[Bugfix][MiniMax-H3] Apply pre-commit formatting

Signed-off-by: hsliuustc0106 <liuhongsheng4@huawei.com>
[Bugfix][MiniMax-H3] Reconcile temporal decode plans across VAE ranks
2026-09-03 08:44:26 +08:00 · 2026-09-03 08:20:00 +08:00 · 2026-09-02 07:08:40 +08:00 · 2026-09-01 23:11:29 +08:00 · 2026-09-01 23:11:29 +08:00 · 2026-09-01 23:11:28 +08:00
509 changed files with 43966 additions and 3250 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 +71,45 @@ 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
+          --deselect='tests/diffusion/offloader/test_diffusion_layerwise_offload.py::test_layerwise_offload_diffusion_model[stabilityai/stable-audio-open-1.0]'

-    - label: "Diffusion Sequence Parallelism Test (Need 4 GPUs)"
+    # TODO: Diagnose unstable Stable Audio layerwise-offload memory savings on ROCm.
+    - label: "Diffusion Stable Audio Layerwise Offload Test (quarantined)"
+      agent_pool: mi300_1
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: NonBlocking
+      commands:
+        - export VLLM_LOGGING_LEVEL=DEBUG
+        - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+        - >-
+          pytest -s -v
+          'tests/diffusion/offloader/test_diffusion_layerwise_offload.py::test_layerwise_offload_diffusion_model[stabilityai/stable-audio-open-1.0]'
+
+    - 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 +190,45 @@ 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"
@@ -195,13 +238,10 @@ steps:
      grade: Blocking
      timeout_in_minutes: 50
      commands:
-        - |
-          timeout 40m 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_base.py tests/e2e/offline_inference/test_qwen3_tts_base.py -m 'advanced_model and cuda' --run-level 'advanced_model'
-          '
+        - export VLLM_LOGGING_LEVEL=DEBUG
+        - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+        - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
+        - timeout 40m pytest -s -v tests/e2e/online_serving/test_qwen3_tts_base.py tests/e2e/offline_inference/test_qwen3_tts_base.py -m "advanced_model and cuda" --run-level "advanced_model"

 # TODO: Bagel test on ROCm is very unstable. @tjtanaa
 # Need to debug before reneable numerical changes across large PRs
@@ -191,10 +191,7 @@ steps:
      mirror_hardwares: [amdproduction]
      grade: Blocking
      commands:
-        - |
-          timeout 45m 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
@@ -85,6 +85,9 @@ steps:
          - vllm_omni/model_executor/models/common/alias_free_activation.py
          - vllm_omni/model_executor/stage_input_processors/qwen3_tts.py
          - vllm_omni/deploy/qwen3_tts.yaml
+          - vllm_omni/entrypoints/openai/serving_speech.py
+          - vllm_omni/entrypoints/openai/tts_adapters/
+          - vllm_omni/engine/stage_pool.py
        commands:
          - |
            timeout 40m bash -c "
@@ -104,6 +107,9 @@ steps:
          - vllm_omni/model_executor/models/common/alias_free_activation.py
          - vllm_omni/model_executor/stage_input_processors/qwen3_tts.py
          - vllm_omni/deploy/qwen3_tts.yaml
+          - vllm_omni/entrypoints/openai/serving_speech.py
+          - vllm_omni/entrypoints/openai/tts_adapters/
+          - vllm_omni/engine/stage_pool.py
          - .buildkite/common/scripts/run_cov_split.sh
          - pyproject.toml
        # 40 -> 60: the coverage split runs the model twice.
@@ -122,6 +128,7 @@ 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 +139,10 @@ 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 +174,7 @@ 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 +225,50 @@ 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
+
+      - label: "Diffusion · MiniMax-H3 L3 DLO DP2 and FastH3"
+        source_file_dependencies:
+          - tests/e2e/online_serving/minimax_h3/
+          - tests/helpers/assertions.py
+          - tests/helpers/mark.py
+          - tests/helpers/media.py
+          - tests/helpers/runtime.py
+          - vllm_omni/diffusion/models/minimax_h3/
+          - vllm_omni/diffusion/lora/
+          - vllm_omni/diffusion/offloader/
+          - vllm_omni/diffusion/distributed/
+          - vllm_omni/entrypoints/openai/
+        # Keep four server lifecycles in one Buildkite job, but never in one
+        # pytest process. DLO uses multiprocessing shared-memory queues; the
+        # parent's resource_tracker can retain a stale /psm_* entry during
+        # teardown and strand the next Ref2VA collective.
+        # A per-file process plus a hard timeout also makes a regression fail
+        # instead of leaving the merge queue stuck indefinitely.
+        timeout_in_minutes: 150
+        commands:
+          - |
+            set -eu
+            export VLLM_IMAGE_FETCH_TIMEOUT=60
+            if [ -f vllm_omni/diffusion/models/minimax_h3/lora.py ]; then
+              export VLLM_TEST_MINIMAX_H3_TURBO_LORA=$(python3 -c 'from huggingface_hub import hf_hub_download; print(hf_hub_download(repo_id="lightx2v/Minimax-h3-Turbo", filename="minimax_h3_fl2v_turbo_4step_v1.0_768p_bf16.safetensors"))')
+            fi
+            if [ -f vllm_omni/diffusion/models/minimax_h3/fasth3.py ]; then
+              export VLLM_TEST_MINIMAX_H3_FASTH3_LORA=$(python3 -c 'from huggingface_hub import hf_hub_download; print(hf_hub_download(repo_id="FastVideo/FastVideo-FastH3-4-step-Preview-v1-LoRA", filename="dense-datafree/adapter_model.safetensors"))')
+            fi
+            timeout 35m pytest -s -v tests/e2e/online_serving/minimax_h3/test_minimax_h3_dlo_dp2_fl2va.py -m 'advanced_model and cuda and diffusion' --run-level 'advanced_model'
+            timeout 35m pytest -s -v tests/e2e/online_serving/minimax_h3/test_minimax_h3_dlo_dp2_ref2va.py -m 'advanced_model and cuda and diffusion' --run-level 'advanced_model'
+            timeout 35m pytest -s -v tests/e2e/online_serving/minimax_h3/test_minimax_h3_turbo_lora.py -m 'advanced_model and cuda and diffusion' --run-level 'advanced_model'
+            timeout 35m pytest -s -v tests/e2e/online_serving/minimax_h3/test_minimax_h3_fasth3_lora.py -m 'advanced_model and cuda and diffusion' --run-level 'advanced_model'
+        mirror_hardwares: h100_2
@@ -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
@@ -251,6 +252,9 @@ steps:
          - vllm_omni/model_executor/models/common/alias_free_activation.py
          - vllm_omni/model_executor/stage_input_processors/qwen3_tts.py
          - vllm_omni/deploy/qwen3_tts.yaml
+          - vllm_omni/entrypoints/openai/serving_speech.py
+          - vllm_omni/entrypoints/openai/tts_adapters/
+          - vllm_omni/engine/stage_pool.py
        commands:
          - |
            timeout 20m bash -c "
@@ -259,25 +263,6 @@ steps:
            "
        mirror_hardwares: l4_1

-      - label: "TTS · Qwen3-TTS Base Test"
-        source_file_dependencies:
-          - tests/e2e/online_serving/test_qwen3_tts_base.py
-          - vllm_omni/model_executor/models/qwen3_tts/
-          - vllm_omni/model_executor/models/common/qwen3_code_predictor.py
-          - vllm_omni/model_executor/models/common/snake_activation.py
-          - vllm_omni/model_executor/models/common/alias_free_activation.py
-          - vllm_omni/model_executor/stage_input_processors/qwen3_tts.py
-          - vllm_omni/deploy/qwen3_tts.yaml
-        commands:
-          - |
-            timeout 20m bash -c "
-              export VLLM_WORKER_MULTIPROC_METHOD=spawn
-              export VLLM_ALLOW_LONG_MAX_MODEL_LEN=1
-              export VLLM_OMNI_USE_V2_RUNNER=1
-              pytest -s -v tests/e2e/online_serving/test_qwen3_tts_base.py -m 'core_model' --run-level 'core_model'
-            "
-        mirror_hardwares: l4_1
-
      - label: "Diffusion · Wan22 Test"
        source_file_dependencies:
          - tests/e2e/online_serving/test_wan22_t2v.py
@@ -337,3 +322,15 @@ 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]
@@ -16,6 +16,7 @@ Easy, fast, and cheap omni-modality model serving for everyone

 *Latest News* 🔥

+- [2026/08] We released [0.28.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.28.0), featuring production-ready [MiniMax H3](recipes/MiniMaxAI/MiniMax-H3.md) serving on GPU and NPU, a unified AR/DiT paged KV cache runtime, and enhanced realtime full-duplex serving for the [MiniCPM-o series](recipes/OpenBMB/MiniCPM-o-4_5.md).
 - [2026/08] [VeRL-Omni](https://github.com/verl-project/verl-omni) `v0.2.0` is released: faster diffusion RL powered by vLLM-Omni (request-level/step-wise batching with FA3), rebuilt Qwen3-Omni multimodal training (DPO & GSPO), plus LTX-2.3, Qwen-Image-Edit support and more. See the [release notes](https://github.com/verl-project/verl-omni/releases/tag/v0.2.0).
 - [2026/08] We released [0.26.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.26.0) - aligned with the vLLM 0.26 release line, featuring [MiniMax H3](recipes/MiniMaxAI/MiniMax-H3.md) joint video/audio generation, an experimental full-duplex realtime runtime for [MiniCPM-o 4.5](recipes/OpenBMB/MiniCPM-o-4_5.md), distributed layerwise diffusion offload, and broader model, hardware, streaming, TTS, and quantization support.
 - [2026/07] We released [0.24.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.24.0) - aligned with the vLLM 0.24 release line, expanding production-ready coverage across TTS, speech, diffusion, image/video generation, and robot-policy serving, with major Omni stage runtime refactoring, diffusion request-level batching, async output materialization, quantization/cache/memory improvements, and broad CUDA/ROCm/XPU/NPU support.
@@ -57,9 +58,9 @@ vLLM-Omni is flexible and easy to use with:
 vLLM-Omni seamlessly supports most popular open-source models on HuggingFace, including:

 - **Omni-modality models** (e.g. Qwen3-Omni, MiniCPM-o 4.5, Cosmos3, HunyuanImage, BAGEL)
- **TTS models** (e.g. Qwen3-TTS, VoxCPM2, Ming-Omni-TTS, CosyVoice3)
- **Diffusion models** — image, video, and audio generation (e.g. MiniMax H3, Qwen-Image, Wan2.2, FLUX)
- **Robot-policy and action models** (e.g. GR00T-N1.7, DreamZero-DROID, InternVLA-A1, Cosmos3 action policy)
+- **TTS models** (e.g. Qwen3-TTS, IndexTTS 2.5, dots.tts, CosyVoice3)
+- **Diffusion models** — image, video, and audio generation (e.g. MiniMax H3, LTX-2.5, SANA-Video, Wan2.2)
+- **Robot-policy and action models** (e.g. π0, GR00T-N1.7, DreamZero-DROID, InternVLA-A1)

 ## Getting Started

@@ -67,6 +68,7 @@ Visit our [documentation](https://vllm-omni.readthedocs.io/en/latest/) to learn

 - [Installation](https://vllm-omni.readthedocs.io/en/latest/getting_started/installation/)
 - [Quickstart](https://vllm-omni.readthedocs.io/en/latest/getting_started/quickstart/)
+- [Nightly CUDA Docker images](https://hub.docker.com/r/vllm/vllm-omni/tags?name=nightly) built from the latest `main`
 - [List of Supported Models](https://vllm-omni.readthedocs.io/en/latest/models/supported_models/)
 - [Deployment Recipes](https://recipes.vllm.ai) for vLLM-Omni model serving

@@ -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
@@ -30,6 +30,7 @@ RUN python3 -m pip install -v --no-deps \
      "stepaudio2-minicpmo>=0.1.1" \
      "step-audio2>=1.0.0"

+ENV VLLM_TARGET_DEVICE=npu
 ENV VLLM_WORKER_MULTIPROC_METHOD=spawn

 ENTRYPOINT []
@@ -1,5 +1,5 @@
 ARG VLLM_ASCEND_IMAGE=quay.io/atlas-ci/vllm-ascend
-ARG VLLM_ASCEND_TAG=v0.27.0-a3
+ARG VLLM_ASCEND_TAG=v0.28.0-a3
 FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}

 # WORKDIR /vllm-workspace/vllm
@@ -30,6 +30,7 @@ RUN python3 -m pip install -v --no-deps \
      "stepaudio2-minicpmo>=0.1.1" \
      "step-audio2>=1.0.0"

+ENV VLLM_TARGET_DEVICE=npu
 ENV VLLM_WORKER_MULTIPROC_METHOD=spawn

 ENTRYPOINT []
@@ -1,5 +1,5 @@
 ARG VLLM_ASCEND_IMAGE=quay.io/atlas-ci/vllm-ascend
-ARG VLLM_ASCEND_TAG=v0.27.0
+ARG VLLM_ASCEND_TAG=v0.28.0
 FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}

 ARG APP_DIR=/vllm-workspace/vllm-omni
@@ -65,6 +65,7 @@ RUN python3 -m pip install -v --no-deps \
      --trusted-host cache-service.nginx-pypi-cache.svc.cluster.local \
      --default-timeout=60

+ENV VLLM_TARGET_DEVICE=npu
 ENV VLLM_WORKER_MULTIPROC_METHOD=spawn

 ENTRYPOINT []
@@ -1,5 +1,5 @@
 ARG VLLM_ASCEND_IMAGE=quay.io/atlas-ci/vllm-ascend
-ARG VLLM_ASCEND_TAG=v0.27.0-a3
+ARG VLLM_ASCEND_TAG=v0.28.0-a3
 FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}

 ARG APP_DIR=/vllm-workspace/vllm-omni
@@ -66,6 +66,7 @@ RUN python3 -m pip install -v --no-deps \
      --trusted-host cache-service.nginx-pypi-cache.svc.cluster.local \
      --default-timeout=60

+ENV VLLM_TARGET_DEVICE=npu
 ENV VLLM_WORKER_MULTIPROC_METHOD=spawn

 ENTRYPOINT []
@@ -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 && \
@@ -5,14 +5,22 @@ nav:
    - getting_started/quickstart.md
    - getting_started/installation/*
  - Serving:
-    - OpenAI-Compatible API:
-      - Chat Completions API: serving/chat_completions_api.md
-      - Image Generation: serving/image_generation_api.md
-      - Image Edit: serving/image_edit_api.md
-      - Text to Speech: serving/speech_api.md
-      - Videos: serving/videos_api.md
-      - Audio Generation: serving/audio_generate_api.md
-      - Streaming Video Input: serving/video_stream_api.md
+    - API Server:
+      - Overview: serving/README.md
+      - Core APIs:
+        - Chat Completions: serving/chat_completions_api.md
+        - Text to Speech: serving/speech_api.md
+        - Audio Generation: serving/audio_generate_api.md
+        - Image Generation: serving/image_generation_api.md
+        - Image Edit: serving/image_edit_api.md
+        - Videos: serving/videos_api.md
+      - Streaming and Realtime:
+        - Realtime Audio: serving/realtime_api.md
+        - Full Duplex: serving/full_duplex_api.md
+        - Streaming Video Input: serving/video_stream_api.md
+        - Streaming Video Output: serving/streaming_video_output_api.md
+        - OpenPI Robot Policy: serving/openpi_api.md
+      - Standalone Experimental Servers: serving/standalone_servers.md
  - Examples:
    - examples/README.md
    - Offline Inference:
@@ -29,7 +37,7 @@ nav:
      - Image-To-Video: user_guide/examples/online_serving/image_to_video.md
      - Speech-To-Video: user_guide/examples/online_serving/speech_to_video.md
      - Text-To-Image: user_guide/examples/online_serving/text_to_image.md
-      - Text-To-Speech: user_guide/examples/online_serving/text_to_speech.md
+      - Text-To-Speech (Online Serving): user_guide/examples/online_serving/text_to_speech.md
      - Text-To-Video: user_guide/examples/online_serving/text_to_video.md
  - General:
    - usage/*
@@ -119,6 +127,8 @@ nav:
        - Async Omni Output Materialization: design/feature/omni_async_output_materialization.md
        - Automatic Prefix Caching: design/feature/prefix_caching.md
        - Realtime AR-Diffusion: design/feature/realtime_ar_diffusion.md
+      - Model-Specific Capabilities:
+        - MiniMax H3 Temporal VAE Chunks: design/feature/minimax_h3_temporal_vae_chunks.md
      - Communication:
        - OmniConnector Implementations:
          - Mooncake Store Connector: design/feature/omni_connectors/mooncake_store_connector.md
@@ -57,9 +57,9 @@ vLLM-Omni is flexible and easy to use with:
 vLLM-Omni seamlessly supports most popular open-source models on HuggingFace, including:

 - Omni-modality models (e.g. Qwen3-Omni, MiniCPM-o 4.5, Cosmos3, HunyuanImage, BAGEL)
- TTS models (e.g. Qwen3-TTS, VoxCPM2, Ming-Omni-TTS, CosyVoice3)
- Diffusion models — image, video, and audio generation (e.g. MiniMax H3, Qwen-Image, Wan2.2, FLUX)
- Robot-policy and action models (e.g. GR00T-N1.7, DreamZero-DROID, InternVLA-A1, Cosmos3 action policy)
+- TTS models (e.g. Qwen3-TTS, IndexTTS 2.5, dots.tts, CosyVoice3)
+- Diffusion models — image, video, and audio generation (e.g. MiniMax H3, LTX-2.5, SANA-Video, Wan2.2)
+- Robot-policy and action models (e.g. π0, GR00T-N1.7, DreamZero-DROID, InternVLA-A1)

 For more information, checkout the following:

@@ -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][]

@@ -2,7 +2,7 @@

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

-For options within a vLLM Engine, please refer to the [vLLM 0.26 configuration guide](https://docs.vllm.ai/en/v0.26.0/configuration/index.html).
+For options within a vLLM Engine, please refer to the [vLLM 0.28 configuration guide](https://docs.vllm.ai/en/v0.28.0/configuration/index.html).

