Merge main and align text encoder TP regression documentation

Signed-off-by: Sy03 <1370724210@qq.com>

.buildkite/amd/scripts/bootstrap-amd-omni.sh

@@ -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
@@ -6,8 +9,13 @@
 
 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
 
+AMD_SUITE_SELECTOR=".buildkite/amd/scripts/select_test_suites.py"
+
 if [[ -z "${RUN_ALL:-}" ]]; then
     RUN_ALL=0
 fi
@@ -24,31 +32,20 @@ if [[ -z "${AMD_MIRROR_HW:-}" ]]; then
     AMD_MIRROR_HW="amdproduction"
 fi
 
-fail_fast() {
-    DISABLE_LABEL="ci-no-fail-fast"
-    # If BUILDKITE_PULL_REQUEST != "false", then we check the PR labels using curl and jq
-    if [ "$BUILDKITE_PULL_REQUEST" != "false" ]; then
-        PR_LABELS=$(curl -s "https://api.github.com/repos/vllm-project/vllm-omni/pulls/$BUILDKITE_PULL_REQUEST" | jq -r '.labels[].name')
-        if [[ $PR_LABELS == *"$DISABLE_LABEL"* ]]; then
-            echo false
-        else
-            echo true
-        fi
-    else
-        echo false  # not a PR or BUILDKITE_PULL_REQUEST not set
+PR_LABELS=""
+# Explicit debug suites do not depend on PR labels.
+if [[ "${BUILDKITE_PULL_REQUEST:-false}" != "false" && -z "${DEBUG_TEST_YAML:-}" ]]; then
+    if ! PR_LABELS=$(curl -fsSL \
+        "https://api.github.com/repos/vllm-project/vllm-omni/pulls/$BUILDKITE_PULL_REQUEST" \
+        | jq -r '.labels[].name'); then
+        echo "ERROR: Could not read PR labels; refusing to select an incorrect AMD test tier." >&2
+        exit 1
     fi
-}
+fi
 
-check_run_all_label() {
-    RUN_ALL_LABEL="ready-run-all-tests"
-    # If BUILDKITE_PULL_REQUEST != "false", then we check the PR labels using curl and jq
-    if [ "$BUILDKITE_PULL_REQUEST" != "false" ]; then
-        PR_LABELS=$(curl -s "https://api.github.com/repos/vllm-project/vllm-omni/pulls/$BUILDKITE_PULL_REQUEST" | jq -r '.labels[].name')
-        if [[ $PR_LABELS == *"$RUN_ALL_LABEL"* ]]; then
-            echo true
-        else
-            echo false
-        fi
+fail_fast() {
+    if [[ "${BUILDKITE_PULL_REQUEST:-false}" != "false" ]]; then
+        echo true
     else
         echo false  # not a PR or BUILDKITE_PULL_REQUEST not set
     fi
@@ -58,11 +55,71 @@ if [[ -z "${COV_ENABLED:-}" ]]; then
     COV_ENABLED=0
 fi
 
+resolve_test_specs() {
+    local selected_specs
+    if ! selected_specs=$(python3 "$AMD_SUITE_SELECTOR" \
+        --branch "$BUILDKITE_BRANCH" \
+        --labels "$PR_LABELS" \
+        --debug-test-yaml "${DEBUG_TEST_YAML:-}" \
+        --nightly "$NIGHTLY"); then
+        exit 1
+    fi
+
+    TEST_SPECS=()
+    while IFS= read -r suite_spec; do
+        [[ -z "$suite_spec" ]] && continue
+        TEST_SPECS+=("$suite_spec")
+    done <<< "$selected_specs"
+}
+
+filter_test_specs_by_skip_ci() {
+    local suite_spec level decision
+    local skip_all=0
+    local -a runnable_specs=()
+
+    for suite_spec in "${TEST_SPECS[@]}"; do
+        case "$suite_spec" in
+            READY_TESTS:*) level="l2" ;;
+            MERGE_TESTS:*) level="l3" ;;
+            NIGHTLY_TESTS:*)
+                # Explicit PR and scheduled nightly selections must survive
+                # L2/L3 diff gating, including docs-only main commits.
+                runnable_specs+=("$suite_spec")
+                continue
+                ;;
+            *)
+                echo "ERROR: unknown AMD test suite spec '$suite_spec'" >&2
+                exit 1
+                ;;
+        esac
+
+        if decision=$(python3 "$SKIP_CI_PY" gate amd "$level"); then
+            echo "Skipping AMD $level suite due to $decision decision."
+            [[ "$decision" == "skip-all" ]] && skip_all=1
+        else
+            runnable_specs+=("$suite_spec")
+        fi
+    done
+
+    TEST_SPECS=("${runnable_specs[@]}")
+    if [[ ${#TEST_SPECS[@]} -eq 0 ]]; then
+        if [[ $skip_all -eq 1 ]]; then
+            buildkite-agent annotate \
+                ":memo: CI skipped — docs or pytest skip-mark changes only" \
+                --style "info" 2>/dev/null || true
+        fi
+        echo "No AMD suites remain after skip-ci filtering."
+        exit 0
+    fi
+}
+
 upload_pipeline() {
     echo "Uploading 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 [[ $BUILDKITE_PIPELINE_SLUG == "fastcheck" ]]; then
@@ -76,7 +133,7 @@ upload_pipeline() {
     # (WIP) Use pipeline generator instead of jinja template
     if [ -e ".buildkite/amd/pipeline_generator/pipeline_generator.py" ]; then
         python -m pip install click pydantic
-        python .buildkite/amd/pipeline_generator/pipeline_generator.py --run_all=$RUN_ALL --list_file_diff="$LIST_FILE_DIFF" --nightly="$NIGHTLY" --mirror_hw="$AMD_MIRROR_HW"
+        python .buildkite/amd/pipeline_generator/pipeline_generator.py --run_all="$RUN_ALL" --list_file_diff="$LIST_FILE_DIFF" --nightly="$NIGHTLY" --mirror_hw="$AMD_MIRROR_HW"
         buildkite-agent pipeline upload .buildkite/amd/pipeline.yaml
         exit 0
     fi
@@ -89,28 +146,46 @@ 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).
-    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
-                exit 1
-                ;;
-        esac
-        echo "DEBUG_TEST_YAML override: using $TEST_YAML"
-    elif [[ $BUILDKITE_BRANCH == "main" ]]; then
-        TEST_YAML="test-amd-merge.yml"
+    # Multiple label-selected or debug-selected suites share one image build.
+    if [[ ${#TEST_SPECS[@]} -eq 1 ]]; then
+        TEST_YAML="${TEST_SPECS[0]#*:}"
     else
-        TEST_YAML="test-amd-ready.yml"
+        TEST_YAML=$(mktemp "${TMPDIR:-/tmp}/amd-selected-tests.XXXXXX.yml")
+        python - "$TEST_YAML" "${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 "AMD test suites: ${TEST_SPECS[*]}"
 
     (
         set -x
@@ -130,19 +205,20 @@ upload_pipeline() {
             > pipeline.yaml
     )
     cat pipeline.yaml
+    if [[ "$TEST_YAML" == "${TMPDIR:-/tmp}/amd-selected-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)
@@ -150,12 +226,10 @@ if [[ $BUILDKITE_BRANCH == "main" ]]; then
     file_diff=$(get_diff_main)
 fi
 
-# Early exit: unified skip-ci (docs / skip marks) and CI-yaml-only level targeting.
-if [[ $BUILDKITE_BRANCH == "main" ]]; then
-    gate_bootstrap_ci amd l3
-else
-    gate_bootstrap_ci amd l2
-fi
+# Resolve PR tier labels before skip-ci so ready/merge suites can be filtered
+# independently while an explicitly selected nightly suite passes through.
+resolve_test_specs
+filter_test_specs_by_skip_ci
 
 patterns=(
     "docker/Dockerfile"
@@ -181,7 +255,7 @@ 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 +265,7 @@ 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
@@ -205,14 +279,6 @@ for file in $file_diff; do
     fi
 done
 
-# Check for ready-run-all-tests label
-LABEL_RUN_ALL=$(check_run_all_label)
-if [[ $LABEL_RUN_ALL == true ]]; then
-    RUN_ALL=1
-    NIGHTLY=1
-    echo "Found 'ready-run-all-tests' label. Running all tests including optional tests."
-fi
-
 # Decide whether to use precompiled wheels
 # Relies on existing patterns array as a basis.
 if [[ -n "${VLLM_USE_PRECOMPILED:-}" ]]; then

.buildkite/amd/scripts/build-ci-image.sh

@@ -0,0 +1,69 @@
+#!/usr/bin/env bash
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
+set -euo pipefail
+
+# Build and push the ROCm CI image with a registry-backed BuildKit cache. The
+# AMD builders are ephemeral, so a local Docker cache cannot survive between
+# jobs. Keeping the cache beside the runtime image also reuses the registry
+# credentials already configured on the amd-cpu agents.
+
+REGISTRY="${ROCM_CI_IMAGE_REGISTRY:-rocm/vllm-omni}"
+DOCKERFILE="docker/Dockerfile.rocm"
+BUILDER_NAME="vllm-omni-rocm-builder"
+ROCM_ARCH="${PYTORCH_ROCM_ARCH:-gfx942}"
+
+if [[ -z "${BUILDKITE_COMMIT:-}" ]]; then
+    echo "ERROR: BUILDKITE_COMMIT is not set" >&2
+    exit 1
+fi
+
+dependency_files=(
+    "$DOCKERFILE"
+    "${DOCKERFILE}.dockerignore"
+    pyproject.toml
+    setup.py
+    tools/install_torchcodec_rocm.sh
+)
+while IFS= read -r dependency_file; do
+    dependency_files+=("$dependency_file")
+done < <(find requirements -maxdepth 1 -type f -name '*.txt' -print | LC_ALL=C sort)
+
+for dependency_file in "${dependency_files[@]}"; do
+    if [[ ! -f "$dependency_file" ]]; then
+        echo "ERROR: cache-key input does not exist: $dependency_file" >&2
+        exit 1
+    fi
+done
+
+CACHE_KEY=$(
+    {
+        printf 'rocm-arch=%s\n' "$ROCM_ARCH"
+        for dependency_file in "${dependency_files[@]}"; do
+            printf 'path=%s\n' "$dependency_file"
+            sha256sum "$dependency_file"
+        done
+    } | sha256sum | cut -c1-16
+)
+CACHE_REF="${REGISTRY}:rocm-deps-cache-${CACHE_KEY}"
+IMAGE_REF="${REGISTRY}:${BUILDKITE_COMMIT}"
+
+echo "ROCm dependency cache: ${CACHE_REF}"
+echo "ROCm CI image: ${IMAGE_REF}"
+
+# The docker-container driver supports exporting the full cache to a registry;
+# the default docker driver on CI does not.
+if ! docker buildx inspect "$BUILDER_NAME" >/dev/null 2>&1; then
+    docker buildx create --name "$BUILDER_NAME" --driver docker-container
+fi
+docker buildx use "$BUILDER_NAME"
+docker buildx inspect --bootstrap
+
+docker buildx build --push --pull --progress=plain \
+    --target test \
+    --build-arg "ARG_PYTORCH_ROCM_ARCH=${ROCM_ARCH}" \
+    --cache-from "type=registry,ref=${CACHE_REF}" \
+    --cache-to "type=registry,ref=${CACHE_REF},mode=max,compression=zstd,ignore-error=true" \
+    --file "$DOCKERFILE" \
+    --tag "$IMAGE_REF" .

.buildkite/amd/scripts/select_test_suites.py

@@ -0,0 +1,100 @@
+#!/usr/bin/env python3
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+"""Select AMD ready/merge/nightly suites after Buildkite creates a build.
+
+This repository-side selector does not decide which GitHub label events start
+the external ``vllm-omni-amd-ci`` pipeline. Its Buildkite PR build condition
+must admit the relevant label event before this code can inspect the labels.
+"""
+
+from __future__ import annotations
+
+import argparse
+import sys
+from collections.abc import Iterable
+
+SUITE_SPECS = {
+    "ready": "READY_TESTS:test-amd-ready.yml",
+    "merge": "MERGE_TESTS:test-amd-merge.yml",
+    "nightly": "NIGHTLY_TESTS:test-amd-nightly.yml",
+}
+
+
+def _parse_debug_suites(value: str) -> tuple[str, ...]:
+    suites = tuple(item.strip().lower() for item in value.split(",") if item.strip())
+    if not suites:
+        raise ValueError("DEBUG_TEST_YAML did not contain a test suite")
+
+    seen: set[str] = set()
+    for suite in suites:
+        if suite not in SUITE_SPECS:
+            raise ValueError(f"DEBUG_TEST_YAML entries must be 'merge', 'nightly', or 'ready', got {suite!r}")
+        if suite in seen:
+            raise ValueError(f"duplicate DEBUG_TEST_YAML suite {suite!r}")
+        seen.add(suite)
+    return suites
+
+
+def select_amd_test_suites(
+    *,
+    branch: str,
+    labels: Iterable[str],
+    debug_test_yaml: str = "",
+    nightly: bool = False,
+) -> tuple[str, ...]:
+    """Return ordered suite names while preserving legacy unlabeled PRs."""
+
+    if debug_test_yaml:
+        return _parse_debug_suites(debug_test_yaml)
+    if branch == "main":
+        return ("nightly",) if nightly else ("merge",)
+
+    label_set = {label.strip() for label in labels if label.strip()}
+    selected = tuple(
+        suite
+        for label, suite in (
+            ("ready", "ready"),
+            ("merge-test", "merge"),
+            ("nightly-test", "nightly"),
+        )
+        if label in label_set
+    )
+    # AMD historically uploaded the ready suite for every admitted PR build.
+    # Preserve that fallback when no explicit tier label is present.
+    return selected or ("ready",)
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--branch", required=True)
+    parser.add_argument("--labels", default="")
+    parser.add_argument("--debug-test-yaml", default="")
+    parser.add_argument("--nightly", type=int, choices=(0, 1), default=0)
+    args = parser.parse_args()
+
+    labels = tuple(args.labels.splitlines())
+    try:
+        suites = select_amd_test_suites(
+            branch=args.branch,
+            labels=labels,
+            debug_test_yaml=args.debug_test_yaml,
+            nightly=bool(args.nightly),
+        )
+    except ValueError as exc:
+        parser.error(str(exc))
+
+    label_set = {label.strip() for label in labels if label.strip()}
+    if args.branch != "main" and not args.debug_test_yaml and not ({"ready", "merge-test", "nightly-test"} & label_set):
+        print(
+            "No AMD ready/merge/nightly label found; preserving the legacy ready-suite fallback.",
+            file=sys.stderr,
+        )
+
+    for suite in suites:
+        print(SUITE_SPECS[suite])
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

.buildkite/amd/test-amd-merge.yml

@@ -9,22 +9,34 @@ steps:
   steps:
     - label: "Simple · Model Executor Test · Shard %N/%t"
       agent_pool: mi300_1
-      parallelism: 2
+      parallelism: 3
       depends_on: amd-build
       mirror_hardwares: [amdproduction]
       grade: Blocking
+      timeout_in_minutes: 90
       commands:
         - export VLLM_ROCM_USE_AITER=0
         # ignore test_teacache_extractors.py because it use rocm gemm kernel from vLLM
         # that is not supported on CPU
-        - "pytest -sv tests/model_executor -m 'core_model and cpu' --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB"
+        - "pytest -sv tests/model_executor -m 'core_model and cpu and not omni' --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB"
 
-    - label: "Simple · Diffusion Test · Shard %N/%t"
+    - label: "Simple · Omni Processor Test"
       agent_pool: mi300_1
-      parallelism: 2
       depends_on: amd-build
       mirror_hardwares: [amdproduction]
       grade: Blocking
+      timeout_in_minutes: 60
+      commands:
+        - export VLLM_ROCM_USE_AITER=0
+        - "pytest -sv tests/model_executor/models/test_omni_processing.py -m 'core_model and cpu and omni'"
+
+    - label: "Simple · Diffusion Test · Shard %N/%t"
+      agent_pool: mi300_1
+      parallelism: 4
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      timeout_in_minutes: 45
       commands:
         - export VLLM_ROCM_USE_AITER=0
         # ignore test_teacache_extractors.py because it use rocm gemm kernel from vLLM
@@ -71,26 +83,45 @@ 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 +202,45 @@ 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 +250,10 @@ 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

.buildkite/amd/test-amd-nightly.yml

@@ -0,0 +1,66 @@
+# AMD MI300 `full_model` nightly coverage during burn-in.
+# All jobs are non-blocking until the lane has stable runtime evidence.
+# Historical build #11646 was red in Function Expansion and Accuracy (one
+# async_chunk similarity mismatch; Seed-TTS WER 0.376480 > 0.35).
+
+env:
+  VLLM_WORKER_MULTIPROC_METHOD: spawn
+  HF_HUB_DOWNLOAD_TIMEOUT: 300
+  HF_HUB_ETAG_TIMEOUT: 60
+
+steps:
+  # MI325 is the repository's legacy ROCm pytest marker. The AMD template maps
+  # agent_pool to the current MI300 Buildkite queues independently of that mark.
+  - group: ":full_moon: Qwen3-Omni MI300 Nightly"
+    steps:
+      - label: "Qwen3-Omni Function Expansion"
+        agent_pool: mi300_2
+        depends_on: amd-build
+        mirror_hardwares: [amdproduction]
+        grade: NonBlocking
+        timeout_in_minutes: 180
+        commands:
+          - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
+          - pytest -s -v tests/e2e/online_serving/test_qwen3_omni_expansion.py -m "full_model and rocm and MI325 and cards_2" --run-level "full_model"
+
+      - label: "Qwen3-Omni Accuracy"
+        agent_pool: mi300_2
+        depends_on: amd-build
+        mirror_hardwares: [amdproduction]
+        grade: NonBlocking
+        timeout_in_minutes: 180
+        artifact_paths:
+          - tests/e2e/accuracy/qwen3_omni/results/qwen_omni_acc/*.json
+        commands:
+          - export SEED_TTS_WER_EVAL=1
+          # PyTorch ROCm uses the torch.cuda device namespace.
+          - export SEED_TTS_EVAL_DEVICE=cuda:1
+          - pytest -s -v tests/e2e/accuracy/qwen3_omni/test_qwen3_omni.py -m "full_model and rocm and MI325 and cards_2" --run-level "full_model"
+          # Native tests run from the image copy under /app/vllm-omni, while
+          # Buildkite's artifact hook searches the checkout workspace.
+          - |
+            if [[ "$$PWD" != "$${BUILDKITE_BUILD_CHECKOUT_PATH:?}" ]]; then
+              artifact_dir="$${BUILDKITE_BUILD_CHECKOUT_PATH}/tests/e2e/accuracy/qwen3_omni/results/qwen_omni_acc"
+              mkdir -p "$$artifact_dir"
+              cp -v tests/e2e/accuracy/qwen3_omni/results/qwen_omni_acc/*.json "$$artifact_dir/"
+            fi
+
+      - label: "Qwen3-Omni Documentation Examples"
+        agent_pool: mi300_2
+        depends_on: amd-build
+        mirror_hardwares: [amdproduction]
+        grade: NonBlocking
+        timeout_in_minutes: 120
+        commands:
+          - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
+          - pytest -s -v tests/examples/online_serving/test_qwen3_omni.py -m "full_model and rocm and MI325 and cards_2" --run-level "full_model"
+
+      - label: "Qwen3-Omni AITER-on Smoke"
+        agent_pool: mi300_2
+        depends_on: amd-build
+        mirror_hardwares: [amdproduction]
+        grade: NonBlocking
+        timeout_in_minutes: 90
+        commands:
+          - export VLLM_ROCM_USE_AITER=1
+          - pytest -s -v tests/e2e/online_serving/test_qwen3_omni_expansion.py::test_text_to_text_audio_001 -m "full_model and rocm and MI325 and cards_2" --run-level "full_model"

.buildkite/amd/test-amd-ready.yml

@@ -9,26 +9,39 @@ steps:
   steps:
     - label: "Simple · Model Executor Test · Shard %N/%t"
       agent_pool: mi300_1
-      parallelism: 2
+      parallelism: 3
       depends_on: amd-build
       mirror_hardwares: [amdproduction]
       grade: Blocking
+      timeout_in_minutes: 90
       commands:
         - export VLLM_ROCM_USE_AITER=0
         # ignore test_teacache_extractors.py because it use rocm gemm kernel from vLLM
         # that is not supported on CPU
-        - "pytest -sv tests/model_executor -m 'core_model and cpu' --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB"
+        - "pytest -sv tests/model_executor -m 'core_model and cpu and not omni' --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB"
 
-    - label: "Simple · Diffusion Test"
+    - label: "Simple · Omni Processor Test"
       agent_pool: mi300_1
       depends_on: amd-build
       mirror_hardwares: [amdproduction]
       grade: Blocking
+      timeout_in_minutes: 60
+      commands:
+        - export VLLM_ROCM_USE_AITER=0
+        - "pytest -sv tests/model_executor/models/test_omni_processing.py -m 'core_model and cpu and omni'"
+
+    - label: "Simple · Diffusion Test · Shard %N/%t"
+      agent_pool: mi300_1
+      parallelism: 4
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      timeout_in_minutes: 45
       commands:
         - export VLLM_ROCM_USE_AITER=0
         # ignore test_teacache_extractors.py because it use rocm gemm kernel from vLLM
         # that is not supported on CPU
-        - "pytest -sv tests/diffusion -m 'core_model and cpu'  --ignore=tests/diffusion/cache/test_teacache_extractors.py"
+        - "pytest -sv tests/diffusion -m 'core_model and cpu' --ignore=tests/diffusion/cache/test_teacache_extractors.py --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB"
 
     - label: "Simple · Engine&Entrypoints Test"
       agent_pool: mi300_1
@@ -69,6 +82,27 @@ steps:
 
 - group: ":card_index_dividers: Diffusion Test"
   steps:
+    # Keep real FlashAttention execution out of the CPU-marked Simple lane.
+    # AITER may need to build several kernels on a cold worker, so run one
+    # pytest worker with job-local build directories and a bounded timeout.
+    - label: "ROCm · FlashAttention/AITER GPU Coverage"
+      agent_pool: mi300_1
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: NonBlocking
+      timeout_in_minutes: 60
+      commands:
+        - export VLLM_ROCM_USE_AITER=1
+        - export AITER_JIT_DIR="/tmp/vllm-omni-aiter-$$BUILDKITE_JOB_ID"
+        - export TORCH_EXTENSIONS_DIR="/tmp/vllm-omni-torch-extensions-$$BUILDKITE_JOB_ID"
+        - mkdir -p "$$AITER_JIT_DIR" "$$TORCH_EXTENSIONS_DIR"
+        - >-
+          timeout --signal=TERM --kill-after=2m 55m
+          pytest -s -v -n 1
+          tests/diffusion/attention/
+          -m 'core_model and rocm and MI325 and cards_1'
+          --run-level "core_model"
+
     - label: "Diffusion · Batch Test"
       agent_pool: mi300_1
       depends_on: amd-build
@@ -107,6 +141,11 @@ steps:
         - export VLLM_LOGGING_LEVEL=DEBUG
         - export VLLM_WORKER_MULTIPROC_METHOD=spawn
         - timeout 20m pytest -s -v tests/diffusion/distributed/test_sequence_parallel.py -m core_model
+        - >-
+          timeout 20m pytest -s -v
+          tests/diffusion/models/ltx2/test_ltx2_transformer_ulysses.py
+          -m 'core_model and rocm and cards_2'
+          --run-level "core_model"
 
     - label: "Diffusion GPU Worker Test"
       agent_pool: mi300_2
@@ -122,7 +161,13 @@ steps:
       mirror_hardwares: [amdproduction]
       grade: Blocking
       commands:
-        - timeout 15m pytest -sv tests/diffusion/models/ -m 'core_model and cuda' --run-level "core_model"
+        # Preserve legacy one-GPU tests without cards_1 while excluding every
+        # registered multi-GPU marker from this one-GPU job.
+        - >-
+          timeout 15m pytest -sv tests/diffusion/models/
+          -m 'core_model and cuda and not
+          (cards_2 or cards_3 or cards_4 or cards_5 or cards_6 or cards_7 or cards_8)'
+          --run-level "core_model"
 
     - label: "Diffusion · Wan22 Test"
       agent_pool: mi300_1
@@ -149,6 +194,39 @@ steps:
 #     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
 #     - timeout 20m pytest -s -v tests/e2e/offline_inference/test_stable_audio_expansion.py -m "advanced_model and diffusion and L4" --run-level advanced_model
 
+- label: "ROCm · Engine & Model Executor GPU Coverage (R2-01)"
+  agent_pool: mi300_1
+  depends_on: amd-build
+  mirror_hardwares: [amdproduction]
+  grade: NonBlocking
+  timeout_in_minutes: 20
+  artifact_paths:
+    - "artifacts/rocm-r2-01/**/*"
+  commands:
+    # Native AMD tests run from /app/vllm-omni, while Buildkite uploads
+    # artifacts relative to its checkout directory.
+    - export R2_01_ARTIFACT_DIR="$$BUILDKITE_BUILD_CHECKOUT_PATH/artifacts/rocm-r2-01"
+    - mkdir -p "$$R2_01_ARTIFACT_DIR"
+    # The CUDA ready job also covers tests/worker/test_batched_omni_output.py.
+    # Keep the initial ROCm burn-in scoped to the requested directories.
+    # Verbose pytest output and JUnit retain node IDs without a second
+    # collection-only pass over the same GPU-backed test tree.
+    # The CUDA/L4-only CosyVoice3 ragged-flow parity test cold-JITs multiple
+    # AITER kernels on ROCm and is covered by the CUDA ready lane.
+    - >-
+      pytest -s -v -ra
+      tests/engine/ tests/model_executor/
+      -m 'core_model and cuda and not
+      (cards_2 or cards_3 or cards_4 or cards_5 or cards_6 or cards_7 or cards_8)'
+      -k 'not test_code2wav_streaming_batch_matches_ragged_flow_numerics'
+      --run-level "core_model"
+      --durations=0
+      --junitxml="$$R2_01_ARTIFACT_DIR/pytest.xml"
+      2>&1 | tee "$$R2_01_ARTIFACT_DIR/pytest.log"
+    - >-
+      tail -n 100 "$$R2_01_ARTIFACT_DIR/pytest.log" >
+      "$$R2_01_ARTIFACT_DIR/pytest-summary.txt"
+
 - label: "Engine Test"
   agent_pool: mi300_1
   depends_on: amd-build
@@ -161,6 +239,71 @@ steps:
               pytest -s -v tests/engine/test_async_omni_engine_abort.py
       '
 
+- label: "ROCm · Entrypoints GPU Coverage (R2-02)"
+  agent_pool: mi300_1
+  depends_on: amd-build
+  mirror_hardwares: [amdproduction]
+  grade: NonBlocking
+  timeout_in_minutes: 30
+  env:
+    VLLM_WORKER_MULTIPROC_METHOD: spawn
+  artifact_paths:
+    - "artifacts/rocm-r2-02/**/*"
+  commands:
+    # Native AMD tests run from /app/vllm-omni, while Buildkite uploads
+    # artifacts relative to its checkout directory.
+    - export R2_02_ARTIFACT_DIR="$$BUILDKITE_BUILD_CHECKOUT_PATH/artifacts/rocm-r2-02"
+    - mkdir -p "$$R2_02_ARTIFACT_DIR"
+    - >-
+      ps -eo pid=,ppid=,lstart=,stat=,comm= >
+      "$$R2_02_ARTIFACT_DIR/processes-before.txt"
+    # Verbose pytest output and JUnit retain selected node IDs without a
+    # second collection-only pass over the same GPU-backed test tree.
+    # The inner timeout leaves five minutes for teardown verification and
+    # artifact generation before the Buildkite step timeout.
+    - >-
+      timeout --signal=TERM --kill-after=1m 25m
+      pytest -s -v -ra
+      tests/entrypoints/
+      -m 'core_model and cuda and not
+      (cards_2 or cards_3 or cards_4 or cards_5 or cards_6 or cards_7 or cards_8)'
+      --run-level "core_model"
+      --durations=0
+      --junitxml="$$R2_02_ARTIFACT_DIR/pytest.xml"
+      2>&1 | tee "$$R2_02_ARTIFACT_DIR/pytest.log"
+    - >-
+      tail -n 100 "$$R2_02_ARTIFACT_DIR/pytest.log" >
+      "$$R2_02_ARTIFACT_DIR/pytest-summary.txt"
+    - |
+      sleep 5
+      ps -eo pid=,ppid=,lstart=,stat=,comm= > "$$R2_02_ARTIFACT_DIR/processes-after.txt"
+      # Compare PID plus process start time so PID reuse cannot hide a leak.
+      # Double dollars preserve awk field references through Buildkite interpolation.
+      awk '
+        NR == FNR {
+          if ($$9 != "ps") {
+            baseline[$$1 "|" $$3 "|" $$4 "|" $$5 "|" $$6 "|" $$7] = 1
+          }
+          next
+        }
+        $$9 != "ps" {
+          identity = $$1 "|" $$3 "|" $$4 "|" $$5 "|" $$6 "|" $$7
+          if (!(identity in baseline)) {
+            print
+            leaked = 1
+          }
+        }
+        END {
+          if (leaked) {
+            print "status=FAIL leaked_processes_detected=1"
+            exit 1
+          }
+          print "status=PASS leaked_processes=0"
+        }
+      ' "$$R2_02_ARTIFACT_DIR/processes-before.txt" \
+        "$$R2_02_ARTIFACT_DIR/processes-after.txt" \
+        2>&1 | tee "$$R2_02_ARTIFACT_DIR/process-cleanup.txt"
+
 # - label: "Omni Model Test Qwen3-Omni"
 #   agent_pool: mi300_2
 #   depends_on: amd-build
@@ -190,11 +333,14 @@ steps:
       depends_on: amd-build
       mirror_hardwares: [amdproduction]
       grade: Blocking
+      # A ROCm hardware hang aborts the interpreter with 134. Let Buildkite
+      # create one fresh pod instead of retrying on the poisoned GPU process.
+      retry:
+        automatic:
+          - exit_status: 134
+            limit: 1
       commands:
-        - |
-          timeout 20m bash -c '
-            pytest -s -v tests/e2e/online_serving/test_cosyvoice3_tts_expansion.py -m "slow" --run-level "core_model"
-          '
+        - pytest -s -v tests/e2e/online_serving/test_cosyvoice3_tts_expansion.py -m "slow" --run-level "core_model"
 
 # TODO: Bagel test on ROCm is very unstable. @tjtanaa
 # Need to debug before reneable numerical changes across large PRs

.buildkite/amd/test-template-amd-omni.j2

@@ -3,7 +3,8 @@
    Last synced: 2026-08-04
    Modifications: Use the shared MI300 queues and Omni command-passing contract
 #}
-{% set docker_image_amd = "rocm/vllm-omni:$BUILDKITE_COMMIT" %}
+{% set docker_registry_amd = "rocm/vllm-omni" %}
+{% set docker_image_amd = docker_registry_amd ~ ":$BUILDKITE_COMMIT" %}
 {% set default_working_dir = "/app/vllm-omni" %}
 
 {# MI300 runs tests natively in Kubernetes; Docker-in-Docker is unavailable. #}
@@ -74,11 +75,12 @@ steps:
         depends_on: ~
         soft_fail: false
         commands:
-          - "docker build -f docker/Dockerfile.rocm -t {{ docker_image_amd }} --target test --build-arg ARG_PYTORCH_ROCM_ARCH=gfx942 --progress plain ."
-          - "docker push {{ docker_image_amd }}"
+          - "bash .buildkite/amd/scripts/build-ci-image.sh"
         key: "amd-build"
         env:
           DOCKER_BUILDKIT: "1"
+          PYTORCH_ROCM_ARCH: "gfx942"
+          ROCM_CI_IMAGE_REGISTRY: "{{ docker_registry_amd }}"
         retry:
           automatic:
             - exit_status: -1  # Agent was lost
@@ -156,6 +158,14 @@ steps:
 {% for path in step.artifact_paths %}
           - "{{ path }}"
 {% endfor %}
+{% endif %}
+{% if step.retry %}
+        retry:
+          automatic:
+{% for retry_rule in step.retry.automatic %}
+            - exit_status: {{ retry_rule.exit_status }}
+              limit: {{ retry_rule.limit }}
+{% endfor %}
 {% endif %}
         {% if step.timeout_in_minutes %}
         timeout_in_minutes: {{ step.timeout_in_minutes }}
@@ -224,6 +234,14 @@ steps:
 {% for path in step.artifact_paths %}
       - "{{ path }}"
 {% endfor %}
+{% endif %}
+{% if step.retry %}
+    retry:
+      automatic:
+{% for retry_rule in step.retry.automatic %}
+        - exit_status: {{ retry_rule.exit_status }}
+          limit: {{ retry_rule.limit }}
+{% endfor %}
 {% endif %}
     {% if step.timeout_in_minutes %}
     timeout_in_minutes: {{ step.timeout_in_minutes }}

