [
+      {
+        "hardware_marks": {
+          "res": {
+            "cuda": "H100"
+          },
+          "num_cards": 1
+        }
+      },
+      "full_model",
+      "diffusion"
+    ],
+    "description": "Single-device baseline",
+    "server_type": "vllm-omni",
+    "server_params": {
+      "model": "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v",
+      "serve_args": {
+        "flow-shift": 5.0,
+        "enable-diffusion-pipeline-profiler": true
+      }
+    },
+    "benchmark_params": [
+      {
+        "name": "832x480_frames33_steps4",
+        "dataset": "random",
+        "task": "t2v",
+        "num-prompts": 10,
+        "max-concurrency": 1,
+        "seed": 42,
+        "enable-negative-prompt": true,
+        "random-request-config": [
+          {
+            "width": 832,
+            "height": 480,
+            "num_inference_steps": 4,
+            "num_frames": 33,
+            "fps": 24,
+            "weight": 1
+          }
+        ],
+        "extra-body": {
+          "guidance_scale": 6.0,
+          "flow_shift": 5.0
+        }
+      }
+    ]
+  },
+  {
+    "test_name": "test_hunyuanvideo15_t2v_cachedit_tp2_vae_patch2",
+    "mark": [
+      {
+        "hardware_marks": {
+          "res": {
+            "cuda": "H100"
+          },
+          "num_cards": 2
+        }
+      },
+      "full_model",
+      "diffusion"
+    ],
+    "description": "CacheDiT + TP=2 + VAE patch parallel=2 + VAE tiling",
+    "server_type": "vllm-omni",
+    "server_params": {
+      "model": "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v",
+      "serve_args": {
+        "flow-shift": 5.0,
+        "cache-backend": "cache_dit",
+        "tensor-parallel-size": 2,
+        "vae-patch-parallel-size": 2,
+        "vae-use-tiling": true,
+        "enable-diffusion-pipeline-profiler": true
+      }
+    },
+    "benchmark_params": [
+      {
+        "name": "832x480_frames33_steps4",
+        "dataset": "random",
+        "task": "t2v",
+        "num-prompts": 10,
+        "max-concurrency": 1,
+        "seed": 42,
+        "enable-negative-prompt": true,
+        "random-request-config": [
+          {
+            "width": 832,
+            "height": 480,
+            "num_inference_steps": 4,
+            "num_frames": 33,
+            "fps": 24,
+            "weight": 1
+          }
+        ],
+        "extra-body": {
+          "guidance_scale": 6.0,
+          "flow_shift": 5.0
+        }
+      }
+    ]
+  }
+]
@@ -43,7 +43,7 @@
        "extra_body": {
          "save_duplex_request_metrics": true
        },
-        "percentile-metrics": "ttft,e2el,audio_ttfp,audio_rtf,audio_duration",
+        "percentile-metrics": "ttft,tpot,itl,e2el,audio_ttfp,audio_rtf,audio_duration",
        "baseline": {
          "H100": {
            "request_throughput": [
@@ -46,9 +46,6 @@
        "percentile-metrics": "ttft,tpot,itl,e2el,audio_rtf,audio_ttfp,audio_duration",