 Each model defines fixed topology in a registered `PipelineConfig` and runtime
 overrides in a deploy YAML.
@@ -151,7 +151,7 @@ their keys only.
 ## Inherited vLLM variables

 vLLM-Omni also reads variables through its aligned vLLM dependency. Refer to
-the [vLLM 0.26 environment-variable reference](https://docs.vllm.ai/en/v0.26.0/configuration/env_vars.html)
+the [vLLM 0.28 environment-variable reference](https://docs.vllm.ai/en/v0.28.0/configuration/env_vars.html)
 for their definitions. This includes vLLM launch, cache, logging, plugin, ROCm,
 XPU, ModelScope, and FlashInfer workspace settings.

@@ -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. |
@@ -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) |

@@ -170,6 +170,10 @@ because the user-visible output is diffusion media rather than a token stream.
 The
 [MiniMax-H3 recipe](https://github.com/vllm-project/vllm-omni/blob/main/recipes/MiniMaxAI/MiniMax-H3.md)
 contains the hardware-specific profiles and warmup requirements.
+The internal, opt-in
+[temporal VAE chunk callback](feature/minimax_h3_temporal_vae_chunks.md)
+documents the narrower released-source contract for publishing committed video
+frames during decode. It does not change the complete-output serving topology.

 ### Cosmos3: unified MoT reasoner and generator

@@ -0,0 +1,260 @@
+# MiniMax H3 temporal VAE chunk callback
+
+!!! warning "Experimental internal capability"
+
+    This feature is an opt-in model integration seam. The current MiniMax H3
+    pipeline still uses complete-output VAE decode, and no CLI option, serving
+    path, transport, or MP4 sink enables temporal chunks by default.
+
+## Status and scope
+
+MiniMax H3 decodes video latents in temporal windows, but the released VAE
+normally exposes only the complete reconstructed video. The temporal callback
+publishes frames after the released decoder has completed overlap blending and
+padding removal. This gives a future runtime consumer a stable point at which
+it can schedule representation conversion, D2H transfer, and encoding while
+the VAE continues decoding later windows.
+
+The implementation remains model-local:
+
+- `vae.py` owns VAE topology and the thin
+  `decode_latent_with_chunks()` facade;
+- `chunked_decode.py` owns the normalized callback contract, metadata
+  validation, and fixed VAE-group rendezvous points; and
+- `temporal_chunks.py` owns the source-gated compatibility planner and the
+  released temporal assembly control flow.
+
+Crop policy, typed media, transport, backpressure, encoding, and public output
+remain runtime responsibilities. The model code does not schedule requests or
+select a presentation format.
+
+## Execution flow
+
+The default and opt-in paths are intentionally separate:
+
+```text
+Default:
+latent -> MiniMaxH3VideoVAE.decode_latent() -> complete RGB video
+
+Opt-in producer seam:
+latent
+  -> MiniMaxH3VideoVAE.decode_latent_with_chunks()
+  -> group-local preflight and callback-owner validation
+  -> released temporal decode / overlap blending / padding removal
+  -> borrowed decoder-domain chunk
+  -> normalize + clone into owned RGB float32 BCTHW [0, 1]
+  -> synchronous callback
+  -> complete RGB video is still returned
+```
+
+Calling `decode_latent_with_chunks()` is the opt-in. Supplying no callback on a
+VAE peer does not select the default decoder; every rank in a VAE patch group
+must enter the chunked method, and only the output rank receives the callback.
+
+## Producer contract
+
+The normalized callback has this structural signature:
+
+```python
+def callback(
+    frames: torch.Tensor,
+    /,
+    *,
+    chunk_index: int,
+    total_chunks: int,
+    frame_start: int,
+    is_final: bool,
+) -> None:
+    ...
+```
+
+For a successful decode:
+
+- `frames` is owned, contiguous RGB float32 in `BCTHW` layout and `[0, 1]`;
+- callback storage does not alias the returned complete video;
+- the consumer may retain or mutate a chunk;
+- `chunk_index` is zero-based, contiguous, and strictly increasing;
+- `total_chunks` is positive and stable for the decode;
+- `frame_start` equals the end of the previous published range;
+- chunks are nonempty and contain only temporally committed frames;
+- exactly one chunk has `is_final=True`; and
+- concatenating chunks along time exactly reconstructs the returned video.
+
+The callback runs synchronously on the producer's current device stream. A
+consumer that reads the tensor on another stream must establish ordering, for
+example with an event, and retain the allocation for that stream's lifetime.
+A slow callback also blocks VAE production. An overlap-oriented runtime
+consumer should therefore acquire bounded capacity, enqueue side-stream work,
+and return promptly instead of encoding frames inside the callback.
+
+Chunks retain the decoded VAE canvas dimensions. A future runtime integration
+must apply the same requested-height and requested-width crop as the complete
+MiniMax H3 pipeline before declaring the media transport-ready.
+
+The raw compatibility callback is deliberately narrower. It receives borrowed
+decoder-domain tensors and must not retain or mutate them. The coordinator is
+the ownership boundary that normalizes and clones raw chunks before invoking
+the public callback.
+
+## Temporal planning and terminal publication
+
+The released configuration advances five latent tokens per temporal window and
+includes a two-token overlap, so each `_adaptive_decode` input normally contains
+seven tokens. After temporal scaling, pre-padding removal, and five-frame
+overlap blending, the normal committed unit is 17 output frames. The final
+remainder depends on the requested frame plan.
+
+Representative released frame plans include:
+
+| Output frames | Published chunks |
+| ---: | --- |
+| 124 | `7 x 17 + 5` |
+| 209 | `12 x 17 + 5` |
+| 243 | `14 x 17 + 5` |
+| 362 | `21 x 17 + 5` |
+
+The terminal callback is held until the complete frame plan passes its logical
+frame, padding, written-frame, chunk-count, and finality checks. Earlier chunks
+are semantically stable, but they do not imply that the overall request has
+succeeded; a consumer must discard or abort partial output if a later decoder
+failure terminates the request.
+
+## Source and configuration gate
+
+The released VAE has no public temporal-callback API. vLLM-Omni therefore owns
+a compatibility implementation for one exact released code contract. The
+reference revision identifies its provenance, but runtime acceptance is based
+on source content, configuration, and structure rather than repository or
+weight metadata:
+
+- reference Hugging Face revision, not independently checked at runtime:
+  `42ed227ee7df40d41602854ae760620d6eb651fe`;
+- sorted 15-file top-level `video_vae/*.py` manifest (excluding
+  `__init__.py`) SHA-256:
+  `30e64afbaa940696bf8bfe2c86003a4b1666c84b22fa3aa12403a3e6a03f705d`;
+- clip length `17`;
+- token chunk, overlap, and drop `5`, `2`, and `3`;
+- temporal ratio `4`;
+- frame pre-padding and overlap `3` and `5`; and
+- no isolated first or last frame.
+
+The adapter does not verify Hugging Face revision metadata, repository identity,
+or model weights. The manifest pin detects Python compatibility drift; it is
+not a security boundary for remote-code execution. The source manifest, listed
+temporal attributes, structural contract, plan, and dtype are validated before
+the first `_adaptive_decode` call. A mismatch disables only the opt-in chunk path.
+`decode_latent()` remains available and performs no source fingerprint or chunk
+rendezvous.
+
+## Supported models
+
+Support in this table means the internal producer contract above, not public
+serving or end-to-end transport support.
+
+| Model or component | Source contract | Status | Validation |
+| --- | --- | --- | --- |
+| MiniMax H3 FL2VA video VAE | Released manifest/listed temporal attributes above (reference `42ed227e`) | Supported, opt-in | Same-environment full-output parity and L20X/B300 feasibility measured on the implementation through PR commit `60e9bbad` |
+| MiniMax H3 Ref2VA video VAE | Same released manifest/listed temporal attributes | Source-compatible; not separately benchmarked | Ref2VA and FL2VA use byte-identical video-VAE source and configuration in the reference snapshot |
+| MiniMax H3 with a different Python manifest or listed temporal attribute | Different enforced contract | Unsupported for chunk mode | Fails closed before decoder collectives; complete decode remains available |
+| Same manifest/temporal attributes with different weights or other component metadata | Not checked by this gate | Not independently qualified | The adapter does not inspect weights, repository identity, revision metadata, or arbitrary component configuration |
+| Other video models | Any | Unsupported | No generic temporal VAE producer ABI is declared by this feature |
+
+## Feature compatibility
+
+| Dimension or combination | Status | Constraint |
+| --- | --- | --- |
+| Existing complete-output decode | Supported and default | No source check, callback object, or callback rendezvous |
+| Explicit chunk decode | Supported internally | Caller invokes `decode_latent_with_chunks()` directly |
+| VAE patch parallel size 1 | Supported by the model seam | Caller supplies the callback only on the request output owner; no WORLD rendezvous is added |
+| VAE patch parallel size equal to the full DiT group | Supported when data parallel size is 1 | Every VAE-group rank calls; exactly group rank 0 owns the callback |
+| Partial VAE patch subgroup | Unsupported | MiniMax H3 currently accepts size 1 or the full DiT group only |
+| Data parallel size greater than 1 with VAE patch parallel size 1 | Model seam only | Per-replica callback ownership is possible, but no serving/runtime consumer is wired |
+| Data parallel size greater than 1 with VAE patch parallel size greater than 1 | Unsupported | Fails before decoder collectives because no replica-local VAE patch group is defined |
+| Too few spatial tiles for the VAE group | Correctness fallback | Decodes rank-locally and restores native tiling state; slower than patch parallel decode |
+| Device memory and full-output assembly | Not reduced or bounded | The compatibility path still assembles and returns the full decode while allocating owned callback chunks; no peak-HBM reduction is claimed |
+| DiT cache, attention, TP, SP, HSDP, LoRA, or quantization features | No direct callback dependency | This seam runs after denoising and does not independently qualify combinations beyond the MiniMax H3 model's own matrix |
+| Layerwise or distributed component offload | Not separately validated | Component residency must place the video VAE before decode |
+| Step execution or request batching | Not integrated | No pipeline caller binds a runtime chunk consumer |
+| Requested-size crop and device uint8 preparation | Not implemented here | Runtime-owned follow-up |
+| Pinned D2H, SHM/ZMQ, or cross-process transport | Not implemented here | Runtime-owned follow-up |
+| Complete MP4 or fragmented MP4 sink | Not implemented here | Existing serving behavior still waits for complete output |
+| Audio VAE chunks and incremental A/V composition | Not supported | Video chunks do not imply audio or request finality |
+| Cancellation and multi-output lease ownership | Not implemented here | Requires a bounded runtime lifecycle |
+| NVIDIA CUDA | Feasibility evidence | L20X and B300 measurements apply to the implementation through `60e9bbad`; the current coordination refactor was not reprofiled |
+| AMD ROCm | Not validated for this feature | Base MiniMax H3 support is documented separately and does not imply temporal-chunk validation |
+| Ascend, Intel, or other accelerators | No feature support claim | Consult the model support table independently; this design adds no platform qualification |
+
+## Distributed and failure semantics
+
+When VAE patch parallelism is active, all VAE-group ranks run the same
+preflight before entering checkpoint collectives. Source/planner errors and the
+resolved temporal concat dtype are reconciled at fixed group rendezvous points.
+
+Callback ownership is also a group decision. Rank `r` contributes `r + 1` when
+it owns a callback and zero otherwise. A group SUM equals one only when rank 0
+is the sole owner; every invalid owner configuration therefore fails
+identically on all ranks before decode.
+
+Callback exceptions are retained on the output rank. Further publication stops,
+but every rank completes the same remaining native decoder collective schedule.
+The original exception is then raised on the owner and
+`MiniMaxH3ChunkCallbackPeerError` on peers.
+
+Exceptions raised inside the checkpoint's native distributed decode cannot in
+general be reconciled by the adapter: a peer may already be blocked inside a
+checkpoint-owned collective. Resolving such failures requires native collective
+handling or supervisor-level timeout and worker-group teardown. The process
+group must not be assumed reusable after such a failure.
+
+## Validation evidence
+
+The contract suite covers ordinary and padded frame plans, blend boundaries,
+owned storage and mutation isolation, source-manifest/listed-temporal-attribute
+drift, callback failure, and healthy subsequent requests. Real Gloo processes cover
+rank-asymmetric preflight failures, dtype disagreement, two-rank callback
+failure propagation, three-rank wrong-owner rejection, insufficient-tile
+fallback, and group recovery for failures that occur at adapter-owned
+rendezvous points.
+
+Four-device L20X and independent four- and eight-device B300 experiments on the
+implementation through PR commit `60e9bbad` showed exact serial-versus-chunked
+frame/MP4 parity within each environment and lower VAE-to-complete-MP4 latency
+when an experimental consumer was attached. Hashes are not claimed to match
+across different hardware/software environments. The later coordination and
+module-boundary refactors were revalidated with CPU and real Gloo tests but were
+not reprofiled on GPUs. These are feasibility measurements, not a promise for
+default serving, because this change does not contain the transport or encoder
+consumer. See [RFC #6872](https://github.com/vllm-project/vllm-omni/issues/6872)
+and [PR #6885](https://github.com/vllm-project/vllm-omni/pull/6885) for the
+measurement provenance and limitations.
+
+## Generalization boundary and follow-ups
+
+This feature does not define a repository-wide `SupportsVideoChunkDecode`
+interface. H3 temporal blending, Wan causal caches, LTX temporal/spatial halos,
+and pipeline-level generation chunks have different commit and failure
+boundaries. A generic producer contract should be extracted only after a second
+model proves the same semantic event without `Any` arguments or model-name
+dispatch.
+
+The reusable future boundary is the emitted, committed video-media chunk, not a
+universal latent-to-VAE method. It should extend the typed media work in
+[RFC #6541](https://github.com/vllm-project/vllm-omni/issues/6541) and
+[PR #6615](https://github.com/vllm-project/vllm-omni/pull/6615), while keeping
+model-specific halo, cache, padding, blending, distributed, and source-gating
+logic inside each model adapter.
+
+Runtime follow-ups own:
+
+- typed video-plus-audio media and request/output identity;
+- requested-size crop and device preparation;
+- bounded leases, backpressure, cancellation, and cleanup;
+- D2H/IPC transport and observability; and
+- complete-MP4 and fragmented-MP4 consumers.
+
+## Related documentation
+
+- [Diffusion model integration](../module/diffusion/diffusion_model_integration.md)
+- [VAE patch parallelism](vae_parallel.md)
+- [Async diffusion output](async_diffusion_output.md)
+- [MiniMax H3 recipe](https://github.com/vllm-project/vllm-omni/blob/main/recipes/MiniMaxAI/MiniMax-H3.md)
@@ -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`. |
@@ -438,11 +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
@@ -459,8 +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`).

 ---

@@ -25,6 +25,10 @@ implementation contract; it is not, by itself, a general support claim.
 - [Automatic Prefix Caching in Omni Models](feature/prefix_caching.md)
 - [Realtime AR-Diffusion Sessions](feature/realtime_ar_diffusion.md)

+### Model-specific capabilities
+
+- [MiniMax H3 Temporal VAE Chunks](feature/minimax_h3_temporal_vae_chunks.md)
+
 ### Communication

 #### OmniConnector implementations
@@ -57,3 +57,10 @@ cross-stage routing.

 Test registry selection, checkpoint loading, minimal inference, input and output
 contracts, and every declared optional capability.
+
+## Model-specific capability documents
+
+- [MiniMax H3 temporal VAE chunk callback](../../feature/minimax_h3_temporal_vae_chunks.md)
+  defines the source-gated producer seam and its boundary with future runtime
+  media transport. It is an internal opt-in capability, not a public serving
+  feature.
@@ -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
@@ -113,6 +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.
@@ -3,4 +3,6 @@
 vLLM-Omni's examples are split into two categories:

 - If you are using vLLM-Omni from within Python code, see the *Offline Inference* section.
- If you are using vLLM-Omni from an HTTP application or client, see the *Online Serving* section.
+- If you are using vLLM-Omni from an HTTP application or client, start with
+  the [API Server guide](../serving/README.md) to choose an endpoint, then use
+  the *Online Serving* section for model-specific examples.
@@ -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; 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).
+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, 0.26.0, and 0.28.0. 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).

 === "NVIDIA CUDA"

@@ -5,7 +5,7 @@
 # --8<-- [end:requirements]
 # --8<-- [start:set-up-using-python]

-vLLM-Omni depends on the matching major/minor release of vLLM. The vLLM-Omni 0.26.x release line uses vLLM 0.26.x.
+vLLM-Omni depends on the matching major/minor release of vLLM. The vLLM-Omni 0.28.x release line uses vLLM 0.28.x.

 !!! note
    PyTorch installed via `conda` will statically link `NCCL` library, which can cause issues when vLLM tries to use `NCCL`. See <gh-issue:8420> for more details.
@@ -95,6 +95,13 @@ uv pip install --no-build-isolation --editable .

 vLLM-Omni offers an official docker image for deployment. These images are built on top of vLLM docker images and available on Docker Hub as [vllm/vllm-omni](https://hub.docker.com/r/vllm/vllm-omni/tags). The version of vLLM-Omni indicates which release of vLLM it is based on.