.buildkite/common/ci_mirror_hardwares.yml

@@ -1,19 +1,36 @@
 # Uploader-only hardware presets for CUDA / NPU test YAML.
 # Referenced as ``mirror_hardwares: l4_1`` on a step; ``upload_pipeline.py`` expands to
-# ``agents`` (+ optional top-level ``image`` for NPU) + ``plugins``, then strips
-# ``mirror_hardwares`` before Buildkite upload.
+# ``agents`` (+ optional top-level ``image`` for NPU) + ``plugins`` (+ ``retry``
+# for ``l4_*``), then strips ``mirror_hardwares`` before Buildkite upload.
+#
+# Omit the key: compose {chip}_{n} from pytest -m SKU + cards_n (max of several
+# positives; not cards_1 with no positive cards_* → that chip's highest existing preset).
+# Unset MIRROR_HW matches H100 or L4 in -m (both → H100); no match skips the step.
+# MIRROR_HW=b200 matches B200 in -m (else the step is skipped).
+# MIRROR_HW must be empty or b200 (case-insensitive); unknown values fail the upload.
+# CUDA preset strings (h100_4, l4_1) are omitted when MIRROR_HW=b200.
+# Unset MIRROR_HW keeps string presets. NPU presets ignore MIRROR_HW.
 #
 # CUDA naming: {chip}_{count}
-#   l4_1, l4_4, h100_1 … h100_4
+#   l4_1, l4_2, l4_3, l4_4, h100_1 … h100_4, b200_1 … b200_4
 # NPU naming: {queue}_{resource_class}
 #   a2b3_npu_1  — ascend-a2b3, resource_class npu-1 (A2/B3 CI image)
 #   a2b3_npu_4  — ascend-a2b3, resource_class npu-4
-#   a3_npu_2    — ascend-a3, resource_class npu-2
+#   a3_npu_2    — ascend-a3, resource_class npu-2 (A3 CI image tag …:$BUILDKITE_COMMIT-a3)
+#   a5_npu_2    — ascend-a5, resource_class npu-2
+#   310p_npu_1  — ascend-310p, resource_class npu-1
 #
-# The ``l4_*`` presets run under the ``docker`` plugin, which does not put
-# ``buildkite-agent`` on PATH inside the container the way the ``kubernetes``
-# presets do, so they set ``mount-buildkite-agent: true``; without it any step
-# calling ``buildkite-agent`` (artifact upload, annotate, meta-data) fails there.
+# B200 (b200-k8s): matches vLLM device ``b200-k8s`` from ci-infra
+# (AgentQueue.B200_K8S + b200_plugin_template). Queue routes the K8s pool;
+# podSpec uses hostNetwork + /raid|/mnt/shared (no H100-style nodeSelector).
+#
+# The ``l4_*`` presets run on the EKS ``l4-k8s`` queue (agent-stack-k8s),
+# packed onto g6.12xlarge L4 nodes (``vllm.ci/gpu-pool=l4x4``). GPU / CPU /
+# memory / shm requests scale with card count (1 GPU = 10 CPU / 40Gi / 24Gi shm).
+# Retry covers lost agents / killed pods, not pytest or OOM (exit 1 / 137).
+# The Kubernetes HF secret deliberately uses VLLM_CI_HF_TOKEN. Scheduled
+# build-level HF_TOKEN values can override pod env entries with the same name;
+# upload_pipeline.py restores the secret-backed value inside the job shell.
 
 x-npu-k8s-plugins: &npu_k8s_plugins
   - kubernetes:
@@ -21,39 +38,224 @@ x-npu-k8s-plugins: &npu_k8s_plugins
         imagePullSecrets:
           - name: swr-secret
 
+# Infra-only retry. Omit exit 1 (pytest failed) and 137 (cgroup OOM / SIGKILL
+# of the test process). ci-infra ``K8S_RETRY`` still retries exit 1.
+x-l4-k8s-retry: &l4_k8s_retry
+  automatic:
+    - exit_status: -1
+      limit: 1
+    - exit_status: 128
+      limit: 1
+    - signal_reason: agent_stop
+      limit: 1
+    - signal_reason: agent_refused
+      limit: 1
+
 
 mirror_hardwares:
   l4_1:
     agents:
-      queue: gpu_1_queue
+      queue: l4-k8s
+    retry: *l4_k8s_retry
     plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          mount-buildkite-agent: true
-          shm-size: "8gb"
-          environment:
-            - HF_HOME=/fsx/hf_cache
-            - HF_TOKEN
-          volumes:
-            - /fsx/hf_cache:/fsx/hf_cache
+      - kubernetes:
+          metadata:
+            annotations:
+              cluster-autoscaler.kubernetes.io/safe-to-evict: "false"
+              karpenter.sh/do-not-disrupt: "true"
+          podSpec:
+            priorityClassName: ci
+            serviceAccountName: buildkite-gpu-jobs
+            tolerations:
+              - key: nvidia.com/gpu
+                operator: Exists
+                effect: NoSchedule
+            nodeSelector:
+              vllm.ci/gpu-pool: l4x4
+            containers:
+              - image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+                resources:
+                  limits:
+                    nvidia.com/gpu: 1
+                  requests:
+                    nvidia.com/gpu: 1
+                    cpu: "10"
+                    memory: 40Gi
+                volumeMounts:
+                  - name: devshm
+                    mountPath: /dev/shm
+                  - name: hf-cache
+                    mountPath: /fsx/hf_cache
+                env:
+                  - name: HF_HOME
+                    value: /fsx/hf_cache
+                  - name: VLLM_CI_HF_TOKEN
+                    valueFrom:
+                      secretKeyRef:
+                        name: hf-token-secret
+                        key: token
+            volumes:
+              - name: devshm
+                emptyDir:
+                  medium: Memory
+                  sizeLimit: 24Gi
+              - name: hf-cache
+                hostPath:
+                  path: /fsx/hf_cache
+                  type: DirectoryOrCreate
+
+  l4_2:
+    agents:
+      queue: l4-k8s
+    retry: *l4_k8s_retry
+    plugins:
+      - kubernetes:
+          metadata:
+            annotations:
+              cluster-autoscaler.kubernetes.io/safe-to-evict: "false"
+              karpenter.sh/do-not-disrupt: "true"
+          podSpec:
+            priorityClassName: ci
+            serviceAccountName: buildkite-gpu-jobs
+            tolerations:
+              - key: nvidia.com/gpu
+                operator: Exists
+                effect: NoSchedule
+            nodeSelector:
+              vllm.ci/gpu-pool: l4x4
+            containers:
+              - image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+                resources:
+                  limits:
+                    nvidia.com/gpu: 2
+                  requests:
+                    nvidia.com/gpu: 2
+                    cpu: "20"
+                    memory: 80Gi
+                volumeMounts:
+                  - name: devshm
+                    mountPath: /dev/shm
+                  - name: hf-cache
+                    mountPath: /fsx/hf_cache
+                env:
+                  - name: HF_HOME
+                    value: /fsx/hf_cache
+                  - name: VLLM_CI_HF_TOKEN
+                    valueFrom:
+                      secretKeyRef:
+                        name: hf-token-secret
+                        key: token
+            volumes:
+              - name: devshm
+                emptyDir:
+                  medium: Memory
+                  sizeLimit: 48Gi
+              - name: hf-cache
+                hostPath:
+                  path: /fsx/hf_cache
+                  type: DirectoryOrCreate
+
+  l4_3:
+    agents:
+      queue: l4-k8s
+    retry: *l4_k8s_retry
+    plugins:
+      - kubernetes:
+          metadata:
+            annotations:
+              cluster-autoscaler.kubernetes.io/safe-to-evict: "false"
+              karpenter.sh/do-not-disrupt: "true"
+          podSpec:
+            priorityClassName: ci
+            serviceAccountName: buildkite-gpu-jobs
+            tolerations:
+              - key: nvidia.com/gpu
+                operator: Exists
+                effect: NoSchedule
+            nodeSelector:
+              vllm.ci/gpu-pool: l4x4
+            containers:
+              - image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+                resources:
+                  limits:
+                    nvidia.com/gpu: 3
+                  requests:
+                    nvidia.com/gpu: 3
+                    cpu: "30"
+                    memory: 120Gi
+                volumeMounts:
+                  - name: devshm
+                    mountPath: /dev/shm
+                  - name: hf-cache
+                    mountPath: /fsx/hf_cache
+                env:
+                  - name: HF_HOME
+                    value: /fsx/hf_cache
+                  - name: VLLM_CI_HF_TOKEN
+                    valueFrom:
+                      secretKeyRef:
+                        name: hf-token-secret
+                        key: token
+            volumes:
+              - name: devshm
+                emptyDir:
+                  medium: Memory
+                  sizeLimit: 72Gi
+              - name: hf-cache
+                hostPath:
+                  path: /fsx/hf_cache
+                  type: DirectoryOrCreate
 
   l4_4:
     agents:
-      queue: gpu_4_queue
+      queue: l4-k8s
+    retry: *l4_k8s_retry
     plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          mount-buildkite-agent: true
-          shm-size: "8gb"
-          environment:
-            - HF_HOME=/fsx/hf_cache
-            - HF_TOKEN
-          volumes:
-            - /fsx/hf_cache:/fsx/hf_cache
+      - kubernetes:
+          metadata:
+            annotations:
+              cluster-autoscaler.kubernetes.io/safe-to-evict: "false"
+              karpenter.sh/do-not-disrupt: "true"
+          podSpec:
+            priorityClassName: ci
+            serviceAccountName: buildkite-gpu-jobs
+            tolerations:
+              - key: nvidia.com/gpu
+                operator: Exists
+                effect: NoSchedule
+            nodeSelector:
+              vllm.ci/gpu-pool: l4x4
+            containers:
+              - image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+                resources:
+                  limits:
+                    nvidia.com/gpu: 4
+                  requests:
+                    nvidia.com/gpu: 4
+                    cpu: "40"
+                    memory: 160Gi
+                volumeMounts:
+                  - name: devshm
+                    mountPath: /dev/shm
+                  - name: hf-cache
+                    mountPath: /fsx/hf_cache
+                env:
+                  - name: HF_HOME
+                    value: /fsx/hf_cache
+                  - name: VLLM_CI_HF_TOKEN
+                    valueFrom:
+                      secretKeyRef:
+                        name: hf-token-secret
+                        key: token
+            volumes:
+              - name: devshm
+                emptyDir:
+                  medium: Memory
+                  sizeLimit: 96Gi
+              - name: hf-cache
+                hostPath:
+                  path: /fsx/hf_cache
+                  type: DirectoryOrCreate
 
   h100_1:
     agents:
@@ -74,7 +276,7 @@ mirror_hardwares:
                 env:
                   - name: HF_HOME
                     value: /root/.cache/huggingface
-                  - name: HF_TOKEN
+                  - name: VLLM_CI_HF_TOKEN
                     valueFrom:
                       secretKeyRef:
                         name: hf-token-secret
@@ -109,7 +311,7 @@ mirror_hardwares:
                 env:
                   - name: HF_HOME
                     value: /root/.cache/huggingface
-                  - name: HF_TOKEN
+                  - name: VLLM_CI_HF_TOKEN
                     valueFrom:
                       secretKeyRef:
                         name: hf-token-secret
@@ -144,7 +346,7 @@ mirror_hardwares:
                 env:
                   - name: HF_HOME
                     value: /root/.cache/huggingface
-                  - name: HF_TOKEN
+                  - name: VLLM_CI_HF_TOKEN
                     valueFrom:
                       secretKeyRef:
                         name: hf-token-secret
@@ -179,7 +381,7 @@ mirror_hardwares:
                 env:
                   - name: HF_HOME
                     value: /root/.cache/huggingface
-                  - name: HF_TOKEN
+                  - name: VLLM_CI_HF_TOKEN
                     valueFrom:
                       secretKeyRef:
                         name: hf-token-secret
@@ -195,6 +397,211 @@ mirror_hardwares:
                   path: /mnt/hf-cache
                   type: DirectoryOrCreate
 
+  # vLLM device: b200-k8s → agents.queue b200-k8s + Buildkite K8s agent-stack podSpec
+  b200_1:
+    agents:
+      queue: b200-k8s
+    plugins:
+      - kubernetes:
+          podSpec:
+            runtimeClassName: nvidia
+            hostNetwork: true
+            dnsPolicy: ClusterFirstWithHostNet
+            imagePullSecrets:
+              - name: k8s-ecr-login-renew-docker-secret
+            containers:
+              - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+                resources:
+                  limits:
+                    nvidia.com/gpu: 1
+                securityContext:
+                  capabilities:
+                    add:
+                      - IPC_LOCK
+                      - SYS_RESOURCE
+                volumeMounts:
+                  - name: devshm
+                    mountPath: /dev/shm
+                  - name: raid
+                    mountPath: /raid
+                  - name: shared
+                    mountPath: /mnt/shared
+                env:
+                  - name: NCCL_CUMEM_HOST_ENABLE
+                    value: "0"
+                  - name: HF_HOME
+                    value: /raid/hf_cache
+                  - name: VLLM_CI_HF_TOKEN
+                    valueFrom:
+                      secretKeyRef:
+                        name: hf-token-secret
+                        key: token
+            volumes:
+              - name: devshm
+                emptyDir:
+                  medium: Memory
+              - name: raid
+                hostPath:
+                  path: /raid
+                  type: DirectoryOrCreate
+              - name: shared
+                hostPath:
+                  path: /mnt/shared
+                  type: DirectoryOrCreate
+
+  b200_2:
+    agents:
+      queue: b200-k8s
+    plugins:
+      - kubernetes:
+          podSpec:
+            runtimeClassName: nvidia
+            hostNetwork: true
+            dnsPolicy: ClusterFirstWithHostNet
+            imagePullSecrets:
+              - name: k8s-ecr-login-renew-docker-secret
+            containers:
+              - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+                resources:
+                  limits:
+                    nvidia.com/gpu: 2
+                securityContext:
+                  capabilities:
+                    add:
+                      - IPC_LOCK
+                      - SYS_RESOURCE
+                volumeMounts:
+                  - name: devshm
+                    mountPath: /dev/shm
+                  - name: raid
+                    mountPath: /raid
+                  - name: shared
+                    mountPath: /mnt/shared
+                env:
+                  - name: NCCL_CUMEM_HOST_ENABLE
+                    value: "0"
+                  - name: HF_HOME
+                    value: /raid/hf_cache
+                  - name: VLLM_CI_HF_TOKEN
+                    valueFrom:
+                      secretKeyRef:
+                        name: hf-token-secret
+                        key: token
+            volumes:
+              - name: devshm
+                emptyDir:
+                  medium: Memory
+              - name: raid
+                hostPath:
+                  path: /raid
+                  type: DirectoryOrCreate
+              - name: shared
+                hostPath:
+                  path: /mnt/shared
+                  type: DirectoryOrCreate
+
+  b200_3:
+    agents:
+      queue: b200-k8s
+    plugins:
+      - kubernetes:
+          podSpec:
+            runtimeClassName: nvidia
+            hostNetwork: true
+            dnsPolicy: ClusterFirstWithHostNet
+            imagePullSecrets:
+              - name: k8s-ecr-login-renew-docker-secret
+            containers:
+              - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+                resources:
+                  limits:
+                    nvidia.com/gpu: 3
+                securityContext:
+                  capabilities:
+                    add:
+                      - IPC_LOCK
+                      - SYS_RESOURCE
+                volumeMounts:
+                  - name: devshm
+                    mountPath: /dev/shm
+                  - name: raid
+                    mountPath: /raid
+                  - name: shared
+                    mountPath: /mnt/shared
+                env:
+                  - name: NCCL_CUMEM_HOST_ENABLE
+                    value: "0"
+                  - name: HF_HOME
+                    value: /raid/hf_cache
+                  - name: VLLM_CI_HF_TOKEN
+                    valueFrom:
+                      secretKeyRef:
+                        name: hf-token-secret
+                        key: token
+            volumes:
+              - name: devshm
+                emptyDir:
+                  medium: Memory
+              - name: raid
+                hostPath:
+                  path: /raid
+                  type: DirectoryOrCreate
+              - name: shared
+                hostPath:
+                  path: /mnt/shared
+                  type: DirectoryOrCreate
+
+  b200_4:
+    agents:
+      queue: b200-k8s
+    plugins:
+      - kubernetes:
+          podSpec:
+            runtimeClassName: nvidia
+            hostNetwork: true
+            dnsPolicy: ClusterFirstWithHostNet
+            imagePullSecrets:
+              - name: k8s-ecr-login-renew-docker-secret
+            containers:
+              - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+                resources:
+                  limits:
+                    nvidia.com/gpu: 4
+                securityContext:
+                  capabilities:
+                    add:
+                      - IPC_LOCK
+                      - SYS_RESOURCE
+                volumeMounts:
+                  - name: devshm
+                    mountPath: /dev/shm
+                  - name: raid
+                    mountPath: /raid
+                  - name: shared
+                    mountPath: /mnt/shared
+                env:
+                  - name: NCCL_CUMEM_HOST_ENABLE
+                    value: "0"
+                  - name: HF_HOME
+                    value: /raid/hf_cache
+                  - name: VLLM_CI_HF_TOKEN
+                    valueFrom:
+                      secretKeyRef:
+                        name: hf-token-secret
+                        key: token
+            volumes:
+              - name: devshm
+                emptyDir:
+                  medium: Memory
+              - name: raid
+                hostPath:
+                  path: /raid
+                  type: DirectoryOrCreate
+              - name: shared
+                hostPath:
+                  path: /mnt/shared
+                  type: DirectoryOrCreate
+
   a2b3_npu_1:
     agents:
       queue: "ascend-a2b3"
@@ -244,3 +651,52 @@ mirror_hardwares:
       resource_class: "npu-16"
     image: "swr.cn-southwest-2.myhuaweicloud.com/modelfoundry/vllm-omni-ci-npu:${BUILDKITE_COMMIT}-a3"
     plugins: *npu_k8s_plugins
+
+  a5_npu_2:
+    agents:
+      queue: "ascend-a5"
+      resource_class: "npu-2"
+    image: "swr.cn-southwest-2.myhuaweicloud.com/modelfoundry/vllm-omni-ci-npu:${BUILDKITE_COMMIT}-a5"
+    plugins: *npu_k8s_plugins
+
+  a5_npu_4:
+    agents:
+      queue: "ascend-a5"
+      resource_class: "npu-4"
+    image: "swr.cn-southwest-2.myhuaweicloud.com/modelfoundry/vllm-omni-ci-npu:${BUILDKITE_COMMIT}-a5"
+    plugins: *npu_k8s_plugins
+
+  a5_npu_8:
+    agents:
+      queue: "ascend-a5"
+      resource_class: "npu-8"
+    image: "swr.cn-southwest-2.myhuaweicloud.com/modelfoundry/vllm-omni-ci-npu:${BUILDKITE_COMMIT}-a5"
+    plugins: *npu_k8s_plugins
+
+  a5_npu_16:
+    agents:
+      queue: "ascend-a5"
+      resource_class: "npu-16"
+    image: "swr.cn-southwest-2.myhuaweicloud.com/modelfoundry/vllm-omni-ci-npu:${BUILDKITE_COMMIT}-a5"
+    plugins: *npu_k8s_plugins
+
+  310p_npu_1:
+    agents:
+      queue: "ascend-310p"
+      resource_class: "npu-1"
+    image: "swr.cn-southwest-2.myhuaweicloud.com/modelfoundry/vllm-omni-ci-npu:${BUILDKITE_COMMIT}-310p"
+    plugins: *npu_k8s_plugins
+
+  310p_npu_2:
+    agents:
+      queue: "ascend-310p"
+      resource_class: "npu-2"
+    image: "swr.cn-southwest-2.myhuaweicloud.com/modelfoundry/vllm-omni-ci-npu:${BUILDKITE_COMMIT}-310p"
+    plugins: *npu_k8s_plugins
+
+  310p_npu_4:
+    agents:
+      queue: "ascend-310p"
+      resource_class: "npu-4"
+    image: "swr.cn-southwest-2.myhuaweicloud.com/modelfoundry/vllm-omni-ci-npu:${BUILDKITE_COMMIT}-310p"
+    plugins: *npu_k8s_plugins

.buildkite/common/ci_source_file_dependencies.yml

@@ -0,0 +1,471 @@
+# Uploader-only path-prefix presets for CUDA L2–L5 and NPU L4 test YAML.
+# Referenced as ``source_file_dependencies: omni_qwen3_omni_function`` (or a list of
+# keys) on a step; ``upload_pipeline.py`` expands the key(s) only.
+#
+# YAML anchors above the registry hold model business-code paths. Job keys
+# ({model}_function / _perf / _accuracy / _reliability / _doc / _cov) alias
+# those anchors and add the scripts the job actually runs. Feature keys below
+# are unchanged. ``source_filter_fallback`` is not a job key: when no job-key
+# prefix matches, a matching diff keeps every job for that upload.
+#
+# YAML anchors are nested lists; the loader flattens them.
+
+x-qwen3-tts: &qwen3_tts
+  - vllm_omni/engine/async_omni_engine.py
+  - vllm_omni/entrypoints/omni_base.py
+  - vllm_omni/worker/gpu_model_runner.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
+  - vllm_omni/entrypoints/openai/serving_speech.py
+  - vllm_omni/entrypoints/openai/tts_adapters/
+  - vllm_omni/engine/stage_pool.py
+  - vllm_omni/worker_v2/
+  - vllm_omni/core/sched/
+  - vllm_omni/distributed/omni_connectors/
+  - vllm_omni/config/stage_config.py
+  - vllm_omni/config/omni_config.py
+  - vllm_omni/engine/orchestrator.py
+  - vllm_omni/engine/stage_engine_core_proc.py
+  - vllm_omni/deploy/qwen3_tts_mrv2.yaml
+  - vllm_omni/deploy/qwen3_tts_high_concurrency_mrv2.yaml
+  - vllm_omni/deploy/qwen3_tts_high_concurrency_mrv2_b4.yaml
+
+x-minicpmo-4-5: &minicpmo_4_5
+  - vllm_omni/engine/async_omni_engine.py
+  - vllm_omni/entrypoints/omni_base.py
+  - vllm_omni/model_executor/models/minicpmo_4_5/
+  - vllm_omni/model_executor/models/step_audio2/
+  - vllm_omni/model_executor/models/cosyvoice3/code2wav_core/
+  - vllm_omni/model_executor/models/cosyvoice3/utils.py
+  - vllm_omni/model_executor/stage_input_processors/minicpmo_4_5_omni.py
+  - vllm_omni/deploy/minicpmo_4_5.yaml
+  - vllm_omni/transformers_utils/chat_templates/minicpmo45_native.jinja
+
+x-minicpmo-4-5-duplex: &minicpmo_4_5_duplex
+  - vllm_omni/deploy/minicpmo_4_5_duplex.yaml
+  - vllm_omni/engine/duplex/
+  - vllm_omni/engine/duplex_omni_engine.py
+  - vllm_omni/engine/duplex_orchestrator.py
+  - vllm_omni/engine/omni_engine_base.py
+  - vllm_omni/entrypoints/duplex/
+  - vllm_omni/entrypoints/duplex_omni.py
+  - vllm_omni/entrypoints/async_omni_base.py
+  - vllm_omni/protocol/
+  - vllm_omni/clients/
+  - vllm_omni/outputs/duplex.py
+  - vllm_omni/model_executor/duplex_sampling.py
+  - vllm_omni/experimental/fullduplex/
+  - vllm_omni/core/sched/
+  - vllm_omni/outputs/multimodal_accumulation.py
+  - vllm_omni/engine/stage_init_utils.py
+  - vllm_omni/engine/orchestrator.py
+  - vllm_omni/entrypoints/cli/serve.py
+  - vllm_omni/entrypoints/openai/api_server.py
+  - vllm_omni/entrypoints/openai/duplex_capability.py
+  - vllm_omni/config/stage_config.py
+
+x-qwen2-5-omni: &qwen2_5_omni
+  - vllm_omni/engine/async_omni_engine.py
+  - vllm_omni/entrypoints/omni_base.py
+  - vllm_omni/model_executor/models/qwen2_5_omni/
+  - vllm_omni/model_executor/stage_input_processors/qwen2_5_omni.py
+  - vllm_omni/deploy/qwen2_5_omni.yaml
+
+x-qwen3-omni: &qwen3_omni
+  - vllm_omni/engine/async_omni_engine.py
+  - vllm_omni/entrypoints/omni_base.py
+  - vllm_omni/entrypoints/openai/serving_chat.py
+  - vllm_omni/model_executor/models/qwen3_omni/
+  - vllm_omni/model_executor/models/common/qwen3_code_predictor.py
+  - vllm_omni/model_executor/models/common/snake_activation.py
+  - vllm_omni/model_executor/models/qwen2_5_omni/qwen2_5_omni_thinker.py
+  - vllm_omni/model_executor/models/qwen3_tts/cuda_graph_decoder_wrapper.py
+  - vllm_omni/model_executor/stage_input_processors/qwen3_omni.py
+  - vllm_omni/deploy/qwen3_omni_moe.yaml
+  - vllm_omni/deploy/qwen3_omni_moe_thinking.yaml
+
+x-wan22: &wan22
+  - vllm_omni/diffusion/models/wan2_2/
+  - vllm_omni/diffusion/models/dmd2/
+
+x-cosmos3: &cosmos3
+  - vllm_omni/diffusion/models/cosmos3/
+  - vllm_omni/diffusion/distributed/autoencoders/autoencoder_kl_wan.py
+  - vllm_omni/experimental/world_models/
+  - vllm_omni/model_extras/cosmos3.py
+
+x-qwen-image: &qwen_image
+  - vllm_omni/diffusion/models/qwen_image/
+  - vllm_omni/diffusion/models/dmd2/
+  - vllm_omni/diffusion/distributed/autoencoders/autoencoder_kl_qwenimage.py
+
+x-hunyuan-image3: &hunyuan_image3
+  - vllm_omni/diffusion/models/hunyuan_image3/
+  - vllm_omni/model_executor/models/hunyuan_image3/
+  - vllm_omni/deploy/hunyuan_image3_dit.yaml
+
+x-minimax-h3: &minimax_h3
+  - vllm_omni/diffusion/models/minimax_h3/
+  - vllm_omni/diffusion/layers/indexed_modulation.py
+
+x-hunyuan-video: &hunyuan_video
+  - vllm_omni/diffusion/models/hunyuan_video/
+  - vllm_omni/model_executor/models/hunyuan_video/
+
+x-ltx2: &ltx2
+  - vllm_omni/diffusion/models/ltx2/
+  - vllm_omni/model_extras/ltx2.py
+
+x-sana-video: &sana_video
+  - vllm_omni/diffusion/models/sana_video/
+  - vllm_omni/model_extras/sana_video.py
+
+x-z-image: &z_image
+  - vllm_omni/diffusion/models/z_image/
+
+x-stable-audio: &stable_audio
+  - vllm_omni/diffusion/models/stable_audio/
+
+x-lingbot-video: &lingbot_video
+  - vllm_omni/diffusion/models/lingbot_video/
+  - vllm_omni/model_extras/lingbot_video.py
+
+x-lingbot-world: &lingbot_world
+  - vllm_omni/diffusion/models/lingbot_world/
+
+
+source_file_dependencies:
+  # Hardware presets and shared test helpers. Not a job key — do not attach this
+  # to a step. Used only when no job-key prefix matches: then a hit keeps every job.
+  source_filter_fallback:
+    - .buildkite/common/ci_mirror_hardwares.yml
+    - tests/helpers/
+
+  # --- features ---
+  diffusion_distributed_attention:
+    - tests/diffusion/distributed/
+    - tests/diffusion/attention/test_attention_sp.py
+    - vllm_omni/diffusion/distributed/
+    - vllm_omni/diffusion/attention/
+    - vllm_omni/diffusion/data.py
+    - vllm_omni/diffusion/models/
+
+  custom_pipeline:
+    - tests/e2e/features/custom_pipeline/
+    - tests/e2e/features/helpers/custom_pipeline.py
+    - vllm_omni/diffusion/
+    - vllm_omni/engine/
+    - vllm_omni/entrypoints/
+    - vllm_omni/inputs/
+    - vllm_omni/outputs/
+
+  rlhf_verl_omni:
+    - tests/e2e/features/rlhf_test/test_verl_omni_e2e.py
+    - tests/e2e/features/helpers/verl_omni_server.py
+    - tests/e2e/features/helpers/custom_pipeline.py
+    - vllm_omni/diffusion/
+    - vllm_omni/engine/
+    - vllm_omni/entrypoints/
+    - vllm_omni/inputs/
+    - vllm_omni/outputs/
+
+  # Tiny-model diffusion jobs (shared across many model trees under models/).
+  diffusion_tiny_model:
+    - tests/model_tests/diffusion/
+    - vllm_omni/diffusion/models/
+
+  diffusion_offloader:
+    - tests/diffusion/offloader/
+    - vllm_omni/diffusion/offloader/
+
+  diffusion_quantization:
+    - tests/diffusion/quantization/
+    - vllm_omni/diffusion/quantization/
+
+  # --- models ---
+  # --- omni / tts jobs ---
+  omni_qwen3_omni_function:
+    - *qwen3_omni
+    - tests/e2e/offline_inference/test_qwen3_omni.py
+    - tests/e2e/offline_inference/test_qwen3_omni_colocate_async.py
+    - tests/e2e/offline_inference/test_qwen3_omni_autoround_w4a16_expansion.py
+    - tests/e2e/offline_inference/test_qwen3_omni_modelopt_nvfp4_w4a4.py
+    - tests/e2e/online_serving/test_qwen3_omni.py
+    - tests/e2e/online_serving/test_qwen3_omni_expansion.py
+    - tests/e2e/online_serving/test_qwen3_omni_multi_replicas.py
+
+  omni_qwen3_omni_accuracy:
+    - *qwen3_omni
+    - tests/e2e/accuracy/qwen3_omni/
+
+  omni_qwen3_omni_perf:
+    - *qwen3_omni
+    - tests/dfx/perf/scripts/run_benchmark.py
+    - tests/dfx/perf/tests/test_qwen3_omni_vllm_text.json
+    - tests/dfx/perf/tests/test_qwen3_omni_async_chunk.json
+    - tests/dfx/perf/tests/test_qwen3_omni_no_async_chunk.json
+    - tests/dfx/perf/tests/test_qwen3_omni_multi_replicas.json
+
+  omni_qwen3_omni_reliability:
+    - *qwen3_omni
+    - tests/dfx/reliability/test_reliability_qwen3_omni.py
+
+  omni_qwen3_omni_doc:
+    - *qwen3_omni
+    - examples/online_serving/qwen3_omni/
+    - examples/online_serving/openai_chat_completion_client_for_multimodal_generation.py
+    - tests/examples/online_serving/test_qwen3_omni.py
+
+  omni_qwen2_5_omni_doc:
+    - *qwen2_5_omni
+    - examples/online_serving/qwen2_5_omni/
+    - examples/online_serving/openai_chat_completion_client_for_multimodal_generation.py
+    - tests/examples/online_serving/test_qwen2_5_omni.py
+
+  omni_minicpmo_4_5_function:
+    - *minicpmo_4_5
+    - tests/e2e/offline_inference/test_minicpmo_4_5.py
+    - tests/e2e/online_serving/test_minicpmo_4_5.py
+    - tests/e2e/online_serving/test_minicpmo_4_5_expansion.py
+
+  omni_minicpmo_4_5_accuracy:
+    - *minicpmo_4_5
+    - tests/e2e/accuracy/minicpmo_4_5/
+
+  omni_minicpmo_4_5_perf:
+    - *minicpmo_4_5
+    - tests/dfx/perf/scripts/run_benchmark.py
+    - tests/dfx/perf/tests/test_minicpmo_4_5_ready.json
+    - tests/dfx/perf/tests/test_minicpmo_4_5.json
+
+  omni_minicpmo_4_5_doc:
+    - *minicpmo_4_5
+    - examples/online_serving/minicpmo/realtime_web/
+    - examples/online_serving/minicpmo/realtime_duplex_demo.py
+    - tests/examples/test_minicpmo_realtime_web_server.py
+    - tests/examples/test_minicpmo_realtime_web_static.py
+    - tests/examples/test_minicpmo_realtime_duplex_simple_demo.py
+
+  omni_minicpmo_4_5_duplex_function:
+    - *minicpmo_4_5
+    - *minicpmo_4_5_duplex
+    - tests/e2e/online_serving/test_minicpmo_4_5_duplex.py
+    - tests/e2e/online_serving/test_minicpmo_4_5_duplex_expansion.py
+    - tests/e2e/online_serving/test_duplex_client_live.py
+    - tests/e2e/online_serving/test_minicpmo_realtime_duplex_drivers.py
+    - tests/e2e/online_serving/helpers/minicpmo_realtime_duplex_scenarios.py
+    - tests/e2e/online_serving/run_minicpmo_realtime_duplex_multi_session.py
+    - tests/e2e/online_serving/run_minicpmo_realtime_duplex_server_vad.py
+    - tests/e2e/online_serving/run_minicpmo_realtime_duplex_soft_interrupt.py
+    - tests/e2e/online_serving/helpers/minicpmo_4_5_duplex.py
+    - tests/assets/minicpmo_4_5/
+    - tests/clients/
+    - tests/engine/duplex/
+    - tests/engine/test_duplex_omni_engine.py
+    - tests/engine/test_duplex_orchestrator.py
+    - tests/entrypoints/duplex/
+    - tests/entrypoints/test_duplex_omni.py
+    - tests/entrypoints/openai_api/test_duplex_api_server.py
+    - tests/model_executor/models/minicpmo_4_5/duplex/
+    - tests/protocol/
+
+  omni_minicpmo_4_5_duplex_perf:
+    - *minicpmo_4_5
+    - *minicpmo_4_5_duplex
+    - tests/dfx/perf/scripts/run_benchmark.py
+    - tests/dfx/perf/tests/test_minicpmo_4_5_duplex_seed_tts.json
+
+  tts_qwen3_tts_function:
+    - *qwen3_tts
+    - tests/e2e/features/prefill_phase/
+    - tests/e2e/offline_inference/test_qwen3_tts_base.py
+    - tests/e2e/offline_inference/test_qwen3_tts_customvoice.py
+    - tests/e2e/online_serving/test_qwen3_tts_base.py
+    - tests/e2e/online_serving/test_qwen3_tts_base_expansion.py
+    - tests/e2e/online_serving/test_qwen3_tts_customvoice.py
+    - tests/e2e/online_serving/test_qwen3_tts_customvoice_expansion.py
+
+  tts_qwen3_tts_perf:
+    - *qwen3_tts
+    - tests/dfx/perf/scripts/run_benchmark.py
+    - tests/dfx/perf/tests/test_tts.json
+
+  tts_qwen3_tts_cov:
+    - *qwen3_tts
+    - .buildkite/common/scripts/run_cov_split.sh
+    - pyproject.toml
+    - tests/e2e/offline_inference/test_qwen3_tts_base.py
+    - tests/e2e/online_serving/test_qwen3_tts_base.py
+
+  # --- diffusion jobs ---
+  diffusion_wan22_function:
+    - *wan22
+    - tests/e2e/offline_inference/test_wan22_t2v.py
+    - tests/e2e/offline_inference/test_wan22_autoround_w4a16_expansion.py
+    - tests/e2e/offline_inference/test_device_postprocess_equivalence.py
+    - tests/e2e/online_serving/test_wan22_t2v.py
+    - tests/e2e/online_serving/test_wan22_expansion.py
+
+  diffusion_wan22_accuracy:
+    - *wan22
+    - tests/e2e/accuracy/wan22_i2v/
+    - tests/e2e/accuracy/test_diffusers_backend_similarity.py
+
+  diffusion_wan22_perf:
+    - *wan22
+    - tests/dfx/perf/scripts/run_diffusion_benchmark.py
+    - tests/dfx/perf/tests/test_wan22_i2v_vllm_omni.json
+
+  diffusion_wan22_reliability:
+    - *wan22
+    - tests/dfx/reliability/test_reliability_wan22.py
+
+  diffusion_image_to_video_doc:
+    - *wan22
+    - *hunyuan_video
+    - *ltx2
+    - *sana_video
+    - *lingbot_video
+    - examples/offline_inference/image_to_video/
+    - tests/examples/offline_inference/test_image_to_video.py
+
+  diffusion_cosmos3_function:
+    - *cosmos3
+    - tests/e2e/online_serving/test_cosmos3.py
+
+  diffusion_cosmos3_accuracy:
+    - *cosmos3
+    - tests/e2e/accuracy/test_cosmos3_similarity.py
+
+  diffusion_cosmos3_perf:
+    - *cosmos3
+    - tests/dfx/perf/scripts/run_diffusion_benchmark.py
+    - tests/dfx/perf/tests/test_cosmos3_vllm_omni.json
+
+  diffusion_qwen_image_function:
+    - *qwen_image
+    - tests/e2e/online_serving/test_qwen_image.py
+    - tests/e2e/online_serving/test_qwen_image_expansion.py
+    - tests/e2e/offline_inference/test_qwen_image_autoround_w4a16_expansion.py
+
+  diffusion_qwen_image_accuracy:
+    - *qwen_image
+    - tests/e2e/accuracy/test_qwen_image.py
+    - tests/e2e/accuracy/test_diffusers_backend_similarity.py
+
+  diffusion_qwen_image_perf:
+    - *qwen_image
+    - tests/dfx/perf/scripts/run_diffusion_benchmark.py
+    - tests/dfx/perf/tests/test_qwen_image_vllm_omni.json
+
+  diffusion_text_to_image_doc:
+    - *qwen_image
+    - *z_image
+    - examples/offline_inference/text_to_image/
+    - examples/online_serving/text_to_image/
+    - tests/examples/offline_inference/test_text_to_image.py
+    - tests/examples/online_serving/test_text_to_image.py
+    - vllm_omni/diffusion/models/nextstep_1_1/
+    - vllm_omni/diffusion/models/longcat_image/
+    - vllm_omni/diffusion/models/ovis_image/
+    - vllm_omni/diffusion/models/omnigen2/
+    - vllm_omni/diffusion/models/sd3/
+    - vllm_omni/diffusion/models/flux/
+    - vllm_omni/diffusion/models/flux2/
+    - vllm_omni/diffusion/models/flux2_klein/
+    - vllm_omni/diffusion/models/hidream_image/
+    - vllm_omni/diffusion/models/krea2/
+    - vllm_omni/diffusion/models/lingbot_video/
+
+  diffusion_hunyuan_image3_function:
+    - *hunyuan_image3
+    - tests/e2e/offline_inference/test_hunyuanimage3_i2t_expansion.py
+
+  diffusion_hunyuan_image3_accuracy:
+    - *hunyuan_image3
+    - tests/e2e/accuracy/test_hunyuan_image3_pixel_accuracy.py
+
+  diffusion_hunyuan_image3_perf:
+    - *hunyuan_image3
+    - tests/dfx/perf/scripts/run_diffusion_benchmark.py
+    - tests/dfx/perf/tests/test_hunyuan_image_tp2_cfgp2.json
+    - tests/dfx/perf/tests/test_hunyuan_image_tp2_sp2.json
+    - tests/dfx/perf/tests/test_hunyuan_image_tp4.json
+
+  diffusion_hunyuan_image3_reliability:
+    - *hunyuan_image3
+    - tests/dfx/reliability/test_reliability_hunyuan_image.py
+
+  diffusion_minimax_h3_function:
+    - *minimax_h3
+    - tests/e2e/online_serving/test_minimax_h3_dlo_dp2_t2va.py
+    - tests/e2e/online_serving/minimax_h3/
+    - tests/diffusion/models/minimax_h3/
+    - vllm_omni/diffusion/lora/
+    - vllm_omni/diffusion/offloader/
+    - vllm_omni/diffusion/distributed/
+    - vllm_omni/entrypoints/openai/
+
+  diffusion_minimax_h3_accuracy:
+    - *minimax_h3
+    - tests/e2e/accuracy/minimax_h3/
+
+  diffusion_minimax_h3_perf:
+    - *minimax_h3
+    - tests/dfx/perf/scripts/run_diffusion_benchmark.py
+    - tests/dfx/perf/tests/test_minimax_h3_vllm_omni.json
+
+  diffusion_hunyuan_video_function:
+    - *hunyuan_video
+    - tests/e2e/online_serving/test_hunyuan_video_15_expansion.py
+
+  diffusion_hunyuan_video_accuracy:
+    - *hunyuan_video
+    - tests/e2e/accuracy/hunyuanvideo15_t2v/
+    - tests/e2e/accuracy/hunyuanvideo15_i2v/
+
+  diffusion_hunyuan_video_perf:
+    - *hunyuan_video
+    - tests/dfx/perf/scripts/run_diffusion_benchmark.py
+    - tests/dfx/perf/tests/test_hunyuanvideo15_t2v_vllm_omni.json
+    - tests/dfx/perf/tests/test_hunyuanvideo15_i2v_vllm_omni.json
+
+  diffusion_z_image_function:
+    - *z_image
+    - tests/e2e/online_serving/test_z_image.py
+    - tests/e2e/online_serving/test_zimage_expansion.py
+
+  diffusion_sana_video_function:
+    - *sana_video
+    - tests/e2e/online_serving/test_sana_video_expansion.py
+    - tests/e2e/accuracy/sana_video/
+
+  diffusion_stable_audio_function:
+    - *stable_audio
+    - tests/e2e/offline_inference/test_stable_audio_expansion.py
+    - tests/e2e/online_serving/test_stable_audio_online_expansion.py
+
+  diffusion_lingbot_function:
+    - *lingbot_video
+    - *lingbot_world
+    - tests/e2e/online_serving/test_lingbot_video.py
+    - tests/e2e/online_serving/test_lingbot_video_moe.py
+    - tests/e2e/offline_inference/test_lingbot_world_v2.py
+
+
+  # Invalid-param jobs: model business code only, plus the reliability scripts.
+  reliability_invalid_param_h100:
+    - *qwen3_omni
+    - *minicpmo_4_5
+    - *qwen_image
+    - *wan22
+    - tests/dfx/reliability/invalid_param_test/
+
+  reliability_invalid_param_l4:
+    - *qwen3_tts
+    - *stable_audio
+    - tests/dfx/reliability/invalid_param_test/