        "baseline": {
          "H100": {
-            "mean_e2el_ms": [
-              3646.797
-            ],
            "mean_audio_rtf": [
              0.1098
            ]
@@ -82,9 +79,6 @@
        "percentile-metrics": "ttft,tpot,itl,e2el,audio_rtf,audio_ttfp,audio_duration",
        "baseline": {
          "H100": {
-            "mean_e2el_ms": [
-              4059.0878
-            ],
            "mean_audio_rtf": [
              0.1335
            ]
@@ -120,9 +114,6 @@
        "percentile-metrics": "ttft,tpot,itl,e2el,audio_rtf,audio_ttfp,audio_duration",
        "baseline": {
          "H100": {
-            "mean_e2el_ms": [
-              5639.5769
-            ],
            "mean_audio_rtf": [
              0.1798
            ]
Author	SHA1	Message	Date
Yueqian Lin	aef61597d3	Merge remote-tracking branch 'origin/main' into HEAD	2026-08-30 03:36:08 -07:00
akshatvishu	c41925689d	[BugFix][Higgs-Audio-V3] Fix transcript and concurrent sampling tests (#6422 ) Signed-off-by: akshatvishu <akshatnayak197@gmail.com>	2026-08-30 08:52:15 +00:00
Yueqian Lin	a6b559cf54	[Bugfix][MiniMax-Music3] Resolve stage subdirs against a real snapshot (#6640 ) Signed-off-by: Yueqian Lin <linyueqian@outlook.com>	2026-08-30 06:51:24 +00:00
baonudesifeizhai	8be601ed7f	[Diffusion] Add native SymmMem Fast Ulysses transport (#6340 ) Signed-off-by: roG0d <baonudesifeizhai@gmail.com>	2026-08-30 04:14:21 +00:00
y-null	99a2ae1fc8	[Bugfix][MiniCPM-o] Anchor duplex soft-interrupt listen sandwich before commit (#6772 ) Signed-off-by: y_null <y_null@qq.com>	2026-08-30 03:25:43 +00:00
Sy03 Nathan Price Eric	21dd4b33ac	[Bugfix][MiniCPM-o] Stabilize native duplex streaming and Stage-1 handoffs (#6529 ) Signed-off-by: Sy03 <1370724210@qq.com> Co-authored-by: Nathan Price <nathan@abridge.com> Co-authored-by: Eric(Qun) <186020953+0xtoward@users.noreply.github.com>	2026-08-29 13:05:24 -07:00
WeiQing ChenandGao Han	9a88d2d21d	[diffusion][bugfix] Separate Diffusers hooks from model metadata (#6749 ) Signed-off-by: david6666666 <530634352@qq.com> Signed-off-by: Gao Han <hgaoaf@connect.ust.hk> Co-authored-by: Gao Han <hgaoaf@connect.ust.hk>	2026-08-29 17:37:14 +00:00
YancyandAsthenia	73893e73ea	[Bugfix] Fix create_error_response import across vLLM versions (#6773 ) Signed-off-by: Asthenia <asthenia0412@gmail.com> Co-authored-by: Asthenia <asthenia0412@gmail.com>	2026-08-29 17:01:51 +00:00
NATURE	87736a3cc5	[Bugfix][MiniCPM-o] Bound full-duplex Talker context with sliding recompute (#6626 ) Signed-off-by: natureofnature <wzliu@connect.hku.hk>	2026-08-30 00:49:42 +08:00
Sy03	3adee400a0	[Bugfix][Qwen3-TTS] Handle codec generations that exhaust their token budget (#6728 ) Signed-off-by: Sy03 <1370724210@qq.com>	2026-08-29 08:59:05 -07:00
Gao Han	676a35fa8b	Fix MiniCPM-o concurrent audio first-packet latency (#6767 ) Signed-off-by: Gao Han <hgaoaf@connect.ust.hk>	2026-08-29 23:27:47 +08:00