+Nightly CUDA images built from the latest `main` are also available:
+
+- [`vllm/vllm-omni:nightly`](https://hub.docker.com/r/vllm/vllm-omni/tags?name=nightly) is the rolling multi-architecture tag.
+- [`vllm/vllm-omni:nightly-<commit>`](https://hub.docker.com/r/vllm/vllm-omni/tags?name=nightly-) provides commit-pinned multi-architecture tags. The newest 14 are retained.
+
+Use a commit-pinned tag when you need a reproducible nightly environment.
+
 Here's an example deployment command that has been verified on 2 x H100's:
 ```bash
 docker run --runtime nvidia --gpus 2 \
@@ -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
@@ -9,8 +9,8 @@ The recommended way to use vLLM-Omni on NPU is through the vLLM-Ascend pre-built

 ```bash
 # vLLM-Ascend image
-# Atlas A2: quay.io/atlas-ci/vllm-ascend:v0.26.0
-# Atlas A3: quay.io/atlas-ci/vllm-ascend:v0.26.0-a3
+# Atlas A2: quay.io/atlas-ci/vllm-ascend:v0.28.0
+# Atlas A3: quay.io/atlas-ci/vllm-ascend:v0.28.0-a3
 docker run --rm \
    --name vllm-omni-npu \
    --shm-size=1g \
@@ -28,11 +28,11 @@ docker run --rm \
    -v /etc/ascend_install.info:/etc/ascend_install.info \
    -v /root/.cache:/root/.cache \
    -p 8000:8000 \
-    -it quay.io/atlas-ci/vllm-ascend:v0.26.0-a3 bash
+    -it quay.io/atlas-ci/vllm-ascend:v0.28.0-a3 bash

 # Inside the container, install vLLM-Omni from source
 cd /vllm-workspace
-git clone -b v0.26.0 https://github.com/vllm-project/vllm-omni.git
+git clone -b v0.28.0 https://github.com/vllm-project/vllm-omni.git
 cd vllm-omni
 pip install -v -e . --no-build-isolation
 # or VLLM_OMNI_TARGET_DEVICE=npu pip install -v -e .
@@ -50,21 +50,11 @@ We are keeping [issue #886](https://github.com/vllm-project/vllm-omni/issues/886

 # --8<-- [start:installation-main]

-You can also build vLLM-Omni from the latest main branch if you want to use the latest features or bug fixes. (But sometimes it will break for a while. You can check [issue #886](https://github.com/vllm-project/vllm-omni/issues/886) for the status of the latest commit of vLLM-Omni main branch on NPU.)
+You can also install vLLM-Omni from the latest main branch if you want to use the latest features or bug fixes. Use the aligned vLLM-Ascend v0.28.0 image described above, and check [issue #886](https://github.com/vllm-project/vllm-omni/issues/886) for the status of the latest vLLM-Omni commit on NPU.

 ```bash
-# Pin vLLM and vLLM-Ascend to the v0.26 release line
-git clone -b v0.26.0 https://github.com/vllm-project/vllm.git
-cd vllm
-VLLM_TARGET_DEVICE=empty pip install -v -e .
-cd ..
-
-git clone -b releases/v0.26.0rc https://github.com/vllm-project/vllm-ascend.git
-cd vllm-ascend
-pip install -v -e .
-cd ..
-
-# Install vLLM-Omni from the latest main branch
+# Inside the vLLM-Ascend v0.28.0 container, install vLLM-Omni from main
+cd /vllm-workspace
 git clone https://github.com/vllm-project/vllm-omni.git
 cd vllm-omni
 pip install -v -e . --no-build-isolation
@@ -88,8 +78,8 @@ Supported images as following.
 Here's an example deployment command that has been verified on 4 x NPUs:

 ```bash
-# Atlas A2: quay.io/ascend/vllm-omni:v0.26.0
-# Atlas A3: quay.io/ascend/vllm-omni:v0.26.0-a3
+# Atlas A2: quay.io/ascend/vllm-omni:v0.28.0
+# Atlas A3: quay.io/ascend/vllm-omni:v0.28.0-a3
 docker run --rm \
    --name vllm-omni-a3 \
    --shm-size=4g \
@@ -114,7 +104,7 @@ docker run --rm \
    -v /etc/ascend_install.info:/etc/ascend_install.info \
    -v ~/.cache:/root/.cache \
    -p 8091:8091 \
-    -it quay.io/ascend/vllm-omni:v0.26.0-a3 bash
+    -it quay.io/ascend/vllm-omni:v0.28.0-a3 bash
 ```

 !!! tip
@@ -91,7 +91,7 @@ if __name__ == "__main__":

 For more usages, please refer to [offline inference](../user_guide/examples/offline_inference/qwen2_5_omni.md)

-## Online Serving with OpenAI-Completions API
+## Online Serving with the API Server

 Text-to-image generation quickstart with vLLM-Omni:

@@ -100,20 +100,16 @@ vllm serve Tongyi-MAI/Z-Image-Turbo --omni --port 8091
 ```

 ```bash
-curl -s http://localhost:8091/v1/chat/completions \
+curl -s http://localhost:8091/v1/images/generations \
  -H "Content-Type: application/json" \
  -d '{
-    "messages": [
-      {"role": "user", "content": "a cup of coffee on the table"}
-    ],
-    "extra_body": {
-      "height": 1024,
-      "width": 1024,
-      "num_inference_steps": 50,
-      "guidance_scale": 4.0,
-      "seed": 42
-    }
-  }' | jq -r '.choices[0].message.content[0].image_url.url' | cut -d',' -f2 | base64 -d > coffee.png
+    "prompt": "a cup of coffee on the table",
+    "size": "1024x1024",
+    "response_format": "b64_json",
+    "seed": 42
+  }' | jq -r '.data[0].b64_json' | base64 -d > coffee.png
 ```

-For more details, please refer to [online serving](../user_guide/examples/online_serving/text_to_image.md).
+See the [API Server guide](../serving/README.md) to choose an endpoint. For
+model-specific details, refer to the [text-to-image online serving
+example](../user_guide/examples/online_serving/text_to_image.md).
@@ -21,20 +21,20 @@ th {
 |--------------|--------|-------------------|------------|---------|-----|-----------|--------|
 | `Qwen3OmniMoeForConditionalGeneration` | Qwen3-Omni | `Qwen/Qwen3-Omni-30B-A3B-Instruct` | ✅︎ | | | | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/Qwen/Qwen3-Omni.md) |
 | `Qwen2_5OmniForConditionalGeneration` | Qwen2.5-Omni | `Qwen/Qwen2.5-Omni-7B`, `Qwen/Qwen2.5-Omni-3B` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
-| `MingFlashOmniForConditionalGeneration` + `MingImagePipeline` | Ming-flash-omni-2.0 (omni-speech + imagegen<sup>1</sup>) | `Jonathan1909/Ming-flash-omni-2.0` | ✅︎ |   |   |   | — |
+| `MingFlashOmniForConditionalGeneration` + `MingImagePipeline` | Ming-flash-omni-2.0 (omni-speech + imagegen<sup>1</sup>) | `Jonathan1909/Ming-flash-omni-2.0` | ✅︎ | | | | — |
 | `BagelForConditionalGeneration` | BAGEL (DiT-only) | `ByteDance-Seed/BAGEL-7B-MoT` | ✅︎ | ✅︎ | | ✅︎ | — |
 | `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`, `Boogu/Boogu-Image-0.1-Edit-Turbo` | ✅︎ | | | ✅︎ | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/Boogu/Boogu-Image.md) |
 | `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` | ✅︎ | | | | — |
@@ -93,16 +95,16 @@ th {
 | `MiMoV2ASRForCausalLM` | MiMo-V2.5-ASR | `XiaomiMiMo/MiMo-V2.5-ASR` | ✅︎ | ✅︎ | | | — |
 | `Flux2Pipeline` | FLUX.2-dev | `black-forest-labs/FLUX.2-dev` | ✅︎ | ✅︎ | | | — |
 | `FishSpeechSlowARForConditionalGeneration` | Fish Speech S2 Pro | `fishaudio/s2-pro` | ✅︎ | ✅︎ | | ✅︎ | — |
-| `SenseNovaU1Pipeline` | SenseNova-U1 (DiT-only) | `SenseNova/SenseNova-U1-8B-MoT` | ✅︎ | | | | — |
+| `SenseNovaU1Pipeline` | SenseNova-U1 (DiT-only), SenseNova-U1.5 | `SenseNova/SenseNova-U1-8B-MoT`, `sensenova/SenseNova-U1.5-8B-MoT` | ✅︎ | | | | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/SenseNova/SenseNova-U1.5.md) |
 | `LancePipeline` | Lance | `bytedance-research/Lance` | ✅︎ | | | | — |
 | `HunyuanVideo15Pipeline` | HunyuanVideo-1.5-T2V | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v`, `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v` | ✅︎ | ✅︎ | | | — |
 | `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` | ✅︎ | | | | — |

@@ -0,0 +1,187 @@
+# API Server
+
+vLLM-Omni exposes OpenAI-compatible HTTP APIs plus vLLM-Omni extensions for
+image, audio, video, realtime, and robot-policy workloads. Use this page to
+choose an endpoint; follow the linked reference page for model-specific fields,
+wire protocols, and response details.
+
+Each server instance hosts one model. An endpoint is usable only when the
+loaded model supports that task.
+
+This guide focuses on vLLM-Omni's primary task endpoints. Compatible models
+may also expose standard routes inherited from vLLM, such as
+`POST /v1/completions`; those routes remain model- and task-dependent.
+
+## Start and Verify the Server
+
+Start a model with the shared serving command:
+
+```bash
+vllm serve <model> --omni --port 8091
+```
+
+Some models require additional flags or a deployment configuration. Use the
+model-specific guide when one is provided.
+
+After the server starts, check its health and served model name:
+
+```bash
+export VLLM_OMNI_BASE_URL=http://localhost:8091
+
+curl "$VLLM_OMNI_BASE_URL/health"
+curl "$VLLM_OMNI_BASE_URL/v1/models" | jq .
+```
+
+Use `http://localhost:8091/v1` as the `base_url` for an OpenAI SDK client. If
+the server was started with `--api-key`, include an `Authorization` header with
+`Bearer <api-key>` in requests.
+
+## Choose a Core HTTP Endpoint
+
+Prefer a task-specific endpoint when one matches your workload. Use Chat
+Completions for conversational or heterogeneous omni pipelines, rather than as
+the default wrapper for every generation task.
+
+| Task | Endpoint | Request | Response | Details |
+|------|----------|---------|----------|---------|
+| Conversation or multimodal understanding/generation | `POST /v1/chat/completions` | JSON | OpenAI-style JSON or SSE | [Chat Completions](chat_completions_api.md) |
+| Text-to-speech | `POST /v1/audio/speech` | JSON | Audio bytes or SSE | [Speech](speech_api.md) |
+| Sound, music, or ambient audio generation | `POST /v1/audio/generate` | JSON | Audio bytes | [Audio Generation](audio_generate_api.md) |
+| Text-to-image | `POST /v1/images/generations` | JSON | Base64 JSON or an image file | [Image Generation](image_generation_api.md) |
+| Image editing | `POST /v1/images/edits` | Multipart form | Base64 JSON or SSE | [Image Edit](image_edit_api.md) |
+| Video generation (recommended) | `POST /v1/videos` | Multipart form | Asynchronous job JSON | [Videos](videos_api.md) |
+| Video generation (blocking) | `POST /v1/videos/sync` | Multipart form | Video bytes | [Videos](videos_api.md#synchronous-response) |
+
+`POST /v1/videos/sync` is intended for tests and simple scripts. Use the
+asynchronous job API for production requests so clients can poll, download,
+and delete generated videos independently.
+
+## Minimal Requests
+
+The examples below assume a compatible model is already running. TTS voices,
+media inputs, generation controls, and output capabilities vary by model.
+
+=== "Chat"
+
+    ```bash
+    curl "$VLLM_OMNI_BASE_URL/v1/chat/completions" \
+      -H "Content-Type: application/json" \
+      -d '{
+        "messages": [{"role": "user", "content": "Describe vLLM-Omni briefly."}],
+        "modalities": ["text"]
+      }'
+    ```
+
+=== "Speech"
+
+    ```bash
+    curl "$VLLM_OMNI_BASE_URL/v1/audio/speech" \
+      -H "Content-Type: application/json" \
+      -d '{
+        "input": "Hello from vLLM-Omni.",
+        "voice": "vivian"
+      }' \
+      --output speech.wav
+    ```
+
+=== "Audio"
+
+    ```bash
+    curl "$VLLM_OMNI_BASE_URL/v1/audio/generate" \
+      -H "Content-Type: application/json" \
+      -d '{
+        "input": "Ocean waves on a quiet beach",
+        "audio_length": 5.0
+      }' \
+      --output audio.wav
+    ```
+
+=== "Image"
+
+    ```bash
+    curl "$VLLM_OMNI_BASE_URL/v1/images/generations" \
+      -H "Content-Type: application/json" \
+      -d '{
+        "prompt": "A small robot reading beside a window",
+        "size": "1024x1024",
+        "response_format": "file"
+      }' \
+      --output image.png
+    ```
+
+=== "Image edit"
+
+    ```bash
+    curl "$VLLM_OMNI_BASE_URL/v1/images/edits" \
+      -F "image=@input.png" \
+      -F "prompt=Turn the background into a snowy mountain" \
+      | jq -r '.data[0].b64_json' \
+      | base64 --decode > edited.png
+    ```
+
+=== "Video"
+
+    ```bash
+    curl "$VLLM_OMNI_BASE_URL/v1/videos" \
+      -F "prompt=A cinematic tracking shot of a mountain lake at sunrise" \
+      -F "size=1280x720" \
+      -F "seconds=5"
+    ```
+
+    The response contains a video job `id`. Poll and download it with:
+
+    ```bash
+    curl "$VLLM_OMNI_BASE_URL/v1/videos/<video_id>"
+    curl -L "$VLLM_OMNI_BASE_URL/v1/videos/<video_id>/content" --output video.mp4
+    ```
+
+## Choose a Streaming or Realtime Endpoint
+
+These WebSocket APIs have different event schemas and cannot be used
+interchangeably.
+
+| Workload | Endpoint | Interaction model | Details |
+|----------|----------|-------------------|---------|
+| Incremental text input for speech synthesis | `WS /v1/audio/speech/stream` | Send text events and receive audio | [Streaming Text to Speech](speech_api.md#streaming-text-input-websocket) |
+| Live video understanding | `WS /v1/video/chat/stream` | Send video frames and receive text/audio | [Streaming Video Input](video_stream_api.md) |
+| Turn-based realtime audio | `WS /v1/realtime` | Stream one audio input and receive transcript/audio events | [Realtime Audio](realtime_api.md) |
+| Continuous speech-to-speech interaction | `WS /v1/realtime?duplex=1` or `WS /v1/duplex` | Listen and speak concurrently with session control | [Full Duplex](full_duplex_api.md) |
+| Generated video chunks | `WS /v1/realtime/video` | Start a diffusion request and receive fragmented MP4 | [Streaming Video Output](streaming_video_output_api.md) |
+| Robot policy inference | `WS /v1/realtime/robot/openpi` | Send MessagePack observations and receive action arrays | [OpenPI Robot Policy](openpi_api.md) |
+
+All six routes are model- or configuration-dependent. In particular,
+`/v1/realtime` is not full duplex unless the client sets `duplex=1` and the
+deployment explicitly enables duplex sessions. Clients should also verify the
+duplex capability payload because the query-parameter form falls back to the
+ordinary realtime handler when duplex is unavailable.
+
+## Related Endpoints
+
+| Purpose | Endpoints | Reference |
+|---------|-----------|-----------|
+| Discovery and readiness | `GET /health`, `GET /v1/models` | This page |
+| Batched conversations | `POST /v1/chat/completions/batch` | [Batch requests](chat_completions_api.md#batch-requests) |
+| Batched speech | `POST /v1/audio/speech/batch` | [Batch speech generation](speech_api.md#batch-speech-generation) |
+| TTS voice management | `GET/POST /v1/audio/voices`, `DELETE /v1/audio/voices/{name}` | [Voices](speech_api.md#voices-endpoint) |
+| Video job lifecycle | `GET /v1/videos`, `GET/DELETE /v1/videos/{video_id}`, `GET /v1/videos/{video_id}/content` | [Video endpoints](videos_api.md#endpoints) |
+| Release and restore stage memory | `POST /v1/omni/sleep`, `POST /v1/omni/wakeup` | [Sleep Mode](../features/sleep_mode.md) |
+
+## Standalone Experimental Servers
+
+PersonaPlex also provides a Moshi-compatible WebSocket server, while JoyVL
+provides a stateful interaction orchestrator in front of another model server.
+These are separate processes with their own routes; they are not additional
+paths on every `vllm serve --omni` instance. See [Standalone Experimental
+Servers](standalone_servers.md) before deploying either one.
+
+## Compatibility Notes
+
+- A registered path does not mean every model supports it. Unsupported tasks
+  return an OpenAI-style error or a WebSocket `error` event.
+- Use JSON for Chat, Speech, Audio Generation, and Image Generation. Use
+  `multipart/form-data` when uploading images, videos, or other files.
+- Generation can take minutes for large diffusion models. Configure client
+  timeouts accordingly; prefer asynchronous video jobs over long blocking
+  requests.
+- Model-specific parameters are vLLM-Omni extensions. Check the endpoint
+  reference before assuming an OpenAI SDK exposes them directly.
@@ -4,14 +4,14 @@ vLLM-Omni provides an API for text-to-audio generation using diffusion-based mod

 Unlike the [Speech API](speech_api.md) which targets text-to-speech synthesis, the Audio Generate API is designed for general-purpose audio generation from text descriptions (sound effects, music, ambient soundscapes, etc.).

-Each server instance runs a single model (specified at startup via `vllm-omni serve <model> --omni`).
+Each server instance runs a single model (specified at startup via `vllm serve <model> --omni`).

 ## Quick Start

 ### Start the Server

 ```bash
-vllm-omni serve stabilityai/stable-audio-open-1.0 \
+vllm serve stabilityai/stable-audio-open-1.0 \
    --host 0.0.0.0 \
    --port 8091 \
    --gpu-memory-utilization 0.9 \
@@ -326,7 +326,7 @@ curl http://localhost:8091/health
 Enable debug logging:

 ```bash
-vllm-omni serve stabilityai/stable-audio-open-1.0 \
+vllm serve stabilityai/stable-audio-open-1.0 \
    --host 0.0.0.0 \
    --port 8091 \
    --gpu-memory-utilization 0.9 \
@@ -1,78 +1,110 @@
 # Chat Completions API

-vLLM-Omni supports generating and editing images via the `/v1/chat/completions`
-endpoint using diffusion models. This page explains how to pass generation
-parameters (such as `num_inference_steps`, `height`, `width`) to diffusion
-models through this endpoint.
+Use `/v1/chat/completions` for conversational and multimodal pipelines that
+follow the OpenAI Chat API schema. Depending on the loaded model, a request can
+contain text, image, audio, or video input and can return text, audio, images,
+or other model-specific output.

-!!! tip
-    For dedicated endpoints that accept generation parameters as top-level
-    fields, see [Image Generation API](image_generation_api.md) and
-    [Image Edit API](image_edit_api.md).
+Some diffusion pipelines also support image generation and editing through
+Chat Completions. Prefer the dedicated [Image Generation
+API](image_generation_api.md) or [Image Edit API](image_edit_api.md) for
+task-specific clients; their request fields and response contracts are more
+direct.