.buildkite/common/scripts/skip_ci.py

@@ -1,5 +1,6 @@
 #!/usr/bin/env python3
 # SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 """Shared Buildkite skip-ci and CI-YAML level targeting logic.
 
 ``resolve_ci_decision(changed_files, ...)`` buckets explicit file lists (tests, dry-run).
@@ -10,12 +11,6 @@ CLI (for bootstrap scripts; exit code is the signal):
   python3 skip_ci.py gate <platform> <level>
       Exit 0 when bootstrap should stop (skip-all, or that L2/L3 target is off).
       Prints ``skip-all`` or ``skip-l23`` on stdout; logs the decision once.
-  python3 skip_ci.py check-skip-all
-      Exit 0 when only docs/skip-mark changes → bootstrap skips entire CI upload.
-  python3 skip_ci.py check-skip-l2-l3 <platform> <level>
-      Exit 0 when that L2/L3 pipeline should be skipped (CI config YAML-only diffs).
-  python3 skip_ci.py print-annotate
-      Print decision message for Buildkite annotation.
 """
 
 from __future__ import annotations
@@ -49,6 +44,7 @@ L3_YAML_FILES: dict[str, str] = {
 L45_YAML_FILES: dict[str, str] = {
     ".buildkite/cuda/test-nightly.yml": "cuda",
     ".buildkite/cuda/test-weekly.yml": "cuda",
+    ".buildkite/amd/test-amd-nightly.yml": "amd",
     ".buildkite/npu/test-npu-nightly.yml": "npu",
 }
 
@@ -258,11 +254,10 @@ class CiDecision:
 
 @dataclass(frozen=True)
 class CiContext:
-    """Skip-ci decision plus git diff inputs used to compute it."""
+    """Skip-ci decision plus changed files used for pipeline filtering."""
 
     decision: CiDecision
     changed_files: list[str] | None
-    diff_range: str | None
 
 
 def _log(message: str) -> None:
@@ -332,7 +327,7 @@ def resolve_ci_context_from_git() -> CiContext:
     diff_range = _resolve_diff_range()
     changed_files = _changed_files_for_diff_range(diff_range)
     decision = resolve_ci_decision(changed_files, diff_range=diff_range)
-    return CiContext(decision=decision, changed_files=changed_files, diff_range=diff_range)
+    return CiContext(decision=decision, changed_files=changed_files)
 
 
 # --- Doc / skip-mark helpers ---
@@ -571,23 +566,6 @@ def main() -> int:
     gate_parser.add_argument("platform", help="cuda, amd, intel, or npu")
     gate_parser.add_argument("level", choices=("l2", "l3"), help="test level")
 
-    subparsers.add_parser(
-        "check-skip-all",
-        help="exit 0 when docs/skip-mark-only diff should skip entire CI upload",
-    )
-
-    skip_parser = subparsers.add_parser(
-        "check-skip-l2-l3",
-        help="exit 0 when PLATFORM l2/l3 pipeline should be skipped (CI config YAML-only diffs)",
-    )
-    skip_parser.add_argument("platform", help="cuda, amd, intel, or npu")
-    skip_parser.add_argument("level", choices=("l2", "l3"), help="test level")
-
-    subparsers.add_parser(
-        "print-annotate",
-        help="print human-readable skip reason for Buildkite annotation",
-    )
-
     args = parser.parse_args()
     decision = resolve_ci_context_from_git().decision
 
@@ -600,16 +578,6 @@ def main() -> int:
             return 0
         return 1
 
-    if args.command == "check-skip-all":
-        return 0 if decision.skip_all else 1
-
-    if args.command == "check-skip-l2-l3":
-        return 0 if not decision.is_run(args.platform, args.level) else 1
-
-    if args.command == "print-annotate":
-        print(decision.message or "CI will run normally")
-        return 0
-
     return 1
 
 

.buildkite/common/scripts/upload_pipeline.py

@@ -10,10 +10,30 @@ Bootstrap mode (``bootstrap-upload-steps.yml``):
   - Detect docs-only, pytest skip-mark-only, or combined skip-ci from git diff.
   - When only CI level YAML changes, enable **L2/L3** upload steps for affected levels only.
 
-Test pipeline mode (e.g. test-merge.yml):
-  - Drop steps whose ``source_file_dependencies`` do not match changed files.
-  - Expand uploader-only ``mirror_hardwares: l4_1`` into ``agents`` (+ optional ``image``
+Test pipeline mode (e.g. test-merge.yml, test-nightly.yml, test-weekly.yml):
+  - Drop steps whose ``source_file_dependencies`` do not match changed files
+    (string/list keys from ci_source_file_dependencies.yml, or inline path
+    prefixes). Filtering applies on **PR label** uploads and on post-merge
+    ``main`` L3 uploads. Scheduled ``main`` uploads that pass ``--all``
+    (NIGHTLY/WEEKLY) or ``--e2e`` keep every selected job and still strip
+    the field. If no job-key prefix matches, a change to the pipeline YAML
+    being uploaded or to a path under the ``source_filter_fallback`` registry
+    key keeps every job so command, env, and hardware edits can be validated
+    before merge. When any job-key prefix already matches, normal filtering
+    wins.
+  - Expand uploader-only ``mirror_hardwares`` into ``agents`` (+ optional ``image``
     for NPU) + ``plugins`` (see ci_mirror_hardwares.yml).
+  - Omit ``mirror_hardwares`` to compose ``{chip}_{n}`` from pytest ``-m`` SKU
+    markers plus ``cards_n`` (max of several positives; ``not cards_1`` with no
+    positive ``cards_*`` uses that chip's highest existing preset). An explicit
+    preset string (``h100_2``) is used as-is (marks are ignored). Names that
+    are not keys in ``ci_mirror_hardwares.yml`` fail the upload.
+
+    Inferred chip: unset ``MIRROR_HW`` matches ``H100`` or ``L4`` in ``-m``
+    (both → H100); no match skips the step. ``MIRROR_HW=b200`` matches
+    ``B200`` in ``-m`` (otherwise skipped). ``-m`` is not rewritten.
+    ``MIRROR_HW`` must be empty or ``b200``; unknown values fail the upload.
+    A CUDA preset string such as ``h100_4`` is omitted when ``MIRROR_HW=b200``.
 
 Usage:
   python3 upload_pipeline.py [--upload] [--all | --e2e] <pipeline.yml>
@@ -25,11 +45,13 @@ from __future__ import annotations
 
 import argparse
 import copy
+import os
+import re
 import subprocess
 import sys
 from functools import lru_cache
 from pathlib import Path
-from typing import Any
+from typing import Any, Literal
 
 try:
     import yaml
@@ -45,11 +67,27 @@ from skip_ci import (
     resolve_ci_context_from_git,
 )
 
+if str(ROOT) not in sys.path:
+    sys.path.insert(0, str(ROOT))
+
+from tests.helpers.mark import (  # noqa: E402
+    get_skus_for_platform,
+    get_supported_card_counts,
+)
+
 # --- Constants ---
 
 LOG = "upload_pipeline"
 BOOTSTRAP_STEPS_FILENAME = "bootstrap-upload-steps.yml"
-BOOTSTRAP_IMAGE_BUILD_KEYS = frozenset({"image-build", "image-build-a2", "image-build-a3"})
+BOOTSTRAP_IMAGE_BUILD_KEYS = frozenset(
+    {
+        "image-build",
+        "image-build-a2",
+        "image-build-a3",
+        "image-build-a5",
+        "image-build-310p",
+    }
+)
 BOOTSTRAP_UPLOAD_IF_KEYS = {
     "upload-ready-pipeline": "ready",
     "upload-merge-pipeline": "merge",
@@ -58,20 +96,19 @@ BOOTSTRAP_UPLOAD_IF_KEYS = {
 }
 E2E_GROUP_MARKER = "E2E Test"
 CI_MIRROR_HARDWARES_PATH = ROOT / ".buildkite/common/ci_mirror_hardwares.yml"
+CI_SOURCE_FILE_DEPENDENCIES_PATH = ROOT / ".buildkite/common/ci_source_file_dependencies.yml"
+# Registry key whose prefixes bypass source filtering. Paths live in
+# ci_source_file_dependencies.yml; do not attach this key to a job.
+SOURCE_FILTER_FALLBACK_KEY = "source_filter_fallback"
+CUDA_HF_TOKEN_ENV = "VLLM_CI_HF_TOKEN"
+CUDA_HF_TOKEN_EXPORT = f'if [ -n "$${{{CUDA_HF_TOKEN_ENV}:-}}" ]; then export HF_TOKEN="$${{{CUDA_HF_TOKEN_ENV}}}"; fi'
 
 # Bootstrap Buildkite ``if`` expressions.
 # ``*_MAIN_IF``: main + env schedule. ``*_LABEL_IF``: PR label (and/or composed with MAIN).
 # ``*_UPLOAD_IF``: full gate for uploading that child pipeline.
 NIGHTLY_MAIN_IF = 'build.branch == "main" && build.env("NIGHTLY") == "1"'
 NIGHTLY_LABEL_IF = (
-    f"({NIGHTLY_MAIN_IF}) || "
-    '(build.branch != "main" && ('
-    'build.pull_request.labels includes "nightly-test" || '
-    'build.pull_request.labels includes "omni-test" || '
-    'build.pull_request.labels includes "tts-test" || '
-    'build.pull_request.labels includes "diffusion-x2iat-test" || '
-    'build.pull_request.labels includes "diffusion-x2v-test"'
-    "))"
+    f'({NIGHTLY_MAIN_IF}) || (build.branch != "main" && build.pull_request.labels includes "nightly-test")'
 )
 WEEKLY_E2E_IF = 'build.branch == "main" && build.env("WEEKLY") == "1"'
 WEEKLY_MAIN_IF = 'build.branch == "main" && (build.env("WEEKLY") == "1" || build.env("NON_CRITICAL") == "1")'
@@ -217,7 +254,7 @@ def _render_bootstrap_pipeline(
     return yaml.safe_dump(doc, sort_keys=False)
 
 
-# --- Test pipeline (test-ready.yml, test-merge.yml) ---
+# --- Test pipeline (test-ready.yml, test-merge.yml, test-nightly.yml) ---
 
 
 @lru_cache(maxsize=1)
@@ -233,32 +270,365 @@ def _load_mirror_hardwares() -> dict[str, dict[str, Any]]:
     return presets
 
 
-def _expand_mirror_hardwares(step: dict[str, Any]) -> dict[str, Any]:
-    """Replace uploader-only ``mirror_hardwares`` with preset fields from ci_mirror_hardwares.yml."""
-    hardware = step.get("mirror_hardwares")
-    if hardware is None:
-        return step
-
-    if not isinstance(hardware, str) or not hardware.strip():
+@lru_cache(maxsize=1)
+def _load_source_file_dependencies() -> dict[str, list[str]]:
+    def flatten(value: Any, *, key: str) -> list[str]:
+        """Flatten YAML-anchor nested lists into a de-duplicated prefix list."""
+        if isinstance(value, str):
+            if not value.strip():
+                raise ValueError(f"empty path in source_file_dependencies[{key!r}]")
+            return [value]
+        if isinstance(value, list):
+            flattened: list[str] = []
+            seen: set[str] = set()
+            for item in value:
+                for path in flatten(item, key=key):
+                    if path not in seen:
+                        seen.add(path)
+                        flattened.append(path)
+            return flattened
         raise ValueError(
-            f"mirror_hardwares must be a non-empty string in step {_get_step_label(step)!r}",
+            f"source_file_dependencies[{key!r}] must be a list of path prefixes, got {type(value).__name__}",
         )
 
-    preset = _load_mirror_hardwares().get(hardware)
+    if not CI_SOURCE_FILE_DEPENDENCIES_PATH.is_file():
+        raise FileNotFoundError(
+            f"missing CI source_file_dependencies registry: {CI_SOURCE_FILE_DEPENDENCIES_PATH}",
+        )
+    doc = yaml.safe_load(CI_SOURCE_FILE_DEPENDENCIES_PATH.read_text(encoding="utf-8"))
+    if not isinstance(doc, dict):
+        raise ValueError(f"invalid CI source_file_dependencies registry: {CI_SOURCE_FILE_DEPENDENCIES_PATH}")
+    presets = doc.get("source_file_dependencies")
+    if not isinstance(presets, dict):
+        raise ValueError(f"source_file_dependencies must be a mapping in {CI_SOURCE_FILE_DEPENDENCIES_PATH}")
+    loaded: dict[str, list[str]] = {}
+    for key, value in presets.items():
+        if not isinstance(key, str) or not key.strip():
+            raise ValueError(
+                f"source_file_dependencies keys must be non-empty strings in {CI_SOURCE_FILE_DEPENDENCIES_PATH}",
+            )
+        loaded[key] = flatten(value, key=key)
+    return loaded
+
+
+_REGISTRY_KEY_RE = re.compile(r"^[a-z][a-z0-9_]*$")
+
+
+def _parse_command_text(step: dict[str, Any]) -> str:
+    """Parse step ``commands`` / ``command`` into a single string."""
+    raw = step.get("commands", step.get("command"))
+    if raw is None:
+        return ""
+    if isinstance(raw, str):
+        return raw
+    if isinstance(raw, list):
+        return "\n".join(str(item) for item in raw if item is not None)
+    return str(raw)
+
+
+def _resolve_source_file_dependencies(step: dict[str, Any]) -> list[str] | None:
+    """Expand a registry key (or list of keys / inline path prefixes)."""
+    deps = step.get("source_file_dependencies")
+    if deps is None:
+        return None
+
+    def dedupe(*groups: list[str]) -> list[str]:
+        merged: list[str] = []
+        seen: set[str] = set()
+        for group in groups:
+            for path in group:
+                if path not in seen:
+                    seen.add(path)
+                    merged.append(path)
+        return merged
+
+    def lookup(key: str) -> list[str]:
+        if key == SOURCE_FILTER_FALLBACK_KEY:
+            raise ValueError(
+                f"source_file_dependencies {key!r} bypasses filtering and cannot be attached to a step "
+                f"({_get_step_label(step)!r})",
+            )
+        registry = _load_source_file_dependencies()
+        paths = registry.get(key)
+        if paths is None:
+            known = ", ".join(sorted(registry))
+            raise ValueError(
+                f"unknown source_file_dependencies {key!r} in step {_get_step_label(step)!r}; known: {known}",
+            )
+        return list(paths)
+
+    prefixes: list[str]
+    if isinstance(deps, str):
+        prefixes = lookup(deps) if _REGISTRY_KEY_RE.fullmatch(deps) else [deps]
+    elif isinstance(deps, list):
+        if not all(isinstance(item, str) for item in deps):
+            raise ValueError(
+                f"source_file_dependencies must be a string key or list of strings in step {_get_step_label(step)!r}",
+            )
+        keyish = [bool(_REGISTRY_KEY_RE.fullmatch(item)) for item in deps]
+        if deps and all(keyish):
+            prefixes = dedupe(*(lookup(key) for key in deps))
+        elif any(keyish):
+            raise ValueError(
+                f"source_file_dependencies in step {_get_step_label(step)!r} mixes registry keys and path prefixes",
+            )
+        else:
+            prefixes = list(deps)
+    else:
+        raise ValueError(
+            f"source_file_dependencies must be a string key or list in step {_get_step_label(step)!r}",
+        )
+    return prefixes
+
+
+@lru_cache(maxsize=1)
+def _cuda_mirror_chips() -> tuple[str, ...]:
+    """Lowercase CUDA SKUs that have at least one ``{chip}_{n}`` preset.
+
+    Longer tokens first so ``h100`` is not matched as a prefix of ``h200``.
+    """
+    skus = get_skus_for_platform("cuda")
+    known = _load_mirror_hardwares()
+    chips = {
+        sku.lower() for sku in skus if any(name == sku.lower() or name.startswith(f"{sku.lower()}_") for name in known)
+    }
+    return tuple(sorted(chips, key=lambda chip: (-len(chip), chip)))
+
+
+def _parse_cards_marks(expr: str) -> tuple[set[int], bool]:
+    """Parse registered ``cards_n`` / ``not cards_n`` from a pytest ``-m`` expr.
+
+    Returns positive card counts and whether any ``not cards_*`` is present.
+    """
+    counts = get_supported_card_counts()
+    alt = "|".join(str(n) for n in sorted(counts, key=lambda n: (-len(str(n)), -n))) if counts else "(?!)"
+    positives: set[int] = set()
+    has_not_cards = False
+    for match in re.finditer(rf"\b(?:(not)\s+)?cards_({alt})\b", expr):
+        if match.group(1):
+            has_not_cards = True
+        else:
+            positives.add(int(match.group(2)))
+    return positives, has_not_cards
+
+
+def _parse_hardware_marks(expr: str) -> set[str]:
+    """Parse lowercase positive CUDA SKUs that have a mirror preset.
+
+    ``not H100`` is ignored. SKUs with no ``{chip}_*`` preset (e.g. H200) are dropped.
+    """
+    skus = sorted(get_skus_for_platform("cuda"), key=lambda s: (-len(s), s))
+    if not skus:
+        return set()
+    alt = "|".join(re.escape(sku) for sku in skus)
+    mirror = set(_cuda_mirror_chips())
+    chips: set[str] = set()
+    for match in re.finditer(rf"\b(?:(not)\s+)?({alt})\b", expr):
+        if match.group(1):
+            continue
+        chip = match.group(2).lower()
+        if chip in mirror:
+            chips.add(chip)
+    return chips
+
+
+# CI policy: not pytest-mark registry.
+_SUPPORTED_MIRROR_HW_SELECTORS = frozenset({"b200"})
+# Unset MIRROR_HW: match H100 then L4 in -m (skip if neither).
+_DEFAULT_INFER_CHIPS = ("h100", "l4")
+_PYTEST_MARKER_ARG = re.compile(r"-m\s+(?:\"([^\"]*)\"|'([^']*)'|(\S+))")
+_CARDS_CHIP_MAX: Literal["max"] = "max"
+
+
+def _get_mirror_hw_selector() -> str:
+    """Return lowercase ``MIRROR_HW``, or empty to keep string presets / marker chips.
+
+    A set value must be ``b200`` (case-insensitive). Unknown values fail closed
+    so a typo cannot silently drop the CUDA pipeline.
+    """
+    selector = os.environ.get("MIRROR_HW", "").strip().lower()
+    if not selector:
+        return ""
+    if selector not in _SUPPORTED_MIRROR_HW_SELECTORS:
+        allowed = " or ".join(repr(s) for s in sorted(_SUPPORTED_MIRROR_HW_SELECTORS))
+        raise ValueError(f"unsupported MIRROR_HW={selector!r}; expected empty or {allowed}")
+    return selector
+
+
+def _parse_pytest_marks(step: dict[str, Any]) -> tuple[set[str], int | Literal["max"] | None]:
+    """Parse pytest ``-m`` from step commands: CUDA SKU chips and cards count.
+
+    *chips* are lowercase SKUs from positive markers (``not H100`` is ignored).
+    *cards* is the max positive ``cards_n``, ``"max"`` when only ``not cards_*``
+    is present, or ``None``.
+    """
+    text = _parse_command_text(step)
+
+    chips: set[str] = set()
+    positives: set[int] = set()
+    has_not_cards = False
+    for match in _PYTEST_MARKER_ARG.finditer(text):
+        expr = match.group(1) or match.group(2) or match.group(3) or ""
+        chips |= _parse_hardware_marks(expr)
+        card_counts, not_cards = _parse_cards_marks(expr)
+        positives.update(card_counts)
+        has_not_cards = has_not_cards or not_cards
+
+    if positives:
+        cards: int | Literal["max"] | None = max(positives)
+    elif has_not_cards:
+        cards = _CARDS_CHIP_MAX
+    else:
+        cards = None
+    return chips, cards
+
+
+def _compose_mirror_hardware_name(
+    chips: set[str],
+    cards: int | Literal["max"] | None,
+    *,
+    step_label: str,
+) -> str | None:
+    """Assemble ``{chip}_{n}`` from pytest ``-m`` chips/cards, or None to skip.
+
+    Unset ``MIRROR_HW``: ``H100`` if in ``-m``, else ``L4``, else skip.
+    Set ``MIRROR_HW``: that chip if in ``-m``, else skip.
+    ``cards`` is a registered ``cards_n``, ``"max"`` for that chip's highest
+    existing preset, or None (error after a chip matched — no ``cards_*``).
+    """
+    selector = _get_mirror_hw_selector()
+    if selector:
+        chip = selector if selector in chips else None
+        skip_reason = f"MIRROR_HW={selector!r} not in -m SKUs {sorted(chips)}"
+    else:
+        chip = next((c for c in _DEFAULT_INFER_CHIPS if c in chips), None)
+        skip_reason = f"no H100 or L4 in -m SKUs {sorted(chips)}"
+    if chip is None:
+        _log(f"skip {step_label}: {skip_reason}")
+        return None
+    if cards is None:
+        raise ValueError(
+            f"step {step_label!r} has a pytest -m SKU marker but no cards_* "
+            f"(or not cards_*); add cards_n or set mirror_hardwares to force a preset",
+        )
+
+    count_source = "not cards_*" if cards == _CARDS_CHIP_MAX else f"cards_{cards}"
+    count = None if cards == _CARDS_CHIP_MAX else cards
+    registered = get_supported_card_counts()
+    if count is not None and count not in registered:
+        supported = ", ".join(f"cards_{n}" for n in sorted(registered)) or "(none)"
+        raise ValueError(
+            f"{count_source} in step {step_label!r} is not a registered cards_* mark; supported: {supported}",
+        )
+    known = _load_mirror_hardwares()
+    if count is None:
+        available = [n for n in sorted(registered) if f"{chip}_{n}" in known]
+        if not available:
+            raise ValueError(
+                f"{count_source} in step {step_label!r} has no CUDA count presets for {chip}",
+            )
+        count = max(available)
+    chosen = f"{chip}_{count}"
+    if chosen not in known:
+        raise ValueError(
+            f"{count_source} in step {step_label!r} has no preset {chosen!r}",
+        )
+    _log(f"{step_label}: {count_source} → {chosen!r}")
+    return chosen
+
+
+def _resolve_mirror_hardware_name(hardware: Any, *, step_label: str) -> str | None:
+    """Map ``mirror_hardwares`` to a ``ci_mirror_hardwares.yml`` preset, or None to skip.
+
+    The value is a preset name (``h100_2``). Unknown names fail later when the
+    registry is loaded. CUDA names are omitted when ``MIRROR_HW`` names a
+    different chip; NPU strings ignore ``MIRROR_HW``.
+    """
+    if isinstance(hardware, bool):
+        raise ValueError(f"mirror_hardwares must not be a boolean in step {step_label!r}")
+    if isinstance(hardware, str):
+        name = hardware.strip()
+    elif isinstance(hardware, int):
+        name = str(hardware)
+    else:
+        raise ValueError(
+            f"mirror_hardwares must be a preset string in step {step_label!r}",
+        )
+    if not name:
+        raise ValueError(f"mirror_hardwares must be a non-empty string in step {step_label!r}")
+
+    selector = _get_mirror_hw_selector()
+    token = name.lower()
+    chip = next(
+        (c for c in _cuda_mirror_chips() if token == c or token.startswith(f"{c}_")),
+        None,
+    )
+    if selector and chip is not None and chip != selector:
+        _log(
+            f"skip {step_label}: preset {name!r} is {chip} hardware; MIRROR_HW={selector!r}",
+        )
+        return None
+    return name
+
+
+def _expand_mirror_hardwares(step: dict[str, Any]) -> dict[str, Any] | None:
+    """Replace ``mirror_hardwares`` (or infer it from ``-m``) with preset agents/plugins.
+
+    Explicit preset string: used as-is; pytest marks are ignored.
+    Omitted key: match H100/L4 (or ``MIRROR_HW``) in ``-m``, then compose
+    ``{chip}_{n}`` from ``cards_n``. No match skips the step. Steps with no
+    SKU/cards and no ``mirror_hardwares`` are left unchanged (CPU jobs).
+
+    Returns None when the current ``MIRROR_HW`` selector does not apply.
+    """
+    step_label = _get_step_label(step)
+    has_pool = step.get("agents") is not None or step.get("plugins") is not None or step.get("image") is not None
+
+    if "mirror_hardwares" not in step:
+        if has_pool:
+            return step
+        chips, cards = _parse_pytest_marks(step)
+        if not chips and cards is None:
+            return step
+        preset_name = _compose_mirror_hardware_name(chips, cards, step_label=step_label)
+    else:
+        if has_pool:
+            raise ValueError(
+                f"step {step_label!r} sets mirror_hardwares together with agents/plugins/image; "
+                f"use mirror_hardwares only",
+            )
+        preset_name = _resolve_mirror_hardware_name(
+            step.get("mirror_hardwares"),
+            step_label=step_label,
+        )
+
+    if preset_name is None:
+        return None
+
+    preset = _load_mirror_hardwares().get(preset_name)
     if preset is None:
         known = ", ".join(sorted(_load_mirror_hardwares()))
         raise ValueError(
-            f"unknown mirror_hardwares {hardware!r} in step {_get_step_label(step)!r}; known: {known}",
-        )
-
-    if step.get("agents") is not None or step.get("plugins") is not None or step.get("image") is not None:
-        raise ValueError(
-            f"step {_get_step_label(step)!r} sets mirror_hardwares together with agents/plugins/image; "
-            "use mirror_hardwares only",
+            f"unknown mirror_hardwares {preset_name!r} in step {step_label!r}; known: {known}",
         )
 
     expanded = copy.deepcopy(preset)
-    return {key: value for key, value in step.items() if key != "mirror_hardwares"} | expanded
+    merged = {key: value for key, value in step.items() if key != "mirror_hardwares"} | expanded
+    # A scheduled build's HF_TOKEN may override a pod env entry with the same
+    # name. The preset injects the Kubernetes secret under a collision-proof
+    # alias; restore the conventional name after Buildkite has merged env.
+    if any(preset_name == chip or preset_name.startswith(f"{chip}_") for chip in _cuda_mirror_chips()):
+        commands = merged.get("commands")
+        if isinstance(commands, list):
+            merged["commands"] = [CUDA_HF_TOKEN_EXPORT, *commands]
+        elif commands is None:
+            merged["commands"] = [CUDA_HF_TOKEN_EXPORT]
+        else:
+            merged["commands"] = [CUDA_HF_TOKEN_EXPORT, commands]
+    # Preset retry (K8S_RETRY on l4_*) must not clobber a step that opted out.
+    if "retry" in step:
+        merged["retry"] = step["retry"]
+    return merged
 
 
 def _match_source_file(changed_files: list[str], prefixes: list[str]) -> bool:
@@ -285,11 +655,7 @@ def _process_test_steps(
             processed.append(step)
             continue
 
-        deps = step.get("source_file_dependencies")
-        if deps is not None and not isinstance(deps, list):
-            raise ValueError(
-                f"source_file_dependencies must be a list in step {_get_step_label(step)!r}",
-            )
+        deps = _resolve_source_file_dependencies(step)
         if changed_files is not None and deps is not None and not _match_source_file(changed_files, deps):
             _log(f"skip {_get_step_label(step)!r} (no changes under {deps})")
             continue
@@ -297,7 +663,7 @@ def _process_test_steps(
         nested = step.get("steps")
         if nested is not None:
             kept_nested = _process_test_steps(nested, changed_files)
-            if changed_files is not None and not kept_nested:
+            if not kept_nested:
                 _log(f"omit empty group {_get_step_label(step)!r}")
                 continue
             new_step = {key: value for key, value in step.items() if key != "source_file_dependencies"}
@@ -305,14 +671,11 @@ def _process_test_steps(
             processed.append(new_step)
             continue
 