Honghan Zhuandamy-why-3459	72a1ce488b	[Bugfix] Avoid invalid TPOT metrics (#6696 ) Signed-off-by: zhuhh97 <zhuhonghan@huawei.com> Co-authored-by: amy-why-3459 <wuhaiyan17@huawei.com>	2026-08-29 11:56:37 +00:00
wangyu	e338f0ece3	[CI]Split GPU jobs by cards_* and reject hand-written SKU marks (#6650 ) Signed-off-by: wangyu <410167048@qq.com> Signed-off-by: [Your Name] <your.email@example.com>	2026-08-29 18:42:33 +08:00
HAAZZZEEEE	c50b7de167	[NPU][Diffusion] MiniMax-H3 RainFusion end_step tail fallback (#6037 ) Signed-off-by: HAAZZZEEEE <wang-taicheng@qq.com>	2026-08-29 18:36:05 +08:00
psv666	a5b8c82788	[Bugfix][MiniCPM-o] Fix duplex camera frame fixture (#6757 ) Signed-off-by: psv666 <2693925048@qq.com>	2026-08-29 09:57:53 +00:00
ooooooye	5a5bc55c25	[Feature][Diffusion] Allow online INT8 quantization with DLO AllGather (#6573 ) Signed-off-by: brandneway <gyuan4892@gmail.com>	2026-08-29 09:50:30 +00:00
Wenjia LiandWeiQing Chen	d9980c75e8	Perf/minimax h3 dit swiglu rope cache npu (#6410 ) Signed-off-by: Wenjia Li <wjialish@gmail.com> Co-authored-by: WeiQing Chen <40507679+david6666666@users.noreply.github.com>	2026-08-29 09:14:52 +00:00
Nguyen Kha Nhat Long Cursor Samit	bb5bf8fd0a	[Bugfix] Fix colocate-async sleep admission race and deliver AR abort tokens (#6367 ) Signed-off-by: knlnguyen1802 <knlnguyen1802@gmail.com> Signed-off-by: Cursor <cursoragent@cursor.com> Signed-off-by: Samit <285365963@qq.com> Co-authored-by: Cursor <cursoragent@cursor.com> Co-authored-by: Samit <285365963@qq.com>	2026-08-29 09:11:39 +00:00
psv666andGao Han	5c47fd0bc8	[Bugfix][Qwen3-Omni] Handle missing packed modules mapping (#6748 ) Signed-off-by: psv666 <2693925048@qq.com> Co-authored-by: Gao Han <hgaoaf@connect.ust.hk>	2026-08-29 15:24:49 +08:00
Zijun HuangandWeiQing Chen	08cdee8dc4	[Feature] Support MiniMax-H3 FlashGen native LoRA with the legacy manager (#6666 ) Signed-off-by: Zijun Huang <zijunhua@usc.edu> Co-authored-by: WeiQing Chen <40507679+david6666666@users.noreply.github.com>	2026-08-29 06:52:19 +00:00
abrahamzewoudie Hongsheng Liu amy-why-3459	143ccc5a2f	Fix Qwen3-Omni audio encoder TP when heads are not divisible by TP size (#4322 ) Signed-off-by: abrahamzewoudie <azewoudi@amd.com> Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com> Co-authored-by: amy-why-3459 <wuhaiyan17@huawei.com>	2026-08-29 14:52:02 +08:00
WeiQing Chen	55a226dcf1	[video][bugfix] Accept semantic video output in offline examples (#6747 ) Signed-off-by: david6666666 <530634352@qq.com>	2026-08-29 06:26:28 +00:00
Jiatai Wang	f66f05633c	[Bugfix] Honor request seed in MOSS-TTS adapters (#6543 ) Signed-off-by: JiataiWang <wangjiatai@proton.me>	2026-08-28 22:44:13 -07:00
ZacheryAUandamy-why-3459	457c6e2793	[bugfix][MiniCPM-o] Re-land empty full-payload and async_chunk=false processor tests (#6745 ) Co-authored-by: amy-why-3459 <wuhaiyan17@huawei.com>	2026-08-29 13:39:54 +08:00