-## Passing Generation Parameters
-
-The `/v1/chat/completions` endpoint follows the OpenAI Chat API schema, which
-does not natively include diffusion-specific fields like `num_inference_steps`
-or `height`. How you pass these extra fields depends on your client.
-
-### curl / Python `requests`
-
-Wrap generation parameters inside an `"extra_body"` key in the JSON body:
+## Basic Request

 ```bash
-curl -s http://localhost:8091/v1/chat/completions \
+curl http://localhost:8091/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [
-      {"role": "user", "content": "A beautiful landscape painting"}
+      {"role": "user", "content": "Describe vLLM-Omni briefly."}
    ],
-    "extra_body": {
-      "num_inference_steps": 50,
-      "seed": 42
-    }
+    "modalities": ["text"]
  }'
 ```

-### OpenAI Python SDK
+Use `stream: true` for Server-Sent Events. Input media syntax, supported output
+`modalities`, and response choices depend on the model; see the
+[model-specific online serving examples](../user_guide/examples/online_serving/qwen3_omni.md).

-Use the `extra_body` **keyword argument**. The SDK automatically merges these
-fields into the top-level request body:
+!!! tip
+    Each server hosts one model. Query `GET /v1/models` for its served model
+    name when your client requires the `model` field.

-```python
-response = client.chat.completions.create(
-    model="Qwen/Qwen-Image",
-    messages=[{"role": "user", "content": "A beautiful landscape painting"}],
-    extra_body={
-        "num_inference_steps": 50,
-        "seed": 42,
-    },
-)
+## vLLM Extension Parameters
+
+Send standard and vLLM-specific Chat parameters as top-level fields in a
+direct HTTP request. When using the OpenAI Python SDK, pass vLLM-specific
+fields through the SDK's `extra_body` keyword; the SDK merges them into the
+top-level JSON sent to the server.
+
+=== "curl"
+
+    ```bash
+    curl http://localhost:8091/v1/chat/completions \
+      -H "Content-Type: application/json" \
+      -d '{
+        "messages": [{"role": "user", "content": "Write a short haiku."}],
+        "top_k": 40
+      }'
+    ```
+
+=== "OpenAI Python SDK"
+
+    ```python
+    from openai import OpenAI
+
+    client = OpenAI(base_url="http://localhost:8091/v1", api_key="none")
+
+    response = client.chat.completions.create(
+        model="your-served-model",
+        messages=[{"role": "user", "content": "Write a short haiku."}],
+        extra_body={"top_k": 40},
+    )
+    ```
+
+??? note "Diffusion compatibility"
+
+    Diffusion models exposed through Chat Completions can accept supported
+    fields such as `num_inference_steps`, `seed`, `height`, and `width`. Send
+    them at the top level with direct HTTP, or in the SDK's `extra_body`
+    keyword argument.
+
+    A literal nested `"extra_body"` JSON object is accepted for compatibility,
+    but it is not recommended for new direct HTTP clients. Do not provide the
+    same parameter in multiple locations; the server returns a `400` error for
+    duplicate diffusion parameters. Prefer the dedicated [Image Generation
+    API](image_generation_api.md) or [Image Edit API](image_edit_api.md) when
+    either endpoint matches the task.
+
+## Batch Requests
+
+`POST /v1/chat/completions/batch` accepts the same shared generation fields,
+but `messages` is a list of conversations. The response contains one choice
+per conversation in input order.
+
+```bash
+curl http://localhost:8091/v1/chat/completions/batch \
+  -H "Content-Type: application/json" \
+  -d '{
+    "messages": [
+      [{"role": "user", "content": "Summarize vLLM in one sentence."}],
+      [{"role": "user", "content": "Summarize vLLM-Omni in one sentence."}]
+    ],
+    "max_tokens": 64
+  }'
 ```

-!!! note "SDK `extra_body` vs. JSON `extra_body`"
-    These two `extra_body` usages look similar but work differently under the
-    hood. The SDK flattens the dict into the top-level request JSON, while the
-    curl/requests approach sends it as a nested `"extra_body"` key. Both are
-    handled correctly by the server.
-
-!!! note "About the `ignored fields` warning"
-    You may see a log message like:
-
-    ```
-    WARNING: The following fields were present in the request but ignored: {'height', 'width', ...}
-    ```
-
-    This is **harmless**. It is emitted by vLLM's request validation layer
-    because these fields are not part of the standard OpenAI
-    `ChatCompletionRequest` schema. The fields are still stored internally
-    and correctly forwarded to the diffusion pipeline.
+Batch Chat Completions does not support streaming, tools, beam search, or
+`n > 1`.

 ## Model-Specific Examples

-For complete examples with full request/response details, see the model-specific
-guides:
+For complete examples with model-specific inputs and outputs, see:

+- [Qwen3-Omni](../user_guide/examples/online_serving/qwen3_omni.md)
+- [Qwen2.5-Omni](../user_guide/examples/online_serving/qwen2_5_omni.md)
 - [Text-to-Image (Qwen-Image)](../user_guide/examples/online_serving/text_to_image.md)
 - [Image-to-Image (Qwen-Image-Edit, Qwen-Image-Layered)](../user_guide/examples/online_serving/image_to_image.md)
 - [GLM-Image](../user_guide/examples/online_serving/glm_image.md)
@@ -0,0 +1,116 @@
+# Full-Duplex WebSocket API
+
+vLLM-Omni provides an experimental full-duplex runtime for models that can
+continue receiving speech while producing speech. It adds persistent session
+state, model-specific turn policy, overlap handling, playback acknowledgement,
+and optional session resume.
+
+Full duplex is distinct from the turn-based [Realtime Audio API](realtime_api.md).
+
+## Choose an Endpoint
+
+| Endpoint | Protocol | Recommended use |
+|----------|----------|-----------------|
+| `WS /v1/realtime?duplex=1` | OpenAI Realtime-style event projection | Applications and browser clients |
+| `WS /v1/duplex` | Native vLLM-Omni duplex events | Runtime integration and low-level testing |
+
+Both endpoints use the same duplex engine and require the same model-side
+adapter. Prefer the Realtime projection unless the native lifecycle events are
+specifically required.
+
+## Enable Full Duplex
+
+The route becomes usable only when the deployment configuration explicitly
+sets:
+
+```yaml
+session_mode: duplex
+```
+
+The selected model pipeline must also provide a duplex serving adapter.
+Model-native deployments configure the corresponding engine runtime extension
+and control plane as part of their registered pipeline.
+
+!!! warning
+
+    `WS /v1/duplex` fails with `Duplex API is not available` when duplex is not
+    enabled. By contrast, `/v1/realtime?duplex=1` falls back to the ordinary
+    turn-based realtime handler when no duplex handler exists. Confirm that
+    `session.created.session.capabilities` is present before treating the
+    connection as full duplex.
+
+The current unified-runtime integrations are:
+
+- MiniCPM-o 4.5, using `vllm_omni/deploy/minicpmo_4_5_duplex.yaml`;
+- PersonaPlex, whose default `vllm_omni/deploy/personaplex.yaml` enables duplex.
+
+JoyVL is a separate HTTP interaction orchestrator and does not use these
+WebSocket endpoints. See [Standalone Experimental Servers](standalone_servers.md).
+
+## MiniCPM-o Quick Start
+
+Start the duplex deployment:
+
+```bash
+vllm serve openbmb/MiniCPM-o-4_5 --omni \
+  --deploy-config vllm_omni/deploy/minicpmo_4_5_duplex.yaml \
+  --trust-remote-code \
+  --port 8091
+```
+
+Stream a mono, PCM16, 16 kHz WAV file with the provided client:
+
+```bash
+python examples/online_serving/minicpmo/realtime_duplex_demo.py \
+  --url 'ws://localhost:8091/v1/realtime?duplex=1' \
+  --model openbmb/MiniCPM-o-4_5 \
+  --input-wav input_16k_mono.wav \
+  --ref-audio reference_voice.wav \
+  --output-dir /tmp/minicpmo-duplex
+```
+
+## Realtime Event Lifecycle
+
+A typical `/v1/realtime?duplex=1` session follows this lifecycle:
+
+1. Send `session.update` with the model, modalities, audio formats, and session
+   options.
+2. Wait for `session.created`; inspect `session.capabilities` instead of
+   assuming every model supports the same controls.
+3. Send `input_audio_buffer.append` events while microphone audio arrives.
+4. Send `input_audio_buffer.commit` at a user-turn boundary when required by
+   the model policy.
+5. Consume `response.created`, transcript deltas, `response.audio.delta`, and
+   `response.done` or `response.listen` events.
+6. Send `playback.ack` after audio has been played when the session advertises
+   playback acknowledgement support.
+7. Send `session.close` and wait for `session.closed`.
+
+Unlike the turn-based realtime endpoint, input may continue while a response
+is active. The server can emit `overlap.decision` to describe whether input was
+deferred, treated as a short acknowledgement, or used to interrupt output.
+
+## Capabilities and Model Differences
+
+The `session.created` payload includes capability fields such as
+`supports_barge_in`, `supports_playback_ack`, `supports_multi_session`,
+`supports_session_resume`, and `chunk_period_ms`. Treat this payload as the
+runtime contract.
+
+For example, PersonaPlex supports native overlapping speech but currently
+advertises `supports_barge_in=false`; destructive output interruption and
+model-state rewind have not been validated for that integration. Capacity and
+session-resume behavior also depend on the selected deployment configuration.
+
+## Native Protocol
+
+`WS /v1/duplex` exposes lower-level lifecycle names including
+`session.create`, `input_audio_buffer.append`, `turn.signal`, `playback.ack`,
+and `session.close`. It returns native session, input, response, overlap, and
+error events. This protocol is experimental and may evolve with the runtime;
+applications should use the provided Realtime client where possible.
+
+See the [MiniCPM-o example](https://github.com/vllm-project/vllm-omni/tree/main/examples/online_serving/minicpmo),
+[PersonaPlex example](https://github.com/vllm-project/vllm-omni/tree/main/examples/online_serving/personaplex),
+and [full-duplex runtime design](https://github.com/vllm-project/vllm-omni/blob/main/vllm_omni/experimental/fullduplex/DESIGN.md)
+for model-specific validation and architecture details.
@@ -0,0 +1,109 @@
+# OpenPI Robot Policy WebSocket API
+
+Use `WS /v1/realtime/robot/openpi` for low-latency robot-policy inference with
+OpenPI-compatible clients. A persistent WebSocket carries observations to the
+loaded policy model and returns action arrays.
+
+Despite the `/realtime` prefix, this endpoint does not use the OpenAI Realtime
+event schema. It uses binary MessagePack frames with NumPy extensions.
+
+## Availability
+
+The endpoint is available only when the loaded diffusion policy configuration
+contains `policy_server_config`. The server sends that model-specific
+configuration to the client as the first binary frame.
+
+For example, DreamZero declares:
+
+```yaml
+policy_server_config:
+  image_resolution: [180, 320]
+  n_external_cameras: 2
+  needs_wrist_camera: true
+  needs_stereo_camera: false
+  needs_session_id: true
+  action_space: joint_position
+```
+
+Without this configuration, the WebSocket returns `Robot policy not available`
+and closes.
+
+## DreamZero Quick Start
+
+Start the bundled DreamZero deployment:
+
+```bash
+vllm serve GEAR-Dreams/DreamZero-DROID --omni --port 8091 \
+  --served-model-name dreamzero-droid \
+  --deploy-config vllm_omni/deploy/dreamzero_tp1_cfg2.yaml \
+  --enforce-eager --disable-log-stats
+```
+
+Install the optional client dependencies, download the sample camera inputs,
+and run the client:
+
+```bash
+pip install openpi-client websockets opencv-python
+
+hf download YangshenDeng/vllm-omni-dreamzero-assets \
+  --repo-type dataset \
+  --local-dir outputs/dreamzero/assets
+
+python examples/online_serving/dreamzero/openpi_client.py \
+  --host 127.0.0.1 \
+  --port 8091 \
+  --video-dir outputs/dreamzero/assets
+```
+
+## Protocol
+
+The connection is request-response after an initial server handshake:
+
+```text
+connect
+  <- msgpack(policy_server_config)
+
+infer
+  -> msgpack({"endpoint": "infer", "session_id": "...", ...observation})
+  <- msgpack(ndarray | dict[str, ndarray])
+
+reset
+  -> msgpack({"endpoint": "reset"})
+  <- msgpack({"status": "reset successful"})
+```
+
+If `endpoint` is omitted, the server treats the message as `infer`. Each
+inference message produces one action response after the engine request
+completes; action tokens or intermediate tensors are not streamed.
+
+NumPy arrays use the marker format implemented by `openpi-client`. The server
+also accepts the legacy vLLM NumPy marker representation on input. JSON text
+frames are not observation messages.
+
+## Sessions and Reset
+
+- The API layer's current-session and first-call counters are scoped to each
+  WebSocket connection.
+- `session_id` identifies model-side state across observations on that
+  connection. If omitted, it defaults to `default`.
+- The first inference for a session is sent to the model with `reset=true`.
+- Changing `session_id`, or sending the `reset` command, causes the next
+  inference to start with `reset=true`.
+- The policy pipeline owns observation transforms and persistent model state,
+  normally keyed by `session_id`; the API layer forwards the raw observation
+  dictionary.
+
+## Limits and Errors
+
+- Maximum inbound payload size is 64 MiB.
+- The server closes an idle connection after 30 seconds.
+- Invalid binary input returns a MessagePack
+  `{"type":"error","message":"Invalid request payload"}` response.
+- Inference failures return a generic `Internal inference error` without
+  exposing an internal traceback.
+- Unsupported NumPy object, structured, and complex dtypes are rejected.
+
+Observation keys, camera layout, state tensors, and action shapes are defined
+by the loaded policy rather than by this transport. Read the handshake before
+constructing observations. See the [DreamZero example](https://github.com/vllm-project/vllm-omni/tree/main/examples/online_serving/dreamzero)
+for a complete OpenPI client and DROID simulation loop.
@@ -0,0 +1,82 @@
+# Realtime Audio WebSocket API
+
+Use `WS /v1/realtime` to stream PCM audio into a compatible omni model and
+receive incremental transcription and synthesized response audio. This is a
+turn-based streaming API: it starts one generation over an audio stream and
+finishes after the client closes that input stream.
+
+For a model that listens while it is already speaking, use the experimental
+[Full-Duplex API](full_duplex_api.md) instead.
+
+## Quick Start
+
+Start a compatible model. The currently documented example uses Qwen3-Omni:
+
+```bash
+vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091
+```
+
+Run the provided client with a mono, 16-bit PCM, 16 kHz WAV file:
+
+```bash
+python examples/online_serving/qwen3_omni/openai_realtime_client.py \
+  --url ws://localhost:8091/v1/realtime \
+  --model Qwen/Qwen3-Omni-30B-A3B-Instruct \
+  --input-wav input_16k_mono.wav \
+  --output-wav response.wav \
+  --output-text response.txt
+```
+
+The client dependency is `websockets`.
+
+## Protocol
+
+Messages in both directions are JSON text frames. Audio carried inside an
+event is base64-encoded raw PCM16.
+
+| Direction | Event | Purpose |
+|-----------|-------|---------|
+| Server to client | `session.created` | Confirms that the WebSocket connection is ready |
+| Client to server | `session.update` | Selects and validates the served model |
+| Client to server | `input_audio_buffer.commit` with `final: false` | Starts generation over the incoming stream |
+| Client to server | `input_audio_buffer.append` | Appends base64 PCM16 audio |
+| Client to server | `input_audio_buffer.commit` with `final: true` | Marks the end of input |
+| Server to client | `transcription.delta` | Carries incremental response text |
+| Server to client | `transcription.done` | Carries final text and token usage |
+| Server to client | `response.audio.delta` | Carries incremental PCM16 response audio |
+| Server to client | `response.audio.done` | Marks the end of response audio |
+| Server to client | `error` | Reports an invalid event, model, or audio payload |
+
+A minimal client sends events in this order:
+
+```json
+{"type":"session.update","model":"Qwen/Qwen3-Omni-30B-A3B-Instruct"}
+{"type":"input_audio_buffer.commit","final":false}
+{"type":"input_audio_buffer.append","audio":"<base64-pcm16>"}
+{"type":"input_audio_buffer.commit","final":true}
+```
+
+The initial non-final commit intentionally starts generation before all audio
+has arrived. Continue sending `input_audio_buffer.append` events while the
+engine is consuming the stream.
+
+## Audio Handling
+
+- Input is mono PCM16 at 16 kHz for the Qwen3-Omni example.
+- `response.audio.delta.audio` contains base64-encoded PCM16 bytes.
+- Read `sample_rate_hz` from each audio event instead of assuming an output
+  rate. Qwen3-Omni output is typically 24 kHz.
+- Concatenate audio deltas in receive order to construct the output waveform.
+- Set `input_audio_buffer.commit.final` to `true` after the last input chunk so
+  the server can terminate the streaming request.
+
+## Availability and Limitations
+
+The path is registered on the unified API server, but it is usable only when
+the loaded pipeline implements realtime audio input and produces compatible
+audio output. Unsupported deployments return an `error` event. The endpoint
+does not provide duplex session resume, playback acknowledgement, overlap
+policy, or barge-in controls.
+
+See the [Qwen3-Omni online serving example](https://github.com/vllm-project/vllm-omni/tree/main/examples/online_serving/qwen3_omni)
+for concurrency options, chunk pacing, and per-delta audio debugging.
@@ -2,7 +2,7 @@

 vLLM-Omni provides an OpenAI-compatible API for text-to-speech (TTS) generation. Supported TTS models include:

- **Qwen3-TTS** (`Qwen/Qwen3-TTS-12Hz-*`) -- Qwen3-based TTS with CustomVoice, VoiceDesign, and Base (voice cloning) task types. Output: 24 kHz.
+- **Qwen3-TTS** (`Qwen/Qwen3-TTS-12Hz-*`) -- Qwen3-based TTS with CustomVoice, VoiceDesign, and Base (voice cloning) task types. Native output: 24 kHz; API output can be resampled to 8 kHz.
 - **Fish Speech S2 Pro** (`fishaudio/s2-pro`) -- Dual-AR TTS with DAC codec. Supports text-to-speech and voice cloning via reference audio. Output: 44.1 kHz.
 - **Voxtral TTS** (`mistralai/Voxtral-4B-TTS-2603`) -- AR + FlowMatching TTS with preset voices. Output: 24 kHz.
 - **CosyVoice3** (`FunAudioLLM/Fun-CosyVoice3-0.5B-2512`) -- 2-stage talker + flow-matching code2wav. Voice cloning via `ref_audio` + `ref_text` (no presets). Output: 24 kHz.
@@ -116,6 +116,7 @@ Content-Type: application/json

 | Parameter | Type | Default | Description |
 |-----------|------|---------|-------------|
+| `sample_rate` | integer | model native | Target output sample rate. Qwen3-TTS supports 8000 or 24000 Hz; the model remains native 24 kHz internally and is resampled before encoding. |
 | `task_type` | string | "CustomVoice" | TTS task type: CustomVoice, VoiceDesign, or Base |
 | `language` | string | "Auto" | Language (see supported languages below) |
 | `instructions` | string | "" | Voice style/emotion instructions |
@@ -0,0 +1,109 @@
+# Standalone Experimental Servers
+
+Most vLLM-Omni endpoints run on the unified server started with
+`vllm serve <model> --omni`. The experimental full-duplex package also contains
+two separate user-facing server processes. Their routes are not added to the
+unified API server.
+
+| Server | Transport | Role |
+|--------|-----------|------|
+| PersonaPlex compatibility server | WebSocket | Hosts the official PersonaPlex browser protocol and a raw-PCM alternative |
+| JoyVL interaction server | HTTP | Adds state, memory, proactive decisions, and delegation in front of a separate OpenAI-compatible model backend |
+
+## PersonaPlex Compatibility Server
+
+Use this server when a client requires the official Moshi/PersonaPlex wire
+protocol or the bundled browser UI. For the engine-owned unified duplex path,
+use [`WS /v1/realtime?duplex=1`](full_duplex_api.md) instead.
+
+Start it with:
+
+```bash
+HF_TOKEN=... CUDA_VISIBLE_DEVICES=0 python -m \
+  vllm_omni.experimental.fullduplex.personaplex.serving.server \
+  --port 8091
+```
+
+| Route | Purpose |
+|-------|---------|
+| `GET /` | Official PersonaPlex browser client, when its assets are available |
+| `GET /health` | Readiness check |
+| `WS /api/chat` | Moshi-compatible tagged binary protocol with Opus audio |
+| `WS /v1/audio/duplex` | Simpler float32 raw-PCM protocol |
+
+### `/api/chat` Protocol
+
+The first byte of every binary frame is a message tag:
+
+| Direction | Tag | Payload |
+|-----------|-----|---------|
+| Server to client | `0x00` | Ready handshake |
+| Client to server | `0x01` | Opus microphone audio at 24 kHz |
+| Server to client | `0x01` | Opus response audio |
+| Server to client | `0x02` | UTF-8 inner-monologue text |
+
+The optional `text_prompt` and `voice_prompt` query parameters select a persona
+and voice. The default server admits one conversation; use `--batch-size N` to
+configure multiple lockstep slots. Connections beyond capacity close with
+WebSocket code 1013.
+
+### `/v1/audio/duplex` Protocol
+
+Send `{"type":"open","persona":"...","voice":"..."}` as a JSON text frame,
+then exchange binary float32 PCM frames. The server returns generated float32
+audio as binary frames and text as `{"type":"text","text":"..."}`. Send
+`{"type":"close"}` and wait for `{"type":"done"}` to flush and finish.
+
+See the [PersonaPlex example](https://github.com/vllm-project/vllm-omni/tree/main/examples/online_serving/personaplex)
+for the browser and headless clients.
+
+## JoyVL Interaction Server
+
+JoyVL is an orchestration layer, not a model-serving engine. It calls a
+separate OpenAI-compatible backend and adds per-session frame history, memory,
+persona policy, and optional delegation.
+
+Start the model backend and orchestrator separately:
+
+```bash
+vllm serve jdopensource/JoyAI-VL-Interaction-Preview \
+  --served-model-name JoyAI-VL-Interaction-Preview \
+  --port 8092 \
+  --max-model-len 131072 \
+  --enable-prefix-caching \
+  --limit-mm-per-prompt '{"image":256,"video":1}'
+
+python -m vllm_omni.experimental.fullduplex.joyvl.serving.server \
+  --port 8091 \
+  --main-backend-url http://127.0.0.1:8092/v1 \
+  --main-model JoyAI-VL-Interaction-Preview
+```
+
+| Method and route | Purpose |
+|------------------|---------|
+| `GET /health` | Readiness check |
+| `GET /v1/models` | Reports the configured interaction model |
+| `POST /v1/chat/completions` | Processes one frame or interaction tick |
+| `POST /reset` | Resets a session |
+| `POST /v1/streaming/reset` | Alias for `/reset` |
+| `POST /v1/streaming/persona` | Changes the session persona |
+
+Send one multimodal Chat Completions request per video frame, normally around
+one frame per second, and identify the session with `x-session-id`. Responses
+include an `interaction` block whose action is `silence`, `response`, or
+`delegate`.
+
+```bash
+curl http://127.0.0.1:8091/v1/chat/completions \
+  -H 'content-type: application/json' \
+  -H 'x-session-id: demo' \
+  -d '{
+    "messages": [{"role": "user", "content": [
+      {"type": "text", "text": "Alert me if a fire appears"},
+      {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,..."}}
+    ]}]
+  }'
+```
+
+See the [JoyVL recipe](https://github.com/vllm-project/vllm-omni/blob/main/recipes/JD/JoyAI-VL-Interaction.md)
+for memory, personas, delegation backends, and the browser UI.
@@ -0,0 +1,92 @@
+# Streaming Video Output WebSocket API
+
+Use `WS /v1/realtime/video` to receive fragmented MP4 chunks while a compatible
+diffusion video model is generating. This endpoint streams generated video
+output; it is unrelated to [`WS /v1/video/chat/stream`](video_stream_api.md),
+which streams live video input into a conversational model.
+
+## Quick Start
+
+Start a model that supports step execution and enable diffusion streaming
+output:
+
+```bash
+vllm serve BestWishYsh/Helios-Distilled --omni \
+  --diffusion-streaming-output \
+  --port 8091
+```
+
+Install the client dependency and run the provided client:
+
+```bash
+pip install av websockets
+
+python examples/online_serving/streaming_video_generation/streaming_video_client.py \
+  --host 127.0.0.1 \
+  --port 8091 \
+  --model BestWishYsh/Helios-Distilled \
+  --prompt "A serene lakeside sunrise with mist over the water" \
+  --output output.mp4
+```
+
+## Protocol
+
+Control events are JSON text frames. Each `video.chunk_metadata` event is
+immediately followed by one binary WebSocket frame containing fragmented MP4
+bytes.
+
+| Direction | Event or frame | Purpose |
+|-----------|----------------|---------|
+| Client to server | `session.start` | Starts generation with a prompt and video parameters |
+| Server to client | `video.start` | Confirms the request ID, format, and accepted configuration |
+| Server to client | `video.chunk_metadata` | Describes the next binary media frame |
+| Server to client | Binary frame | Carries one fragmented MP4 chunk |
+| Client to server | `session.interaction` | Queues an optional prompt update during generation |
+| Server to client | `session.interaction.queued` | Confirms that an interaction was queued |
+| Client to server | `session.ping` | Refreshes the stall clock |
+| Server to client | `session.pong` | Acknowledges a ping |
+| Client to server | `session.stop` | Cancels the active generation |
+| Server to client | `session.done` | Reports completion or cancellation and the chunk count |
+| Server to client | `error` | Reports validation, generation, or timeout failures |
+
+Start a minimal session with:
+
+```json
+{
+  "type": "session.start",
+  "model": "BestWishYsh/Helios-Distilled",
+  "prompt": "A serene lakeside sunrise",
+  "format": "m4s",
+  "width": 640,
+  "height": 384,
+  "fps": 16,
+  "num_frames": 99
+}
+```
+
+Only `m4s` output is currently accepted. Concatenate the binary frames in
+receive order, then remux the fragmented stream if the target player requires
+a progressive MP4 file.
+
+## Mid-Generation Interaction
+
+Models that implement interaction updates can change the active prompt:
+
+```json
+{
+  "type": "session.interaction",
+  "interaction": {
+    "event_id": "sunlight-update",
+    "event": {"prompt": "Sunlight breaks through the mist"},
+    "transition_chunks": 2
+  }
+}
+```
+
+This feature is pipeline-dependent. An enabled endpoint does not imply that
+the loaded model supports midway prompt updates.
+
+The default start timeout is 10 seconds and the server reports a stall after
+about 60 seconds without generation progress or a `session.ping`. See the
+[streaming video generation example](https://github.com/vllm-project/vllm-omni/tree/main/examples/online_serving/streaming_video_generation)
+for prompt-update scheduling and browser playback.
@@ -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

@@ -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**           |     ❌     |     ✅      |           ❌           |       ✅        |         ❌         |          ❌          |   ❌    |             ✅             |          ❌           |       ❌        |        ❌         |
@@ -137,7 +138,7 @@ The following tables show which models support each feature:
 | **Qwen-Image-Edit**      |     ✅     |     ✅      |           ✅           |       ✅        |         ✅         |          ❌          |   ✅    |             ✅             |      ✅ (decode)      |       ❌        |        ❌         |
 | **Qwen-Image-Edit-2509** |     ✅     |     ✅      |           ✅           |       ✅        |         ✅         |          ❌          |   ✅    |        ✅ (decode)         |          ✅           |       ❌        |        ❌         |
 | **Qwen-Image-Layered**   |     ✅     |     ✅      |           ✅           |       ✅        |         ✅         |          ❌          |   ✅    |             ✅             |      ✅ (decode)      |       ❌        |        ❌         |
-| **SenseNova-U1**         |     ❌     |     ✅      |           ❌           |       ✅        |         ✅         |          ❌          |   ❌    |             ✅             |          ❌           |       ❌        |        ❌         |
+| **SenseNova-U1 / U1.5**  |     ❌     |     ✅      |           ❌           |       ✅        |         ✅         |          ❌          |   ❌    |             ✅             |          ❌           |       ❌        |        ❌         |
 | **Stable-Diffusion-XL**  |     ❌     |     ❌      |           ✅           |       ✅        |         ✅         |          ❌          |   ✅    |             ✅             |      ✅ (decode)      |       ❌        |        ❌         |
 | **Stable-Diffusion3.5**  |     ❌     |     ✅      |           ❌           |       ✅        |         ✅         |          ❌          |   ❌    |             ✅             |      ✅ (decode)      |       ❌        |        ❌         |
 | **Z-Image**              |     ✅     |     ✅      |           ✅           |       ❓        |   ✅ (TP=2 only)   |          ❌          |   ✅    |             ❌             |      ✅ (decode)      |       ✅        |        ❌         |
@@ -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.
@@ -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:
@@ -49,7 +49,7 @@ scenario below for live barge-in validation on the target GPU.
 Default:

 ```bash
-vllm-omni serve openbmb/MiniCPM-o-4_5 \
+vllm serve openbmb/MiniCPM-o-4_5 \
    --omni \
    --deploy-config vllm_omni/deploy/minicpmo_4_5.yaml \
    --trust-remote-code \
@@ -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:
@@ -149,6 +155,23 @@ def _response_in_progress(events: list[dict[str, object]]) -> bool:
    )


+async def _commit_input_after_playback_checkpoint(client: RealtimeDuplexClient) -> float:
+    """Commit input only after preserving already-played assistant history.
+
+    Native duplex can finish several responses while the user is still
+    streaming.  Under the ``ack_only`` playback policy, acknowledging those
+    responses after the final input commit is intentionally rejected: it could
+    append an old assistant message after the newly committed user message.
+    Checkpoint playback first so the server commits each assistant item at its
+    original history position.  A second acknowledgement after the commit can
+    then safely advance response-local playback that drained in the meantime.
+    """
+    await client.acknowledge_playback()
+    commit_sent_at_s = time.monotonic()
+    await client.commit()
+    return commit_sent_at_s
+
+
 def _event_count_after(
    events: list[dict[str, object]],
    event_type: str,
@@ -159,14 +182,258 @@ 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 +451,12 @@ 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)
@@ -196,8 +465,7 @@ async def run_demo(args: argparse.Namespace) -> dict[str, object]:
            chunk_period_ms=_chunk_period_ms(client.events.events),
        )
        wait_for_post_commit_decision = False
-        commit_sent_at_s = time.monotonic()
-        await client.commit()
+        commit_sent_at_s = await _commit_input_after_playback_checkpoint(client)
        wait_error: str | None = None
        committed_index: int | None = None
        post_commit_decision: str | None = None
@@ -310,6 +578,10 @@ 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 +628,48 @@ 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 +685,7 @@ 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:

@@ -37,7 +37,7 @@ During generation the client normally sends only `session.start` and then receiv
 ## Install Client Dependency

 ```bash
-pip install websockets
+pip install av websockets
 # For the Gradio demo:
 pip install 'vllm-omni[demo]' websockets
 ```
@@ -8,7 +8,7 @@ the received bytes to disk when the session finishes or is interrupted.
 Image/reference input is intentionally not supported in this example yet.

 Requirements:
-    pip install websockets
+    pip install av websockets
 """

 import argparse
@@ -1,3 +1,6 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
 """OpenAI-compatible client for Qwen3-TTS via /v1/audio/speech endpoint.

 This script demonstrates how to use the OpenAI-compatible speech API
@@ -74,6 +77,8 @@ def run_tts_generation(args) -> None:
        "voice": args.speaker,
        "response_format": args.response_format,
    }
+    if args.sample_rate is not None:
+        payload["sample_rate"] = args.sample_rate

    # Add optional parameters
    if args.instructions:
@@ -246,6 +251,13 @@ def parse_args():
        choices=["wav", "mp3", "flac", "pcm", "aac", "opus"],
        help="Audio output format (default: wav)",
    )
+    parser.add_argument(
+        "--sample-rate",
+        type=int,
+        default=None,
+        choices=[8000, 24000],
+        help="Output sample rate in Hz (default: model native)",
+    )
    parser.add_argument(
        "--output",
        "-o",
@@ -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",
 ]
@@ -1,11 +1,14 @@
 # Boogu-Image

-> Text-to-image and image-editing online serving (Boogu-Image-0.1-Base / -Edit)
+> Text-to-image and image-editing online serving
+(Boogu-Image-0.1-Base / -Edit / -Edit-Turbo)

 ## Summary

 - Vendor: Boogu
- Model: `Boogu/Boogu-Image-0.1-Base` (text-to-image), `Boogu/Boogu-Image-0.1-Edit` (image editing)
+- Model: `Boogu/Boogu-Image-0.1-Base` (text-to-image),
+  `Boogu/Boogu-Image-0.1-Edit` (image editing), and
+  `Boogu/Boogu-Image-0.1-Edit-Turbo` (four-step image editing)
 - Task: Text-to-image generation and text-guided image editing (TI2I)
 - Mode: Online serving with the OpenAI-compatible API
 - Maintainer: Community
@@ -13,19 +16,23 @@
 ## When to use this recipe

 Use this recipe when you want a known-good starting point for serving
-`Boogu/Boogu-Image-0.1-Base` with vLLM-Omni's native pipeline (no
+`Boogu/Boogu-Image-0.1-Base`, `Boogu/Boogu-Image-0.1-Edit`, or
+`Boogu/Boogu-Image-0.1-Edit-Turbo` with vLLM-Omni's native pipeline (no
 `--diffusion-load-format diffusers`, and the upstream `boogu` package is not
 required).

 Boogu-Image-0.1 is an Apache-2.0 unified image generation and editing model
-family. This recipe covers the Base text-to-image checkpoint, which pairs a
-Qwen3-VL multimodal encoder with a Diffusion Transformer (DiT) and a flow-match
-Euler scheduler with time-shift. It handles photorealistic generation and
-Chinese/English text rendering.
+family. The Base text-to-image checkpoint pairs a Qwen3-VL multimodal encoder
+with a Diffusion Transformer (DiT) and a flow-match Euler scheduler with
+time-shift. It handles photorealistic generation and Chinese/English text
+rendering. The Edit and Edit-Turbo checkpoints use the same native
+`BooguImagePipeline` for image editing.

 ## References

 - Upstream model card: <https://huggingface.co/Boogu/Boogu-Image-0.1-Base>
+- Edit model card: <https://huggingface.co/Boogu/Boogu-Image-0.1-Edit>
+- Edit-Turbo 1K hotfix: <https://huggingface.co/Boogu/Boogu-Image-0.1-Edit-Turbo/tree/hotfix-1k-20260708>
 - Project page: <https://boogu.org>
 - GitHub: <https://github.com/boogu-project/Boogu-Image>
 - Related example: [`examples/online_serving/text_to_image/`](../../examples/online_serving/text_to_image/README.md)
@@ -93,7 +100,7 @@ curl -s http://localhost:8091/v1/chat/completions \
 - **Memory usage:** ~34.6 GiB on GPU. Use `--vae-use-slicing --vae-use-tiling`
  to trim peak VRAM if needed.
 - **Key flags:**
-  - `--omni` — enables vLLM-Omni diffusion serving.
+    - `--omni` — enables vLLM-Omni diffusion serving.
 - **Guidance:** Boogu-Image uses `guidance_scale` (mapped to the upstream
  `text_guidance_scale`); the default is `4.0`. Classifier-free guidance is
  active whenever `guidance_scale > 1.0`.
@@ -111,13 +118,13 @@ the image-editing (TI2I) path activates automatically when a request carries a
 reference image. The Base text-to-image path is unaffected (no reference image
 is sent).