-        if deps is not None:
-            processed.append(
-                _expand_mirror_hardwares(
-                    {key: value for key, value in step.items() if key != "source_file_dependencies"},
-                ),
-            )
-        else:
-            processed.append(_expand_mirror_hardwares(step))
+        leaf = {key: value for key, value in step.items() if key != "source_file_dependencies"}
+        expanded = _expand_mirror_hardwares(leaf)
+        if expanded is None:
+            continue
+        processed.append(expanded)
 
     return processed
 
@@ -331,18 +694,44 @@ def _select_e2e_group_steps(steps: list[Any]) -> list[Any]:
     return selected
 
 
+def _any_source_dependency_match(steps: list[Any], changed_files: list[str]) -> bool:
+    """True when any step with ``source_file_dependencies`` matches *changed_files*."""
+    for step in steps:
+        if not isinstance(step, dict):
+            continue
+        deps = _resolve_source_file_dependencies(step)
+        if deps is not None and _match_source_file(changed_files, deps):
+            return True
+        nested = step.get("steps")
+        if isinstance(nested, list) and _any_source_dependency_match(nested, changed_files):
+            return True
+    return False
+
+
 def _render_test_pipeline(
     doc: dict[str, Any],
     changed_files: list[str] | None,
     *,
+    pipeline_path: Path | None = None,
     e2e_only: bool = False,
 ) -> dict[str, Any]:
     """Filter steps by PR diff and strip uploader-only ``source_file_dependencies`` metadata."""
+    # Validate MIRROR_HW once up front. Per-step skip must not run for typos
+    # (e.g. b20o), or a pipeline can shrink to leftover CPU-only steps and
+    # still upload successfully.
+    _get_mirror_hw_selector()
     steps = doc.get("steps")
     if not isinstance(steps, list):
         return doc
     if e2e_only:
         steps = _select_e2e_group_steps(steps)
+    # Bypass is a fallback: only when no job-key prefix matched.
+    if changed_files is not None and pipeline_path is not None:
+        if not _any_source_dependency_match(steps, changed_files):
+            bypass = _source_filter_fallback_reason(changed_files, pipeline_path)
+            if bypass is not None:
+                _log(f"keep all jobs (no source_file_dependencies match; bypassed by {bypass})")
+                changed_files = None
     steps = _process_test_steps(steps, changed_files)
     return {**doc, "steps": steps}
 
@@ -350,6 +739,60 @@ def _render_test_pipeline(
 # --- Entry (read file → bootstrap or test render → YAML string) ---
 
 
+def _repo_relative_posix(path: Path) -> str:
+    resolved = path.resolve()
+    try:
+        return resolved.relative_to(ROOT).as_posix()
+    except ValueError:
+        return path.as_posix().replace("\\", "/")
+
+
+def _source_filter_fallback_prefixes() -> list[str]:
+    """Shared uploader/registry prefixes from ``source_filter_fallback``. Not a job key."""
+    registry = _load_source_file_dependencies()
+    prefixes = registry.get(SOURCE_FILTER_FALLBACK_KEY)
+    if not prefixes:
+        raise ValueError(
+            f"source_file_dependencies[{SOURCE_FILTER_FALLBACK_KEY!r}] must list shared uploader paths "
+            f"in {CI_SOURCE_FILE_DEPENDENCIES_PATH}",
+        )
+    return prefixes
+
+
+def _source_filter_fallback_reason(changed_files: list[str] | None, pipeline_path: Path) -> str | None:
+    """Return the changed path that should disable source filtering, if any."""
+    if not changed_files:
+        return None
+    changed = set(changed_files)
+    pipeline_rel = _repo_relative_posix(pipeline_path)
+    if pipeline_rel in changed:
+        return pipeline_rel
+    prefixes = _source_filter_fallback_prefixes()
+    for path in changed_files:
+        if _match_source_file([path], prefixes):
+            return path
+    return None
+
+
+def _changed_files_for_source_filter(
+    ctx,
+    *,
+    force_all: bool,
+    e2e_only: bool,
+) -> list[str] | None:
+    """Return the diff to filter against, or None to keep every step.
+
+    ``--all`` / ``--e2e`` disable filtering (scheduled NIGHTLY/WEEKLY full
+    uploads and weekly E2E sweeps). Post-merge ``main`` L3 and PR-label
+    uploads filter against the commit/PR diff. Pipeline YAML /
+    ``source_filter_fallback`` matches are applied later in
+    ``_render_test_pipeline`` only when no job-key prefix already matched.
+    """
+    if force_all or e2e_only:
+        return None
+    return ctx.changed_files
+
+
 def _render_pipeline(
     path: Path,
     *,
@@ -367,16 +810,18 @@ def _render_pipeline(
 
     text = path.read_text(encoding="utf-8")
     ctx = resolve_ci_context_from_git()
-    if force_all or e2e_only:
-        changed_files = None
-    else:
-        changed_files = ctx.changed_files
+    changed_files = _changed_files_for_source_filter(ctx, force_all=force_all, e2e_only=e2e_only)
 
     doc = yaml.safe_load(text)
     if not isinstance(doc, dict):
         raise ValueError(f"invalid pipeline YAML: {path}")
 
-    doc = _render_test_pipeline(doc, changed_files, e2e_only=e2e_only)
+    doc = _render_test_pipeline(
+        doc,
+        changed_files,
+        pipeline_path=path,
+        e2e_only=e2e_only,
+    )
     return yaml.safe_dump(doc, sort_keys=False)
 
 

.buildkite/cuda/bootstrap-upload-steps.yml

@@ -22,7 +22,8 @@ steps:
     agents:
       queue: "cpu_queue_premerge"
 
-  # L3 Test — PR merge-test label (diff-aware) or main+WEEKLY=1 (--e2e, full E2E group)
+  # L3 Test — PR merge-test label or post-merge on main (both diff-aware);
+  # main+WEEKLY=1 uploads the E2E group only (--e2e).
   - label: "Upload Merge Pipeline"
     depends_on: image-build
     key: upload-merge-pipeline
@@ -36,20 +37,30 @@ steps:
     agents:
       queue: "cpu_queue_premerge"
 
-  # L4 Test — main+NIGHTLY=1 (scheduled), or PR with specific label (e.g. add label then Rebuild)
+  # L4 Test — main+NIGHTLY=1 (full pipeline), or PR nightly-test label (diff-aware)
   - label: "Upload Nightly Pipeline"
     depends_on: image-build
     key: upload-nightly-pipeline
     commands:
-      - python3 .buildkite/common/scripts/upload_pipeline.py --upload .buildkite/cuda/test-nightly.yml
+      - |
+        EXTRA=""
+        if [ "${NIGHTLY:-}" = "1" ] && [ "${BUILDKITE_BRANCH}" = "main" ]; then
+          EXTRA="--all"
+        fi
+        python3 .buildkite/common/scripts/upload_pipeline.py --upload $$EXTRA .buildkite/cuda/test-nightly.yml
     agents:
       queue: "cpu_queue_premerge"
 
-  # L5 Test — main+WEEKLY=1 / NON_CRITICAL=1 (scheduled), or PR with weekly-test label
+  # L5 Test — main+WEEKLY=1 / NON_CRITICAL=1 (full pipeline), or PR weekly-test label (diff-aware)
   - label: "Upload Weekly Pipeline"
     depends_on: image-build
     key: upload-weekly-pipeline
     commands:
-      - python3 .buildkite/common/scripts/upload_pipeline.py --upload .buildkite/cuda/test-weekly.yml
+      - |
+        EXTRA=""
+        if [ "${BUILDKITE_BRANCH}" = "main" ]; then
+          EXTRA="--all"
+        fi
+        python3 .buildkite/common/scripts/upload_pipeline.py --upload $$EXTRA .buildkite/cuda/test-weekly.yml
     agents:
       queue: "cpu_queue_premerge"

.buildkite/cuda/test-merge.yml

@@ -1,6 +1,9 @@
+# CUDA CI L3 (`advanced_model` e2e) plus L1 CPU Simple Test (`core_model and cpu`).
+# Uploaded by Upload Merge Pipeline: PR label `merge-test`, or post-merge on main
+# (both diff-aware via source_file_dependencies).
+
 env:
   VLLM_WORKER_MULTIPROC_METHOD: spawn
-  VLLM_USE_FLASHINFER_MOE_FP16: 0
   HF_HUB_DOWNLOAD_TIMEOUT: 300
   HF_HUB_ETAG_TIMEOUT: 60
 
@@ -63,7 +66,9 @@ steps:
         # 0.26 startup regression is understood and fixed.
         timeout_in_minutes: 90
         commands:
-          - pytest -s -v tests/entrypoints/ -m "advanced_model and cuda and H100" --run-level "advanced_model"
+          # Turn-based server VAD over /v1/realtime is deferred to RFC #7181 PR 5
+          # (see the P0 thread on #7413); the test stays in the tree and runs again there.
+          - pytest -s -v tests/entrypoints/ -m "advanced_model and cuda and H100" --run-level "advanced_model" --deselect tests/entrypoints/openai_api/test_qwen3_omni_realtime_websocket.py::TestQwen3OmniRealtimeWebSocket::test_server_vad_multi_turn_without_client_commit
         mirror_hardwares: h100_2
 
       - label: "Entrypoint Test with L4"
@@ -76,36 +81,18 @@ steps:
     depends_on: upload-merge-pipeline
     steps:
       - label: "TTS · Qwen3-TTS CustomVoice Test"
-        source_file_dependencies:
-          - tests/e2e/online_serving/test_qwen3_tts_customvoice.py
-          - tests/e2e/offline_inference/test_qwen3_tts_customvoice.py
-          - vllm_omni/model_executor/models/qwen3_tts/
-          - vllm_omni/model_executor/models/common/qwen3_code_predictor.py
-          - vllm_omni/model_executor/models/common/snake_activation.py
-          - vllm_omni/model_executor/models/common/alias_free_activation.py
-          - vllm_omni/model_executor/stage_input_processors/qwen3_tts.py
-          - vllm_omni/deploy/qwen3_tts.yaml
+        source_file_dependencies: tts_qwen3_tts_function
         commands:
           - |
             timeout 40m bash -c "
               export VLLM_LOGGING_LEVEL=DEBUG
               export VLLM_ALLOW_LONG_MAX_MODEL_LEN=1
-              pytest -s -v tests/e2e/online_serving/test_qwen3_tts_customvoice.py tests/e2e/offline_inference/test_qwen3_tts_customvoice.py -m 'advanced_model and cuda' --run-level 'advanced_model'
+              pytest -s -v tests/e2e/online_serving/test_qwen3_tts_customvoice.py tests/e2e/offline_inference/test_qwen3_tts_customvoice.py tests/e2e/features/prefill_phase/test_chunked_prefill_tail.py -m 'advanced_model and cuda' --run-level 'advanced_model'
             "
         mirror_hardwares: l4_1
 
       - label: "TTS · Qwen3-TTS Base Test"
-        source_file_dependencies:
-          - tests/e2e/online_serving/test_qwen3_tts_base.py
-          - tests/e2e/offline_inference/test_qwen3_tts_base.py
-          - vllm_omni/model_executor/models/qwen3_tts/
-          - vllm_omni/model_executor/models/common/qwen3_code_predictor.py
-          - vllm_omni/model_executor/models/common/snake_activation.py
-          - vllm_omni/model_executor/models/common/alias_free_activation.py
-          - vllm_omni/model_executor/stage_input_processors/qwen3_tts.py
-          - vllm_omni/deploy/qwen3_tts.yaml
-          - .buildkite/common/scripts/run_cov_split.sh
-          - pyproject.toml
+        source_file_dependencies: tts_qwen3_tts_cov
         # 40 -> 60: the coverage split runs the model twice.
         timeout_in_minutes: 60
         commands:
@@ -120,81 +107,34 @@ steps:
         mirror_hardwares: l4_1
 
       - label: "Omni · Qwen3-Omni Test"
-        source_file_dependencies:
-          - tests/e2e/offline_inference/test_qwen3_omni.py
-          - tests/e2e/online_serving/test_qwen3_omni.py
-          - tests/helpers/runtime.py
-          - vllm_omni/entrypoints/openai/serving_chat.py
-          - vllm_omni/model_executor/models/qwen3_omni/
-          - vllm_omni/model_executor/models/common/qwen3_code_predictor.py
-          - vllm_omni/model_executor/models/common/snake_activation.py
-          - vllm_omni/model_executor/models/qwen2_5_omni/qwen2_5_omni_thinker.py
-          - vllm_omni/model_executor/models/qwen3_tts/cuda_graph_decoder_wrapper.py
-          - vllm_omni/model_executor/stage_input_processors/qwen3_omni.py
-          - vllm_omni/deploy/qwen3_omni_moe.yaml
+        source_file_dependencies: omni_qwen3_omni_function
         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"
-        source_file_dependencies:
-          - tests/e2e/offline_inference/test_minicpmo_4_5.py
-          - tests/e2e/online_serving/test_minicpmo_4_5.py
-          - vllm_omni/model_executor/models/minicpmo_4_5/
-          - vllm_omni/model_executor/models/step_audio2/
-          - vllm_omni/model_executor/models/cosyvoice3/code2wav_core/
-          - vllm_omni/model_executor/models/cosyvoice3/utils.py
-          - vllm_omni/model_executor/stage_input_processors/minicpmo_4_5_omni.py
-          - vllm_omni/deploy/minicpmo_4_5.yaml
+        source_file_dependencies: omni_minicpmo_4_5_function
         timeout_in_minutes: 50
         commands:
           - pytest -s -v tests/e2e/offline_inference/test_minicpmo_4_5.py tests/e2e/online_serving/test_minicpmo_4_5.py -m 'advanced_model and cuda' --run-level 'advanced_model'
         # h100_2, not h100_1: advanced_model asserts transcribe every audio
-        # response with Whisper, and tests/helpers/media.py only uses an
-        # accelerator for Whisper when a spare GPU exists. On a single card it
-        # grades on CPU: ~8-10 min per clip vs ~30 s, which alone overruns the
-        # 50-minute job timeout (build 2956).
+        # response with Whisper, and tests/helpers/media.py requires at least
+        # 16 GiB free VRAM on a device to run Whisper on GPU. When the model
+        # server leaves insufficient memory, Whisper falls back to CPU:
+        # ~8-10 min per clip vs ~30 s, which overruns the 50-minute job timeout
+        # (build 2956). h100_2 guarantees a second GPU with ample free VRAM.
         mirror_hardwares: h100_2
 
       - label: "Omni · MiniCPM-o 4.5 Duplex Test"
-        source_file_dependencies:
-          - tests/assets/minicpmo_4_5/response_required_16k.wav
-          - tests/assets/minicpmo_4_5/soft_interrupt_16k.wav
-          - tests/e2e/online_serving/test_minicpmo_4_5_duplex.py
-          - tests/e2e/online_serving/test_minicpmo_4_5_duplex_expansion.py
-          - tests/e2e/online_serving/test_minicpmo_realtime_duplex_drivers.py
-          - tests/e2e/online_serving/helpers/minicpmo_realtime_duplex_scenarios.py
-          - tests/e2e/online_serving/run_minicpmo_realtime_duplex_multi_session.py
-          - tests/e2e/online_serving/run_minicpmo_realtime_duplex_soft_interrupt.py
-          - vllm_omni/deploy/minicpmo_4_5.yaml
-          - tests/e2e/online_serving/helpers/minicpmo_4_5_duplex.py
-          - vllm_omni/experimental/fullduplex/
-          - vllm_omni/model_executor/models/minicpmo_4_5/
-          - vllm_omni/model_executor/models/step_audio2/
-          - vllm_omni/model_executor/models/cosyvoice3/code2wav_core/
-          - vllm_omni/model_executor/models/cosyvoice3/utils.py
-          - vllm_omni/model_executor/stage_input_processors/minicpmo_4_5_omni.py
-          - vllm_omni/core/sched/
-          - vllm_omni/outputs/multimodal_accumulation.py
-          - vllm_omni/engine/stage_init_utils.py
-          - vllm_omni/engine/async_omni_engine.py
-          - vllm_omni/engine/orchestrator.py
-          - vllm_omni/entrypoints/cli/serve.py
-          - vllm_omni/entrypoints/openai/api_server.py
-          - vllm_omni/entrypoints/openai/duplex_capability.py
-          - vllm_omni/config/stage_config.py
+        source_file_dependencies: omni_minicpmo_4_5_duplex_function
         timeout_in_minutes: 50
         commands:
           - pytest -s -v tests/e2e/online_serving/test_minicpmo_4_5_duplex.py -m 'advanced_model and cuda' --run-level 'advanced_model'
         mirror_hardwares: h100_1
 
       - label: "Diffusion · Qwen Image Test"
-        source_file_dependencies:
-          - tests/e2e/online_serving/test_qwen_image.py
-          - vllm_omni/diffusion/models/qwen_image/
-          - vllm_omni/diffusion/models/dmd2/
-          - vllm_omni/diffusion/distributed/autoencoders/autoencoder_kl_qwenimage.py
+        source_file_dependencies: diffusion_qwen_image_function
         commands:
           - |
             timeout 40m bash -c '
@@ -204,11 +144,7 @@ steps:
         mirror_hardwares: h100_1
 
       - label: "Diffusion · Wan22 Test"
-        source_file_dependencies:
-          - tests/e2e/offline_inference/test_wan22_t2v.py
-          - tests/e2e/online_serving/test_wan22_t2v.py
-          - vllm_omni/diffusion/models/wan2_2/
-          - vllm_omni/diffusion/models/dmd2/
+        source_file_dependencies: diffusion_wan22_function
         commands:
           - |
             timeout 40m bash -c "
@@ -216,3 +152,38 @@ 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: diffusion_hunyuan_video_function
+        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: diffusion_minimax_h3_function
+        # 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

.buildkite/cuda/test-nightly.yml

@@ -1,6 +1,9 @@
+# CUDA CI L4 (`full_model`): nightly and release share this file.
+# Uploaded by Upload Nightly Pipeline: NIGHTLY=1, or PR labels (nightly-test / omni-test / ...).
+# Hardware: omit mirror_hardwares to compose from -m SKU + cards_n; or set a preset string to force.
+
 env:
   VLLM_WORKER_MULTIPROC_METHOD: spawn
-  VLLM_USE_FLASHINFER_MOE_FP16: 0
   HF_HUB_DOWNLOAD_TIMEOUT: 300
   HF_HUB_ETAG_TIMEOUT: 60
 
@@ -9,146 +12,170 @@ steps:
   - group: ":card_index_dividers: Omni Model Test"
     key: nightly-omni-test-group
     depends_on: upload-nightly-pipeline
-    if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test" || build.pull_request.labels includes "omni-test"
+    if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test"
     steps:
 
-      - label: ":full_moon: Omni · Function Test with H100"
+      - label: ":full_moon: Omni · Function Test with H100 · Single-GPU"
+        source_file_dependencies:
+          - omni_qwen3_omni_function
+          - omni_minicpmo_4_5_function
         timeout_in_minutes: 120
         commands:
-          - pytest -sv tests/e2e --run-level "full_model" -m "full_model and H100 and omni" --ignore=tests/e2e/accuracy --ignore=tests/e2e/online_serving/test_qwen3_omni_multi_replicas.py --ignore=tests/e2e/online_serving/test_minicpmo_4_5_duplex_expansion.py
-        mirror_hardwares: h100_2
+          - pytest -sv tests/e2e --run-level "full_model" -m "full_model and H100 and B200 and omni and cards_1" --ignore=tests/e2e/accuracy --ignore=tests/e2e/online_serving/test_minicpmo_4_5_duplex_expansion.py
+
+      - label: ":full_moon: Omni · Function Test with H100 · 2-GPU"
+        source_file_dependencies:
+          - omni_qwen3_omni_function
+          - omni_minicpmo_4_5_function
+        timeout_in_minutes: 90
+        commands:
+          - pytest -sv tests/e2e --run-level "full_model" -m "full_model and H100 and B200 and omni and cards_2" --ignore=tests/e2e/accuracy --ignore=tests/e2e/online_serving/test_qwen3_omni_multi_replicas.py
 
       - label: ":full_moon: Omni · MiniCPM-o 4.5 Duplex Test"
+        source_file_dependencies: omni_minicpmo_4_5_duplex_function
         timeout_in_minutes: 50
         commands:
-          - pytest -s -v tests/e2e/online_serving/test_minicpmo_4_5_duplex_expansion.py -m "full_model and cuda and H100 and omni" --run-level "full_model"
-        mirror_hardwares: h100_1
+          - pytest -s -v tests/e2e/online_serving/test_minicpmo_4_5_duplex_expansion.py -m "full_model and cuda and H100 and B200 and omni and cards_1" --run-level "full_model"
 
-      - label: ":full_moon: Omni · Doc Test with L4"
+      - label: ":full_moon: Omni · Doc Test with L4 · 4-GPU"
+        source_file_dependencies: omni_qwen2_5_omni_doc
         timeout_in_minutes: 90
         commands:
           - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
-          - pytest -s -v tests/examples/ -m "full_model and omni and L4" --run-level "full_model"
-        mirror_hardwares: l4_4
+          - pytest -s -v tests/examples/ -m "full_model and omni and L4 and B200 and cards_4" --run-level "full_model"
 
-      - label: ":full_moon: Omni · Doc Test with H100"
+      - label: ":full_moon: Omni · Doc Test with H100 · 2-GPU"
+        source_file_dependencies: omni_qwen3_omni_doc
         timeout_in_minutes: 90
         commands:
-          - pytest -s -v tests/examples/ -m "full_model and omni and H100" --run-level "full_model"
-        mirror_hardwares: h100_2
+          - pytest -s -v tests/examples/ -m "full_model and omni and H100 and B200 and cards_2" --run-level "full_model"
 
       - label: ":full_moon: Omni · Accuracy Test"
+        source_file_dependencies: omni_qwen3_omni_accuracy
         timeout_in_minutes: 180
         commands:
           - export SEED_TTS_WER_EVAL=1
           - export SEED_TTS_EVAL_DEVICE=cuda:1
           - |
             set +e
-            pytest -s -v tests/e2e/accuracy/qwen3_omni/test_qwen3_omni.py -m "full_model" --run-level full_model
+            pytest -s -v tests/e2e/accuracy/qwen3_omni/test_qwen3_omni.py -m "full_model and H100 and B200 and cards_2" --run-level full_model
             EXIT=$$?
             buildkite-agent artifact upload "tests/e2e/accuracy/qwen3_omni/results/qwen_omni_acc/*.json"
             exit $$EXIT
-        mirror_hardwares: h100_2
 
       - label: ":full_moon: Omni · MiniCPM-o 4.5 · Accuracy Test"
+        source_file_dependencies: omni_minicpmo_4_5_accuracy
         timeout_in_minutes: 180
         commands:
           - export SEED_TTS_WER_EVAL=1
           - export SEED_TTS_EVAL_DEVICE=cuda:1
           - |
             set +e
-            pytest -s -v tests/e2e/accuracy/minicpmo_4_5/test_minicpmo_4_5.py -m "full_model and cuda" --run-level full_model
+            pytest -s -v tests/e2e/accuracy/minicpmo_4_5/test_minicpmo_4_5.py -m "full_model and H100 and B200 and cuda and (cards_1 or cards_2)" --run-level full_model
             EXIT=$$?
             buildkite-agent artifact upload "tests/e2e/accuracy/minicpmo_4_5/results/*.json"
             exit $$EXIT
-        mirror_hardwares: h100_2
 
       - label: ":full_moon: Omni · Perf Test · No Async Chunk"
+        source_file_dependencies: omni_qwen3_omni_perf
         key: nightly-omni-performance-no-async-chunk
         timeout_in_minutes: 300
         commands:
           - export BENCHMARK_DIR=tests/dfx/perf/results
           - |
             set +e
-            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen3_omni_no_async_chunk.json
+            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen3_omni_no_async_chunk.json -m "H100 and B200 and cards_2"
             EXIT=$$?
             buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
             exit $$EXIT
-        mirror_hardwares: h100_2
 
       - label: ":full_moon: Omni · Perf Test · Async Chunk"
+        source_file_dependencies: omni_qwen3_omni_perf
         key: nightly-omni-performance-async-chunk
         timeout_in_minutes: 180
         commands:
           - export BENCHMARK_DIR=tests/dfx/perf/results
           - |
             set +e
-            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen3_omni_async_chunk.json -m "H100 and full_model"
+            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen3_omni_async_chunk.json -m "H100 and B200 and full_model and cards_2"
             EXIT=$$?
             buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
             exit $$EXIT
-        mirror_hardwares: h100_2
 
       - label: ":full_moon: Omni · MiniCPM-o 4.5 · Perf Test"
+        source_file_dependencies: omni_minicpmo_4_5_perf
         key: nightly-omni-performance-minicpmo-4-5
         timeout_in_minutes: 180
         commands:
           - export BENCHMARK_DIR=tests/dfx/perf/results
           - |
             set +e
-            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_minicpmo_4_5.json
+            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_minicpmo_4_5.json -m "H100 and B200 and cards_1"
             EXIT=$$?
             buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
             exit $$EXIT
-        mirror_hardwares: h100_1
 
       - label: ":full_moon: Omni · MiniCPM-o 4.5 · Duplex Seed-TTS Perf Test"
+        source_file_dependencies: omni_minicpmo_4_5_duplex_perf
         key: nightly-omni-performance-minicpmo-4-5-duplex-seed-tts
         timeout_in_minutes: 180
         commands:
           - export BENCHMARK_DIR=tests/dfx/perf/results
           - |
             set +e
-            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_minicpmo_4_5_duplex_seed_tts.json
+            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_minicpmo_4_5_duplex_seed_tts.json -m "H100 and B200 and cards_1"
             EXIT=$$?
             buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
             exit $$EXIT
-        mirror_hardwares: h100_1
 
       - label: ":full_moon: Omni · Multi-Replica Startup Test with 4x H100"
+        source_file_dependencies: omni_qwen3_omni_function
         timeout_in_minutes: 45
         commands:
-          - pytest -s -v tests/e2e/online_serving/test_qwen3_omni_multi_replicas.py -m "full_model" --run-level "core_model"
-        mirror_hardwares: h100_4
+          - pytest -s -v tests/e2e/online_serving/test_qwen3_omni_multi_replicas.py -m "full_model and H100 and B200 and cards_4" --run-level "core_model"
 
   - group: ":card_index_dividers: TTS Model Test"
     key: nightly-tts-test-group
     depends_on: upload-nightly-pipeline
-    if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test" || build.pull_request.labels includes "tts-test"
+    if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test"
     steps:
 
       - label: ":full_moon: TTS · Function Test with L4"
+        source_file_dependencies: tts_qwen3_tts_function
         timeout_in_minutes: 120
         commands:
           - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
           - export VLLM_USE_DEEP_GEMM="0"
           - export VLLM_MOE_USE_DEEP_GEMM="0"
-          - pytest -s -v tests/e2e/ -m "full_model and L4 and tts" --run-level "full_model" --ignore=tests/e2e/accuracy
-        mirror_hardwares: l4_1
+          - pytest -s -v tests/e2e/ -m "full_model and L4 and B200 and tts and cards_1" --run-level "full_model" --ignore=tests/e2e/accuracy
 
-      - label: ":full_moon: TTS · Perf Test"
-        key: nightly-tts-performance
+      - label: ":full_moon: TTS · Perf Test · Single-GPU"
+        source_file_dependencies: tts_qwen3_tts_perf
+        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 B200 and cards_1"
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
+            exit $$EXIT
+
+      - label: ":full_moon: TTS · Perf Test · 2-GPU"
+        source_file_dependencies: tts_qwen3_tts_perf
+        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 B200 and cards_2"
             EXIT=$$?
             buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
             exit $$EXIT
-        mirror_hardwares: h100_2
 
   - group: ":card_index_dividers: Tiny Model Tests [Multi-GPU]"
     key: nightly-tiny-model-test-group
@@ -158,7 +185,8 @@ steps:
       build.pull_request.labels includes "nightly-test"
     steps:
 
-      - label: ":full_moon: Tiny Model Tests · Diffusion (Multi-GPU accelerations)"
+      - label: ":full_moon: Tiny Model Tests · Diffusion (Multi-GPU accelerations) · 2-GPU"
+        source_file_dependencies: diffusion_tiny_model
         timeout_in_minutes: 30
         # NOTE: Single-GPU (core_model) tiny model tests run on the ready label,
         # so nightly only adds the multi-GPU parallelism configurations.
@@ -168,216 +196,328 @@ steps:
         # for the CI, since these tests should pass irrespective of whether or not we use
         # the full weights or not.
         commands:
-          - timeout 25m pytest -sv tests/model_tests/diffusion -m 'full_model and cuda' --run-level "core_model"
-        mirror_hardwares: l4_4
+          - pytest -sv tests/model_tests/diffusion -m 'full_model and L4 and B200 and cuda and cards_2' --run-level "core_model"
+
+      - label: ":full_moon: Tiny Model Tests · Diffusion (Multi-GPU accelerations) · 4-GPU"
+        source_file_dependencies: diffusion_tiny_model
+        timeout_in_minutes: 30
+        # CFG+TP extra_test_groups request 2×2=4 devices (see get_required_device_count).
+        commands:
+          - pytest -sv tests/model_tests/diffusion -m 'full_model and L4 and B200 and cuda and cards_4' --run-level "core_model"
 
   - group: ":card_index_dividers: Diffusion X2I(&A&T) Model Test"
     key: nightly-diffusion-x2iat-group
     depends_on: upload-nightly-pipeline
     if: >-
       build.env("NIGHTLY") == "1" ||
-      build.pull_request.labels includes "nightly-test" ||
-      build.pull_request.labels includes "diffusion-x2iat-test"
+      build.pull_request.labels includes "nightly-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"
+        source_file_dependencies: diffusion_qwen_image_function
         timeout_in_minutes: 120
         commands:
           - >-
             pytest -sv
             tests/e2e/online_serving/test_qwen_image_expansion.py
-            -m "full_model and diffusion and H100 and not distributed_cuda"
+            -m "full_model and diffusion and H100 and B200 and cards_1"
+            --run-level "full_model"
+
+      - label: ":robot_face: Robot Policy OpenPI · π0.5 · H100 · Single-GPU"
+        timeout_in_minutes: 120
+        commands:
+          - pip install --no-deps "openpi-client @ git+https://github.com/Physical-Intelligence/openpi.git@215abfb217dbac7d5f1273282331b9b1866c0479#subdirectory=packages/openpi-client"
+          - >-
+            pytest -sv
+            tests/e2e/online_serving/test_pi05_expansion.py
+            -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"
+        source_file_dependencies: diffusion_qwen_image_function
         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 B200 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 · Single-GPU"
+        source_file_dependencies: diffusion_qwen_image_function
         timeout_in_minutes: 120
         commands:
-          - pytest -sv tests/e2e/ -k "not test_wan and not hunyuan" -m "full_model and diffusion and L4" --run-level "full_model" --ignore=tests/e2e/accuracy
-        mirror_hardwares: l4_4
+          - pytest -sv tests/e2e/offline_inference/test_qwen_image_autoround_w4a16_expansion.py -m "full_model and diffusion and L4 and B200 and cards_1" --run-level "full_model"
+
 
       - label: ":full_moon: Diffusion X2I(&A&T) · Doc Test"
+        source_file_dependencies: diffusion_text_to_image_doc
         timeout_in_minutes: 60
         commands:
-          - pytest -s -v tests/examples/*/test_text_to_image.py -m "full_model and example and H100" --run-level "full_model"
-        mirror_hardwares: h100_2
+          - pytest -s -v tests/examples/*/test_text_to_image.py -m "full_model and example and H100 and B200 and (cards_1 or cards_2)" --run-level "full_model"
 
       - label: ":full_moon: Diffusion X2I(&A&T) · Accuracy Test"
+        source_file_dependencies: diffusion_qwen_image_accuracy
         timeout_in_minutes: 180
         commands:
           - export VLLM_HTTP_TIMEOUT_KEEP_ALIVE=120
-          - pytest -s -v tests/e2e/accuracy/test_qwen_image.py --run-level full_model
-          - pytest -s -v tests/e2e/accuracy/test_diffusers_backend_similarity.py -k '2i_matches_diffusers' -m full_model --run-level full_model
-        mirror_hardwares: h100_1
+          - pytest -s -v tests/e2e/accuracy/test_qwen_image.py -m "H100 and B200 and cards_1" --run-level full_model
+          - pytest -s -v tests/e2e/accuracy/test_diffusers_backend_similarity.py -k '2i_matches_diffusers' -m "full_model and H100 and B200 and cards_1" --run-level full_model
 
       - label: ":full_moon: HunyuanImage3-DIT · Accuracy Test"
+        source_file_dependencies: diffusion_hunyuan_image3_accuracy
         timeout_in_minutes: 180
         commands:
           - export HUNYUAN_IMAGE3_MODEL="tencent/HunyuanImage-3.0-Instruct"
           - export HUNYUAN_IMAGE3_DEVICES="0,1,2,3"
-          - pytest -s -v tests/e2e/accuracy/test_hunyuan_image3_pixel_accuracy.py -m full_model --run-level full_model
-        mirror_hardwares: h100_4
+          - pytest -s -v tests/e2e/accuracy/test_hunyuan_image3_pixel_accuracy.py -m "full_model and H100 and B200 and cards_4" --run-level full_model
 
-      - label: ":full_moon: HiDream-O1-Image · Accuracy Test"
-        timeout_in_minutes: 180
-        commands:
-          - 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"
+        source_file_dependencies: diffusion_qwen_image_perf
+        key: nightly-diffusion-x2iat-performance-qwen-image-single
         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
+            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 B200 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
+
+      - label: ":full_moon: Diffusion X2I(&A&T) · Perf Test · Qwen-Image · 4-GPU"
+        source_file_dependencies: diffusion_qwen_image_perf
+        key: nightly-diffusion-x2iat-performance-qwen-image-4gpu
+        timeout_in_minutes: 180
+        commands:
+          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
+          - export CACHE_DIT_VERSION=1.5.0
+          # Do not pin DIFFUSION_ATTENTION_BACKEND: H100 auto-selects FLASH_ATTN;
+          # B200 rejects explicit FLASH_ATTN without FA4 and uses CUDNN/TRTLLM.
+          - |
+            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 B200 and cards_4"
             EXIT=$$?
             buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
             buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
             exit $$EXIT
-        mirror_hardwares: h100_4
 