boatman	bfe8967ca2	[Bugfix][StepAudio2] Fix async-chunk metadata mismatch and boundary glitches (#5917 ) Signed-off-by: boatman <109857087+sphinxkkkbc@users.noreply.github.com>	2026-08-28 21:45:24 -07:00
WeiQing ChenandOpenAI Codex	fbc93a0e08	[ci][bugfix] Fix MiniMax-H3 FP8 quality test OOM (#6742 ) Signed-off-by: david6666666 <530634352@qq.com> Co-authored-by: OpenAI Codex <codex@openai.com>	2026-08-29 12:11:31 +08:00
Bob Tian	d934a5f7b8	[Bugfix] Report engine-queued requests as waiting in vllm_omni:num_requests gauges (#6549 ) Signed-off-by: Bob Tian <bobzetian@google.com>	2026-08-29 03:58:26 +00:00
Inesh Reddy Chappidiandamy-why-3459	bbb3ae01a1	[CI] Gate qwen3-omni no-async-chunk perf on mean_audio_rtf (#6743 ) Signed-off-by: Inesh Reddy <ineshreddy249@gmail.com> Co-authored-by: amy-why-3459 <wuhaiyan17@huawei.com>	2026-08-28 20:28:58 -07:00
Zhang BowenandHongsheng Liu	46cfa8435b	[Model][Frontend] MiniMax-H3: Parallelize MP4 response frame conversion (#6499 ) Signed-off-by: MosCloud <bwzhang1991@163.com> Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-28 21:22:00 -06:00
fan2956andWeiQing Chen	0dc8f45906	[Feat] Minimax H3 support ref and tgt video sparse attention on NPU (#6518 ) Signed-off-by: fan2956 <zhoufan53@huawei.com> Co-authored-by: WeiQing Chen <40507679+david6666666@users.noreply.github.com>	2026-08-29 11:10:53 +08:00
Allen Wu TRAE CLI TRAE CLI	d94061201d	[Bugfix][Engine] Make abort idempotent during shutdown (#6327 ) Signed-off-by: Allen Wu <allenwu2795@gmail.com> Co-authored-by: TRAE CLI <noreply@bytedance.com> Co-authored-by: TRAE CLI <traecli@bytedance.com>	2026-08-28 19:51:25 -07:00
Mu GuanLinandWeiQing Chen	9558fd2ea8	[Bugfix] Scope MiniMax-H3 cuDNN SDPA state (#6710 ) Signed-off-by: mglyn <1203789601@qq.com> Co-authored-by: WeiQing Chen <40507679+david6666666@users.noreply.github.com>	2026-08-28 19:40:50 -07:00
Mu GuanLin	55a8bd6b80	[CI][Bugfix] Align the LTX-2 FP8 quality gate with the default recipe (#5831 ) Signed-off-by: mglyn <1203789601@qq.com>	2026-08-29 02:24:09 +00:00
Honghan Zhu	f584729f24	[BugFix] Fix local media access for diffusion speech (#6622 ) Signed-off-by: zhuhh97 <zhuhonghan@huawei.com>	2026-08-29 02:01:24 +00:00
Chendi.Xue	034af1f657	[CI][XPU]switch to use vLLM base docker for XPU (#6727 ) Signed-off-by: Chendi Xue <chendi.xue@intel.com>	2026-08-29 09:03:00 +08:00
Hongsheng Liuandhsliuustc0106	8bfad23f98	[Core] Restore topology-aware diffusion worker titles (#6722 ) Signed-off-by: hsliuustc0106 <hsliuustc0106@users.noreply.github.com> Co-authored-by: hsliuustc0106 <hsliuustc0106@users.noreply.github.com>	2026-08-29 08:45:57 +08:00
Gao Han	6b9ce05feb	Revert "[bugfix][MiniCPM-o] Fix offline_inference/test_minicpmo_4_5.py and online_serving/ one" (#6730 )	2026-08-29 08:14:02 +08:00
ZacheryAUandamy-why-3459	6d84409a68	[bugfix][MiniCPM-o] Fix offline_inference/test_minicpmo_4_5.py and online_serving/ one (#5464 ) Co-authored-by: amy-why-3459 <wuhaiyan17@huawei.com>	2026-08-28 19:20:09 +00:00