-#### Command
+### Command

 ```bash
 vllm serve Boogu/Boogu-Image-0.1-Edit --omni --port 8091
 ```

-#### Verification
+### Verification

 Edit an image with `/v1/images/edits` (the model-card example — change a photo
 to a colored-pencil drawing). Diffusion parameters are plain multipart form
@@ -157,14 +164,14 @@ curl -s http://localhost:8091/v1/chat/completions \
  }' | jq -r '.choices[0].message.content[0].image_url.url' | cut -d',' -f2- | base64 -d > edited.png
 ```

-#### Notes
+### Notes

 - **Single reference image:** only one input image is supported for now (the
  upstream "Only support 1 reference image for now" limit).
 - **Guidance semantics:**
-  - `guidance_scale` = text guidance (upstream `text_guidance_scale`, default
+    - `guidance_scale` = text guidance (upstream `text_guidance_scale`, default
    `4.0`); `> 1.0` enables text CFG. Editing typically uses `5.0`.
-  - `guidance_scale_2` = image guidance (upstream `image_guidance_scale`,
+    - `guidance_scale_2` = image guidance (upstream `image_guidance_scale`,
    default `1.0` = off). Setting it `> 1.0` enables the double-guidance path
    (3 model predictions per step), steering more strongly toward the reference
    image.
@@ -178,3 +185,69 @@ curl -s http://localhost:8091/v1/chat/completions \
  supports Boogu-Image-Edit directly
  (`--model Boogu/Boogu-Image-0.1-Edit --guidance-scale 5.0`, optional
  `--guidance-scale-2`).
+
+## Fast image editing (Boogu-Image-0.1-Edit-Turbo)
+
+Edit-Turbo is the distilled image-editing checkpoint. Its `model_index.json`
+declares `BooguImagePipeline`, so it uses the same native TI2I path as Edit.
+The upstream 1K hotfix is recommended for stable results and is pinned below
+to avoid silently loading the older checkpoint from the repository's default
+revision.
+
+### Command
+
+```bash
+vllm serve Boogu/Boogu-Image-0.1-Edit-Turbo \
+  --omni \
+  --revision hotfix-1k-20260708 \
+  --port 8091
+```
+
+### Verification
+
+Edit an image with the distilled four-step settings:
+
+```bash
+curl -s http://localhost:8091/v1/images/edits \
+  -F model="Boogu/Boogu-Image-0.1-Edit-Turbo" \
+  -F image="@input.png" \
+  -F prompt="Change the style to a colored pencil drawing." \
+  -F num_inference_steps=4 \
+  -F guidance_scale=1.0 \
+  -F seed=42 \
+  | jq -r '.data[0].b64_json' | base64 -d > edited-turbo.png
+```
+
+Or via chat completions:
+
+```bash
+curl -s http://localhost:8091/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "messages": [
+      {"role": "user", "content": [
+        {"type": "image_url", "image_url": {"url": "data:image/png;base64,<BASE64>"}},
+        {"type": "text", "text": "Change the style to a colored pencil drawing."}
+      ]}
+    ],
+    "extra_body": {
+      "num_inference_steps": 4,
+      "guidance_scale": 1.0,
+      "seed": 42
+    }
+  }' | jq -r '.choices[0].message.content[0].image_url.url' | cut -d',' -f2- | base64 -d > edited-turbo.png
+```
+
+### Notes
+
+- **Pinned revision:** use `hotfix-1k-20260708`; upstream recommends the 1K
+  hotfix over the 1.5K variant for more stable results.
+- **Recommended settings:** `num_inference_steps=4` and
+  `guidance_scale=1.0`. Edit-Turbo is guidance-distilled, meaning the guidance
+  behavior is already baked into the distilled checkpoint. A scale of `1.0`
+  avoids applying additional classifier-free guidance; unlike the regular Edit
+  checkpoint, it should not be increased to `5.0`.
+- **Reference images:** the same single-reference and `align_res` behavior as
+  the regular Edit checkpoint applies.
+- **Known limitations:** the same single-GPU limitations as Base and Edit apply;
+  CPU offload, Cache-DiT, and multi-GPU parallelism are not yet validated.
@@ -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.
@@ -69,11 +69,12 @@ reference-video preparation and MP4 output.

 For CUDA and ROCm deployments, non-streaming MP4 responses are encoded on the
 host CPU through PyAV/libx264 after generation. The response encoder selects the
-path automatically at runtime: inputs with a supported frame shape, common dtype,
-and RGB per-channel contiguous layout use direct planar frames; other inputs
-fall back to the legacy path before the PyAV container is opened. No CLI flag,
-model declaration, or user configuration is required. Streaming fMP4 output is
-unchanged.
+path automatically at runtime. The server-owned parallel converter accepts
+supported frame shapes and dtypes with either per-channel-contiguous or strided
+RGB planes, including interleaved arrays materialized by output transport.
+Standalone callers without a parallel converter retain the legacy fallback for
+strided planes. No CLI flag, model declaration, or user configuration is
+required. Streaming fMP4 output is unchanged.

 A community benchmark on 2x Xeon 8480C reported the following comparison
 between the legacy and direct planar paths
@@ -860,6 +861,129 @@ 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.
+
+### FastH3 adapter
+
+[FastH3](https://haoailab.com/blogs/fasth3-preview/) is FastVideo's four-step
+DMD2 student of H3-Base. It reuses H3's text encoder, VAEs, tokenizers, and
+schedulers unchanged, replacing 49 denoiser evaluations with four. It is fused
+into the checkpoint at load time rather than switched per request, because it
+carries full-rank deltas that no LoRA layer can express.
+
+The bundle publishes four variants, so download one and point `--lora-path` at
+it; the repository root is refused rather than guessed at:
+
+```bash
+export FASTH3_DIR=/path/to/fasth3
+hf download FastVideo/FastVideo-FastH3-4-step-Preview-v1-LoRA \
+  dense-datafree/adapter_model.safetensors --local-dir "${FASTH3_DIR}"
+export FASTH3_LORA="${FASTH3_DIR}/dense-datafree/adapter_model.safetensors"
+```
+
+Add `--task-type fl2va --lora-path "${FASTH3_LORA}"` to a non-offloaded server
+command. T2VA is served by the FL2VA partition, so `--task-type fl2va` is
+correct even though FastH3 preview v1 distills T2VA only. Because the adapter is
+fused, `--lora-backend` does not apply and a request carrying a `lora=` field is
+rejected rather than served without the adapter it asked for.
+
+```bash
+-F 'num_inference_steps=4' \
+-F 'extra_params={"task":"t2va","duration":4.4}'
+```
+
+Requests must ask for `num_inference_steps=4` and `task=t2va`: the release's five
+sigma points bound four denoiser evaluations, and that count is what the step
+scheduler admits a request on. The server denoises on the release's own ladder,
+keeping H3's per-modality shifts at the checkpoint values, so a request that
+overrides `flow_shift` or `audio_flow_shift` is rejected - it would sample the
+student at noise levels it was never distilled at.
+
+Only a release that identifies itself as FastH3 is fused; any other
+`fastvideo-lora-v2` adapter stays on the dynamic LoRA route. A claimed artifact
+is then held to its own metadata: one that misdeclares its tensor counts or
+leaves a transformer block unedited is refused at startup instead of serving
+mostly base H3 weights on a four-step schedule. Offload is refused for the same
+reason - `--enable-cpu-offload`, `--enable-layerwise-offload` and
+`--enable-distributed-layerwise-offload` all bypass the fusion, so they fail fast.
+
+> [!NOTE]
+> Only the **Dense / Data-Free** variant is supported today. The three VSA
+> variants carry `.set_weight` compression gates for modules that do not exist
+> in vLLM-Omni's dense H3 attention, and the release states dense attention is
+> not a supported substitute for them, so they are refused at startup rather
+> than silently run wrong. Video Sparse Attention for H3's packed
+> `[text | cond | audio | video]` sequence is follow-up work.
+
+Measured on 8x NVIDIA B300 with USP8, VAE patch-parallel 8, `TRTLLM_ATTN`, at
+1344x768, 4.4 s, seed 1101, one warmup excluded and two runs recorded:
+
+| Adapter | Steps | End-to-end | Diffusion engine |
+| --- | ---: | ---: | ---: |
+| none (base H3) | 50 | 25.8 / 26.4 s | 16.22 / 16.28 s |
+| FastH3 Dense | 4 (5 sigma points) | 11.7 / 11.8 s | 2.37 / 2.36 s |
+
+The denoising speedup is 6.9x. End-to-end is 2.2x because text encoding, VAE
+decoding and muxing are a fixed cost that dominates a clip this short; longer
+generations move the end-to-end figure toward the denoising one. Fusing the
+adapter does not measurably change startup: weight loading took 77.3 s with it
+against 85.8 s without.
+
 ## Key parameters

 | Parameter | Recommended value | Notes |
@@ -1018,3 +1142,4 @@ vllm serve "${MODEL_ROOT}/FL2VA" \
 - [Supported models](../../docs/models/supported_models.md)
 - [Video API](../../docs/serving/videos_api.md)
 - [Diffusion parallelism](../../docs/user_guide/diffusion/parallelism/overview.md)
+- [Temporal VAE chunk callback design](../../docs/design/feature/minimax_h3_temporal_vae_chunks.md)
@@ -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
@@ -78,6 +78,7 @@ recipes/
 | [`Qwen/Qwen3-Omni.md`](./Qwen/Qwen3-Omni.md) | Online serving for multimodal chat | 1x A100 80GB |
 | [`Qwen/Qwen3-TTS.md`](./Qwen/Qwen3-TTS.md) | Text-to-speech serving (CustomVoice / VoiceDesign / Base) | 1x H100/A100 80GB / 1x AMD MI300X |
 | [`SenseNova/SenseNova-U1.md`](./SenseNova/SenseNova-U1.md) | Unified image generation and understanding | 1x or 2x H200 / 1x AMD MI300X |
+| [`SenseNova/SenseNova-U1.5.md`](./SenseNova/SenseNova-U1.5.md) | Unified image generation and understanding, with an 8-step distilled LoRA | 1x A800 80GB |
 | [`StabilityAI/Stable-Audio-Open.md`](./StabilityAI/Stable-Audio-Open.md) | Offline + online text-to-audio generation (Stable Audio Open) | 1x RTX 4090 24GB / 1x AMD MI300X |
 | [`Tencent/Covo-Audio-Chat.md`](./Tencent/Covo-Audio-Chat.md) | Online serving for audio chat | 1x A100 80GB |
 | [`Tencent/HunyuanImage-3.0-Instruct.md`](./Tencent/HunyuanImage-3.0-Instruct.md) | DiT-only text-to-image serving and benchmark, including ModelOpt mixed FP8/NVFP4 | 4x H100/H800 80GB / 2x B200 |
@@ -0,0 +1,159 @@
+# SenseNova-U1.5
+
+> Unified image generation and understanding, with an official 8-step distilled LoRA
+
+## Summary
+
+- Vendor: SenseNova
+- Model: `sensenova/SenseNova-U1.5-8B-MoT`
+- LoRA: `sensenova/SenseNova-U1.5-8B-MoT-LoRAs` (`SenseNova-U1.5-8B-MoT-LoRA-8step.safetensors`)
+- Task: text2img, img2img, img2text (visual understanding), text2text (chat)
+- Mode: Offline inference, Online serving (OpenAI-compatible API)
+- Maintainer: Community
+
+## When to use this recipe
+
+U1.5 runs on the same `SenseNovaU1Pipeline` as U1 — the checkpoint keeps
+`model_type: neo_chat`, so it resolves without `--model-class-name`. Relative to U1 the
+config flips two fields, `use_pixel_head` to `true` (the flow-matching head becomes a
+`ConvDecoder`) and `noise_scale_max_value` from 8.0 to 16.0; both are already read by
+`SenseNovaU1Config`. The checkpoint is 13 shards / 50.2 GB on disk, 30.3 GB of that fp32, and
+loads to roughly 34 GB in bf16.
+
+Use this recipe for U1.5 specifically, including its distilled few-step LoRA. For U1 see
+[SenseNova-U1](SenseNova-U1.md).
+
+## Hardware Support
+
+### GPU
+
+#### 1x A800 80GB
+
+- 1x NVIDIA A800 80GB PCIe with an Intel Xeon Gold 6336Y, driver 595.84, Linux
+- Python 3.12.13, vLLM 0.28.0, torch 2.13.0+cu130, CUDA 13.0, diffusers 0.40.0,
+  transformers 5.14.1, flashinfer 0.6.16.post3, BF16, TP=1
+- Peak GPU memory: 34.3 GB at 1024x1024, 36.4 GB at 1536x2720
+
+##### Text-to-Image
+
+```bash
+python examples/offline_inference/text_to_image/text_to_image.py \
+    --model sensenova/SenseNova-U1.5-8B-MoT \
+    --prompt "Close portrait of an elderly woman by a farmhouse window, warm natural light." \
+    --width 1024 --height 1024 \
+    --seed 42 --num-inference-steps 50 --cfg-scale 4.0 \
+    --extra-body '{"think": false, "cfg_norm": "none", "timestep_shift": 3.0, "t_eps": 0.02}' \
+    --output sensenova_u15_t2i.png
+```
+
+Think mode (`"think": true`) is recommended for higher image quality.
+
+##### Image-to-Image Editing
+
+```bash
+python examples/offline_inference/image_to_image/image_edit.py \
+    --model sensenova/SenseNova-U1.5-8B-MoT \
+    --prompt "Turn this into an oil painting" \
+    --image input.png --resolution 1024 \
+    --seed 42 --num-inference-steps 25 --cfg-scale 4.0 \
+    --extra-args '{"think": true, "img_cfg_scale": 1.0, "cfg_norm": "none", "timestep_shift": 3.0}' \
+    --output sensenova_u15_edit.png
+```
+
+##### 8-step distilled LoRA
+
+The distilled LoRA is fused into the generation tower at load time:
+
+```bash
+python examples/offline_inference/text_to_image/text_to_image.py \
+    --model sensenova/SenseNova-U1.5-8B-MoT \
+    --lora-path SenseNova-U1.5-8B-MoT-LoRA-8step.safetensors --lora-backend distill \
+    --prompt "Close portrait of an elderly woman by a farmhouse window, warm natural light." \
+    --width 1024 --height 1024 \
+    --seed 42 --num-inference-steps 8 --cfg-scale 1.0 \
+    --extra-body '{"think": false, "cfg_norm": "none", "timestep_shift": 3.0, "t_eps": 0.02}' \
+    --output sensenova_u15_lora8.png
+```
+
+**Use `--cfg-scale 1.0` with this LoRA.** It is distilled with DMD and runs without
+classifier-free guidance; the default `4.0` applies guidance twice and produces a
+blown-out, posterised image.
+
+##### Online serving
+
+```bash
+vllm serve sensenova/SenseNova-U1.5-8B-MoT --omni --port 8091
+
+python examples/online_serving/sensenova_u1/openai_chat_client.py \
+    -s http://127.0.0.1:8091 -m img2text -i input.png -p "Describe this image."
+```
+
+`-s` takes the base URL; the client appends `/v1` itself.
+
+#### Measured latency (1x A800 80GB, 25 steps, median of 3 after a warmup)
+
+| Resolution | Step latency | Total |
+| --- | --- | --- |
+| 1024x1024 | 209.5 ms | 5.24 s |
+| 1536x1536 | 458.5 ms | 11.46 s |
+
+At 1536x2720 with 50 steps, end-to-end is 43.0 s.
+
+#### Measured latency (1x NVIDIA H200 139GB, single run, no warmup)
+
+Reported by @hsliuustc0106 against PR head `29c090d`, on one reserved CUDA device.
+
+- Python 3.12.13, vLLM 0.28.0, torch 2.13.0+cu130, CUDA 13.0, diffusers 0.40.0,
+  transformers 5.14.1, flashinfer 0.6.16.post3, BF16, TP=1
+- `sensenova/SenseNova-U1.5-8B-MoT` with `SenseNova-U1.5-8B-MoT-LoRA-8step.safetensors`,
+  seed 42, 1024x1024
+
+| Case | Stage latency | Peak GPU memory |
+| --- | --- | --- |
+| 50 steps, cfg 4.0, think off | 9017 ms | 34384 MiB |
+| 8 steps, cfg 1.0, no LoRA control | 624.00 ms, 77.85 ms/step | 34376 MiB |
+| 8 steps, cfg 1.0, distilled LoRA | 623.85 ms, 77.98 ms/step | 34364 MiB |
+| 50 steps, cfg 4.0, think on, `VLLM_OMNI_SENSENOVA_PAGED_DECODE=1` | 81622 ms | 34594 MiB |
+
+Each row runs the Text-to-Image command above with `--num-inference-steps` and
+`--cfg-scale` as listed and `think` set through `--extra-body`; the LoRA row adds
+`--lora-path` and `--lora-backend distill`.
+
+Single runs without a preceding warmup, so the think-on row carries the first-request
+compile rather than a steady state. The distilled LoRA fused into 168 parameters, and its
+image differs from the same-seed no-LoRA control in 1,048,574 of 1,048,576 pixels, MAE 58.62.
+
+```bash
+pytest -m "cpu and not cuda" tests/diffusion/models/sensenova_u1/ tests/diffusion/lora/test_loader.py -q
+pytest -m "cpu and not cuda" tests/config/test_environment_variables.py tests/diffusion/test_diffusion_worker.py -q
+pytest -m cuda tests/diffusion/models/sensenova_u1/test_sensenova_u1_attention_gqa.py \
+    tests/diffusion/models/sensenova_u1/test_sensenova_u1_paged_decode.py -q --tb=short
+```
+
+71, 13 and 9 passed. Serving reached `/health`; `img2text` returned a 1921-character
+description and `text2text` the expected answer.
+
+#### Verification
+
+```bash
+pytest -q tests/diffusion/models/sensenova_u1/
+```
+
+## Notes
+
+- Both `use_pixel_head` and `noise_scale_max_value` come from `config.json`; no flag is needed.
+- The checkpoint carries no `configuration_neo_chat.py`, so the loader logs a few
+  "does not appear to have a file named configuration_neo_chat.py" errors during startup and
+  then proceeds on the in-tree config. Generation is unaffected.
+- The LoRA targets the generation tower only (`*_mot_gen`); understanding-tower weights are
+  untouched.
+- Autoregressive decode (think, text-to-text and image-to-text) runs on a paged K/V cache under
+  a captured CUDA graph. It falls back to the ordinary cache when the device or the bundled
+  `flash_attn_varlen_func` cannot support it; set `VLLM_OMNI_SENSENOVA_PAGED_DECODE=0` to force
+  that fallback.
+- The first request after startup costs about 0.7 s more than the steady state whether the paged
+  path is on or off. Measured on one A800 with the inductor, triton and vLLM compile caches all
+  cleared, median of three runs: 718 ms above steady with the path on, 679 ms with it off.
+- Each request captures its own graphs, and a think request captures twice because the sequence
+  grows past the 512 bucket, so the capture cost is paid per request rather than once at
+  startup.
@@ -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
@@ -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
 """Benchmark legacy and automatic MP4 response encoding on synthetic input."""

 from __future__ import annotations
@@ -11,13 +11,16 @@ import statistics
 import time
 from collections.abc import Callable
 from dataclasses import dataclass
+from functools import partial
 from pathlib import Path
+from typing import cast

 import numpy as np

 from vllm_omni.entrypoints.openai.video_api_utils import (
    _encode_video_bytes,
    _encode_video_bytes_legacy,
+    _PlanarFrameConverter,
 )


@@ -42,12 +45,19 @@ def _build_inputs(
    fps: int,
    audio_sample_rate: int,
    seed: int,
+    input_layout: str = "planar-backed",
 ) -> tuple[np.ndarray, np.ndarray]:
    rng = np.random.default_rng(seed)
    planar = rng.random((3, frames, height, width), dtype=np.float32)
    video = planar.transpose(1, 2, 3, 0)
-    if not all(video[..., channel].flags.c_contiguous for channel in range(3)):
+    if input_layout == "interleaved":
+        video = np.ascontiguousarray(video)
+        if not video.flags.c_contiguous:
+            raise RuntimeError("synthetic video is not C-contiguous")
+    elif input_layout == "planar-backed" and not all(video[..., channel].flags.c_contiguous for channel in range(3)):
        raise RuntimeError("synthetic video does not expose contiguous channel planes")
+    elif input_layout != "planar-backed":
+        raise ValueError(f"unsupported input layout: {input_layout}")

    sample_count = max(1, round(frames / fps * audio_sample_rate))
    timeline = np.arange(sample_count, dtype=np.float32) / audio_sample_rate
@@ -86,8 +96,8 @@ def _measure(

 def _summarize(records: list[dict[str, object]], label: str) -> dict[str, object]:
    selected = [record for record in records if record["label"] == label]
-    wall_values = [float(record["wall_ms"]) for record in selected]
-    cpu_values = [float(record["process_cpu_ms"]) for record in selected]
+    wall_values = [cast(float, record["wall_ms"]) for record in selected]
+    cpu_values = [cast(float, record["process_cpu_ms"]) for record in selected]
    return {
        "runs": len(selected),
        "wall_ms": {
@@ -113,6 +123,16 @@ def _parse_args() -> argparse.Namespace:
    parser.add_argument("--warmup", type=int, default=1)
    parser.add_argument("--rounds", type=_positive_int, default=3)
    parser.add_argument("--seed", type=int, default=20260817)
+    parser.add_argument(
+        "--input-layout",
+        choices=("planar-backed", "interleaved"),
+        default="planar-backed",
+    )
+    parser.add_argument(
+        "--frame-conversion-workers",
+        type=_positive_int,
+        default=8,
+    )
    parser.add_argument("--output-json", type=Path)
    args = parser.parse_args()
    if args.warmup < 0:
@@ -129,33 +149,41 @@ def main() -> None:
        fps=args.fps,
        audio_sample_rate=args.audio_sample_rate,
        seed=args.seed,
+        input_layout=args.input_layout,
    )
    baseline = BenchmarkVariant("legacy", _encode_video_bytes_legacy)
-    candidate = BenchmarkVariant("automatic", _encode_video_bytes)
+    converter = _PlanarFrameConverter(max_workers=args.frame_conversion_workers)
+    candidate = BenchmarkVariant(
+        "automatic",
+        partial(_encode_video_bytes, frame_converter=converter),
+    )

-    for _ in range(args.warmup):
-        for variant in (baseline, candidate):
-            _measure(
-                variant,
-                video=video,
-                audio=audio,
-                fps=args.fps,
-                audio_sample_rate=args.audio_sample_rate,
-            )
+    try:
+        for _ in range(args.warmup):
+            for variant in (baseline, candidate):
+                _measure(
+                    variant,
+                    video=video,
+                    audio=audio,
+                    fps=args.fps,
+                    audio_sample_rate=args.audio_sample_rate,
+                )

-    records: list[dict[str, object]] = []
-    for round_index in range(args.rounds):
-        order = (baseline, candidate) if round_index % 2 == 0 else (candidate, baseline)
-        for variant in order:
-            _, record = _measure(
-                variant,
-                video=video,
-                audio=audio,
-                fps=args.fps,
-                audio_sample_rate=args.audio_sample_rate,
-            )
-            record["round"] = round_index + 1
-            records.append(record)
+        records: list[dict[str, object]] = []
+        for round_index in range(args.rounds):
+            order = (baseline, candidate) if round_index % 2 == 0 else (candidate, baseline)
+            for variant in order:
+                _, record = _measure(
+                    variant,
+                    video=video,
+                    audio=audio,
+                    fps=args.fps,
+                    audio_sample_rate=args.audio_sample_rate,
+                )
+                record["round"] = round_index + 1
+                records.append(record)
+    finally:
+        converter.shutdown()

    output_hashes = {str(record["output_sha256"]) for record in records}
    if len(output_hashes) != 1:
@@ -165,8 +193,8 @@ def main() -> None:
    paired_savings_percent = []
    for round_index in range(args.rounds):
        pair = [record for record in records if record["round"] == round_index + 1]
-        baseline_ms = float(next(record["wall_ms"] for record in pair if record["label"] == baseline.label))
-        candidate_ms = float(next(record["wall_ms"] for record in pair if record["label"] == candidate.label))
+        baseline_ms = cast(float, next(record["wall_ms"] for record in pair if record["label"] == baseline.label))
+        candidate_ms = cast(float, next(record["wall_ms"] for record in pair if record["label"] == candidate.label))
        paired_savings_ms.append(baseline_ms - candidate_ms)
        paired_savings_percent.append((baseline_ms - candidate_ms) / baseline_ms * 100)

@@ -180,6 +208,8 @@ def main() -> None:
            "warmup": args.warmup,
            "rounds": args.rounds,
            "seed": args.seed,
+            "input_layout": args.input_layout,
+            "frame_conversion_workers": args.frame_conversion_workers,
            "video_shape": list(video.shape),
            "video_strides": list(video.strides),
            "codec_options": {"preset": "ultrafast", "threads": "0"},
@@ -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
@@ -9,22 +9,98 @@ import asyncio
 import base64
 import json
 import time
+from argparse import Namespace
 from types import SimpleNamespace

 import pytest
 from pytest_mock import MockerFixture
 from vllm.benchmarks.lib.endpoint_request_func import RequestFuncInput

+from vllm_omni.benchmarks.data_modules.seed_tts_dataset import (
+    SeedTTSSampleRequest,
+    SeedTTSTextSampleRequest,
+)
 from vllm_omni.benchmarks.patch.patch import (
    MixRequestFuncOutput,
+    _apply_stage0_token_timings,
+    _attach_seed_tts_to_request_func_input,
    async_request_openai_chat_omni_completions,
    async_request_openai_realtime_duplex,
+    should_request_stage_metrics,
 )
 from vllm_omni.experimental.fullduplex.client import RealtimeEventCollector

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


+def _seed_tts_request_func_input() -> RequestFuncInput:
+    return RequestFuncInput(
+        model="test-model",
+        model_name="test-model",
+        prompt="target text",
+        api_url="http://test.com/v1/chat/completions",
+        prompt_len=2,
+        output_len=20,
+        extra_body={"modalities": ["text", "audio"]},
+    )
+
+
+def test_seed_tts_chat_request_carries_reference_audio_once() -> None:
+    reference_audio_url = "data:audio/wav;base64,AAAA"
+    sample = SeedTTSSampleRequest(
+        prompt="target text",
+        prompt_len=2,
+        expected_output_len=20,
+        multi_modal_data=None,
+        seed_tts_system_prompt="Clone the supplied voice.",
+        seed_tts_speech_extra={
+            "ref_audio": reference_audio_url,
+            "ref_text": "reference text",
+            "task_type": "Base",
+        },
+    )
+    request_func_input = _seed_tts_request_func_input()
+
+    _attach_seed_tts_to_request_func_input(sample, request_func_input)
+
+    assert request_func_input.omni_chat_messages == [
+        {
+            "role": "system",
+            "content": [{"type": "text", "text": "Clone the supplied voice."}],
+        },
+        {"role": "user", "content": [{"type": "text", "text": "target text"}]},
+    ]
+    assert request_func_input.extra_body["ref_audio"] == reference_audio_url
+    serialized_request = json.dumps(
+        {"messages": request_func_input.omni_chat_messages, **request_func_input.extra_body},
+    )
+    assert serialized_request.count(reference_audio_url) == 1
+
+
+def test_seed_tts_text_chat_messages_do_not_add_reference_audio() -> None:
+    sample = SeedTTSTextSampleRequest(
+        prompt="target text",
+        prompt_len=2,
+        expected_output_len=20,
+        multi_modal_data=None,
+        seed_tts_system_prompt="Use the configured voice.",
+        seed_tts_speech_extra=None,
+    )
+    request_func_input = _seed_tts_request_func_input()
+
+    _attach_seed_tts_to_request_func_input(sample, request_func_input)
+
+    assert getattr(request_func_input, "seed_tts_row", False) is True
+    assert request_func_input.omni_chat_messages == [
+        {
+            "role": "system",
+            "content": [{"type": "text", "text": "Use the configured voice."}],
+        },
+        {"role": "user", "content": [{"type": "text", "text": "target text"}]},
+    ]
+    assert "ref_audio" not in request_func_input.extra_body
+
+
 class MockResponse:
    """Mock aiohttp response for testing"""

@@ -102,7 +178,24 @@ 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 +236,7 @@ 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 +261,10 @@ 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"]
@@ -198,11 +296,160 @@ 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()


+def test_chat_text_timing_metrics_request_stage_metrics():
+    args = Namespace(
+        backend="openai-chat-omni",
+        percentile_metrics="ttft,tpot,itl,e2el",
+        print_stage=False,
+        extra_body={},
+    )
+
+    assert should_request_stage_metrics(args) is True
+
+
+@pytest.mark.asyncio
+async def test_bundled_first_text_chunk_uses_stage0_token_timings(mocker: MockerFixture):
+    """Engine timings recover TPOT when every text token arrives together."""
+    request_input = RequestFuncInput(
+        model="test-model",
+        model_name="test-model",
+        prompt="test prompt",
+        api_url="http://test.com/v1/chat/completions",
+        prompt_len=10,
+        output_len=20,
+    )
+    chunks = [
+        create_sse_chunk(
+            {
+                "choices": [{"delta": {"content": "ABCD"}}],
+                "modality": "text",
+                "usage": {"prompt_tokens": 10, "completion_tokens": 4, "total_tokens": 14},
+                "metrics": {
+                    "stage_metrics": {
+                        "0": {
+                            "num_tokens_out": 4,
+                            "vllm_itls_ms": [10.0, 11.0, 12.0],
+                            "vllm_tpot_ms": 11.0,
+                        }
+                    }
+                },
+            }
+        ),
+        b"data: [DONE]\n\n",
+    ]
+    mock_response = MockResponse(200, chunks)
+    mock_session = mocker.AsyncMock()
+    mock_session.post = mocker.MagicMock(return_value=mock_response)
+
+    output = await async_request_openai_chat_omni_completions(request_input, mock_session)
+
+    assert output.success is True
+    assert output.output_tokens == 4
+    assert output.itl == pytest.approx([0.010, 0.011, 0.012])
+    assert output.text_latency - output.ttft == pytest.approx(0.033)
+    assert output.tpot_measured is True
+
+
+@pytest.mark.asyncio
+async def test_positive_client_text_timings_take_precedence_over_stage0(mocker: MockerFixture):
+    request_input = RequestFuncInput(
+        model="test-model",
+        model_name="test-model",
+        prompt="test prompt",
+        api_url="http://test.com/v1/chat/completions",
+        prompt_len=10,
+        output_len=20,
+    )
+    chunks = [
+        create_sse_chunk(
+            {
+                "choices": [{"delta": {"content": "A"}}],
+                "modality": "text",
+                "usage": {"prompt_tokens": 10, "completion_tokens": 1, "total_tokens": 11},
+            }
+        ),
+        create_sse_chunk(
+            {
+                "choices": [{"delta": {"content": "B"}}],
+                "modality": "text",
+                "usage": {"prompt_tokens": 10, "completion_tokens": 2, "total_tokens": 12},
+                "metrics": {
+                    "stage_metrics": {
+                        "0": {
+                            "num_tokens_out": 2,
+                            "vllm_itls_ms": [1000.0],
+                            "vllm_tpot_ms": 1000.0,
+                        }
+                    }
+                },
+            }
+        ),
+        b"data: [DONE]\n\n",
+    ]
+    mock_response = MockResponse(200, chunks, delay_between_chunks=0.01)
+    mock_session = mocker.AsyncMock()
+    mock_session.post = mocker.MagicMock(return_value=mock_response)
+
+    output = await async_request_openai_chat_omni_completions(request_input, mock_session)
+
+    assert output.success is True
+    assert len(output.itl) == 1
+    assert 0.0 < output.itl[0] < 0.1
+
+
 # ============================================================================
 # output_tokens Tests
 # ============================================================================
@@ -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):
@@ -1,3 +1,6 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
 """Tests for SeedTTSTextDataset, SeedTTSTextSampleRequest, SeedTTSDesignDataset,
 and SeedTTSDesignSampleRequest.

@@ -234,34 +237,6 @@ def test_seed_tts_design_dataset_rejects_missing_description(seed_tts_design_roo
        assert req.seed_tts_utterance_id == "ok"


-def test_attach_sets_seed_tts_row_even_without_extra_body():
-    """seed_tts_row=True must be set for SeedTTSTextSampleRequest (no extra body)."""
-    from vllm_omni.benchmarks.data_modules.seed_tts_dataset import SeedTTSTextSampleRequest
-
-    req = SeedTTSTextSampleRequest(
-        prompt="hello world",
-        prompt_len=2,
-        expected_output_len=100,
-        multi_modal_data=None,
-        request_id="test-0",
-        seed_tts_speech_extra=None,
-        seed_tts_ref_wav_path="",
-    )
-    assert req.seed_tts_speech_extra is None
-    assert req.seed_tts_ref_wav_path == ""
-    # The fix ensures that even with speech_extra=None, the function
-    # sets seed_tts_row=True. We verify the source code has the fix.
-    import inspect
-
-    import vllm_omni.benchmarks.patch.patch as patch_mod
-
-    src = inspect.getsource(patch_mod._attach_seed_tts_to_request_func_input)
-    # seed_tts_row must be set BEFORE the 'if not ex: return' check
-    row_pos = src.index("seed_tts_row")
-    not_ex_pos = src.index("if not ex:")
-    assert row_pos < not_ex_pos, "seed_tts_row must be set before 'if not ex: return'"
-
-
 def test_seed_tts_whisper_transcribe_passes_attention_mask(monkeypatch):
    from vllm_omni.benchmarks.data_modules import seed_tts_eval

@@ -0,0 +1,42 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
+from pathlib import Path
+from shlex import split
+
+import pytest
+import yaml
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
+
+AMD_MERGE_PIPELINE = Path(".buildkite/amd/test-amd-merge.yml")
+
+
+def _find_step(label: str) -> dict:
+    pipeline = yaml.safe_load(AMD_MERGE_PIPELINE.read_text(encoding="utf-8"))
+
+    def walk(steps: list[dict]) -> dict | None:
+        for step in steps:
+            if step.get("label") == label:
+                return step
+            if nested := walk(step.get("steps", [])):
+                return nested
+        return None
+
+    step = walk(pipeline.get("steps", []))
+    assert step is not None, f"missing AMD pipeline step: {label}"
+    return step
+
+
+def test_qwen3_tts_base_preserves_advanced_model_arguments() -> None:
+    step = _find_step("Qwen3-TTS Base E2E Test")
+    commands = step["commands"]
+
+    assert all("bash -c" not in command for command in commands)
+    pytest_command = next(command for command in commands if "pytest" in command)
+    argv = split(pytest_command)
+
+    marker_index = argv.index("-m")
+    run_level_index = argv.index("--run-level")
+    assert argv[marker_index + 1] == "advanced_model and cuda"
+    assert argv[run_level_index + 1] == "advanced_model"
@@ -34,12 +34,18 @@ 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

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

+SINGLE_STAGE_PIPE_CFG = (StagePipelineConfig(stage_id=0, model_stage="a", final_output=True),)
+# Validation strings to match against in post init
+NO_STAGES_MATCH_STR = "no stages"
+NO_TERMINAL_STAGE_MATCH_STR = "No terminal stage"
+

@pytest.fixture(autouse=True)
 def _stable_test_platform(monkeypatch):
@@ -429,6 +435,20 @@ 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 +554,7 @@ 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 +579,18 @@ 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)],
@@ -590,6 +623,7 @@ class TestPipelineDiscovery:
        p = PipelineConfig(
            model_type="custom_collide",
            hf_architectures=("SomeCollidingArch",),
+            stages=SINGLE_STAGE_PIPE_CFG,
        )
        assert p.hf_architectures == ("SomeCollidingArch",)

@@ -611,33 +645,63 @@ class TestStagePipelineConfig:


 class TestPipelineConfigNew:
-    def test_frozen(self):
-        p = PipelineConfig(model_type="t", model_arch="A")
-        with pytest.raises(AttributeError):
-            p.model_type = "changed"
+    def test_simple_init(self):
+        # Ensure pipeline config validates in post init.
+        PipelineConfig(
+            model_type="t",
+            model_arch="A",
+            stages=SINGLE_STAGE_PIPE_CFG,
+        )

-    def test_validate_valid(self):
+    def test_frozen(self):
        p = PipelineConfig(
+            model_type="t",
+            model_arch="A",
+            stages=SINGLE_STAGE_PIPE_CFG,
+        )
+        with pytest.raises(AttributeError):
+            p.model_type = "changed"
+
+    def test_validate_no_stages(self):
+        with pytest.raises(ValueError, match=NO_STAGES_MATCH_STR):
+            PipelineConfig(model_type="t", model_arch="A")
+
+    def test_validate_no_terminal_stage(self):
+        """A pipeline with no ``final_output`` stage can never emit a result."""
+        with pytest.raises(ValueError, match=NO_TERMINAL_STAGE_MATCH_STR):
+            PipelineConfig(
+                model_type="t",
+                model_arch="A",
+                stages=(
+                    StagePipelineConfig(stage_id=0, model_stage="a"),
+                    StagePipelineConfig(stage_id=1, model_stage="b", input_sources=(0,)),
+                ),