   - group: ":card_index_dividers: Diffusion X2V Model Test"
     key: nightly-diffusion-x2v-group
     depends_on: upload-nightly-pipeline
     if: >-
       build.env("NIGHTLY") == "1" ||
-      build.pull_request.labels includes "nightly-test" ||
-      build.pull_request.labels includes "diffusion-x2v-test"
+      build.pull_request.labels includes "nightly-test"
     steps:
 
-      - label: ":full_moon: Diffusion X2V · Wan2.2 T2V Function Test"
+      - label: ":full_moon: Diffusion X2V · Wan2.2 T2V Function Test · Single-GPU"
+        source_file_dependencies: diffusion_wan22_function
         timeout_in_minutes: 90
         commands:
-          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "t2v" -m "full_model and cuda" --run-level "full_model"
-        mirror_hardwares: h100_2
+          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "t2v" -m "full_model and cuda and H100 and B200 and cards_1" --run-level "full_model"
 
-      - label: ":full_moon: Diffusion X2V · Wan2.2 I2V/TI2V Function Test"
+      - label: ":full_moon: Diffusion X2V · Wan2.2 T2V Function Test · 2-GPU"
+        source_file_dependencies: diffusion_wan22_function
         timeout_in_minutes: 90
         commands:
-          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "i2v" -m "full_model and cuda" --run-level "full_model"
-        mirror_hardwares: h100_2
+          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "t2v" -m "full_model and cuda and H100 and B200 and cards_2" --run-level "full_model"
+
+      - label: ":full_moon: Diffusion X2V · Wan2.2 I2V/TI2V Function Test · Single-GPU"
+        source_file_dependencies: diffusion_wan22_function
+        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 B200 and cards_1" --run-level "full_model"
+
+      - label: ":full_moon: Diffusion X2V · Wan2.2 I2V/TI2V Function Test · 2-GPU"
+        source_file_dependencies: diffusion_wan22_function
+        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 B200 and cards_2" --run-level "full_model"
+
+      - label: ":full_moon: Diffusion X2V · HunyuanVideo-1.5 Function Test"
+        source_file_dependencies: diffusion_hunyuan_video_function
+        timeout_in_minutes: 90
+        commands:
+          - pytest -s -v tests/e2e/online_serving/test_hunyuan_video_15_expansion.py -m "full_model and H100 and B200 and cuda and (cards_1 or cards_2)" --run-level "full_model"
+
+      - label: ":full_moon: Diffusion X2V · LingBot Function Test"
+        source_file_dependencies: diffusion_lingbot_function
+        timeout_in_minutes: 120
+        commands:
+          - >-
+            pytest -s -v
+            tests/e2e/online_serving/test_lingbot_video.py
+            tests/e2e/online_serving/test_lingbot_video_moe.py
+            tests/e2e/offline_inference/test_lingbot_world_v2.py
+            -m "full_model and diffusion and H100 and B200 and cards_1"
+            --run-level "full_model"
 
       - label: ":full_moon: Diffusion X2V · Doc Test"
+        source_file_dependencies: diffusion_image_to_video_doc
         timeout_in_minutes: 60
         commands:
-          - pytest -s -v tests/examples/offline_inference/test_image_to_video.py -m "full_model and example and H100" --run-level "full_model"
-        mirror_hardwares: h100_1
+          - pytest -s -v tests/examples/offline_inference/test_image_to_video.py -m "full_model and example and H100 and B200 and cards_1" --run-level "full_model"
 
-      - label: ":full_moon: Diffusion X2V · Other Function Test"
+      - label: ":full_moon: Diffusion X2V · Wan2.2 Autoround Function Test"
+        source_file_dependencies: diffusion_wan22_function
         timeout_in_minutes: 90
         commands:
-          - pytest -s -v tests/e2e/offline_inference/test_wan22_autoround_w4a16_expansion.py -m "full_model and cuda" --run-level "full_model"
-        mirror_hardwares: h100_2
+          - pytest -s -v tests/e2e/offline_inference/test_wan22_autoround_w4a16_expansion.py -m "full_model and H100 and B200 and cuda and (cards_1 or cards_2)" --run-level "full_model"
+
+      - label: ":full_moon: Diffusion X2V · Wan2.2 Device-Postprocess Equivalence Test"
+        source_file_dependencies: diffusion_wan22_function
+        timeout_in_minutes: 90
+        commands:
+          - |
+            VLLM_OMNI_DEVICE_POSTPROCESS_MODEL=Wan-AI/Wan2.2-TI2V-5B-Diffusers \
+              pytest -s -v tests/e2e/offline_inference/test_device_postprocess_equivalence.py -m "full_model and H100 and B200 and cuda and cards_1" --run-level "full_model"
 
       - label: ":full_moon: Diffusion X2V · Wan2.2 Accuracy Test"
+        source_file_dependencies: diffusion_wan22_accuracy
         timeout_in_minutes: 180
         commands:
-          - pytest -s -v tests/e2e/accuracy/wan22_i2v/test_wan22_i2v_video_similarity.py -m full_model --run-level full_model
-          - pytest -s -v tests/e2e/accuracy/test_diffusers_backend_similarity.py -k '2v_matches_diffusers' -m full_model --run-level full_model
-        mirror_hardwares: h100_2
+          - pytest -s -v tests/e2e/accuracy/wan22_i2v/test_wan22_i2v_video_similarity.py -m "full_model and H100 and B200 and (cards_1 or cards_2)" --run-level full_model
+          - pytest -s -v tests/e2e/accuracy/test_diffusers_backend_similarity.py -k '2v_matches_diffusers' -m "full_model and H100 and B200 and cards_1" --run-level full_model
 
       - label: ":full_moon: Diffusion X2V · MiniMax H3 I2VA/Ref2VA Accuracy Test"
+        source_file_dependencies: diffusion_minimax_h3_accuracy
         timeout_in_minutes: 180
         commands:
-          - pytest -s -v tests/e2e/accuracy/minimax_h3/test_minimax_h3_i2va_ref2va_similarity.py -m full_model --run-level full_model
-        mirror_hardwares: h100_4
+          - pytest -s -v tests/e2e/accuracy/minimax_h3/test_minimax_h3_i2va_ref2va_similarity.py -m "full_model and H100 and B200 and cards_4" --run-level full_model
 
-      - label: ":full_moon: Diffusion X2V · Other Accuracy Test"
+      - label: ":full_moon: Diffusion X2V · HunyuanVideo-1.5 Accuracy Test"
+        source_file_dependencies: diffusion_hunyuan_video_accuracy
         timeout_in_minutes: 180
         commands:
-          - pytest -s -v tests/e2e/accuracy/hunyuanvideo15_t2v/test_hunyuanvideo15_t2v_video_similarity.py -m full_model --run-level full_model
-          - pytest -s -v tests/e2e/accuracy/hunyuanvideo15_i2v/test_hunyuanvideo15_i2v_video_similarity.py -m full_model --run-level full_model
-        mirror_hardwares: h100_2
+          - pytest -s -v tests/e2e/accuracy/hunyuanvideo15_t2v/test_hunyuanvideo15_t2v_video_similarity.py -m "full_model and H100 and B200 and (cards_1 or cards_2)" --run-level full_model
+          - pytest -s -v tests/e2e/accuracy/hunyuanvideo15_i2v/test_hunyuanvideo15_i2v_video_similarity.py -m "full_model and H100 and B200 and (cards_1 or cards_2)" --run-level full_model
 
-      - label: ":full_moon: Diffusion X2V · Perf Test"
-        key: nightly-diffusion-x2v-performance
+      - label: ":full_moon: Diffusion X2V · Perf Test · Single-GPU"
+        source_file_dependencies: diffusion_wan22_perf
+        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 B200 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
+
+      - label: ":full_moon: Diffusion X2V · Perf Test · 2-GPU"
+        source_file_dependencies:
+          - diffusion_wan22_perf
+          - diffusion_cosmos3_perf
+        key: nightly-diffusion-x2v-performance-2gpu
+        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_wan22_i2v_vllm_omni.json -m "H100 and B200 and cards_2"
             EXIT1=$$?
-            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_cosmos3_vllm_omni.json
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_cosmos3_vllm_omni.json -m "H100 and B200 and cards_2"
+            EXIT2=$$?
+            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 $$EXIT2
+
+      - label: ":full_moon: Diffusion X2V · Perf Test · HunyuanVideo-1.5"
+        source_file_dependencies: diffusion_hunyuan_video_perf
+        key: nightly-diffusion-x2v-performance-hunyuanvideo15
+        timeout_in_minutes: 180
+        commands:
+          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_hunyuanvideo15_t2v_vllm_omni.json -m "H100 and B200 and (cards_1 or cards_2)"
+            EXIT1=$$?
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_hunyuanvideo15_i2v_vllm_omni.json -m "H100 and B200 and (cards_1 or 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 · MiniMax-H3"
+        source_file_dependencies: diffusion_minimax_h3_perf
         key: nightly-diffusion-x2v-performance-minimax-h3
         timeout_in_minutes: 180
         commands:
           - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
-          - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
           - |
             set +e
-            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_minimax_h3_vllm_omni.json
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_minimax_h3_vllm_omni.json -m "H100 and B200 and cards_4"
             EXIT=$$?
             buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
             buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
             exit $$EXIT
-        mirror_hardwares: h100_4
 
   - group: ":card_index_dividers: Diffusion Test"
     key: nightly-diffusion-test-group
     depends_on: upload-nightly-pipeline
     if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test"
     steps:
-      - label: ":full_moon: Diffusion · Distributed Test with L4"
+      - label: ":full_moon: Diffusion · Distributed Test with L4 · 2-GPU"
+        source_file_dependencies: diffusion_distributed_attention
         timeout_in_minutes: 60
         commands:
-          - pytest -sv tests/diffusion/distributed/ -m 'full_model and cuda and L4' --run-level "full_model"
-        mirror_hardwares: l4_4
+          - pytest -sv tests/diffusion/distributed/ -m 'full_model and cuda and L4 and B200 and cards_2' --run-level "full_model"
 
-      - label: ":full_moon: Diffusion · Distributed Test with H100"
+      - label: ":full_moon: Diffusion · Distributed Test with L4 · 3-GPU"
+        source_file_dependencies: diffusion_distributed_attention
         timeout_in_minutes: 60
         commands:
-          - pytest -sv tests/diffusion/distributed/ -m 'full_model and cuda and H100' --run-level "full_model"
-        mirror_hardwares: h100_4
+          - pytest -sv tests/diffusion/distributed/ -m 'full_model and cuda and L4 and B200 and cards_3' --run-level "full_model"
+
+      - label: ":full_moon: Diffusion · Distributed Test with L4 · 4-GPU"
+        source_file_dependencies: diffusion_distributed_attention
+        timeout_in_minutes: 60
+        commands:
+          - pytest -sv tests/diffusion/distributed/ -m 'full_model and cuda and L4 and B200 and cards_4' --run-level "full_model"
+
+      - label: ":full_moon: Diffusion · Distributed Test with H100 · 2-GPU"
+        source_file_dependencies: diffusion_distributed_attention
+        timeout_in_minutes: 60
+        commands:
+          - pytest -sv tests/diffusion/distributed/ -m 'full_model and cuda and H100 and B200 and cards_2' --run-level "full_model"
+
+      - label: ":full_moon: Diffusion · Distributed Test with H100 · 4-GPU"
+        source_file_dependencies: diffusion_distributed_attention
+        timeout_in_minutes: 60
+        commands:
+          - pytest -sv tests/diffusion/distributed/ -m 'full_model and cuda and H100 and B200 and cards_4' --run-level "full_model"
 
       - label: ":full_moon: Diffusion · Offloader Test"
+        source_file_dependencies: diffusion_offloader
         timeout_in_minutes: 60
         commands:
-          - pytest -sv tests/diffusion/offloader -m "full_model and cuda"
-        mirror_hardwares: l4_1
+          - pytest -sv tests/diffusion/offloader -m "full_model and L4 and B200 and cuda and cards_1"
 
       - label: ":full_moon: Diffusion · Quantization Test with H100 · Single-GPU"
+        source_file_dependencies: diffusion_quantization
         timeout_in_minutes: 60
         commands:
-          - pytest -sv tests/diffusion/quantization -m 'full_model and cuda and H100' --run-level "full_model"
-        mirror_hardwares: h100_1
+          - pytest -sv tests/diffusion/quantization -m 'full_model and cuda and H100 and B200 and cards_1' --run-level "full_model"
 
       - label: ":full_moon: Diffusion · MiniMax H3 FP8 Quality Test with H100 · 2-GPU"
+        source_file_dependencies: diffusion_minimax_h3_function
         timeout_in_minutes: 60
         commands:
-          - pytest -sv tests/diffusion/models/minimax_h3/test_minimax_h3_quantization_quality.py -m 'full_model and cuda and H100' --run-level "full_model"
-        mirror_hardwares: h100_2
+          - pytest -sv tests/diffusion/models/minimax_h3/test_minimax_h3_quantization_quality.py -m 'full_model and cuda and H100 and B200 and cards_2' --run-level "full_model"
 
       - label: ":full_moon: Diffusion · Quantization Test with L4"
+        source_file_dependencies: diffusion_quantization
         timeout_in_minutes: 60
         commands:
           - pip install "vllm-gguf-plugin==0.0.4"
-          - pytest -sv tests/diffusion/quantization -m 'full_model and cuda and L4' --run-level "full_model"
-        mirror_hardwares: l4_1
+          - pytest -sv tests/diffusion/quantization -m 'full_model and cuda and L4 and B200 and cards_1' --run-level "full_model"
 
   # NOTE: ":email: Nightly Collection & Email" is deprecated and will be removed soon.
   # Do not add new depends_on entries or extend this step.
@@ -386,19 +526,25 @@ 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"

.buildkite/cuda/test-ready.yml

@@ -1,6 +1,8 @@
+# CUDA CI L2 (`core_model` e2e) plus L1 CPU Simple Test (`core_model and cpu`).
+# Uploaded by Upload Ready Pipeline: PR label `ready`, or main+WEEKLY=1 with --e2e.
+
 env:
   VLLM_WORKER_MULTIPROC_METHOD: spawn
-  VLLM_USE_FLASHINFER_MOE_FP16: 0
   HF_HUB_DOWNLOAD_TIMEOUT: 300
   HF_HUB_ETAG_TIMEOUT: 60
 
@@ -36,6 +38,12 @@ steps:
     depends_on: upload-ready-pipeline
     steps:
 
+      - label: "Diffusion · FlashAttention Test"
+        timeout_in_minutes: 20
+        commands:
+          - timeout 15m pytest -s -v tests/diffusion/attention/ -m 'core_model and cuda and L4 and cards_1' --run-level "core_model"
+        mirror_hardwares: l4_1
+
       - label: "Diffusion · Offloader Test"
         commands:
           - timeout 40m pytest -s -v tests/diffusion/offloader -m "core_model and cuda" --run-level "core_model"
@@ -51,17 +59,23 @@ steps:
           - timeout 15m pytest -sv tests/diffusion/batching -m 'core_model and cuda' --run-level "core_model"
         mirror_hardwares: l4_1
 
-      - label: "Diffusion · Distributed & Attention Test"
-        source_file_dependencies:
-          - tests/diffusion/distributed/
-          - tests/diffusion/attention/test_attention_sp.py
-          - vllm_omni/diffusion/distributed/
-          - vllm_omni/diffusion/attention/
-          - vllm_omni/diffusion/data.py
-          - vllm_omni/diffusion/models/
+      - label: "Diffusion · Distributed Test · Single-GPU"
+        source_file_dependencies: diffusion_distributed_attention
         commands:
-          - timeout 20m pytest -sv tests/diffusion/distributed -m 'core_model and cuda' --run-level "core_model"
-          - timeout 20m pytest -sv tests/diffusion/attention/test_attention_sp.py -m 'core_model and cuda' --run-level "core_model"
+          - timeout 20m pytest -sv tests/diffusion/distributed -m 'core_model and cuda and L4 and cards_1' --run-level "core_model"
+        mirror_hardwares: l4_1
+
+      - label: "Diffusion · Distributed Test · 2-GPU"
+        source_file_dependencies: diffusion_distributed_attention
+        commands:
+          - timeout 20m pytest -sv tests/diffusion/distributed -m 'core_model and cuda and L4 and cards_2' --run-level "core_model"
+        mirror_hardwares: l4_2
+
+      - label: "Diffusion · Distributed & Attention Test · 4-GPU"
+        source_file_dependencies: diffusion_distributed_attention
+        commands:
+          - timeout 20m pytest -sv tests/diffusion/distributed -m 'core_model and cuda and L4 and cards_4' --run-level "core_model"
+          - timeout 20m pytest -sv tests/diffusion/attention/test_attention_sp.py -m 'core_model and cuda and L4 and cards_4' --run-level "core_model"
         mirror_hardwares: l4_4
 
       - label: "Diffusion · Model Test"
@@ -71,7 +85,7 @@ steps:
 
       - label: "Diffusion · Other Test"
         commands:
-          - timeout 15m pytest -sv tests/diffusion/*.py -m 'core_model and cuda' --run-level "core_model"
+          - timeout 15m pytest -sv tests/diffusion/*.py tests/diffusion/ar_diffusion -m 'core_model and cuda' --run-level "core_model"
         mirror_hardwares: l4_1
 
   - group: ":card_index_dividers: Tiny Model Tests [Single GPU]"
@@ -88,7 +102,7 @@ steps:
     commands:
       - |
         timeout 15m bash -c "
-                  pytest -sv tests/engine/ tests/model_executor/ -m 'core_model and cuda' --run-level 'core_model'
+                  pytest -sv tests/engine/ tests/model_executor/ tests/worker/test_batched_omni_output.py tests/worker_v2/ -m 'core_model and cuda' --run-level 'core_model'
         "
     mirror_hardwares: l4_1
 
@@ -115,29 +129,14 @@ steps:
     depends_on: upload-ready-pipeline
     steps:
       - label: "Custom Pipeline Test"
-        source_file_dependencies:
-          - tests/e2e/features/custom_pipeline/
-          - tests/e2e/features/helpers/custom_pipeline.py
-          - vllm_omni/diffusion/
-          - vllm_omni/engine/
-          - vllm_omni/entrypoints/
-          - vllm_omni/inputs/
-          - vllm_omni/outputs/
+        source_file_dependencies: custom_pipeline
         commands:
           - timeout 20m pytest -s -v tests/e2e/features/custom_pipeline/ -m "core_model"
         mirror_hardwares: l4_1
 
 
       - label: "RLHF · VeRL-Omni E2E Test"
-        source_file_dependencies:
-          - tests/e2e/features/rlhf_test/test_verl_omni_e2e.py
-          - tests/e2e/features/helpers/verl_omni_server.py
-          - tests/e2e/features/helpers/custom_pipeline.py
-          - vllm_omni/diffusion/
-          - vllm_omni/engine/
-          - vllm_omni/entrypoints/
-          - vllm_omni/inputs/
-          - vllm_omni/outputs/
+        source_file_dependencies: rlhf_verl_omni
         commands:
           - |
             timeout 20m bash -c "
@@ -146,17 +145,7 @@ steps:
         mirror_hardwares: l4_1
 
       - label: "Omni · Qwen3-Omni Test"
-        source_file_dependencies:
-          - tests/e2e/online_serving/test_qwen3_omni.py
-          - tests/helpers/runtime.py
-          - vllm_omni/entrypoints/openai/serving_chat.py
-          - vllm_omni/model_executor/models/qwen3_omni/
-          - vllm_omni/model_executor/models/common/qwen3_code_predictor.py
-          - vllm_omni/model_executor/models/common/snake_activation.py
-          - vllm_omni/model_executor/models/qwen2_5_omni/qwen2_5_omni_thinker.py
-          - vllm_omni/model_executor/models/qwen3_tts/cuda_graph_decoder_wrapper.py
-          - vllm_omni/model_executor/stage_input_processors/qwen3_omni.py
-          - vllm_omni/deploy/qwen3_omni_moe.yaml
+        source_file_dependencies: omni_qwen3_omni_function
         commands:
           - |
             timeout 20m bash -c "
@@ -165,14 +154,7 @@ steps:
         mirror_hardwares: h100_2
 
       - label: "Omni · MiniCPM-o 4.5 Offline Test"
-        source_file_dependencies:
-          - tests/e2e/offline_inference/test_minicpmo_4_5.py
-          - vllm_omni/model_executor/models/minicpmo_4_5/
-          - vllm_omni/model_executor/models/step_audio2/
-          - vllm_omni/model_executor/models/cosyvoice3/code2wav_core/
-          - vllm_omni/model_executor/models/cosyvoice3/utils.py
-          - vllm_omni/model_executor/stage_input_processors/minicpmo_4_5_omni.py
-          - vllm_omni/deploy/minicpmo_4_5.yaml
+        source_file_dependencies: omni_minicpmo_4_5_function
         commands:
           - |
             timeout 20m bash -c "
@@ -181,14 +163,7 @@ steps:
         mirror_hardwares: h100_1
 
       - label: "Omni · MiniCPM-o 4.5 Online Test"
-        source_file_dependencies:
-          - tests/e2e/online_serving/test_minicpmo_4_5.py
-          - vllm_omni/model_executor/models/minicpmo_4_5/
-          - vllm_omni/model_executor/models/step_audio2/
-          - vllm_omni/model_executor/models/cosyvoice3/code2wav_core/
-          - vllm_omni/model_executor/models/cosyvoice3/utils.py
-          - vllm_omni/model_executor/stage_input_processors/minicpmo_4_5_omni.py
-          - vllm_omni/deploy/minicpmo_4_5.yaml
+        source_file_dependencies: omni_minicpmo_4_5_function
         commands:
           - |
             timeout 20m bash -c "
@@ -197,60 +172,24 @@ steps:
         mirror_hardwares: h100_1
 
       - label: "Omni · MiniCPM-o 4.5 Duplex Test"
-        source_file_dependencies:
-          - tests/assets/minicpmo_4_5/response_required_16k.wav
-          - tests/assets/minicpmo_4_5/soft_interrupt_16k.wav
-          - tests/e2e/online_serving/test_minicpmo_4_5_duplex.py
-          - tests/e2e/online_serving/test_minicpmo_4_5_duplex_expansion.py
-          - tests/e2e/online_serving/test_minicpmo_realtime_duplex_drivers.py
-          - tests/e2e/online_serving/helpers/minicpmo_realtime_duplex_scenarios.py
-          - tests/e2e/online_serving/run_minicpmo_realtime_duplex_multi_session.py
-          - tests/e2e/online_serving/run_minicpmo_realtime_duplex_soft_interrupt.py
-          - vllm_omni/deploy/minicpmo_4_5.yaml
-          - tests/e2e/online_serving/helpers/minicpmo_4_5_duplex.py
-          - vllm_omni/experimental/fullduplex/
-          - vllm_omni/model_executor/models/minicpmo_4_5/
-          - vllm_omni/model_executor/models/step_audio2/
-          - vllm_omni/model_executor/models/cosyvoice3/code2wav_core/
-          - vllm_omni/model_executor/models/cosyvoice3/utils.py
-          - vllm_omni/model_executor/stage_input_processors/minicpmo_4_5_omni.py
-          - vllm_omni/core/sched/
-          - vllm_omni/outputs/multimodal_accumulation.py
-          - vllm_omni/engine/stage_init_utils.py
-          - vllm_omni/engine/async_omni_engine.py
-          - vllm_omni/engine/orchestrator.py
-          - vllm_omni/entrypoints/cli/serve.py
-          - vllm_omni/entrypoints/openai/api_server.py
-          - vllm_omni/entrypoints/openai/duplex_capability.py
-          - vllm_omni/config/stage_config.py
+        source_file_dependencies: omni_minicpmo_4_5_duplex_function
         commands:
           - |
-            timeout 20m bash -c "
-              pytest -s -v tests/e2e/online_serving/test_minicpmo_4_5_duplex.py -m 'core_model and cuda' --run-level 'core_model'
+            timeout 35m bash -c "
+              pytest -s -v tests/e2e/online_serving/test_minicpmo_4_5_duplex.py tests/e2e/online_serving/test_duplex_client_live.py -m 'core_model and cuda' --run-level 'core_model'
             "
         mirror_hardwares: h100_1
 
       - label: "Omni · MiniCPM-o 4.5 Perf Test"
         timeout_in_minutes: 60
-        source_file_dependencies:
-          - tests/dfx/perf/tests/test_minicpmo_4_5_ready.json
-          - vllm_omni/deploy/minicpmo_4_5.yaml
-          - vllm_omni/model_executor/models/minicpmo_4_5/
-          - vllm_omni/model_executor/stage_input_processors/minicpmo_4_5_omni.py
+        source_file_dependencies: omni_minicpmo_4_5_perf
         commands:
           - export BENCHMARK_DIR=tests/dfx/perf/results
           - pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_minicpmo_4_5_ready.json -m 'core_model and cuda'
         mirror_hardwares: h100_1
 
       - label: "TTS · Qwen3-TTS CustomVoice Test"
-        source_file_dependencies:
-          - tests/e2e/online_serving/test_qwen3_tts_customvoice.py
-          - vllm_omni/model_executor/models/qwen3_tts/
-          - vllm_omni/model_executor/models/common/qwen3_code_predictor.py
-          - vllm_omni/model_executor/models/common/snake_activation.py
-          - vllm_omni/model_executor/models/common/alias_free_activation.py
-          - vllm_omni/model_executor/stage_input_processors/qwen3_tts.py
-          - vllm_omni/deploy/qwen3_tts.yaml
+        source_file_dependencies: tts_qwen3_tts_function
         commands:
           - |
             timeout 20m bash -c "
@@ -259,30 +198,8 @@ 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
-          - vllm_omni/diffusion/models/wan2_2/
-          - vllm_omni/diffusion/models/dmd2/
+        source_file_dependencies: diffusion_wan22_function
         commands:
           - |
             timeout 40m bash -c "
@@ -292,12 +209,7 @@ steps:
         mirror_hardwares: h100_1
 
       - label: "Diffusion · Cosmos3 Test"
-        source_file_dependencies:
-          - tests/e2e/online_serving/test_cosmos3.py
-          - vllm_omni/diffusion/models/cosmos3/
-          - vllm_omni/diffusion/distributed/autoencoders/autoencoder_kl_wan.py
-          - vllm_omni/experimental/world_models/
-          - vllm_omni/model_extras/cosmos3.py
+        source_file_dependencies: diffusion_cosmos3_function
         commands:
           - |
             timeout 40m bash -c "
@@ -306,12 +218,19 @@ steps:
             "
         mirror_hardwares: h100_1
 
+      - label: "Diffusion · MiniMax-H3 DLO DP2 T2VA Test"
+        timeout_in_minutes: 90
+        source_file_dependencies: diffusion_minimax_h3_function
+        commands:
+          - |
+            timeout 80m bash -c "
+              export VLLM_IMAGE_FETCH_TIMEOUT=60
+              pytest -s -v tests/e2e/online_serving/test_minimax_h3_dlo_dp2_t2va.py -m 'core_model and cuda and diffusion' --run-level 'core_model'
+            "
+        mirror_hardwares: h100_2
+
       - label: "Diffusion · Qwen Image Test"
-        source_file_dependencies:
-          - tests/e2e/online_serving/test_qwen_image.py
-          - vllm_omni/diffusion/models/qwen_image/
-          - vllm_omni/diffusion/models/dmd2/
-          - vllm_omni/diffusion/distributed/autoencoders/autoencoder_kl_qwenimage.py
+        source_file_dependencies: diffusion_qwen_image_function
         commands:
           - |
             timeout 40m bash -c "
@@ -319,3 +238,13 @@ 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: diffusion_hunyuan_video_function
+        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

.buildkite/cuda/test-weekly.yml

@@ -1,6 +1,8 @@
+# CUDA CI L5 (`slow` / reliability / extra coverage).
+# Uploaded by Upload Weekly Pipeline: WEEKLY=1 / NON_CRITICAL=1, or PR label `weekly-test`.
+
 env:
   VLLM_WORKER_MULTIPROC_METHOD: spawn
-  VLLM_USE_FLASHINFER_MOE_FP16: 0
   HF_HUB_DOWNLOAD_TIMEOUT: 300
   HF_HUB_ETAG_TIMEOUT: 60
 
@@ -27,30 +29,42 @@ steps:
     steps:
 
       - label: "Reliability Test · qwen3-omni"
+        source_file_dependencies: omni_qwen3_omni_reliability
         timeout_in_minutes: 180
         commands:
           - pytest -s -v tests/dfx/reliability/test_reliability_qwen3_omni.py -m "slow"
         mirror_hardwares: h100_2
 
       - label: "Reliability Test · wan22"
+        source_file_dependencies: diffusion_wan22_reliability
         timeout_in_minutes: 180
         commands:
           - pytest -s -v tests/dfx/reliability/test_reliability_wan22.py -m "slow"
         mirror_hardwares: h100_2
 
       - label: "Reliability Test · hunyuan-image"
+        source_file_dependencies: diffusion_hunyuan_image3_reliability
         timeout_in_minutes: 180
         commands:
           - 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"
+        source_file_dependencies: reliability_invalid_param_h100
+        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"
+        source_file_dependencies: reliability_invalid_param_h100
+        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"
+        source_file_dependencies: reliability_invalid_param_l4
         timeout_in_minutes: 120
         commands:
           - pytest -s -v tests/dfx/reliability/invalid_param_test/ -m "slow and L4"
@@ -62,6 +76,7 @@ steps:
     steps:
 
       - label: ":full_moon: Omni · Perf Test · vLLM Text"
+        source_file_dependencies: omni_qwen3_omni_perf
         key: weekly-omni-performance-vllm-text
         timeout_in_minutes: 180
         commands:
@@ -88,6 +103,7 @@ steps:
         mirror_hardwares: h100_2
 
       - label: ":full_moon: Omni · Perf Test · Multi-Replica"
+        source_file_dependencies: omni_qwen3_omni_perf
         key: weekly-omni-performance-multi-replicas
         timeout_in_minutes: 180
         commands:
@@ -105,27 +121,44 @@ steps:
     if: build.env("NON_CRITICAL") == "1"
     steps:
 
-      - label: "Omni · L4"
+      # Online Qwen2.5 expansion is 2-card; offline expansion + AutoRound are 4-card.
+      - label: "Omni · L4 · 2-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/online_serving/test_qwen2_5_omni_expansion.py -m "slow and L4 and omni and cards_2" --run-level "core_model"
+        mirror_hardwares: l4_2
+
+      - label: "Omni · L4 · 4-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
+          - pytest -s -v tests/e2e/offline_inference/test_qwen2_5_omni_expansion.py -m "slow and L4 and omni and cards_4" --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 cards_4"
             --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
@@ -158,40 +191,350 @@ steps:
       # from downgrading transformers; see
       # examples/offline_inference/text_to_speech/README.md.
       - label: "TTS · Gepard-1.0 · L4"
-        timeout_in_minutes: 60
+        timeout_in_minutes: 90
         commands:
           - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
           - printf '%s\n' "transformers>=5.5.3" "huggingface-hub>=1.0" > /tmp/gepard-overrides.txt
           - uv pip install --python "$(python3 -c 'import sys; print(sys.executable)')" "nemo_toolkit[tts]==2.7.3" --overrides /tmp/gepard-overrides.txt
           - >-
             pytest -s -v tests/e2e/offline_inference/test_gepard_tts.py
+            tests/e2e/online_serving/test_gepard_tts.py
             -m "tts and cuda"
             --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_4
+        mirror_hardwares: l4_1
 
-      - label: "Diffusion · H100 · Single-GPU"
-        timeout_in_minutes: 150
+      - label: "Diffusion · L4 · 2-GPU"
+        timeout_in_minutes: 60
         commands:
           - >-
             pytest -sv
-            tests/e2e/ -m "slow and diffusion and H100 and not distributed_cuda"
+            tests/e2e/ -m "slow and diffusion and L4 and cards_2"
+            --run-level "full_model"
+        mirror_hardwares: l4_2
+
+      - label: "Diffusion · L4 · 4-GPU"
+        timeout_in_minutes: 60
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/ -m "slow and diffusion and L4 and cards_4"
+            --run-level "full_model"
+        mirror_hardwares: l4_4
+
+      # Split the old shared H100 Single-GPU sweep: one pytest process collected
+      # every slow+cards_1 diffusion file and exceeded 150 minutes.
+      - label: "Diffusion · Qwen-Image · H100 · Single-GPU"
+        timeout_in_minutes: 120
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/online_serving/test_qwen_image_edit.py
+            tests/e2e/online_serving/test_qwen_image_edit_expansion.py
+            tests/e2e/online_serving/test_qwen_image_layered.py
+            tests/e2e/online_serving/test_qwen_image_layered_expansion.py
+            tests/e2e/accuracy/test_qwen_image_edit.py
+            tests/e2e/accuracy/test_qwen_image_layered.py
+            -m "slow and diffusion and H100 and cards_1"
             --run-level "full_model"
         mirror_hardwares: h100_1
 