Khairul Kabir khairulkabir1661 Claude Opus 4.6	ebb3b20576	[Diffusion] Make Flux2Pipeline text_encoder_out_layers configurable (#6390 ) Co-authored-by: khairulkabir1661 <khairulkabir1661@users.noreply.github.com> Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>	2026-08-28 09:05:41 -06:00
Wallbreazzz Canlin Guo Canlin Guo	be335a86f9	fix(npu): extend bf16 autocast to NPU in MossAudioTokenizer decode (#6664 ) Signed-off-by: Wallbreazzz <110282866+Wallbreazzz@users.noreply.github.com> Signed-off-by: Canlin Guo <961750412@qq.com> Co-authored-by: Canlin Guo <961750412@qq.com> Co-authored-by: Canlin Guo <canlinguosdu@gmail.com>	2026-08-28 04:01:49 -07:00
Hongsheng Liu	d5aced9296	Revert "[Core] Make diffusion worker titles topology-aware" (#6717 )	2026-08-28 17:30:03 +08:00
Wallbreazzz	4ad455e61e	[Feature][TTS] adaptive chunk ramp (Phase 2 buffer-feedback controller) (#6001 ) Signed-off-by: Wallbreazzz <110282866+Wallbreazzz@users.noreply.github.com>	2026-08-28 02:11:05 -07:00
amy-why-3459	da94c3ebe1	[Bugfix][MiniCPM-o] Bind omni-duplex video frames to the unit they close (#6404 ) Signed-off-by: amy-why-3459 <wuhaiyan17@huawei.com>	2026-08-28 16:58:20 +08:00
prettygirlisnotme	13173d6bbb	[Core] Make diffusion worker titles topology-aware (#6307 ) Signed-off-by: Dragonborn <1078757180@qq.com>	2026-08-28 16:00:30 +08:00
Sy03	009b992c5b	[Bugfix][MiniCPM-o] Isolate duplex handoff failures and barge-in (#6170 ) Signed-off-by: Sy03 <1370724210@qq.com>	2026-08-28 15:46:41 +08:00
Mu GuanLinandHongsheng Liu	04b97a5922	[Perf] MiniMax-H3 VAE Decoder Ops (#6607 ) Signed-off-by: mglyn <1203789601@qq.com> Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-28 15:14:33 +08:00
TJianandroot	15c6ab8fa7	[AMD][CI] Fix AMD CI (Partial) (#6704 ) Signed-off-by: root <root@tw020.mia.tensorwave.lan> Signed-off-by: tjtanaa <tunjian.tan@embeddedllm.com> Co-authored-by: root <root@tw020.mia.tensorwave.lan>	2026-08-28 14:34:11 +08:00
zzh	c201fd77c6	Add HYvideo1.5 benchmark (#6349 ) Signed-off-by: zzh <943967662@qq.com>	2026-08-28 14:31:03 +08:00
Canlin Guo lishunyang12 yuanwu	b6a4baa659	[Feature] Support text encoder Disaggregation for H3 (#5885 ) Signed-off-by: Canlin Guo <canlinguosdu@gmail.com> Signed-off-by: lishunyang12 <lishunyang12@163.com> Signed-off-by: yuanwu <yuan.wu@intel.com> Co-authored-by: lishunyang12 <lishunyang12@163.com> Co-authored-by: yuanwu <yuan.wu@intel.com>	2026-08-28 14:27:05 +08:00
WeiQing Chen	ed837e350e	[Test] Stabilize MiniMax-H3 reference accuracy compile (#6688 ) Signed-off-by: david6666666 <530634352@qq.com>	2026-08-28 14:18:28 +08:00
BruceLoveDecimalandamy-why-3459	ab8d45d3cc	[Bugfix][MiniCPM-o] Retire CFM DiT CUDA graphs a generation at a time (#6587 ) Signed-off-by: BruceLoveDecimal <liuqihao970610@gmail.com> Co-authored-by: amy-why-3459 <wuhaiyan17@huawei.com>	2026-08-28 14:07:57 +08:00
Eugene Zhao	f8f8aa8eb7	[Bugfix] Honor stage selection in online profiler endpoints (#6609 ) Signed-off-by: qujing226 <qujing226@gmail.com>	2026-08-28 11:41:47 +08:00