+            )
+
+    def test_pipeline_can_have_final_output_in_any_stage(self):
+        """Ensure any stage may carry ``final_output``."""
+        PipelineConfig(
            model_type="t",
            model_arch="A",
            stages=(
-                StagePipelineConfig(stage_id=0, model_stage="a"),
+                StagePipelineConfig(stage_id=0, model_stage="a", final_output=True),
                StagePipelineConfig(stage_id=1, model_stage="b", input_sources=(0,)),
            ),
        )
-        assert p.validate() == []
-
-    def test_validate_no_stages(self):
-        p = PipelineConfig(model_type="t", model_arch="A")
-        assert any("no stages" in e.lower() for e in p.validate())


 class TestPipelineRegistration:
    def test_resolve_pipeline_prefers_deploy_pipeline_key(self, clean_pipeline_registry, tmp_path):
        deploy_key = "deploy_selected_pipeline"
        model_type_key = "hf_model_type_pipeline"
-        deploy_pipe = PipelineConfig(model_type=deploy_key)
-        model_type_pipe = PipelineConfig(model_type=model_type_key)
+        deploy_pipe = PipelineConfig(
+            model_type=deploy_key,
+            stages=SINGLE_STAGE_PIPE_CFG,
+        )
+        model_type_pipe = PipelineConfig(
+            model_type=model_type_key,
+            stages=SINGLE_STAGE_PIPE_CFG,
+        )
        register_pipeline(deploy_pipe)
        register_pipeline(model_type_pipe)

@@ -663,7 +727,11 @@ class TestPipelineRegistration:
        tmp_path,
    ):
        model_type_key = "registered_model_type_pipeline"
-        register_pipeline(PipelineConfig(model_type=model_type_key))
+        pipeline_cfg = PipelineConfig(
+            model_type=model_type_key,
+            stages=SINGLE_STAGE_PIPE_CFG,
+        )
+        register_pipeline(pipeline_cfg)

        deploy_path = tmp_path / "deploy.yaml"
        deploy_path.write_text("pipeline: missing_pipeline\n", encoding="utf-8")
@@ -698,6 +766,7 @@ class TestPipelineRegistration:
        pipe_cfg = PipelineConfig(
            model_type=pipeline_key,
            hf_architectures=("ArchitectureFallbackForTest",),
+            stages=SINGLE_STAGE_PIPE_CFG,
        )
        register_pipeline(pipe_cfg)

@@ -728,15 +797,18 @@ class TestPipelineRegistration:
            model_type="rejected_by_predicate",
            hf_architectures=(shared_arch,),
            hf_config_predicate=rejecting_predicate,
+            stages=SINGLE_STAGE_PIPE_CFG,
        )
        raises_cfg = PipelineConfig(
            model_type="raises_in_predicate",
            hf_architectures=(shared_arch,),
            hf_config_predicate=raising_predicate,
+            stages=SINGLE_STAGE_PIPE_CFG,
        )
        accept_cfg = PipelineConfig(
            model_type="accepted_by_predicate",
            hf_architectures=(shared_arch,),
+            stages=SINGLE_STAGE_PIPE_CFG,
        )
        register_pipeline(reject_cfg)
        register_pipeline(raises_cfg)
@@ -770,6 +842,7 @@ class TestPipelineRegistration:
            model_type="predicate_without_arch_match",
            hf_architectures=("DifferentArchitectureForTest",),
            hf_config_predicate=predicate,
+            stages=SINGLE_STAGE_PIPE_CFG,
        )
        register_pipeline(pipe_cfg)

@@ -793,6 +866,7 @@ class TestPipelineRegistration:
        resolved_cfg = PipelineConfig(
            model_type="callable_resolved_pipeline",
            hf_architectures=("CallableArchitectureForTest",),
+            stages=SINGLE_STAGE_PIPE_CFG,
        )
        seen_hf_configs = []

@@ -866,7 +940,10 @@ class TestPipelineRegistration:
    def test_pipeline_registration(self, clean_pipeline_registry):
        """Ensure that we can register and create a custom pipeline config."""
        new_model_type = "new_model_type"
-        pipe_cfg = PipelineConfig(model_type=new_model_type)
+        pipe_cfg = PipelineConfig(
+            model_type=new_model_type,
+            stages=SINGLE_STAGE_PIPE_CFG,
+        )

        # Register the new PipelineConfig
        assert new_model_type not in OMNI_PIPELINES
@@ -909,7 +986,10 @@ class TestPipelineRegistration:
        def custom_resolver(
            hf_config: FakeConfig,
        ) -> PipelineConfig:
-            return PipelineConfig(model_type=resolved_type)
+            return PipelineConfig(
+                model_type=resolved_type,
+                stages=SINGLE_STAGE_PIPE_CFG,
+            )

        # Register the new PipelineConfig
        assert new_model_type not in OMNI_PIPELINES
@@ -966,7 +1046,7 @@ class TestPipelineRegistration:
        pipe_cfg = PipelineConfig(
            model_type=pipeline_key,
            model_arch="DeployOnlyArch",
-            stages=(StagePipelineConfig(stage_id=0, model_stage="diffusion"),),
+            stages=SINGLE_STAGE_PIPE_CFG,
        )
        register_pipeline(pipe_cfg)

@@ -999,7 +1079,11 @@ class TestPipelineRegistration:

    def test_structured_path_loads_explicit_deploy_config_once(self, clean_pipeline_registry, tmp_path):
        pipeline_key = "single_load_pipeline"
-        register_pipeline(PipelineConfig(model_type=pipeline_key))
+        pipeline_cfg = PipelineConfig(
+            model_type=pipeline_key,
+            stages=SINGLE_STAGE_PIPE_CFG,
+        )
+        register_pipeline(pipeline_cfg)
        deploy_path = tmp_path / "single_load.yaml"
        deploy_path.write_text(f"pipeline: {pipeline_key}\n", encoding="utf-8")

@@ -1029,6 +1113,7 @@ class TestPipelineRegistration:
        pipeline = PipelineConfig(
            model_type="pipeline_with_default",
            default_deploy_config_name=default_name,
+            stages=SINGLE_STAGE_PIPE_CFG,
        )

        with patch(
@@ -1048,8 +1133,16 @@ class TestPipelineRegistration:
            OmniServingCapability.COMPLETIONS,
            "pipeline_a blocks completions",
        )
-        pipe_a = PipelineConfig(model_type="detect_type", endpoint_restrictions=(restriction,))
-        pipe_b = PipelineConfig(model_type="override_type", endpoint_restrictions=())
+        pipe_a = PipelineConfig(
+            model_type="detect_type",
+            endpoint_restrictions=(restriction,),
+            stages=SINGLE_STAGE_PIPE_CFG,
+        )
+        pipe_b = PipelineConfig(
+            model_type="override_type",
+            endpoint_restrictions=(),
+            stages=SINGLE_STAGE_PIPE_CFG,
+        )
        register_pipeline(pipe_a)
        register_pipeline(pipe_b)

@@ -1131,7 +1224,7 @@ 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 +1318,22 @@ 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 +1342,7 @@ 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 +1394,8 @@ 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 +1404,13 @@ 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 +1431,91 @@ 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 +1551,29 @@ 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)))
@@ -1466,6 +1682,7 @@ stages:
                    model_stage="ar",
                    execution_type=StageExecutionType.LLM_AR,
                    requires_multimodal_data=True,
+                    final_output=True,
                ),
            ),
        )
@@ -1486,6 +1703,7 @@ stages:
                    model_stage="ar",
                    execution_type=StageExecutionType.LLM_AR,
                    scheduler_cls=scheduler_cls,
+                    final_output=True,
                ),
            ),
        )
@@ -1731,7 +1949,6 @@ class TestQwen3OmniPipeline:
        assert isinstance(p, PipelineConfig)
        assert p.model_arch == "Qwen3OmniMoeForConditionalGeneration"
        assert len(p.stages) == 3
-        assert p.validate() == []

    def test_thinker(self):
        p = resolve_pipeline_config(
@@ -1785,7 +2002,6 @@ class TestQwen2_5OmniPipeline:
        assert isinstance(p, PipelineConfig)
        assert p.model_arch == "Qwen2_5OmniForConditionalGeneration"
        assert len(p.stages) == 3
-        assert p.validate() == []

    def test_thinker(self):
        p = resolve_pipeline_config("qwen2_5_omni")
@@ -1834,7 +2050,6 @@ class TestQwen3TTSPipeline:
        assert p is not None
        assert p.model_arch == "Qwen3TTSTalkerForConditionalGeneration"
        assert len(p.stages) == 2
-        assert p.validate() == []

    def test_talker_stage(self):
        p = resolve_pipeline_config("qwen3_tts")
@@ -1914,7 +2129,6 @@ class TestMingFlashOmniPipeline:
        assert isinstance(p, PipelineConfig)
        assert p.model_arch == "MingFlashOmniForConditionalGeneration"
        assert len(p.stages) == 2
-        assert p.validate() == []

    def test_thinker_stage(self):
        p = resolve_pipeline_config("ming_flash_omni")
@@ -1971,7 +2185,6 @@ class TestMingFlashOmniPipeline:
        assert isinstance(p, PipelineConfig)
        assert p.model_arch == "MingFlashOmniTalkerForConditionalGeneration"
        assert len(p.stages) == 1
-        assert p.validate() == []

    def test_tts_stage(self):
        p = resolve_pipeline_config("ming_flash_omni_tts")
@@ -2028,7 +2241,6 @@ class TestMingFlashOmniPipeline:
        assert isinstance(p, PipelineConfig)
        assert p.model_arch == "MingFlashOmniForConditionalGeneration"
        assert len(p.stages) == 1
-        assert p.validate() == []

    def test_thinker_only_stage(self):
        p = resolve_pipeline_config("ming_flash_omni_thinker_only")
@@ -2067,7 +2279,6 @@ class TestMingFlashOmniPipeline:
        assert p is not None
        assert p.model_arch == "MingFlashOmniForConditionalGeneration"
        assert len(p.stages) == 2
-        assert p.validate() == []

    def test_image_thinker_stage(self):
        s = resolve_pipeline_config("ming_flash_omni_image").get_stage(0)
@@ -2188,6 +2399,21 @@ class TestBaseConfigInheritance:
        # CI overrides max_tokens
        assert s0["max_tokens"] == 150

+    def test_qwen3_omni_colocate_async_bounds_only_rocm_kv_cache(self):
+        ci_path = Path(get_deploy_config_path("ci/qwen3_omni_moe_colocate_async.yaml"))
+        pipeline = resolve_pipeline_config("qwen3_omni_moe_thinker_only")
+        assert isinstance(pipeline, PipelineConfig)
+
+        cuda = _apply_platform_overrides(load_deploy_config(ci_path), platform="cuda")
+        cuda_stage = merge_pipeline_deploy(pipeline, cuda)[0]
+        assert cuda_stage.yaml_engine_args["gpu_memory_utilization"] == 0.9
+        assert "kv_cache_memory_bytes" not in cuda_stage.yaml_engine_args
+
+        rocm = _apply_platform_overrides(load_deploy_config(ci_path), platform="rocm")
+        rocm_stage = merge_pipeline_deploy(pipeline, rocm)[0]
+        assert "gpu_memory_utilization" not in rocm_stage.yaml_engine_args
+        assert rocm_stage.yaml_engine_args["kv_cache_memory_bytes"] == 2 * 1024**3
+
    def test_pure_inheritance_overlay(self, tmp_path):
        """An overlay with only ``base_config`` inherits everything."""
        base = Path(get_deploy_config_path("qwen3_omni_moe.yaml"))
@@ -2248,6 +2474,20 @@ 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(
@@ -2635,6 +2875,54 @@ stages:
        assert omega_config.engine_args.max_model_len == 8192
        assert omega_config.engine_args.enforce_eager is True

+    def test_cli_attention_shorthand_replaces_yaml_structured_default(self):
+        stage = StageConfig(
+            stage_id=0,
+            model_stage="dit",
+            stage_type=StageType.DIFFUSION,
+            yaml_engine_args={
+                "diffusion_attention_config": {
+                    "default": {"backend": "FLASH_ATTN"},
+                    "per_role": {"cross": {"backend": "TORCH_SDPA"}},
+                },
+            },
+            runtime_overrides={"diffusion_attention_backend": "SAGE_ATTN"},
+        )
+
+        engine_args = stage.to_omegaconf().engine_args
+
+        assert engine_args.diffusion_attention_backend == "SAGE_ATTN"
+        assert "default" not in engine_args.diffusion_attention_config
+        assert engine_args.diffusion_attention_config.per_role.cross.backend == "TORCH_SDPA"
+
+    def test_cli_attention_shorthand_keeps_yaml_per_role_only_config(self):
+        stage = StageConfig(
+            stage_id=0,
+            model_stage="dit",
+            stage_type=StageType.DIFFUSION,
+            yaml_engine_args={"diffusion_attention_config": {"per_role": {"cross": {"backend": "TORCH_SDPA"}}}},
+            runtime_overrides={"diffusion_attention_backend": "SAGE_ATTN"},
+        )
+
+        engine_args = stage.to_omegaconf().engine_args
+
+        assert engine_args.diffusion_attention_backend == "SAGE_ATTN"
+        assert engine_args.diffusion_attention_config == {"per_role": {"cross": {"backend": "TORCH_SDPA"}}}
+
+    def test_cli_attention_config_replaces_yaml_shorthand(self):
+        stage = StageConfig(
+            stage_id=0,
+            model_stage="dit",
+            stage_type=StageType.DIFFUSION,
+            yaml_engine_args={"diffusion_attention_backend": "TORCH_SDPA"},
+            runtime_overrides={"diffusion_attention_config": {"default": {"backend": "SAGE_ATTN"}}},
+        )
+
+        engine_args = stage.to_omegaconf().engine_args
+
+        assert "diffusion_attention_backend" not in engine_args
+        assert engine_args.diffusion_attention_config.default.backend == "SAGE_ATTN"
+

 class TestSentinelDefaultPrecedence:
    """Caller-typed (non-None) values win over YAML; None values fall through
@@ -167,7 +167,7 @@ def test_inventory_matches_reviewed_snapshot_counts():
        EnvironmentVariableCategory.PUBLIC_OMNI: 24,
        EnvironmentVariableCategory.INHERITED_VLLM: 20,
        EnvironmentVariableCategory.PLATFORM_EXTERNAL: 27,
-        EnvironmentVariableCategory.MODEL_SPECIFIC: 56,
+        EnvironmentVariableCategory.MODEL_SPECIFIC: 57,
        EnvironmentVariableCategory.BENCHMARK_TRANSITIONAL: 20,
        EnvironmentVariableCategory.INTERNAL: 2,
    }
@@ -178,7 +178,7 @@ def test_inventory_matches_reviewed_snapshot_counts():
        if item.category is EnvironmentVariableCategory.MODEL_SPECIFIC
    )
    assert {disposition: disposition_counts[disposition] for disposition in ModelEnvironmentVariableDisposition} == {
-        ModelEnvironmentVariableDisposition.PROMOTE: 33,
+        ModelEnvironmentVariableDisposition.PROMOTE: 34,
        ModelEnvironmentVariableDisposition.REQUEST_SCOPE: 6,
        ModelEnvironmentVariableDisposition.EXTERNAL: 0,
        ModelEnvironmentVariableDisposition.INTERNALIZE: 11,
@@ -45,6 +45,7 @@ from vllm_omni.config.stage_config import (
    PipelineConfig,
    StageDeployConfig,
    StageExecutionType,
+    StagePipelineConfig,
    load_deploy_config,
    merge_pipeline_deploy,
 )
@@ -92,10 +93,23 @@ 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 +119,7 @@ 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 +147,7 @@ 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 +595,7 @@ 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 +654,11 @@ 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 +860,23 @@ 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 +971,35 @@ 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")
@@ -976,7 +1040,10 @@ def test_from_pipeline_config_uses_hf_config_for_callable_resolver():


 def test_from_pipeline_config_accepts_pre_resolved_pipeline():
-    resolved_pipeline = PipelineConfig(model_type="callable_resolved_variant")
+    resolved_pipeline = PipelineConfig(
+        model_type="callable_resolved_variant",
+        stages=(StagePipelineConfig(stage_id=0, model_stage="a", final_output=True),),
+    )

    omni_config = VllmOmniConfig.from_pipeline_config(resolved_pipeline)

@@ -1009,6 +1076,7 @@ def test_from_pipeline_config_default_deploy_name_ignores_cwd(monkeypatch, tmp_p
    pipeline = PipelineConfig(
        model_type="pipeline_with_default",
        default_deploy_config_name=default_name,
+        stages=(StagePipelineConfig(stage_id=0, model_stage="a", final_output=True),),
    )
    loaded_paths = []

@@ -1,16 +1,22 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 """Tests for out of tree registration to OMNI_PIPELINES."""

 import pytest
 from transformers import PretrainedConfig

 from vllm_omni.config.pipeline_registry import OMNI_PIPELINES, register_pipeline
-from vllm_omni.config.stage_config import PipelineConfig, pipeline_cfg_resolver
+from vllm_omni.config.stage_config import PipelineConfig, StagePipelineConfig, pipeline_cfg_resolver

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


+def build_fake_pipeline_config(model_type: str) -> PipelineConfig:
+    return PipelineConfig(
+        model_type=model_type, stages=(StagePipelineConfig(stage_id=0, model_stage="a", final_output=True),)
+    )
+
+
@pytest.fixture
 def custom_resolver():
    """Build a reusable custom resolver for PipelineConfigs."""
@@ -22,7 +28,7 @@ def custom_resolver():
    def custom_resolver(
        hf_config: CustomConfigType,
    ) -> PipelineConfig:
-        return PipelineConfig(model_type="resolved_type")
+        return build_fake_pipeline_config("resolved_type")

    return custom_resolver

@@ -30,7 +36,7 @@ def custom_resolver():
 def test_register_pipeline_config(clean_pipeline_registry):
    """Ensure that we can register a custom pipeline config to OMNI_PIPELINES."""
    new_model_type = "new_model_type"
-    pipe_cfg = PipelineConfig(model_type=new_model_type)
+    pipe_cfg = build_fake_pipeline_config(new_model_type)
    assert new_model_type not in OMNI_PIPELINES
    register_pipeline(pipe_cfg)
    assert new_model_type in OMNI_PIPELINES
@@ -41,7 +47,7 @@ def test_register_pipeline_config_with_model_type(clean_pipeline_registry):
    """Ensure that we can register a custom pipeline config with an explicit model_type to OMNI_PIPELINES."""
    new_model_type = "new_model_type"
    unused_model_type = "foo"
-    pipe_cfg = PipelineConfig(model_type=unused_model_type)
+    pipe_cfg = build_fake_pipeline_config(unused_model_type)
    assert new_model_type not in OMNI_PIPELINES
    assert unused_model_type not in OMNI_PIPELINES

@@ -66,3 +72,10 @@ 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