-      - label: "Diffusion · H100 · Multi-GPU"
+      - label: "Diffusion · Wan2.1 VACE · H100 · Single-GPU"
         timeout_in_minutes: 90
         commands:
           - >-
             pytest -sv
-            tests/e2e/ -m "slow and diffusion and H100 and distributed_cuda"
+            tests/e2e/online_serving/test_wan_2_1_vace_expansion.py
+            -m "slow and diffusion and H100 and cards_1"
             --run-level "full_model"
+        mirror_hardwares: h100_1
+
+      - label: "Diffusion · LongCat · H100 · Single-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/online_serving/test_longcat_image_expansion.py
+            tests/e2e/online_serving/test_longcat_image_edit_expansion.py
+            -m "slow and diffusion and H100 and cards_1"
+            --run-level "full_model"
+        mirror_hardwares: h100_1
+
+      - label: "Diffusion · Bagel · H100 · Single-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/online_serving/test_bagel.py
+            tests/e2e/online_serving/test_bagel_expansion.py
+            tests/e2e/offline_inference/test_bagel.py
+            tests/e2e/offline_inference/test_bagel_expansion.py
+            -m "slow and diffusion and H100 and cards_1"
+            --run-level "full_model"
+        mirror_hardwares: h100_1
+
+      - label: "Diffusion · Boogu · H100 · Single-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/online_serving/test_boogu_image.py
+            tests/e2e/online_serving/test_boogu_image_edit.py
+            tests/e2e/online_serving/test_boogu_image_expansion.py
+            tests/e2e/online_serving/test_boogu_image_edit_expansion.py
+            -m "slow and diffusion and H100 and cards_1"
+            --run-level "full_model"
+        mirror_hardwares: h100_1
+
+      - label: "Diffusion · Flux · H100 · Single-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/online_serving/test_flux_2_dev_expansion.py
+            tests/e2e/offline_inference/test_flux1_schnell_expansion.py
+            -m "slow and diffusion and H100 and cards_1"
+            --run-level "full_model"
+        mirror_hardwares: h100_1
+
+      - label: "Diffusion · LTX · H100 · Single-GPU"
+        timeout_in_minutes: 120
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/online_serving/test_ltx.py
+            tests/e2e/online_serving/test_ltx25.py
+            tests/e2e/accuracy/ltx/test_ltx_official_similarity.py
+            tests/e2e/accuracy/ltx/test_ltx25_official_similarity.py
+            -m "slow and diffusion and H100 and cards_1"
+            --run-level "full_model"
+        mirror_hardwares: h100_1
+
+      - label: "Diffusion · Krea2 · H100 · Single-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/online_serving/test_krea2_expansion.py
+            tests/e2e/offline_inference/test_krea2_expansion.py
+            -m "slow and diffusion and H100 and cards_1"
+            --run-level "full_model"
+        mirror_hardwares: h100_1
+
+      - label: "Diffusion · HiDream-I1 · H100 · Single-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/online_serving/test_hidream_i1_full_expansion.py
+            -m "slow and diffusion and H100 and cards_1"
+            --run-level "full_model"
+        mirror_hardwares: h100_1
+
+      - label: "Diffusion · SANA-Video · H100 · Single-GPU"
+        source_file_dependencies: diffusion_sana_video_function
+        timeout_in_minutes: 180
+        commands:
+          - export SANA_VIDEO_GOLDEN_BASE_URL=https://vllm-public-assets.s3.us-west-2.amazonaws.com/omni-assets/sana-video/v2
+          - >-
+            pytest -sv
+            tests/e2e/online_serving/test_sana_video_expansion.py
+            tests/e2e/accuracy/sana_video/
+            -m "slow and diffusion and H100 and cards_1"
+            --run-level "full_model"
+        mirror_hardwares: h100_1
+
+      - label: "Diffusion · SenseNova-U1 · H100 · Single-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/offline_inference/test_sensenova_u1_expansion.py
+            tests/e2e/offline_inference/test_sensenova_u1_text2img_expansion.py
+            tests/e2e/offline_inference/test_sensenova_u1_img2img_expansion.py
+            -m "slow and diffusion and H100 and cards_1"
+            --run-level "full_model"
+        mirror_hardwares: h100_1
+
+      - label: "Diffusion · MammothModa2 · H100 · Single-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/offline_inference/test_mammoth_moda2_expansion.py
+            -m "slow and diffusion and H100 and cards_1"
+            --run-level "full_model"
+        mirror_hardwares: h100_1
+
+      - label: "Diffusion · Helios · H100 · Single-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/accuracy/test_video_streaming_output_similarity.py
+            -m "slow and diffusion and H100 and cards_1"
+            --run-level "full_model"
+        mirror_hardwares: h100_1
+
+      # Split the old shared H100 2-GPU sweep: one pytest process collected every
+      # slow+cards_2 diffusion file and exceeded 90 minutes.
+      - label: "Diffusion · Qwen-Image · H100 · 2-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/online_serving/test_qwen_image_edit_expansion.py
+            tests/e2e/online_serving/test_qwen_image_layered_expansion.py
+            -m "slow and diffusion and H100 and cards_2"
+            --run-level "full_model"
+        mirror_hardwares: h100_2
+
+      - label: "Diffusion · Wan2.1 VACE · H100 · 2-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/online_serving/test_wan_2_1_vace_expansion.py
+            -m "slow and diffusion and H100 and cards_2"
+            --run-level "full_model"
+        mirror_hardwares: h100_2
+
+      - label: "Diffusion · LongCat · H100 · 2-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/online_serving/test_longcat_image_expansion.py
+            tests/e2e/online_serving/test_longcat_image_edit_expansion.py
+            -m "slow and diffusion and H100 and cards_2"
+            --run-level "full_model"
+        mirror_hardwares: h100_2
+
+      - label: "Diffusion · Bagel · H100 · 2-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/online_serving/test_bagel_expansion.py
+            -m "slow and diffusion and H100 and cards_2"
+            --run-level "full_model"
+        mirror_hardwares: h100_2
+
+      - label: "Diffusion · Boogu · H100 · 2-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/online_serving/test_boogu_image_expansion.py
+            tests/e2e/online_serving/test_boogu_image_edit_expansion.py
+            -m "slow and diffusion and H100 and cards_2"
+            --run-level "full_model"
+        mirror_hardwares: h100_2
+
+      - label: "Diffusion · Flux · H100 · 2-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/online_serving/test_flux_2_dev_expansion.py
+            tests/e2e/offline_inference/test_flux1_kontext_expansion.py
+            -m "slow and diffusion and H100 and cards_2"
+            --run-level "full_model"
+        mirror_hardwares: h100_2
+
+      - label: "Diffusion · GLM-Image · H100 · 2-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/online_serving/test_glm_image_expansion.py
+            tests/e2e/offline_inference/test_glm_image_autoround_w4a16_expansion.py
+            -m "slow and diffusion and H100 and cards_2"
+            --run-level "full_model"
+        mirror_hardwares: h100_2
+
+      - label: "Diffusion · LTX · H100 · 2-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/online_serving/test_ltx.py
+            -m "slow and diffusion and H100 and cards_2"
+            --run-level "full_model"
+        mirror_hardwares: h100_2
+
+      - label: "Diffusion · Krea2 · H100 · 2-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/offline_inference/test_krea2_expansion.py
+            -m "slow and diffusion and H100 and cards_2"
+            --run-level "full_model"
+        mirror_hardwares: h100_2
+
+      - label: "Diffusion · DreamZero · H100 · 2-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/online_serving/test_dreamzero_expansion.py
+            tests/e2e/accuracy/test_dreamzero.py
+            -m "slow and diffusion and H100 and cards_2"
+            --run-level "full_model"
+        mirror_hardwares: h100_2
+
+      - label: "Diffusion · H100 · 3-GPU"
+        timeout_in_minutes: 120
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/ -m "slow and diffusion and H100 and cards_3"
+            --run-level "full_model"
+        mirror_hardwares: h100_3
+
+      - 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"
+            --ignore=tests/e2e/online_serving/test_magi2.py
+        mirror_hardwares: h100_4
+
+      # Dedicated: HiDream-O1 accuracy needs a long TP2 run; the shared H100
+      # 2-GPU sweep is only 90 minutes.
+      - label: "Diffusion · HiDream-O1-Image Accuracy · H100 · 2-GPU"
+        timeout_in_minutes: 180
+        commands:
+          - pytest -s -v tests/e2e/accuracy/test_hidream_o1_image.py -m "slow and diffusion and cuda and H100 and cards_2" --run-level "full_model"
+        mirror_hardwares: h100_2
+
+      # Dedicated: Boogu CFG-parallel size 3 needs 3 GPUs; weekly has no H100 3-GPU sweep.
+      - label: "Diffusion · Boogu Edit CFG · H100 · 3-GPU"
+        timeout_in_minutes: 120
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/online_serving/test_boogu_image_edit_expansion.py
+            -k "test_double_guidance_cfg_parallel and cfg_parallel_3_full"
+            -m "slow and diffusion and cuda and H100 and cards_3"
+            --run-level "full_model"
+        mirror_hardwares: h100_3
+
+      # Dedicated: MAGI-2 Preview serving smoke needs ~30 min server init; the
+      # shared H100 4-GPU sweep is only 90 minutes.
+      - label: "Diffusion · MAGI-2 Preview Serving Smoke · H100 · 4-GPU"
+        timeout_in_minutes: 180
+        commands:
+          - pytest -s -v tests/e2e/online_serving/test_magi2.py -m "slow and diffusion and cuda and H100 and cards_4" --run-level "full_model"
         mirror_hardwares: h100_4

.buildkite/intel/pipeline-intel.yml

@@ -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: "v0.29.0"
         priority: 100
         timeout_in_minutes: 60
         soft_fail: false

.buildkite/intel/scripts/run-xpu-test.sh

@@ -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}"

.buildkite/npu/bootstrap-upload-steps.yml

@@ -3,10 +3,10 @@
 steps:
   - label: ":buildkit: Build and Push NPU Test Image - A2"
     key: image-build-a2
-    agents:
+    agents: &npu-image-build-agents
       queue: "ascend-a2b3"
       resource_class: "npu-1"
-    plugins:
+    plugins: &npu-image-build-plugins
       - kubernetes:
           metadata:
             annotations:
@@ -46,136 +46,67 @@ steps:
       IMAGE_NAME: "vllm-omni-ci-npu"
       IMAGE_REGISTRY: "swr.cn-southwest-2.myhuaweicloud.com/modelfoundry"
       BUILDKITD_ADDR: "tcp://buildkitd-service.buildkitd:1234"
-    command: |
-      set -ex
-
-      echo "--- Building and pushing NPU Test Image"
-      echo "Image: $${IMAGE_REGISTRY}/$${IMAGE_NAME}:$${VLLM_IMAGE_TAG}"
-      echo "buildkitd address: $${BUILDKITD_ADDR}"
-
-      if ! command -v buildctl &> /dev/null; then
-        echo "Installing buildctl..."
-        mkdir -p /tmp/buildkit
-        BUILDKIT_VERSION="v0.29.0"
-        wget -q "https://github.com/moby/buildkit/releases/download/$${BUILDKIT_VERSION}/buildkit-$${BUILDKIT_VERSION}.linux-arm64.tar.gz" -O /tmp/buildkit.tar.gz
-        tar -xzf /tmp/buildkit.tar.gz -C /tmp/buildkit
-        cp /tmp/buildkit/bin/buildctl /usr/local/bin/
-      fi
-
-      export DOCKER_CONFIG=/home/user/.docker
-      buildctl \
-        --addr="$${BUILDKITD_ADDR}" \
-        --tlscacert=/home/user/.docker/ca.pem \
-        --tlscert=/home/user/.docker/cert.pem \
-        --tlskey=/home/user/.docker/key.pem \
-        build \
-        --frontend dockerfile.v0 \
-        --local context=. \
-        --local dockerfile=./docker \
-        --opt filename=Dockerfile.npu.ci \
-        --secret id=dockerconfig,src=/home/user/.docker/config.json \
-        --output type=image,name=$${IMAGE_REGISTRY}/$${IMAGE_NAME}:$${VLLM_IMAGE_TAG},push=true \
-        --progress=plain
-
-      echo "--- Image pushed successfully"
-      echo "$${IMAGE_REGISTRY}/$${IMAGE_NAME}:$${VLLM_IMAGE_TAG}"
+    command: bash .buildkite/npu/scripts/build-npu-image.sh Dockerfile.npu.ci "$${VLLM_IMAGE_TAG}"
 
   - label: ":buildkit: Build and Push NPU Test Image - A3"
     key: image-build-a3
-    agents:
-      queue: "ascend-a2b3"
-      resource_class: "npu-1"
-    plugins:
-      - kubernetes:
-          metadata:
-            annotations:
-              vault.hashicorp.com/agent-init-first: "true"
-              vault.hashicorp.com/agent-inject: "true"
-              vault.hashicorp.com/agent-inject-perms-ca.pem: "0400"
-              vault.hashicorp.com/agent-inject-perms-cert.pem: "0400"
-              vault.hashicorp.com/agent-inject-perms-config.json: "0400"
-              vault.hashicorp.com/agent-inject-perms-key.pem: "0400"
-              vault.hashicorp.com/agent-inject-secret-ca.pem: internal/data/ascend/buildkitd
-              vault.hashicorp.com/agent-inject-secret-cert.pem: internal/data/ascend/buildkitd
-              vault.hashicorp.com/agent-inject-secret-config.json: internal/data/ascend/buildkitd
-              vault.hashicorp.com/agent-inject-secret-key.pem: internal/data/ascend/buildkitd
-              vault.hashicorp.com/agent-inject-template-ca.pem: "{{- with secret \"internal/data/ascend/buildkitd\" -}}\n{{ .Data.data.RootCA }}\n{{- end }}"
-              vault.hashicorp.com/agent-inject-template-cert.pem: "{{- with secret \"internal/data/ascend/buildkitd\" -}}\n{{ .Data.data.ClientCaCert }}\n{{- end }}"
-              vault.hashicorp.com/agent-inject-template-config.json: "{{- with secret \"internal/data/ascend/buildkitd\" -}}\n{{ .Data.data.dockerConfig }}\n{{- end }}"
-              vault.hashicorp.com/agent-inject-template-key.pem: "{{- with secret \"internal/data/ascend/buildkitd\" -}}\n{{ .Data.data.ClientCaKey }}\n{{- end }}"
-              vault.hashicorp.com/agent-pre-populate-only: "true"
-              vault.hashicorp.com/agent-run-as-group: "1000"
-              vault.hashicorp.com/agent-run-as-user: "1000"
-              vault.hashicorp.com/agent-service-account-token-volume-name: token-vol
-              vault.hashicorp.com/role: ascend-gha-runners
-              vault.hashicorp.com/secret-volume-path: /home/user/.docker/
-              vault.hashicorp.com/tls-skip-verify: "true"
-          podSpecPatch:
-            volumes:
-            - name: token-vol
-              projected:
-                defaultMode: 420
-                sources:
-                - serviceAccountToken:
-                    audience: api
-                    expirationSeconds: 600
-                    path: token
+    agents: *npu-image-build-agents
+    plugins: *npu-image-build-plugins
     env:
       VLLM_IMAGE_TAG: "${BUILDKITE_COMMIT}-a3"
       IMAGE_NAME: "vllm-omni-ci-npu"
       IMAGE_REGISTRY: "swr.cn-southwest-2.myhuaweicloud.com/modelfoundry"
       BUILDKITD_ADDR: "tcp://buildkitd-service.buildkitd:1234"
-    command: |
-      set -ex
+    command: bash .buildkite/npu/scripts/build-npu-image.sh Dockerfile.npu.ci "$${VLLM_IMAGE_TAG}" -a3
 
-      echo "--- Building and pushing NPU Test Image"
-      echo "Image: $${IMAGE_REGISTRY}/$${IMAGE_NAME}:$${VLLM_IMAGE_TAG}"
-      echo "buildkitd address: $${BUILDKITD_ADDR}"
+  - label: ":buildkit: Build and Push NPU Test Image - A5"
+    key: image-build-a5
+    agents: *npu-image-build-agents
+    plugins: *npu-image-build-plugins
+    env:
+      VLLM_IMAGE_TAG: "${BUILDKITE_COMMIT}-a5"
+      IMAGE_NAME: "vllm-omni-ci-npu"
+      IMAGE_REGISTRY: "swr.cn-southwest-2.myhuaweicloud.com/modelfoundry"
+      BUILDKITD_ADDR: "tcp://buildkitd-service.buildkitd:1234"
+    command: bash .buildkite/npu/scripts/build-npu-image.sh Dockerfile.npu.ci "$${VLLM_IMAGE_TAG}" -a5
 
-      if ! command -v buildctl &> /dev/null; then
-        echo "Installing buildctl..."
-        mkdir -p /tmp/buildkit
-        BUILDKIT_VERSION="v0.29.0"
-        wget -q "https://github.com/moby/buildkit/releases/download/$${BUILDKIT_VERSION}/buildkit-$${BUILDKIT_VERSION}.linux-arm64.tar.gz" -O /tmp/buildkit.tar.gz
-        tar -xzf /tmp/buildkit.tar.gz -C /tmp/buildkit
-        cp /tmp/buildkit/bin/buildctl /usr/local/bin/
-      fi
-
-      export DOCKER_CONFIG=/home/user/.docker
-      buildctl \
-        --addr="$${BUILDKITD_ADDR}" \
-        --tlscacert=/home/user/.docker/ca.pem \
-        --tlscert=/home/user/.docker/cert.pem \
-        --tlskey=/home/user/.docker/key.pem \
-        build \
-        --frontend dockerfile.v0 \
-        --local context=. \
-        --local dockerfile=./docker \
-        --opt filename=Dockerfile.npu.ci.a3 \
-        --secret id=dockerconfig,src=/home/user/.docker/config.json \
-        --output type=image,name=$${IMAGE_REGISTRY}/$${IMAGE_NAME}:$${VLLM_IMAGE_TAG},push=true \
-        --progress=plain
-
-      echo "--- Image pushed successfully"
-      echo "$${IMAGE_REGISTRY}/$${IMAGE_NAME}:$${VLLM_IMAGE_TAG}"
+  - label: ":buildkit: Build and Push NPU Test Image - 310P"
+    key: image-build-310p
+    agents: *npu-image-build-agents
+    plugins: *npu-image-build-plugins
+    env:
+      VLLM_IMAGE_TAG: "${BUILDKITE_COMMIT}-310p"
+      IMAGE_NAME: "vllm-omni-ci-npu"
+      IMAGE_REGISTRY: "swr.cn-southwest-2.myhuaweicloud.com/modelfoundry"
+      BUILDKITD_ADDR: "tcp://buildkitd-service.buildkitd:1234"
+    command: bash .buildkite/npu/scripts/build-npu-image.sh Dockerfile.npu.ci "$${VLLM_IMAGE_TAG}" -310p
 
   - label: "Upload Ready Pipeline"
     depends_on:
       - image-build-a2
       - image-build-a3
+      - image-build-a5
+      - image-build-310p
     key: upload-ready-pipeline
     agents:
       queue: "cpu_queue_premerge"
     command: |
       python3 .buildkite/common/scripts/upload_pipeline.py --upload .buildkite/npu/test-npu-ready.yml
 
-  # L4 Test — main+NIGHTLY=1 (scheduled), or PR with specific label (e.g. add label then Rebuild)
+  # L4 Test — main+NIGHTLY=1 (scheduled), or PR nightly-test label
   - label: "Upload Nightly Pipeline"
     depends_on:
       - image-build-a2
       - image-build-a3
+      - image-build-a5
+      - image-build-310p
     key: upload-nightly-pipeline
     commands:
-      - python3 .buildkite/common/scripts/upload_pipeline.py --upload .buildkite/npu/test-npu-nightly.yml
+      - |
+        EXTRA=""
+        if [ "${NIGHTLY:-}" = "1" ] && [ "${BUILDKITE_BRANCH}" = "main" ]; then
+          EXTRA="--all"
+        fi
+        python3 .buildkite/common/scripts/upload_pipeline.py --upload $$EXTRA .buildkite/npu/test-npu-nightly.yml
     agents:
       queue: "cpu_queue_premerge"

.buildkite/npu/pipeline-npu-a3.yml

@@ -96,13 +96,7 @@
   #   key: upload-nightly-pipeline
   #   if: >-
   #     (build.branch == "main" && build.env("NIGHTLY") == "1") ||
-  #     (build.branch != "main" && (
-  #       build.pull_request.labels includes "nightly-test" ||
-  #       build.pull_request.labels includes "omni-test" ||
-  #       build.pull_request.labels includes "tts-test" ||
-  #       build.pull_request.labels includes "diffusion-x2iat-test" ||
-  #       build.pull_request.labels includes "diffusion-x2v-test"
-  #     ))
+  #     (build.branch != "main" && build.pull_request.labels includes "nightly-test")
   #   commands:
   #     - buildkite-agent pipeline upload .buildkite/npu/test-npu-nightly.yml
   #   agents:

.buildkite/npu/scripts/build-npu-image.sh

@@ -0,0 +1,48 @@
+#!/usr/bin/env bash
+set -ex
+
+dockerfile=${1:?Dockerfile is required}
+image_tag=${2:?Image tag is required}
+device_suffix=${3:-}
+
+image_name=${IMAGE_NAME:-vllm-omni-ci-npu}
+image_registry=${IMAGE_REGISTRY:-swr.cn-southwest-2.myhuaweicloud.com/modelfoundry}
+buildkitd_addr=${BUILDKITD_ADDR:-tcp://buildkitd-service.buildkitd:1234}
+
+echo "--- Building and pushing NPU Test Image"
+echo "Image: ${image_registry}/${image_name}:${image_tag}"
+echo "Dockerfile: ${dockerfile}"
+
+if ! command -v buildctl &> /dev/null; then
+  mkdir -p /tmp/buildkit
+  buildkit_version=v0.29.0
+  wget -q \
+    "https://github.com/moby/buildkit/releases/download/${buildkit_version}/buildkit-${buildkit_version}.linux-arm64.tar.gz" \
+    -O /tmp/buildkit.tar.gz
+  tar -xzf /tmp/buildkit.tar.gz -C /tmp/buildkit
+  cp /tmp/buildkit/bin/buildctl /usr/local/bin/
+fi
+
+build_args=()
+if [[ -n "${device_suffix}" ]]; then
+  build_args+=(--opt "build-arg:VLLM_ASCEND_DEVICE_SUFFIX=${device_suffix}")
+fi
+
+export DOCKER_CONFIG=/home/user/.docker
+buildctl \
+  --addr="${buildkitd_addr}" \
+  --tlscacert=/home/user/.docker/ca.pem \
+  --tlscert=/home/user/.docker/cert.pem \
+  --tlskey=/home/user/.docker/key.pem \
+  build \
+  --frontend dockerfile.v0 \
+  --local context=. \
+  --local dockerfile=./docker \
+  --opt "filename=${dockerfile}" \
+  "${build_args[@]}" \
+  --secret id=dockerconfig,src=/home/user/.docker/config.json \
+  --output "type=image,name=${image_registry}/${image_name}:${image_tag},push=true" \
+  --progress=plain
+
+echo "--- Image pushed successfully"
+echo "${image_registry}/${image_name}:${image_tag}"

.buildkite/npu/test-npu-nightly.yml

@@ -4,10 +4,10 @@ steps:
     depends_on: upload-nightly-pipeline
     if: >-
       build.env("NIGHTLY") == "1" ||
-      build.pull_request.labels includes "nightly-test" ||
-      build.pull_request.labels includes "omni-test"
+      build.pull_request.labels includes "nightly-test"
     steps:
       - label: ":full_moon: Omni · Perf Test · No Async Chunk"
+        source_file_dependencies: omni_qwen3_omni_perf
         key: nightly-omni-performance-no-async-chunk
         timeout_in_minutes: 180
         mirror_hardwares: a3_npu_4
@@ -24,6 +24,7 @@ steps:
             buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
             exit $$EXIT
       - label: ":full_moon: Omni · Perf Test · Async Chunk"
+        source_file_dependencies: omni_qwen3_omni_perf
         key: nightly-omni-performance-async-chunk
         timeout_in_minutes: 180
         mirror_hardwares: a3_npu_4
@@ -40,6 +41,7 @@ steps:
             buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
             exit $$EXIT
       - label: ":full_moon: Omni · MiniCPM-o 4.5 Test"
+        source_file_dependencies: omni_minicpmo_4_5_function
         mirror_hardwares: a3_npu_2
         env:
           VLLM_WORKER_MULTIPROC_METHOD: spawn
@@ -48,6 +50,7 @@ steps:
         commands:
           - pytest -s -v tests/e2e/offline_inference/test_minicpmo_4_5.py tests/e2e/online_serving/test_minicpmo_4_5.py tests/e2e/online_serving/test_minicpmo_4_5_expansion.py -m 'full_model' --run-level 'full_model'
       - label: ":full_moon: Omni · MiniCPM-o 4.5 · Duplex Test"
+        source_file_dependencies: omni_minicpmo_4_5_duplex_function
         timeout_in_minutes: 180
         mirror_hardwares: a3_npu_2
         env:
@@ -62,6 +65,7 @@ steps:
             -m "npu and A3 and omni"
             --run-level full_model
       - label: ":full_moon: Omni · MiniCPM-o 4.5 · Accuracy Test"
+        source_file_dependencies: omni_minicpmo_4_5_accuracy
         timeout_in_minutes: 180
         mirror_hardwares: a3_npu_2
         env:
@@ -82,6 +86,7 @@ steps:
             buildkite-agent artifact upload "tests/e2e/accuracy/minicpmo_4_5/results/*.json"
             exit $$EXIT
       - label: ":full_moon: Omni · MiniCPM-o 4.5 · Perf Test"
+        source_file_dependencies: omni_minicpmo_4_5_perf
         key: nightly-omni-performance-minicpmo-4-5-npu
         timeout_in_minutes: 180
         mirror_hardwares: a3_npu_2
@@ -98,6 +103,7 @@ steps:
             buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
             exit $$EXIT
       - label: ":full_moon: Omni · MiniCPM-o 4.5 · Perf Test (A2)"
+        source_file_dependencies: omni_minicpmo_4_5_perf
         key: nightly-omni-performance-minicpmo-4-5-npu-a2
         timeout_in_minutes: 180
         mirror_hardwares: a2b3_npu_1
@@ -115,6 +121,7 @@ steps:
             buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
             exit $$EXIT
       - label: ":full_moon: Omni · MiniCPM-o 4.5 · Duplex Seed-TTS Perf Test"
+        source_file_dependencies: omni_minicpmo_4_5_duplex_perf
         key: nightly-omni-performance-minicpmo-4-5-duplex-seed-tts-npu
         timeout_in_minutes: 180
         mirror_hardwares: a3_npu_2
@@ -133,9 +140,10 @@ steps:
   - group: ":card_index_dividers: TTS Model Test"
     key: nightly-tts-test-group
     depends_on: upload-nightly-pipeline
-    if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test" || build.pull_request.labels includes "tts-test"
+    if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test"
     steps:
       - label: ":full_moon: TTS · Perf Test"
+        source_file_dependencies: tts_qwen3_tts_perf
         timeout_in_minutes: 180
         mirror_hardwares: a3_npu_2
         env:
@@ -151,15 +159,15 @@ steps:
             EXIT=$$?
             buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
             exit $$EXIT
-  - group: ":card_index_dividers: Diffusion I2I Model Test"
-    key: nightly-HunyuanImage3-x2i-test-group
+  - group: ":card_index_dividers: Diffusion X2I(&A&T) Model Test"
+    key: nightly-diffusion-x2iat-group
     depends_on: upload-nightly-pipeline
     if: >-
       build.env("NIGHTLY") == "1" ||
-      build.pull_request.labels includes "nightly-test" ||
-      build.pull_request.labels includes "diffusion-x2iat-test"
+      build.pull_request.labels includes "nightly-test"
     steps:
       - label: ":full_moon: HunyuanImage3-DIT · Accuracy Test"
+        source_file_dependencies: diffusion_hunyuan_image3_accuracy
         timeout_in_minutes: 180
         mirror_hardwares: a3_npu_4
         env:
@@ -169,15 +177,8 @@ steps:
           DIFFUSION_ATTENTION_BACKEND: TORCH_SDPA
         commands:
           - pytest -s -v tests/e2e/accuracy/test_hunyuan_image3_pixel_accuracy.py -m "full_model and A3" --run-level full_model
-  - group: ":card_index_dividers: Diffusion Benchmark Perf Test"
-    key: nightly-diffusion-benchmark-group
-    depends_on: upload-nightly-pipeline
-    if: >-
-      build.env("NIGHTLY") == "1" ||
-      build.pull_request.labels includes "nightly-test" ||
-      build.pull_request.labels includes "diffusion-x2iat-test"
-    steps:
       - label: ":full_moon: Diffusion Perf · HunyuanImage3 TP=2 CFGP=2"
+        source_file_dependencies: diffusion_hunyuan_image3_perf
         timeout_in_minutes: 180
         mirror_hardwares: a3_npu_4
         env:
@@ -194,6 +195,7 @@ steps:
             buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
             exit $$EXIT
       - label: ":full_moon: Diffusion Perf · HunyuanImage3 TP=2 SP=2"
+        source_file_dependencies: diffusion_hunyuan_image3_perf
         timeout_in_minutes: 180
         mirror_hardwares: a3_npu_4
         env:
@@ -210,6 +212,7 @@ steps:
             buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
             exit $$EXIT
       - label: ":full_moon: Diffusion Perf · HunyuanImage3 TP=4"
+        source_file_dependencies: diffusion_hunyuan_image3_perf
         timeout_in_minutes: 180
         mirror_hardwares: a3_npu_4
         env:
@@ -230,10 +233,41 @@ steps:
     depends_on: upload-nightly-pipeline
     if: >-
       build.env("NIGHTLY") == "1" ||
-      build.pull_request.labels includes "nightly-test" ||
-      build.pull_request.labels includes "diffusion-x2v-test"
+      build.pull_request.labels includes "nightly-test"
     steps:
-      - label: ":full_moon: Diffusion X2V · Function Test"
+      - label: ":full_moon: Diffusion X2V · HunyuanVideo-1.5 Function Test"
+        source_file_dependencies: diffusion_hunyuan_video_function
+        timeout_in_minutes: 180
+        mirror_hardwares: a2b3_npu_4
+        env:
+          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
+          HF_TOKEN: "${HF_TOKEN}"
+          HF_HUB_OFFLINE: "1"
+        commands:
+          - pytest -s -v tests/e2e/online_serving/test_hunyuan_video_15_expansion.py -m "npu and A2" --run-level "full_model"
+      - label: ":full_moon: Diffusion X2V · HunyuanVideo-1.5 Perf Test"
+        source_file_dependencies: diffusion_hunyuan_video_perf
+        key: nightly-diffusion-x2v-performance-hunyuanvideo15
+        timeout_in_minutes: 180
+        mirror_hardwares: a3_npu_2
+        env:
+          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
+          HF_TOKEN: "${HF_TOKEN}"
+          HF_HUB_OFFLINE: "1"
+          DIFFUSION_ATTENTION_BACKEND: TORCH_SDPA
+        commands:
+          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py -m "npu and A3" --test-config-file tests/dfx/perf/tests/test_hunyuanvideo15_t2v_vllm_omni.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
+            buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
+            exit $$EXIT
+      - label: ":full_moon: Diffusion X2V · Wan 2.2 Function Test"
+        source_file_dependencies: diffusion_wan22_function
         mirror_hardwares: a2b3_npu_4
         env:
           VLLM_WORKER_MULTIPROC_METHOD: spawn
@@ -241,7 +275,8 @@ steps:
           HF_TOKEN: "${HF_TOKEN}"
         commands:
           - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k npu_i2v --run-level "full_model"
-      - label: ":full_moon: Diffusion X2V · Perf Test"
+      - label: ":full_moon: Diffusion X2V · Wan 2.2 Perf Test"
+        source_file_dependencies: diffusion_wan22_perf
         key: nightly-diffusion-x2v-performance
         timeout_in_minutes: 180
         mirror_hardwares: a3_npu_4

.buildkite/npu/test-npu-ready.yml

@@ -1,14 +1,59 @@
 steps:
-  - label: "Platforms Test"
-    key: ready-platforms-test
+  - group: ":card_index_dividers: Simple Test"
     depends_on: upload-ready-pipeline
-    mirror_hardwares: a3_npu_2
-    commands:
-      - >-
-        pytest -s -v
-        tests/platforms/npu/test_minicpmo_code2wav_npugraph.py
-        -m 'core_model and npu and A3 and omni'
-        --run-level 'core_model'
+    steps:
+      - label: "Platforms Test"
+        key: ready-platforms-test
+        mirror_hardwares: a3_npu_2
+        commands:
+          - >-
+            pytest -s -v
+            tests/platforms/npu/test_minicpmo_code2wav_npugraph.py
+            -m 'core_model and npu and A3 and omni'
+            --run-level 'core_model'
+
+      - label: "Diffusion NPU layer tests - A2"
+        key: ready-diffusion-npu-layer-tests-a2
+        mirror_hardwares: a2b3_npu_1
+        commands:
+          - >-
+            pytest -s -v
+            tests/diffusion/layers/test_activation.py
+            -m 'core_model and npu and A2'
+            --run-level 'core_model'
+
+      - label: "Diffusion NPU layer tests - A3"
+        key: ready-diffusion-npu-layer-tests
+        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
+            tests/diffusion/models/minimax_h3/test_minimax_h3_qwen3vl_swiglu.py
+            tests/diffusion/models/minimax_h3/test_minimax_h3_modulation.py
+            -m 'core_model and npu and A3'
+            --run-level 'core_model'
+
+      - label: "Diffusion NPU layer tests - A5"
+        key: ready-diffusion-npu-layer-tests-a5
+        mirror_hardwares: a5_npu_2
+        commands:
+          - >-
+            pytest -s -v
+            tests/diffusion/layers/test_activation.py
+            -m 'core_model and npu and A5'
+            --run-level 'core_model'
+
+      - label: "Diffusion NPU layer tests - 310P"
+        key: ready-diffusion-npu-layer-tests-310p
+        mirror_hardwares: 310p_npu_1
+        commands:
+          - >-
+            pytest -s -v
+            tests/diffusion/layers/test_activation.py
+            -m 'core_model and npu and 310P'
+            --run-level 'core_model'
 
   - group: ":card_index_dividers: Omni Model Test"
     key: ready-omni-group

.claude/skills/add-diffusion-model/SKILL.md

@@ -1,6 +1,6 @@
 ---
 name: add-diffusion-model
-description: Add a new diffusion model (text-to-image, text-to-video, image-to-video, text-to-audio, image editing) to vLLM-Omni, including Cache-DiT acceleration and parallelism support (TP, SP/USP, CFG-Parallel, HSDP). Use when integrating a new diffusion model, porting a diffusers pipeline or a custom model repo to vllm-omni, creating a new DiT transformer adapter, adding diffusion model support, or enabling multi-GPU parallelism and cache acceleration for an existing model.
+description: Add a new diffusion model (text-to-image, text-to-video, image-to-video, text-to-audio, image editing) to vLLM-Omni, including native non-Diffusers ports, reference-parity validation, Cache-DiT, offload, and parallelism support (TP, SP/USP, CFG-Parallel, HSDP). Use when integrating or reviewing a new diffusion model, porting a Diffusers pipeline or custom model repository, creating a DiT adapter, reusing shared examples, or qualifying multi-GPU and memory optimizations.
 ---
 
 # Adding a Diffusion Model to vLLM-Omni
@@ -26,9 +26,16 @@ Check the model's HF repo for `model_index.json`. This determines your path:
 |----------|----------------|----------------|
 | **Already supported** | `_class_name` in `model_index.json` matches a key in `_DIFFUSION_MODELS` in `registry.py` | Skip implementation, then validate model-specific examples, tests, and docs as needed |
 | **Diffusers-based** | Has standard `model_index.json` with `_diffusers_version`, subfolders for `transformer/`, `vae/`, etc. | Follow **Path A** below |
-| **Custom/private repo** | No diffusers `model_index.json`, weights in non-standard format, custom model code in a separate git repo | Follow **Path B** below |
+| **Native non-Diffusers model** | No Diffusers index, non-standard checkpoint hierarchy, or custom architecture in a separate repo | Follow **Path B** below; port the runtime natively unless an external adapter was explicitly requested |
 | **Hybrid** | Has some diffusers components (VAE) but custom transformer/fusion | Mix of Path A and Path B |
 
+Before coding, write a short integration contract covering runtime ownership,
+checkpoint discovery, reference revision, I/O geometry, attention semantics,
+CFG behavior, auxiliary components, target hardware, and the default deployment.
+For Path B, hybrid models, or any optimization work, read
+[references/native-model-integration-checklist.md](references/native-model-integration-checklist.md)
+and use its phase gates.
+
 ## Path A: Diffusers-Based Model
 
 For models with a standard diffusers layout. See [references/transformer-adaptation.md](references/transformer-adaptation.md) for detailed code patterns.
@@ -103,9 +110,15 @@ For custom loop structures, follow `vllm_omni/diffusion/models/progress_bar.py`
 
 ---
 
-## Path B: Custom/Private Repo Model
+## Path B: Native / Non-Diffusers Model
 
-For models without a diffusers pipeline — weights in custom format, model code in a private repo. Real examples: BAGEL, HunyuanImage3. See [references/custom-model-patterns.md](references/custom-model-patterns.md) for concrete custom-model integration patterns.
+For models without a Diffusers pipeline—weights in custom formats and model
+code in another public or private repository. Treat that repository as a pinned
+correctness oracle. A request for native support means the vLLM-Omni runtime
+must not import the reference implementation; an external adapter is appropriate
+only when the requested scope explicitly permits that dependency. See
+[references/custom-model-patterns.md](references/custom-model-patterns.md) for
+concrete integration patterns.
 