Hongsheng Liu	6ec5ab11bd	[Bugfix] Make HWR filesystem lifecycle transitions fail-closed (#6692 ) Signed-off-by: hsliuustc0106 <liuhongsheng4@huawei.com>	2026-08-28 09:30:17 +08:00
Hongsheng Liuandhsliuustc0106	4245fd6955	[Fix] Restore MammothModa2 support (#6694 ) Signed-off-by: hsliuustc0106 <hsliuustc0106@users.noreply.github.com> Co-authored-by: hsliuustc0106 <hsliuustc0106@users.noreply.github.com>	2026-08-28 08:32:32 +08:00
Mu GuanLinandHongsheng Liu	5e4daaf197	[Diffusion] Add LTX-2.5 Diffusion VAE decoder support (#6189 ) Signed-off-by: mglyn <1203789601@qq.com> Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-27 23:54:40 +08:00
NATUREandamy-why-3459	262ff14693	[Bugfix][MiniCPM-o] Bound native auto-response continuation (#6630 ) Signed-off-by: natureofnature <wzliu@connect.hku.hk> Co-authored-by: amy-why-3459 <wuhaiyan17@huawei.com>	2026-08-27 19:51:47 +08:00
Deep Shahandamy-why-3459	069e6c5fbc	[CI/Build][MiniCPM-o] Derive duplex admission-probe limit from the deploy config (#6678 ) Signed-off-by: Deep Shah <deep@socratic.co> Co-authored-by: amy-why-3459 <wuhaiyan17@huawei.com>	2026-08-27 19:20:01 +08:00
NATURE	54c1fc64b7	[Bugfix] Gate async-chunk segment resume by session mode (#6680 ) Signed-off-by: natureofnature <wzliu@connect.hku.hk>	2026-08-27 18:52:09 +08:00
Zhanshenzhuiandamy-why-3459	c6df39e9f7	[Benchmark][MiniCPM-o] Add Omni-DuplexEval support (#6634 ) Signed-off-by: Zhanshenzhui <99891002+zyforsure@users.noreply.github.com> Co-authored-by: amy-why-3459 <wuhaiyan17@huawei.com>	2026-08-27 16:31:50 +08:00
Zheng Wengangandamy-why-3459	94b1546ce3	[Model] Qwen3-Omni: register thinker-only pipeline for Instruct serve (#6284 ) Signed-off-by: ZhengWG <zwg0606@gmail.com> Signed-off-by: Zheng Wengang <zwg0606@gmail.com> Co-authored-by: amy-why-3459 <wuhaiyan17@huawei.com>	2026-08-27 08:21:03 +00:00
ooooooye changetheway Claude Opus 4.8 WeiQing Chen	19da238246	[Bugfix][NPU] Eager-import mindiesd for every diffusion attention backend (#6054 ) Signed-off-by: brandneway <gyuan4892@gmail.com> Co-authored-by: changetheway <guotaoyuan1@h-partners.com> Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com> Co-authored-by: WeiQing Chen <40507679+david6666666@users.noreply.github.com>	2026-08-27 14:41:36 +08:00
Zhou TaichangandClaude Opus 5	651b03225b	[Rebase] Rebase to vllm 0.28.0 (#6606 ) Signed-off-by: tzhouam <tzhouam@connect.ust.hk> Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>	2026-08-27 06:12:55 +00:00
Hongsheng Liuandhsliuustc0106	da4a08b65b	[Diffusion] Add FLUX.2-klein BF16 HWR contract (#6651 ) Signed-off-by: hsliuustc0106 <hsliuustc0106@users.noreply.github.com> Signed-off-by: hsliuustc0106 <liuhongsheng4@huawei.com> Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com> Co-authored-by: hsliuustc0106 <hsliuustc0106@users.noreply.github.com>	2026-08-27 12:51:15 +08:00
SYLARandHongsheng Liu	0e44fc7406	[diffusion][feature] Add offline SVDQuant W4A4 support (#6162 ) Signed-off-by: lishunyang12 <lishunyang12@163.com> Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-27 08:45:53 +08:00
Sy03andsy03	432b2c6ca3	[Model] Add native full-duplex Nemotron VoiceChat serving (#6089 ) Signed-off-by: Sysy <1370724210@qq.com> Signed-off-by: sy03 <137072421@qq.com> Co-authored-by: sy03 <137072421@qq.com>	2026-08-26 13:19:20 -07:00
ChoHee Claude Gao Han	c2723979c0	[Bugfix] Validate per-stage device layout before spawning workers (#5003 ) (#5742 ) Signed-off-by: ChoHee15 <cc5281@126.com> Signed-off-by: Gao Han <hgaoaf@connect.ust.hk> Co-authored-by: Claude <noreply@anthropic.com> Co-authored-by: Gao Han <hgaoaf@connect.ust.hk>	2026-08-26 19:25:22 +00:00
vOv	2d4724eb74	[Bugfix][Diffusion] Fix FLASH_ATTN cross-attention key-padding unpad (#5866 ) Signed-off-by: cr-gao <gaochenrui@sjtu.edu.cn>	2026-08-26 17:20:59 +00:00
Anurag	91dd7fcaec	[Bugfix] Reject instructions changes on locked native duplex sessions (#6318 ) Signed-off-by: ANURAG KANADE <anuragkanade6@gmail.com>	2026-08-26 16:50:31 +00:00
harley	926e7fbfa2	[Recipe] zai-org/GLM-TTS (2x non-standard RTX 4090 48GB) (#5769 ) Signed-off-by: harley <480497125@qq.com>	2026-08-26 22:16:49 +08:00
Yueqian LinandYueqian Lin	8cfede4309	[Perf][Engine] Event-driven orchestration loop (opt-in) — S1 of #4855 (#5221 ) Signed-off-by: Yueqian Lin <gao@yueqian.us> Signed-off-by: Yueqian Lin <linyueqian@outlook.com> Co-authored-by: Yueqian Lin <gao@yueqian.us>	2026-08-26 22:15:25 +08:00
Mu GuanLin	d3c990dca8	[Model][Bugfix] Improve LTX audio parity and similarity guards (#6342 ) Signed-off-by: mglyn <1203789601@qq.com>	2026-08-26 20:59:10 +08:00
psv666andamy-why-3459	5ec576ab4c	[Bugfix] Fix missing Realtime audio completion (#6564 ) Signed-off-by: psv666 <2693925048@qq.com> Co-authored-by: amy-why-3459 <wuhaiyan17@huawei.com>	2026-08-26 18:27:25 +08:00
WeiQing Chen	030fccb2bf	[Test] Add MiniMax-H3 DLO DP2 T2VA smoke (#6555 ) Signed-off-by: david6666666 <530634352@qq.com>	2026-08-26 17:17:10 +08:00
Yueqian Lin	1ad90653c5	[CI/Build] Add dots.tts weekly e2e coverage dots.tts landed in #4765 with no GPU coverage: nothing under tests/e2e exercises it, so the DiT sampler, streaming vocoder and AR loopback have no CI signal at all. Add an offline e2e module marked slow + tts + L4, which the existing weekly 'TTS · L4' step collects via -m 'slow and L4 and tts'. No pipeline YAML change is needed; the weekly TTS steps are marker driven. Three cases, the latter two guarding specific review findings from #4765: 1. zero-shot synthesis produces finite, non-silent audio 2. mixed prefill+decode batch keeps per-request logits rows aligned 3. the same text twice yields the same audio (seeded FM noise) Verified selector behaviour: 3 collected under the weekly selector, 0 collected under the nightly one. Signed-off-by: Yueqian Lin <linyueqian@outlook.com>	2026-08-13 11:24:50 -07:00