 ### B1. Understand the reference repo
 
@@ -126,16 +139,25 @@ This is the key design decision for custom models. Follow these placement rules:
 | **Pipeline orchestration** (init, forward, denoise loop) | `vllm_omni/diffusion/models/<name>/pipeline_<name>.py` | Always required |
 | **Custom transformer/backbone** (ported and adapted to vllm-omni) | `vllm_omni/diffusion/models/<name>/<name>_transformer.py` or similar | `wan2_2.py`, `fusion.py`, `bagel_transformer.py` |
 | **Custom sub-models** (VAE, fusion, autoencoder) | `vllm_omni/diffusion/models/<name>/` as separate files | `autoencoder.py`, `fusion.py` |
-| **External dependency code** (original repo utilities) | **External repo**, installed via download script or pip | original-repo package via git clone |
+| **Reference-only code** | Keep outside the runtime; use a pinned revision for golden outputs and architecture analysis | Reference inference script |
+| **Explicit external adapter dependency** | External package, only when the requested scope and maintainer direction allow it | Compatibility adapter, not native support |
 | **Hardcoded model configs** | Module-level dicts in pipeline file | `VIDEO_CONFIG`, `AUDIO_CONFIG` dicts |
 | **Download/setup script** | `examples/offline_inference/<name>/download_<name>.py` | `download_<name>.py` |
 | **Custom `model_index.json`** | Generated by download script, placed at model root | Minimal: `{"_class_name": "YourPipeline", ...}` |
 
 ### B3. Handle external dependencies
 
-If the model's code lives in a separate git repo:
+If the model's code lives in a separate git repo, first decide whether the
+requested deliverable is native support or an external adapter. Do not silently
+choose the adapter path.
 
-**Option 1: Import with graceful fallback** (recommended for models with external utils)
+**Option 1: Port the code directly** (default for native support)
+
+Copy the essential model files into `vllm_omni/diffusion/models/<name>/` and
+adapt them to shared vLLM-Omni contracts. Keep checkpoint loading strict and
+use the pinned reference only to generate parity evidence.
+
+**Option 2: Import with graceful fallback** (adapter scope only)
 
 ```python
 try:
@@ -147,11 +169,9 @@ except ImportError:
     )
 ```
 
-**Option 2: Port the code directly** (preferred when feasible)
-
-Copy the essential model files into `vllm_omni/diffusion/models/<name>/` and adapt them. This avoids external dependencies. BAGEL does this — `autoencoder.py` and `bagel_transformer.py` are ported directly.
-
-**Decision criteria**: Port if the code is self-contained and won't diverge. Use external deps if the model repo is actively maintained and the code is complex.
+Use an external runtime dependency only when the user explicitly requested an
+adapter or a maintainer approved the exception. Document the dependency,
+pinned revision, installation path, and unsupported native features.
 
 ### B4. Handle custom weight loading
 
@@ -221,7 +241,8 @@ class CustomPipeline(nn.Module):
 
 ### B5. Create the `model_index.json`
 
-Custom models need a `model_index.json` at the model root for vllm-omni to discover them. For custom models, this is minimal:
+Prefer a `model_index.json` at the model root when vLLM-Omni owns an assembled
+checkpoint directory. For custom models, this is minimal:
 
 ```json
 {
@@ -230,7 +251,14 @@ Custom models need a `model_index.json` at the model root for vllm-omni to disco
 }
 ```
 
-The `_class_name` must match a key in `_DIFFUSION_MODELS` in `registry.py`. Additional keys are model-specific (accessed via `od_config.model_config`).
+The `_class_name` must match a key in `_DIFFUSION_MODELS` in `registry.py`.
+Additional keys are model-specific (accessed via `od_config.model_config`).
+
+If the released repository is immutable and has neither a root `config.json`
+nor a Diffusers index, add it to a generic native-checkpoint signature resolver:
+match the exact Hub ID or a distinctive set of local files and return the
+pipeline class. Do not use model-name substrings or add parallel one-off
+predicates in CLI, config, and serving consumers.
 
 If the model's weights come from multiple HF repos, write a **download script** that:
 1. Downloads from each repo
@@ -292,7 +320,9 @@ Use the appropriate existing example script:
 | Image-to-Image | `examples/offline_inference/image_to_image/image_edit.py` |
 | Text-to-Audio | `examples/offline_inference/text_to_audio/text_to_audio.py` |
 
-For custom/Omni models that don't fit these categories, create a dedicated example script.
+Reuse these shared scripts even for custom models when their request and output
+contracts fit. Create a dedicated model script only when the shared category
+cannot represent the protocol, and document that gap in the PR.
 
 **Validation**: No errors, output is meaningful, quality matches reference implementation.
 
@@ -300,13 +330,17 @@ See [references/troubleshooting.md](references/troubleshooting.md) for common er
 
 ### Step 6: Add Example Scripts
 
-For Omni or custom models, create:
+Only when the shared category scripts cannot represent the model, create:
 - `examples/offline_inference/your_model_name/` — offline script + README
 - `examples/online_serving/your_model_name/` — server script + client
 - Download script if weights require assembly from multiple sources
 
 ### Step 7: Update Documentation
 
+Follow the [`add-recipe` skill](../add-recipe/SKILL.md) to add or update the
+model-family recipe and its `recipes/README.md` row with verified specifications,
+hardware, commands, feature links, and qualification evidence.
+
 Required updates:
 1. `docs/user_guide/diffusion/parallelism/overview.md` — parallelism support overview/table
 2. `docs/user_guide/diffusion/cpu_offload.md` — if CPU offload supported (add to supported models table)
@@ -338,7 +372,7 @@ 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.
 
@@ -346,107 +380,20 @@ Classify the model's **CI priority** first:
 
 **Keep model-specific code inside test modules — not `tests/helpers/{slug}.py`:** deploy constants, prompts, sampling dicts, and inline `request_config` / `form_data` belong in each `test_{slug}.py` and `test_{slug}_expansion.py`. Do not add per-model files under `tests/helpers/`; reuse only repo-wide harness (`mark`, `media`, `runtime`, `stage_config`, `assertions`). **L2+ online/offline e2e:** reuse or add `send_*_request` in `tests/helpers/runtime.py` — tests call the handler, not raw `omni.generate` / HTTP. See [vllm-omni-test skill](../vllm-omni-test/SKILL.md) § **Runtime send helpers**.
 
+Keep the model suite proportional using the six distinct failure owners in the
+native integration checklist. Combine supported features into a few
+parametrized E2E rows instead of creating one test per optimization.
+
 ### Step 9: Add Cache-DiT Acceleration
 
-Cache-DiT accelerates inference by caching intermediate computation results across denoising steps. After your model is working correctly on a single GPU, add cache-dit support.
+Add caching only after uncached single-device correctness. Read
+[references/cache-dit-patterns.md](references/cache-dit-patterns.md), use the
+automatic single-block-list path when possible, and add a registered
+`BlockAdapter` only for genuinely custom block topology.
 
-See [references/cache-dit-patterns.md](references/cache-dit-patterns.md) for detailed code patterns.
-
-#### 9a. Determine your model type
-
-| Model Type | Description | Action |
-|------------|-------------|--------|
-| **Standard single-transformer** | One transformer with one `ModuleList` of blocks | No code needed — `CacheDiTBackend` auto-detects via `enable_cache_for_dit()` |
-| **Multi-block-list** | One transformer with multiple block lists (e.g., `transformer_blocks` + `single_transformer_blocks`) | Write custom enabler with `BlockAdapter` |
-| **Dual-transformer** | Two transformers (e.g., high-noise + low-noise) | Write custom enabler with `BlockAdapter` wrapping both |
-
-#### 9b. Standard models — verify automatic support
-
-For standard single-transformer models, test directly:
-
-```python
-omni = Omni(
-    model="your-model-name",
-    cache_backend="cache_dit",
-    cache_config={
-        "Fn_compute_blocks": 1,
-        "Bn_compute_blocks": 0,
-        "max_warmup_steps": 4,
-    }
-)
-```
-
-Check logs for "Cache-dit enabled successfully on xxx". If it works, skip to Step 9e.
-
-#### 9c. Custom architectures — write a custom enabler
-
-For multi-block-list or dual-transformer models, write a custom enabler function:
-
-```python
-from cache_dit import BlockAdapter, ForwardPattern, ParamsModifier, DBCacheConfig
-
-def enable_cache_for_your_model(pipeline, cache_config):
-    db_cache_config = DBCacheConfig(
-        num_inference_steps=None,
-        Fn_compute_blocks=cache_config.Fn_compute_blocks,
-        Bn_compute_blocks=cache_config.Bn_compute_blocks,
-        max_warmup_steps=cache_config.max_warmup_steps,
-        max_cached_steps=cache_config.max_cached_steps,
-        max_continuous_cached_steps=cache_config.max_continuous_cached_steps,
-        residual_diff_threshold=cache_config.residual_diff_threshold,
-    )
-
-    cache_dit.enable_cache(
-        BlockAdapter(
-            transformer=pipeline.transformer,
-            blocks=[
-                pipeline.transformer.transformer_blocks,
-                pipeline.transformer.single_transformer_blocks,
-            ],
-            forward_pattern=[ForwardPattern.Pattern_1, ForwardPattern.Pattern_1],
-            params_modifiers=[ParamsModifier(...)],
-        ),
-        cache_config=db_cache_config,
-    )
-
-    def refresh_cache_context(pipeline, num_inference_steps, verbose=True):
-        cache_dit.refresh_context(
-            pipeline.transformer, num_inference_steps=num_inference_steps, verbose=verbose
-        )
-    return refresh_cache_context
-```
-
-#### 9d. Register the custom enabler
-
-Add your enabler to `CUSTOM_DIT_ENABLERS` in
-`vllm_omni/diffusion/cache/cachedit/model_specific.py`:
-
-```python
-CUSTOM_DIT_ENABLERS = {
-    "Wan22Pipeline": enable_cache_for_wan22,
-    "LongCatImagePipeline": enable_cache_for_longcat_image,
-    "YourModelPipeline": enable_cache_for_your_model,  # Add here
-}
-```
-
-#### 9e. Test Cache-DiT
-
-```python
-omni = Omni(
-    model="your-model-name",
-    cache_backend="cache_dit",
-    cache_config={
-        "Fn_compute_blocks": 1, "Bn_compute_blocks": 0,
-        "max_warmup_steps": 4, "residual_diff_threshold": 0.24,
-    }
-)
-images = omni.generate("a beautiful landscape",
-    OmniDiffusionSamplingParams(num_inference_steps=50))
-```
-
-**Verify**: 1) logs show cache enabled, 2) 1.5-2x speedup, 3) output quality acceptable vs baseline.
-
-If quality degrades, lower `residual_diff_threshold` (try 0.12-0.18) or increase `max_warmup_steps` (try 6-8).
+Verify a real cache hit and compare quality with the uncached baseline. Make a
+speed claim only at a realistic step count where warmup permits hits; an
+all-warmup smoke proves integration, not acceleration.
 
 ---
 
@@ -460,107 +407,30 @@ See [references/parallelism-patterns.md](references/parallelism-patterns.md) for
 
 #### 10a. Tensor Parallelism (TP)
 
-Shards DiT linear layers across GPUs. Requires code changes in the transformer.
-
-**What to change in the transformer**:
-1. Replace `nn.Linear` with `ColumnParallelLinear` / `RowParallelLinear` / `QKVParallelLinear`
-2. Update `load_weights()` to handle QKV fusion with `stacked_params_mapping`
-3. Use `self.to_qkv.num_heads` (local heads) instead of total heads for split sizes
-
-```python
-from vllm.model_executor.layers.linear import (
-    QKVParallelLinear, RowParallelLinear, ColumnParallelLinear,
-)
-
-# Attention: QKV → RowParallel output
-self.to_qkv = QKVParallelLinear(dim, head_dim, num_heads, num_kv_heads)
-self.to_out = RowParallelLinear(dim, dim, input_is_parallel=True)
-
-# FFN: ColumnParallel → RowParallel
-self.w1 = ColumnParallelLinear(dim, ffn_dim)
-self.w2 = RowParallelLinear(ffn_dim, dim, input_is_parallel=True)
-```
-
-**Constraints**: `num_heads % tp_size == 0` and `num_kv_heads % tp_size == 0`.
-
-**Test**: `--tensor-parallel-size 2`
+Replace compatible projections with vLLM parallel linears, preserve checkpoint
+fusion/loading, and use local head counts. Require query/KV head divisibility
+and compare a multi-rank forward with the one-rank oracle.
 
 #### 10b. Sequence Parallelism (SP / USP)
 
-Splits sequence tokens across GPUs. Non-intrusive via `_sp_plan` on the transformer class — no changes to `forward()`.
-
-**What to change in the transformer**:
-
-Add `_sp_plan` class attribute:
-
-```python
-from vllm_omni.diffusion.distributed.sp_plan import (
-    SequenceParallelInput, SequenceParallelOutput,
-)
-
-class YourTransformer(nn.Module):
-    _sp_plan = {
-        "blocks.0": {
-            "hidden_states": SequenceParallelInput(split_dim=1, expected_dims=3),
-        },
-        "proj_out": SequenceParallelOutput(gather_dim=1, expected_dims=3),
-    }
-```
-
-If inline tensor ops (e.g., `torch.cat`) exist between shard/gather points, extract them into `nn.Module` submodules so hooks can intercept them.
-
-For RoPE that needs splitting, add an entry for the RoPE module with `split_output=True`.
-
-**Test**: `--ulysses-degree 2` (offline) or `--usp 2` (online serving)
+Prefer the declarative `_sp_plan`. For packed variable-length attention or
+learned-sink LSE correction, keep model math explicit and reuse shared exchange
+utilities. Validate uneven sequence splits, RoPE coordinates, and outputs
+against the one-rank oracle.
 
 #### 10c. CFG Parallel
 
-Distributes positive/negative CFG branches across 2 GPUs. Requires the pipeline to inherit `CFGParallelMixin`.
-
-**What to change in the pipeline**:
-
-```python
-from vllm_omni.diffusion.distributed.cfg_parallel import CFGParallelMixin
-
-class YourPipeline(nn.Module, CFGParallelMixin):
-    def diffuse(self, ...) -> torch.Tensor:
-        for i, t in enumerate(timesteps):
-            positive_kwargs = {...}
-            negative_kwargs = {...} if do_true_cfg else None
-            noise_pred = self.predict_noise_maybe_with_cfg(
-                do_true_cfg=do_true_cfg, true_cfg_scale=cfg_scale,
-                positive_kwargs=positive_kwargs, negative_kwargs=negative_kwargs,
-            )
-            latents = self.scheduler_step_maybe_with_cfg(
-                noise_pred, t, latents, do_true_cfg
-            )
-        return latents
-```
-
-Override `predict_noise()` if your transformer call is non-standard. Override `combine_cfg_noise()` for multi-output models (e.g., video + audio).
-
-**Constraint**: Exactly 2 GPUs. Only for models using classifier-free guidance.
-
-**Test**: `--cfg-parallel-size 2`
+Confirm the model uses CFG. Then reuse `CFGParallelMixin`, overriding prediction
+or recombination only for non-standard or multi-output pipelines. Distinguish a
+packed positive/negative implementation from two independent branches and
+validate the two-rank result against the packed one-rank oracle.
 
 #### 10d. HSDP (Hybrid Sharded Data Parallel)
 
-Shards transformer weights via PyTorch FSDP2 to reduce per-GPU VRAM. No code changes to the forward pass — just add a class attribute.
-
-**What to change in the transformer**:
-
-```python
-class YourTransformer(nn.Module):
-    @staticmethod
-    def _is_transformer_block(name: str, module) -> bool:
-        return "blocks" in name and name.split(".")[-1].isdigit()
-
-    _hsdp_shard_conditions = [_is_transformer_block]
-```
-
-**Constraint**: Cannot combine with TP. For standalone HSDP, set `hsdp_shard_size` explicitly.
-
-**Test**: `--use-hsdp` or `DiffusionParallelConfig(use_hsdp=True)`
+Declare layer shard conditions and ignored rank-local modules; preserve mixed
+checkpoint dtypes. HSDP cannot combine with TP. Measure parameter loading,
+FSDP materialization, warm HBM, and host PSS rather than assuming sharding saves
+peak memory. Keep the resident layout as default if HSDP is worse.
 
 #### 10e. Update parallelism documentation
 
@@ -599,6 +469,12 @@ Pipelines without `SupportsComponentDiscovery` fall back to scanning
 well-known attribute names (`transformer`, `text_encoder`, `vae`,
 etc.), which fails for non-standard names.
 
+Keep model-specific checkpoint paths, nested block aliases, layout transforms,
+and component lifecycles in the model package. Change a shared offloader only
+for a general contract, demonstrate another consumer or a framework-level bug,
+and add one focused shared regression. Avoid `if ModelName` branches in shared
+backends.
+
 ### Step 12: Performance Profiling
 
 After verifying correctness and implementing parallelism/caching, profile the model's performance to identify bottlenecks and ensure optimal execution.
@@ -608,6 +484,12 @@ See the [Profiling Single-Stage Diffusion](../../../docs/contributing/profiling.
 2. Using Nsight Systems (`nsys`) with `profiler: "cuda"` for low-overhead CUDA traces.
 3. Controlling profiling via `omni.start_profile()` and `omni.stop_profile()`.
 
+Report cold E2E, warm user latency, steady-state wave time, peak allocated and
+reserved HBM, host PSS for the full process tree, and stage boundaries. State
+whether prompt encoding and VAE/audio decoding are included. For multi-device
+layouts, plot user latency against throughput per device and keep different
+denoising-step counts on separate Pareto frontiers.
+
 ---
 
 ## Pre-commit conventions
@@ -635,7 +517,7 @@ shellcheck). In particular:
 3. **Use small models**: Test with smaller variants first
 4. **Check tensor shapes**: Most errors are reshape mismatches in attention
 5. **Add features incrementally**: Single GPU → TP → SP → CFG → HSDP → Cache-DiT
-6. **For custom models**: Get the model running with the original code first, then progressively replace components with vllm-omni equivalents
+6. **For custom models**: Run the pinned reference separately, then port the runtime natively; do not ship temporary imports from the reference implementation
 7. **Cache-DiT before parallelism tuning**: Cache-DiT is lossy — verify quality at baseline before combining with parallelism
 8. **Combine lossless + lossy**: e.g., TP + SP + Cache-DiT for maximum throughput
 
@@ -644,6 +526,7 @@ shellcheck). In particular:
 - [vllm-omni-test skill](../vllm-omni-test/SKILL.md) — L1–L4 markers, naming, Buildkite wiring, run commands
 - [Transformer Adaptation](references/transformer-adaptation.md) — porting transformers from diffusers
 - [Custom Model Patterns](references/custom-model-patterns.md) — patterns for non-diffusers models
+- [Native Model Integration Checklist](references/native-model-integration-checklist.md) — ownership boundaries, phase gates, qualification matrix, and review evidence
 - [Parallelism Patterns](references/parallelism-patterns.md) — TP, SP/USP, CFG parallel, HSDP implementation details
 - [Cache-DiT Patterns](references/cache-dit-patterns.md) — cache-dit acceleration for standard and custom architectures
 - [Troubleshooting](references/troubleshooting.md) — common errors and fixes

.claude/skills/add-diffusion-model/references/cache-dit-patterns.md

@@ -9,7 +9,9 @@ Three caching strategies:
 - **TaylorSeer**: Calibration-based prediction using Taylor expansion to estimate block outputs
 - **SCM** (Step Computation Masking): Dynamic step skipping based on configurable policies
 
-**Typical speedup**: 1.5-2.5x depending on model and configuration.
+Speedup is model-, step-, and policy-dependent. Treat a cache-enabled run as
+integration evidence until logs or a focused test show real hits and repeated
+timings show an improvement over the uncached baseline.
 
 **Official docs**: https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/cache_dit
 
@@ -243,8 +245,10 @@ vllm serve your-model --omni --port 8098 \
 
 **Verification checklist**:
 1. Logs show "Cache-dit enabled successfully on xxx"
-2. Performance: 1.5-2x speedup vs no cache
-3. Quality: compare output with `cache_backend=None`
+2. A focused test or summary confirms at least one real cache hit
+3. Quality is compared with `cache_backend=None`
+4. Performance uses a realistic step count, repeated timings, and a policy
+   whose warmup does not consume the whole run
 
 ## Excluded Models
 

.claude/skills/add-diffusion-model/references/custom-model-patterns.md

@@ -16,7 +16,7 @@ vllm_omni/diffusion/models/wan2_2/
 
 The transformer is loaded separately via `weights_sources` + `load_weights()`. Non-transformer components (VAE, text encoder) are loaded in `__init__` via `from_pretrained()`.
 
-### Custom model with external deps
+### External adapter (not native support)
 
 ```
 vllm_omni/diffusion/models/<name>/
@@ -28,7 +28,10 @@ examples/offline_inference/<task>/
 └── download_<name>.py             # Downloads weights and/or clones code repo
 ```
 
-All weights loaded eagerly in `__init__`. `load_weights()` is a no-op. External dependency imported with try/except.
+All weights are loaded eagerly in `__init__`. `load_weights()` is a no-op. The
+external dependency is imported with try/except. Use this pattern only when the
+requested scope explicitly allows an adapter; it does not satisfy a request for
+a native vLLM-Omni implementation.
 
 ### Custom model with ported code (e.g., BAGEL)
 
@@ -86,7 +89,7 @@ init → load ALL weights eagerly via custom helpers → load_weights() = no-op
 - `load_weights()` is `pass`
 - Weights may come from multiple HF repos in different formats (`.pth`, `.safetensors`)
 
-Use this when:
+Use this only for an explicitly approved external adapter when:
 - The original model has complex, well-tested loading code you don't want to rewrite
 - Weights span multiple HF repos
 - Weight format is non-standard (e.g., a single `.pth` file, not sharded safetensors)
@@ -112,7 +115,14 @@ Custom model `model_index.json` (minimal):
 }
 ```
 
-The only **required** field is `_class_name` — it must match a key in `_DIFFUSION_MODELS` in `registry.py`. Other fields are model-specific and accessible via `od_config.model_config` dict.
+The only **required** field is `_class_name` — it must match a key in
+`_DIFFUSION_MODELS` in `registry.py`. Other fields are model-specific and
+accessible via `od_config.model_config`.
+
+When the released repository cannot be modified and contains neither this file
+nor a root `config.json`, use the generic native-checkpoint signature resolver.
+Match the exact Hub ID or a distinctive local file set; never infer support from
+a model-name substring.
 
 ## External Dependency Management
 
@@ -141,6 +151,10 @@ def download_dependency():
 
 Copy essential files from the original repo into `vllm_omni/diffusion/models/<name>/`. Adapt imports to use vllm-omni utilities. Benefits: no external dependency, no git clone step. Drawback: must maintain the ported code.
 
+This is the default for native model support. Keep the reference repository at
+a pinned revision for architecture analysis and golden outputs, but do not
+import it from production code.
+
 ## Multi-Modal Input/Output Protocols
 
 Custom models that handle images, audio, or video I/O should implement protocol classes:

.claude/skills/add-diffusion-model/references/native-model-integration-checklist.md

@@ -0,0 +1,131 @@
+# Native Diffusion Model Integration Checklist
+
+Use this checklist for native non-Diffusers models, hybrid pipelines, and
+model-specific optimization work.
+
+## 1. Freeze the contract before coding
+
+Record:
+
+- Reference repository and immutable revision.
+- Official checkpoint ID, local layout, license, and required auxiliary files.
+- Pipeline stages: prompt/image encoding, denoising, video/image/audio decoding,
+  and muxing.
+- Token packing, RoPE coordinates, attention sinks/LSE behavior, MoE layout,
+  scheduler equations, CFG formulation, dtypes, and checkpoint transforms.
+- Output geometry, frame count/FPS, audio rate/channels, supported tasks, and
+  default generation steps.
+- Available local hardware versus the official qualification hardware.
+- Intended default deployment and optional optimization paths.
+
+State whether the deliverable is a native implementation or an external
+adapter. For native support, use the reference as an oracle only.
+
+## 2. Use phase gates
+
+| Phase | Required exit evidence |
+|---|---|
+| Architecture | Component/weight inventory; unsupported reference dependencies identified; ownership decisions documented |
+| Native correctness | Strict checkpoint load; tiny forward or one-step golden vs pinned reference; output contract validated |
+| Framework integration | Registry/discovery, shared request types, existing category example, serving route, docs, and recipe work without model-name branching in shared scripts |
+| Lossless distribution | One-rank oracle vs each supported TP/SP/CFG/HSDP/VAE layout; released-checkpoint smoke for the recommended layout |
+| Memory optimization | Ordinary/layerwise/DLO behavior, peak HBM, full-tree host PSS, and stage timeline; shared backend unchanged unless a general contract is proven |
+| Lossy optimization | Cache hit demonstrated; quality delta measured; speed claim made only when warmup permits hits |
+| Full qualification | Default-resolution E2E at official step count for every supported task; media metadata and hashes recorded |
+| Review readiness | Focused tests, Ruff/format, strict docs, DCO, current-main rebase, issue/RFC for large changes, concise PR evidence and limitations |
+
+Do not start performance tuning before native one-step parity. Do not claim a
+feature from topology validation alone when released weights can exercise it.
+
+## 3. Keep ownership narrow
+
+Default placement:
+
+| Concern | Owner |
+|---|---|
+| Architecture, checkpoint remap, packed attention math, nested block alias, auxiliary lifecycle | Model package |
+| Kernel/version dispatch already useful to multiple models | Shared attention utility |
+| Request-dependent example defaults | Typed model-extra entry consumed through a registry |
+| Exact-ID/local-file detection for repositories without standard metadata | Generic native-checkpoint signature table |
+| General offload/cache/HSDP bug | Shared layer plus one focused shared regression |
+
+Before changing shared code, answer:
+
+1. Is this a framework contract or only this checkpoint's layout?
+2. Can the model expose an existing declarative hook instead?
+3. Is there a second consumer or a framework-level failing test?
+4. Can the shared change avoid importing or naming the model?
+
+If the answers do not justify a shared change, keep it model-local.
+
+## 4. Reuse examples without leaking model logic
+
+- Reuse the existing `x_to_y.py` category script.
+- Resolve the pipeline class through standard metadata or native signature
+  discovery, not path substrings.
+- Put dynamic model defaults and validation in `model_extras`; let shared
+  examples consume only the generic typed API.
+- Do not create image/speech/default abstractions without a real consumer.
+- Add a dedicated example only when the shared request/output protocol cannot
+  express the model.
+
+## 5. Build a bounded evidence matrix
+
+Start with the recommended resident layout. Add only supported, useful points:
+
+- Single request: TP, SP, or hybrid TP/SP.
+- Throughput: DLO/DP layouts when concurrent requests are valid.
+- CFG: compare CFG-parallel output with the packed-CFG oracle only if the model
+  actually uses CFG.
+- HSDP: record loading and materialization peaks; it can use more HBM than a
+  resident SP layout.
+- VAE patch parallelism: compare decoded tensors or media exactly where the
+  algorithm should be lossless.
+- Cache: separate cache integration from acceleration evidence.
+
+With four devices, qualify four-device layouts such as DP4, DP2SP2, SP4, or
+CFG2xSP2. Do not present CFG2xSP4 or an official eight-device runtime as locally
+validated.
+
+For each measured layout record:
+
+- Cold E2E and warm user latency.
+- Steady-state wave time and throughput per device.
+- Prompt, denoise, video decode, audio decode, and mux times.
+- Peak allocated/reserved HBM and full-process-tree host PSS.
+- Step count, seed, output geometry, frame/FPS/audio metadata, and parity metric.
+
+Plot latency versus throughput/device. Keep one-step, four-step, and full-step
+results separate because they are not quality-equivalent.
+
+## 6. Keep tests proportional
+
+Prefer a small set with distinct failure ownership:
+
+1. Strict weight-key/dtype/load contract.
+2. Tiny native golden or one-step reference parity.
+3. One parametrized distributed-parity test covering supported layouts.
+4. Pipeline/request/output geometry and serving smoke.
+5. Shared-example/default test when the category script changes.
+6. One shared regression only for a genuine shared-layer fix.
+
+Avoid duplicating the same shape or configuration assertion across
+model-specific files. Keep expensive released-checkpoint evidence in the PR or
+recipe unless the repository's CI tier explicitly requires it.
+
+## 7. Write evidence, not marketing
+
+In the PR description include:
+
+- A compact architecture/data-flow explanation.
+- Default deployment and why it is recommended.
+- Code-change counts by component.
+- Accuracy and distributed-parity evidence tied to commands/scripts.
+- Separate Pareto figures for different step counts.
+- Supported versus validated versus planned optimizations.
+- Hardware gaps and untested official-runtime claims.
+
+Call a result a bounded local qualification unless it includes repeated timing
+and variance. State whether latency includes text/image encoding and all
+decoders. Preserve the reference revision and exact commands needed to
+reproduce the evidence.

.claude/skills/add-recipe/SKILL.md

@@ -0,0 +1,126 @@
+---
+name: add-recipe
+description: Add or update an in-repository vLLM-Omni model recipe with verified task, input, output, hardware, command, feature, and validation contracts. Use when creating files under recipes/, restructuring a recipe after review, documenting a newly supported model, or synchronizing recipe claims with support tables and shared feature guides.
+---
+
+# Add a vLLM-Omni Recipe
+
+## Source the contract
+
+Read [`recipes/TEMPLATE.md`](../../../recipes/TEMPLATE.md) and
+[`recipes/README.md`](../../../recipes/README.md) before editing. Inspect the
+canonical model card/repository, implementation, shared examples, tests, and
+available local qualification evidence.
+
+Do not infer support from a model name, sibling recipe, registry entry, or
+topology validator. Mark unexecuted configurations as configuration-only.
+
+## Route by model family
+
+Read only the contributor and user documentation relevant to the model:
+
+| Family | Contributor guide | User-facing documentation to synchronize |
+|---|---|---|
+| Diffusion | [`adding a diffusion model`](../add-diffusion-model/SKILL.md) | `docs/user_guide/diffusion_features.md`, applicable diffusion feature guides, and shared image/video/audio examples |
+| TTS | [`adding a TTS model`](../add-tts-model/SKILL.md) | `examples/offline_inference/text_to_speech/README.md`, `examples/online_serving/text_to_speech/README.md`, and `docs/serving/speech_api.md` |
+| Omni | [`adding an omni model`](../../../docs/contributing/model/adding_omni_model.md) | model-family offline/online example docs, `docs/serving/chat_completions_api.md`, and `docs/user_guide/feature_compatibility.md` |
+
+All families must update `docs/models/supported_models.md` and the matching row
+in `recipes/README.md`. Do not add a modality-specific support document when
+the repository has no such table; update the closest shared user contract.
+
+## Structure the recipe
+
+Keep the recipe task-oriented and use one hardware-specific file named
+`recipes/<vendor>/<model>-<hardware>.md`. A recipe may contain multiple
+deployment topologies on that hardware, but each additional accelerator model
+or platform requires its own suffixed file.
+
+1. **Summary:** vendor, exact model ID, runtime, modes, named hardware,
+   recommended deployment, and maintainer.
+2. **Supported model contract:** put task, input, output, and provided-profile
+   tables before setup or commands.
+3. **References:** link the canonical model source, shared examples, supported
+   model table, and feature matrix.
+4. **Checkpoint/setup:** pin revisions when possible and state required assets.
+5. **Hardware:** document the single accelerator named by the file suffix,
+   including every validated device-count/topology profile on it.
+6. **Commands:** reuse shared `examples/` entrypoints unless the model contract
+   truly requires a dedicated script. Include offline and online paths that
+   were validated.
+7. **Supported features:** use a compact model-specific topology/status table
+   with links to shared feature guides.
+8. **Verification:** provide a quick command and exact expected output
+   contract.
+9. **Qualification evidence:** report correctness, memory, and timing evidence
+   with its measurement scope and caveats.
+
+## Separate hardware from software
+
+For every locally validated hardware profile, record:
+
+- accelerator vendor/model and per-device memory;
+- number of devices;
+- interconnect (`NVLink`, `PCIe`, or the platform equivalent);
+- host memory when CPU staging/offload is material;
+- whether the profile is runtime-qualified or configuration-only.
+
+Record OS, Python, driver/runtime, framework versions, and the vLLM-Omni
+revision in a separate software-environment table. Keep precision, worker
+count, and DP/TP/SP/PP sizes with the deployment profile or exact command.
+
+Never generalize a result from one accelerator family to another or combine
+NVIDIA, AMD, NPU, or distinct accelerator models in one recipe. Create a
+separate hardware-suffixed recipe and `recipes/README.md` row. Distinguish
+upstream requirements from hardware exercised by the PR.
+
+## Keep shared documentation concise
+
+Link the applicable diffusion, TTS, omni, serving, or design guide for generic
+feature semantics and launch instructions. The recipe should contain only the
+model-specific status, valid topology, required flag difference, and evidence
+boundary.
+
+Put unsupported combinations in the same feature table. Avoid repeating the
+same model-specific prose below the global feature matrix; the recipe is the
+detailed source of truth.
+
+## Report evidence precisely
+
+For every performance or memory value, state:
+
+- checkpoint/revision and workload;
+- accelerator and device count;
+- precision and topology;
+- cold/warm scope, step count, and concurrency;
+- peak HBM and host-memory metric when measured;
+- output/quality guard and sample count;
+- monitoring overhead or other known variance.
+
+Call a single bounded run qualification evidence, not a benchmark. Do not add
+a benchmark script unless the contribution explicitly requires one.
+
+## Synchronize repository documentation
+
+Update all applicable locations:
+
+- the row in `recipes/README.md`;
+- `docs/models/supported_models.md`;
+- the family-specific documentation selected in **Route by model family**;
+- shared example documentation when commands or flags change.
+
+Keep global support tables compact. Link to the recipe instead of duplicating
+its deployment explanation.
+
+## Validate
+
+Run, at minimum:
+
+```bash
+pre-commit run --files <changed recipe/docs/skill files>
+mkdocs build --strict
+git diff --check
+```
+
+Remove generated documentation artifacts after validation. Confirm every
+recipe link resolves and every claimed profile has matching code or evidence.

.claude/skills/add-tts-model/SKILL.md

@@ -499,6 +499,13 @@ Common pre-commit failures, recovery commands for missing sign-off, and the
 full `pre-commit run` invocation for a TTS model:
 [references/precommit-dco.md](references/precommit-dco.md).
 
+## Documentation and recipe
+
+Follow the [`add-recipe` skill](../add-recipe/SKILL.md) to add or update the
+in-repository model-family recipe, the `recipes/README.md` index, the supported
+models table, TTS example documentation, and the speech API contract using
+validated evidence.
+
 ## Integration Checklist
 
 Use this checklist when integrating a new TTS model:
@@ -543,6 +550,7 @@ Use this checklist when integrating a new TTS model:
 - [ ] Buildkite entries match level: `test-ready.yml` / `test-merge.yml` or nightly TTS job / `test-nightly.yml`
 - [ ] Gradio demo working
 - [ ] Documentation added (offline + online docs, nav, supported models)
+- [ ] In-repository recipe and `recipes/README.md` row added via the `add-recipe` skill
 
 ### Phase 4: Async Chunk
 - [ ] Pipeline declares the async handoff processor

.claude/skills/add-tts-model/references/precommit-dco.md

@@ -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 |

.claude/skills/production-add-diffusion-model/SKILL.md

@@ -0,0 +1,458 @@
+---
+name: production-add-diffusion-model
+description: Productionize a vLLM-Omni diffusion model after its Day-0 vertical slice works. Use when work requires official API parity and input limits, feature-combination evidence, online FP8, distributed layerwise offload, per-request Cache-DiT, CUDA/ROCm/NPU/XPU recipes, faster or sparse attention, fused operators, USP or disaggregation analysis, continuous batching and abort handling, long-running RPS stability, or accuracy/performance/reliability CI. For initial architecture porting, registry wiring, and basic weight loading, use add-diffusion-model first.
+---
+
+# Productionizing a Diffusion Model
+
+## Scope
+
+Use this skill to move a working model integration from Day-0 support to a
+measured, fail-closed production deployment. It is deliberately separate from
+[`add-diffusion-model`](../add-diffusion-model/SKILL.md); do not modify or copy
+that skill when productionizing a model. If registry, pipeline, or basic loader
+work is incomplete, read that skill first and finish the Day-0 vertical slice.
+
+MiniMax-H3 [PR #5691](https://github.com/vllm-project/vllm-omni/pull/5691)
+is the merged Day-0 case study. Its open optimization
+[issue #5700](https://github.com/vllm-project/vllm-omni/issues/5700) is an
+actively maintained roadmap and evidence map, not a list of already supported
+features. It can lag merged implementation: read its current revision, cited
+PRs, target source, and maintained recipes, then leave every unverified row
+`not tested` until the target revision has scoped evidence.
+
+Read these references before the corresponding gate:
+
+- [API and hardware recipes](references/api-and-recipes.md)
+- [Production feature patterns](references/feature-patterns.md)
+- [Performance patterns and code examples](references/performance-patterns.md)
+- [Serving, soak, and CI validation](references/production-validation.md)
+
+## Non-negotiable evidence contract
+
+Create a matrix whose row key is at least:
+
+```text
+task x API mode x execution mode/capacity x shape/schedule x attention backend x
+packed layout x cache policy x quantization x offload mode x topology x
+hardware x dtype x output representation/transport
+```
+
+Use exactly these states:
+
+| State | Meaning |
+|---|---|
+| `validated` | The exact row passed correctness and measured deployment tests, with reproducible artifacts. |
+| `limited` | A bounded subset passed; the limitation and rejection/fallback behavior are explicit. |
+| `unsupported` | A stable architectural/platform restriction is proven and fails early with an actionable error. |
+| `not tested` | No adequate evidence exists. This is the default. |
+
+Never infer compatibility from import success, server startup, another task,
+another card, or another topology. Record the model/checkpoint revision,
+vLLM-Omni commit, commands, request assets and hashes, seed/schedule/shape,
+raw output artifacts, environment, and result for every `validated` row.
+
+## Workflow
+
+Follow the gates in order. Keep a dense BF16 single-device path as the
+correctness oracle until every advertised fast path has parity evidence.
+
+### Gate 0: Freeze the official contract
+
+Pin the official implementation and checkpoint revision. Build a matrix for:
+
+- constructor/call arguments, defaults, scheduler and sigma schedule;
+- task-to-source modality/count/order rules;
+- prompt, MIME, bytes, pixels, frames, duration, FPS, steps, seed, guidance,
+  output count, and output format limits;
+- offline API and every public sync/async serving endpoint;
+- checkpoint partitions and task selection.
+
+Normalize offline, sync, and async requests through one validation contract.
+Reject an invalid task/source combination before downloads, upload persistence,
+temporary files, async job creation, or engine submission. Use 400 for invalid
+semantics and 413 for payload limits; clean partial resources on every failure.
+See [API and hardware recipes](references/api-and-recipes.md).
+
+### Gate 1: Re-prove correctness under production shapes
+
+1. Make loading strict: track expected, loaded, unexpected, and intentionally
+   ignored tensors. A fused destination is complete only after every source
+   shard arrives. Missing retained parameters abort startup.
+2. Fix reference revision, asset hashes, seed, schedule, and shape. Compare
+   component outputs, intermediate denoise latents, and final artifacts.
+3. Test unequal packed samples. Verify `cu_seqlens`, padding exclusion,
+   modality/CFG ownership, split/gather boundaries, and one output per request.
+4. Test partial process groups with non-member ranks and group size smaller
+   than world size. Do not construct a private group per component or request.
+5. Run negative API cases before positive E2E tests to prove side-effect-free
+   rejection and cleanup.
+
+### Gate 2: Wire shared fast primitives
+
+Prefer explicit, testable vLLM-Omni/vLLM operators with native fallbacks.
+`torch.compile` remains a separately validated regional optimization; it is
+not a substitute for correct operator selection or lifecycle-safe boundaries.
+
+#### RMSNorm + RoPE baseline
+
+Use the shared cross-platform operators, preserving the official Q/K norm and
+RoPE order:
+
+```python
+from vllm_omni.diffusion.layers.norm import RMSNorm
+from vllm_omni.diffusion.layers.rope import (
+    RotaryEmbedding,
+    apply_rope_to_qk,
+)
+
+self.norm_q = RMSNorm(head_dim, eps=eps)
+self.norm_k = RMSNorm(head_dim, eps=eps)
+self.rope = RotaryEmbedding(is_neox_style=False)
+
+query = self.norm_q(query)  # [B, S, Hq, D] or packed [T, Hq, D]
+key = self.norm_k(key)      # [B, S, Hkv, D] or packed [T, Hkv, D]
+query, key = apply_rope_to_qk(
+    self.rope,
+    query,
+    key,
+    (cos, sin),
+)
+```
+
+Verify `is_neox_style`, `half_head_dim`, partial rotary dimensions, cos/sin
+layout, and packed row ownership against the official model. `False` means
+interleaved/GPT-J style; do not assume the default is NeoX. Shared RMSNorm and
+RoPE are two dispatched operators, not automatically one fused kernel.
+
+For packed non-interleaved RoPE, use the shared fused boundary when its contract
+matches the model:
+
+```python
+from vllm_omni.diffusion.layers.fused_qk_norm_rope import fused_qk_norm_rope
+
+query, key = fused_qk_norm_rope(
+    query,                    # [T, Hq, D]
+    key,                      # [T, Hkv, D]
+    self.norm_q.weight,       # [D]
+    self.norm_k.weight,       # [D]
+    rope_table,               # [T, rotary_dim] = [cos | sin]
+    self.norm_q.variance_epsilon,
+)
+```
+
+The current CUDA fast path specializes BF16 `head_dim=128`, `rotary_dim=96`;
+the public function keeps an eager fallback for unsupported inputs. Verify the
+packed row/frequency contract and trace the actual fast path. Do not reshape an
+incompatible official RoPE layout merely to enter this kernel.
+
+MiniMax-H3-style partial NeoX RoPE rotates a prefix and passes the suffix
+through:
+
+```python
+self.q_norm = RMSNorm(head_dim, eps=eps, dtype=torch.bfloat16)
+self.k_norm = RMSNorm(head_dim, eps=eps, dtype=torch.bfloat16)
+self.rope = RotaryEmbedding(is_neox_style=True, half_head_dim=False)
+
+def apply_partial_rope(self, x, freqs, rot_dim):
+    x_rot, x_pass = x[..., :rot_dim], x[..., rot_dim:]
+    cos, sin = torch.cos(freqs).to(x.dtype), torch.sin(freqs).to(x.dtype)
+    return torch.cat((self.rope(x_rot, cos, sin), x_pass), dim=-1)
+```
+
+The partial-RoPE snippet assumes `import torch` and lives on the owning
+attention module; adapt names and validated frequency shapes rather than
+copying it at module scope.
+
+Call the modules normally so `CustomOp` selects CUDA/HIP/NPU/XPU/native
+implementations. Do not call `forward_cuda()` directly. Compare the shared
+path with native BF16 at operator, block, denoise-trajectory, and artifact
+levels before claiming either accuracy or speed.
+
+#### Faster BF16 attention and fused projections
+
+Use local TP head counts and role-aware shared attention:
+
+```python
+from vllm.model_executor.layers.linear import QKVParallelLinear
+from vllm_omni.diffusion.attention.layer import Attention
+
+self.qkv_proj = QKVParallelLinear(
+    hidden_size=hidden_size,
+    head_size=head_dim,
+    total_num_heads=num_heads,
+    total_num_kv_heads=num_kv_heads,
+    bias=False,
+    quant_config=quant_config,
+    prefix=f"{prefix}.qkv_proj",
+)
+self.attn = Attention(
+    num_heads=self.qkv_proj.num_heads,
+    num_kv_heads=self.qkv_proj.num_kv_heads,
+    head_size=head_dim,
+    softmax_scale=head_dim**-0.5,
+    causal=False,
+    qkv_layout="BSND",
+    role="self",
+    prefix=prefix,
+)
+```
+
+Prove the selected backend from runtime logs/trace and compare it with dense
+SDPA. Packed metadata must express real sample boundaries; do not build masks
+that the selected backend ignores. A backend that is faster for one shape or
+platform is not a global default. See [performance patterns](references/performance-patterns.md)
+for QKV, SwiGLU, AdaLN, sparse attention, redundancy, and fusion examples.
+
+### Gate 3: Add online FP8 without breaking the loader
+
+Route the active config to each component, then pass a stable runtime prefix
+and config into every quantizable vLLM linear:
+
+```python
+def resolve_component_quant_config(quant_config, component):
+    return quant_config.resolve(component) if hasattr(quant_config, "resolve") else quant_config
+
+transformer_quant_config = resolve_component_quant_config(
+    od_config.quantization_config,
+    "transformer",
+)
+self.transformer = MyDiT(
+    od_config,
+    quant_config=transformer_quant_config,
+    prefix="transformer",
+)
+
+self.qkv_proj = QKVParallelLinear(
+    hidden_size=hidden_size,
+    head_size=head_dim,
+    total_num_heads=num_heads,
+    total_num_kv_heads=num_kv_heads,
+    quant_config=quant_config,
+    prefix=f"{prefix}.qkv_proj",
+)
+```
+
+Use `build_quant_config()` for direct construction/testing:
+
+```python
+from vllm_omni.quantization import build_quant_config
+
+quant_config = build_quant_config({
+    "transformer": {"method": "fp8"},
+    "text_encoder": None,
+    "vae": None,
+    "default": None,
+})
+```
+
+Preserve each parameter's vLLM `weight_loader`; perform checkpoint layout
+conversion before invoking it. Account for every QKV/gate-up source shard.
+Keep precision-sensitive norm/modulation/embedders in BF16 unless separately
+proven, using the current `safe_quant_config` pattern where applicable.
+
+Validate resident DiT FP8 first: prove which modules are quantized, HBM saved,
+BF16-vs-FP8 trajectory/artifact quality, latency, and throughput. Text encoder,
+VAE, other hardware, TP/cache/DLO combinations are separate rows.
+Pre-quantized, pruned, rotated, or adapter-modified checkpoints are separate
+model/loading lanes; compare them with the released BF16 model without calling
+the result a pure quantization ablation. Read
+[`../quantization/SKILL.md`](../quantization/SKILL.md) if present and prefer the
+target revision's implementation/docs when sibling guidance differs.
+
+### Gate 4: Make every component offloadable
+
+Declare topology instead of relying on heuristic discovery:
+
+```python
+from typing import ClassVar
+
+import torch.nn as nn
+
+from vllm_omni.diffusion.models.interface import SupportsComponentDiscovery
+from vllm_omni.diffusion.offloader import OffloadPlan
+
+class MyPipeline(nn.Module, SupportsComponentDiscovery):
+    _dit_modules: ClassVar[list[str]] = ["transformer"]
+    _encoder_modules: ClassVar[list[str]] = ["text_encoder"]
+    _vae_modules: ClassVar[list[str]] = ["vae"]
+    _resident_modules: ClassVar[list[str]] = []
+    _offload_plan: ClassVar[OffloadPlan] = OffloadPlan(
+        on_demand_component_paths=frozenset({"text_encoder", "vae"}),
+        block_attrs={"transformer": ("blocks",)},
+        offload_submodules={"token_refiner": "blocks"},
+        resident_dit_paths=frozenset({"transformer"}),
+        encoder_block_attrs={"text_encoder": ("encoder.layers",)},
+    )
+```
+
+Add a test that resolves every dotted component/block path and fails if it is
+missing or is not the expected module/container; discovery may otherwise warn
+and skip. Validate ordinary layerwise offload and distributed layerwise
+offload (DLO) independently.
+
+Keep component lifecycle and host-weight storage separate. `OffloadPlan`
+declares what can be streamed; the diffusion loader owns any `HostWeightPlan`
+and hands the exact prevalidated plan to DLO. Do not make the offloader rescan
+checkpoint files or duplicate loader name/shape/dtype decisions. Direct mmap is
+a fail-closed optimization: current preflight requires TP1, no HSDP, no online
+quantization, complete bindings, and represented transforms. Otherwise the
+ordinary loader remains authoritative.
+
+If the target revision supports Host Weight Runtime (HWR), treat it as a
+separate, exact-identity startup/cache path rather than generic offload or
+zero-copy execution. Qualify `preferred` population/fallback and `required`
+consume-only failure independently; verify immutable manifests, final-layout
+restore, lease cleanup, corruption/quarantine, and node-local sharing. Current
+model contracts may be BF16 no-AllGather-only, so reject AllGather, online
+quantization, HSDP, LoRA/adapted weights, and unrecognized load formats unless
+the exact target revision explicitly adds them.
+
+On small-HBM cards, record load/materialization peak, resident peak, encode,
+each denoise wave, decode, transient per-rank HBM, host PSS, H2D, and collective
+time. Test DLO AllGather and `--dlo-no-use-allgather` as different deployments.
+Sweep the supported resident-block policies and publish the non-dominated
+latency-HBM frontier; a single minimum-memory or fastest point is not the DLO
+trade-off. Keep request-specific preparation replica-local. Only weight
+materialization may enter the declared DLO collective group; never use WORLD
+for per-request work in a DP deployment.
+Direct checkpoint mmap preflight is limited to TP1 without HSDP or online
+quantization; TP>1 falls back to the ordinary TP-aware loader and can still feed
+DLO AllGather or no-AllGather after scoped E2E. HSDP remains incompatible with
+DLO AllGather. A target revision may allow finalized per-tensor online FP8
+through the ordinary loader and AllGather; keep every other online quantizer
+fail-closed, and require model/card/topology E2E before promotion.
+Use deploy YAML for DP under `--omni`; vLLM DP CLI flags are rejected.
+
+### Gate 5: Add request-scoped acceleration safely
+
+Server-wide Cache-DiT support does not imply a per-request quality API. Only
+add request policy after defining calibrated quality tiers and a safe hook
+lifecycle using `SupportsRequestScopedCacheDiT`, `CacheDiTRequestSpec`, and
+`RequestScopedCacheDiTRuntime`. Validate alternating and concurrent quality
+tiers, real cache hits, refresh/reset, success, error, disconnect, abort, and
+the next uncached request. Do not batch different quality policies unless the
+batch compatibility key and hook ownership make that safe.
+
+Then test Cache-DiT against FP8, DLO, TP/SP, compile, and batching one
+combination at a time. See [production feature patterns](references/feature-patterns.md)
+for the lifecycle skeleton and compatibility matrix.
+
+### Gate 6: Optimize only from profiles
+
+Freeze one canonical workload manifest before profiling. Keep lossless runtime
+A/Bs, accelerated paths with numerical/quality trade-offs, and production
+topology studies in separate result lanes. Reconcile the client boundary as
+queue + encode + denoise + video/audio decode + output transport/codec +
+residual; do not hide a material residual in another stage.
+
+Profile stage time, GPU kernels, CPU/GPU synchronization, allocations, H2D,
+output payload/copies, and collectives before choosing work. Apply and A/B one
+change at a time:
+
+1. Remove synchronization and redundant denoise-loop work: hoist static prompt
+   conditioning/token refinement, RoPE tables, masks/metadata, bounded AdaLN
+   schedule projections, and reference scans; remove per-layer `.item()` and
+   unused masks/allocations.
+2. Select the fastest correct BF16 attention backend for each role and shape.
+3. Prefer explicit fused QKV, gate-up + `SiluAndMul`, RMSNorm/RoPE, AdaLN, and
+   layout/residual operators with guarded native fallbacks. Preserve every
+   materialized dtype/rounding boundary consumed by the unfused graph.
+4. Enable sparse attention only when platform, shape, topology, realized
+   sparsity, quality, fallback, and end-to-end speedup are all proven.
+5. Profile USP all-to-all count/bytes, packing, contiguous copies, overlap, and
+   scaling efficiency. Audit replicated cross-attention before using
+   `skip_sequence_parallel`. Qualify regular and accelerated Ulysses transport
+   independently; activation logs, JIT/readiness time, workspace growth,
+   stream ownership, maximum-shape warmup, and numerical drift are gates.
+   Preserve the checkpoint's Q:KV head ratio when padding GQA for Ulysses, and
+   validate strided QKV staging rather than assuming packed contiguity.
+6. Evaluate text-encoder or VAE disaggregation only if stage share, transfer
+   volume, reuse/concurrency, and independent scaling justify it. Tiling,
+   offload, or patch parallelism is not disaggregation.
+
+Do not combine independent optimizations into one performance claim. Preserve
+raw before/after runs with fixed workload and warmup policy.
+
+### Gate 7: Make serving interruptible and load-stable
+
+Treat request-mode batching and step-wise execution as separate capabilities:
+
+- request batching requires `supports_request_batch = True` and
+  `DiffusionRequestBatch -> list[DiffusionOutput]`, exactly one output per
+  request;
+- step execution requires the complete `SupportsStepExecution` contract:
+  `prepare_encode`, `denoise_step`, `step_scheduler`, and `post_decode`.
+
+Keep all mutable scheduler, RNG, latent, mask, cache, and step state request
+scoped. First validate `--step-execution --max-num-seqs 1` and step-boundary
+abort; only then test heterogeneous multi-request waves. Step continuous
+batching is experimental and is not automatically compatible with Cache-DiT.
+Before recommending it, predeclare a useful case and success threshold, then
+A/B it against request mode under the same arrival process. Structural support
+or a generic bridge is not latency, throughput, or cancellation evidence for a
+model.
+
+Test queued and in-flight abort, client disconnect, timeout, OOM, worker error,
+and restart. Require one terminal result, idempotent cleanup, no post-decode
+after abort, no cache/temp/VRAM/request-ID leak, and a successful next request.
+Inject late worker results/exceptions after cancellation and prove the result
+pump stays alive instead of raising `InvalidStateError`. For large batched or
+long-window video outputs, freeze dtype, range, layout, contiguity, ownership,
+payload size, serializer/codec limits, and offline-versus-HTTP semantics.
+Validate device-side output preparation, D2H/IPC or shared handles, remote
+encoding, event-loop responsiveness, and client materialization as separate
+stages, including abort and consumer failure cleanup.
+A model/VAE callback that publishes ordered chunks is only a producer contract;
+it is not transport, backpressure, cancellation, public streaming, or
+time-to-first-frame evidence. Distinguish complete-response faster-than-playback
+(`client E2E / output duration <= 1`) from streaming and report first-chunk,
+steady cadence, finalization, and complete-artifact latency separately.
+Run below-, near-, and above-saturation mixed-RPS soaks and report success/error
+rate, throughput, queue time, memory slope, temp growth, and worker health.
+Report p50/p95/p99 only from a declared arrival model with enough measured
+requests for the claimed percentile. See
+[serving and CI validation](references/production-validation.md).
+
+### Gate 8: Publish recipes and four independent CI tracks
+
+For every target vendor/card/topology, publish exact environment, serve/deploy
+command, one complete JSON or multipart curl per supported task, expected
+output checks, benchmark command, warmup/repeat/concurrency policy, raw metrics,
+per-rank HBM/host PSS, accuracy artifact, and limitations. Mark each
+CUDA/ROCm/NPU/XPU row `validated`, `unsupported`, or `not tested`; never inherit
+support from the platform abstraction or another card. Re-run DLO on each
+small-HBM recipe.
+
+Maintain separate CI gates:
+
+1. Function: strict load, API/offline positive and negative contracts.
+2. Accuracy: fixed reference revision/assets/seed/schedule and scoped tolerance.
+3. Performance: fixed best deployment plus memory-constrained/DLO row, raw JSON,
+   explicit warmup, enough samples for each claimed statistic, and owned
+   regression thresholds.
+4. Reliability: long mixed-RPS soak, abort/disconnect/fault cleanup, memory and
+   temporary-resource trend.
+
+## Production Definition of Done
+
+A model is production-ready only when:
+
+- every official advertised task passes API parity, offline/online parity,
+  strict loading, fixed-reference quality, and negative validation;
+- the recommended deployment row passes correctness, isolation, abort/error
+  cleanup, benchmark, and soak gates on named hardware;
+- online FP8 and DLO are each implemented and either validated in scoped rows
+  or explicitly limited/unsupported with fail-fast behavior;
+- per-request cache quality tiers have measured speed/quality frontiers and
+  request isolation, if advertised;
+- CUDA/ROCm/NPU/XPU recipes describe evidence rather than inferred support;
+- Function, Accuracy, Performance, and Reliability CI have owners, artifacts,
+  and actionable failure output;
+- accelerated paths identify every precision/quality trade-off, and the raw
+  offline plus online encoded output contracts are versioned and tested;
+- all unverified task/backend/cache/quant/offload/topology/hardware combinations
+  remain `not tested` and are not presented as supported.
+
+Day-0 support is still a valid milestone, but title, docs, and matrix must say
+which representative task passed and which production gates remain.

.claude/skills/production-add-diffusion-model/agents/openai.yaml

@@ -0,0 +1,4 @@
+interface:
+  display_name: "Production Add Diffusion Model"
+  short_description: "Productionize vLLM-Omni diffusion models"
+  default_prompt: "Use $production-add-diffusion-model to take this integrated diffusion model from Day-0 support to evidence-backed production readiness."

.claude/skills/production-add-diffusion-model/references/api-and-recipes.md

@@ -0,0 +1,315 @@
+# API Contract and Hardware Recipes
+
+## Contents
+
+1. [Freeze the upstream contract](#freeze-the-upstream-contract)
+2. [Validate before side effects](#validate-before-side-effects)
+3. [Task request templates](#task-request-templates)
+4. [Serving benchmark template](#serving-benchmark-template)
+5. [Hardware recipe contract](#hardware-recipe-contract)
+6. [Best deployment selection](#best-deployment-selection)
+
+## Freeze the upstream contract
+
+Pin three revisions independently:
+
+- official source implementation commit or package version;
+- checkpoint/model-card revision and partition;
+- vLLM-Omni target commit.
+
+Build a table before editing code. One row is one official task/partition.
+
+| Field | Required content |
+|---|---|
+| Task and partition | Official task name, checkpoint subdirectory, task selection rules |
+| Call contract | Constructor and call arguments, types, required/optional, official defaults |
+| Prompt/input | Prompt count/length, modality, MIME, order, count, decoded shape |
+| Size/time | Width/height alignment, pixels, frames, duration, FPS, start offset |
+| Denoise | Scheduler, sigma schedule, steps, CFG/guidance, seed behavior |
+| Output | Count, format, codec/sample rate, return type |
+| API | Offline mapping, sync endpoint, async endpoint/job lifecycle |
+| Rejection | Status code and stable error class/message for every invalid boundary |
+
+Official API parity includes limits and defaults, not just names. Do not add a
+generic default that changes a distilled checkpoint's fixed schedule, or accept
+an input combination the official task rejects.
+
+For every accepted boundary, add the neighboring rejected values. Examples:
+`max_images` and `max_images + 1`, smallest/largest legal duration, aligned and
+misaligned dimensions, omitted and illegal steps, one and too many outputs.
+
+## Validate before side effects
+
+Use one normalization/validation path for offline, sync, and async requests.
+The ordering must be:
+
+```text
+parse -> normalize aliases -> validate task/source matrix -> validate declared
+limits -> stream with byte caps -> inspect/decode with frame/pixel/duration caps
+-> persist only if needed -> submit to engine
+```
+
+Fail before download, persistence, job creation, or engine submit whenever the
+available metadata is sufficient. For streamed uploads, enforce:
+
+- total reference count and per-modality count before reading bodies;
+- per-file bytes while streaming;
+- aggregate bytes across all files;
+- decoded frames, duration, pixels, and media type before engine submission.
+
+On rejection, close streams and remove every partial temporary resource. Use
+HTTP 400 for invalid task/field combinations and 413 for payload limits. Test
+both sync and async paths; an invalid async request must not create a job ID.
+
+Do not trust client MIME alone. Sniff/validate content and keep ordered repeated
+multipart fields ordered. Avoid logging prompts, raw references, signed URLs,
+or base64 payloads.
+
+## Task request templates
+
+These are endpoint-shape templates, not model recipes. Replace placeholders
+with the exact official defaults and validated assets for the target model.
+Every production recipe must include all advertised tasks and an expected
+output check.
+
+### Text to image: JSON
+
+```bash
+curl --fail-with-body -sS -X POST http://127.0.0.1:8091/v1/images/generations \
+  -H 'Content-Type: application/json' \
+  -d '{
+    "model": "<model>",
+    "prompt": "<validated prompt>",
+    "size": "1024x1024",
+    "num_inference_steps": 30,
+    "seed": 42,
+    "response_format": "b64_json"
+  }' | jq -er '.data[0].b64_json' | base64 -d > output.png
+file output.png
+```
+
+### Image edit: multipart
+
+```bash
+curl --fail-with-body -sS -X POST http://127.0.0.1:8091/v1/images/edits \
+  -F 'model=<model>' \
+  -F 'prompt=<validated edit prompt>' \
+  -F 'image=@/path/to/reference.png;type=image/png' \
+  -F 'size=1024x1024' \
+  -F 'num_inference_steps=30' \
+  -F 'seed=42' \
+  -F 'output_format=png' \
+  | jq -er '.data[0].b64_json' | base64 -d > edited.png
+file edited.png
+```
+
+Use repeated `image` fields only if the model's official input matrix allows
+multiple ordered sources. Never describe a file upload as JSON-only.
+
+### Public request fields versus internal sampling fields
+
+Use the field exposed by the target revision's endpoint schema; do not rename
+it from the pipeline's internal attribute. For example, the current video JSON
+and multipart contracts expose `extra_params`, while serving translates that
+object into `DiffusionSamplingParams.extra_args`. Other request paths may
+expose canonical `extra_args` or nest it under `extra_body`. Inspect the exact
+Pydantic model/FastAPI form dependency and exercise the generated curl before
+publishing a recipe. A deprecation warning in a different endpoint is not
+evidence that an unrecognized multipart form field works here.
+
+Current endpoint map (re-check it at the target commit):
+
+| Endpoint | Public model-specific field |
+|---|---|
+| `/v1/videos`, `/v1/videos/sync` | Multipart `extra_params` JSON string |
+| `/v1/images/generations` | JSON `extra_params` object |
+| `/v1/chat/completions` diffusion | `extra_body.extra_args` |
+| `/v1/images/edits` | Declared typed form fields only |
+| `/v1/audio/generate` | Declared typed JSON fields only |
+
+Prefer declared top-level fields. Use a generic model-specific envelope only
+when the endpoint actually exposes it.
+
+### Text to video: multipart
+
+```bash
+curl --fail-with-body -sS -X POST http://127.0.0.1:8091/v1/videos/sync \
+  -H 'Accept: video/mp4' \
+  -F 'model=<model>' \
+  -F 'prompt=<validated prompt>' \
+  -F 'size=1280x720' \
+  -F 'num_frames=81' \
+  -F 'fps=16' \
+  -F 'num_inference_steps=30' \
+  -F 'seed=42' \
+  -F 'extra_params={"task":"<official-task>"}' \
+  -o output.mp4
+ffprobe -v error -show_entries stream=codec_name,width,height,r_frame_rate \
+  -of json output.mp4
+```
+
+### Image/video to video: multipart
+
+```bash
+curl --fail-with-body -sS -X POST http://127.0.0.1:8091/v1/videos/sync \
+  -H 'Accept: video/mp4' \
+  -F 'model=<model>' \
+  -F 'prompt=<validated prompt>' \
+  -F 'num_frames=81' \
+  -F 'fps=16' \
+  -F 'num_inference_steps=30' \
+  -F 'seed=42' \
+  -F 'extra_params={"task":"<official-task>","start_time_seconds":0}' \
+  -F 'input_reference=@/path/to/reference.png;type=image/png' \
+  -o i2v.mp4
+
+curl --fail-with-body -sS -X POST http://127.0.0.1:8091/v1/videos/sync \
+  -H 'Accept: video/mp4' \
+  -F 'model=<model>' \
+  -F 'prompt=<validated prompt>' \
+  -F 'num_frames=81' \
+  -F 'num_inference_steps=30' \
+  -F 'seed=43' \
+  -F 'extra_params={"task":"<official-task>","start_time_seconds":0}' \
+  -F 'input_reference=@/path/to/reference.mp4;type=video/mp4' \
+  -o v2v.mp4
+```
+
+For multiple references, repeat the model's accepted multipart field in
+official order. Include complete downloadable or repository fixtures plus
+SHA256 hashes. A placeholder-only recipe is not runnable evidence.
+
+### Text to audio: JSON
+
+```bash
+curl --fail-with-body -sS -X POST http://127.0.0.1:8091/v1/audio/generate \
+  -H 'Content-Type: application/json' \
+  -d '{
+    "model": "<model>",
+    "input": "<validated audio prompt>",
+    "audio_length": 10.0,
+    "num_inference_steps": 100,
+    "seed": 42,
+    "response_format": "wav"
+  }' -o output.wav
+ffprobe -v error -show_entries stream=codec_name,sample_rate,channels \
+  -of json output.wav
+```
+
+Use `/v1/audio/generate` for general diffusion audio. Do not substitute the
+speech API unless the model is actually served through the speech contract.
+
+### Async video lifecycle
+
+Alongside `/v1/videos/sync`, include `POST /v1/videos`, status polling,
+content retrieval, cancellation/abort if public, and cleanup assertions. The
+same request must normalize to the same engine sampling parameters in both
+modes.
+
+## Serving benchmark template
+
+Run a smoke first, then fixed-concurrency and arrival-rate tests. Video task
+names for the harness are `t2v`, `i2v`, `ti2v`, or `v2v`; model-specific task
+selection belongs in `--extra-body` when required.
+
+```bash
+python benchmarks/diffusion/diffusion_benchmark_serving.py \
+  --base-url http://127.0.0.1:8091 \
+  --endpoint /v1/videos \
+  --model '<model>' \
+  --dataset random \
+  --task t2v \
+  --num-prompts 100 \
+  --width 1280 --height 720 \
+  --num-frames 81 --fps 16 \
+  --num-inference-steps 30 \
+  --request-rate 0.2 \
+  --max-concurrency 4 \
+  --warmup-requests 4 \
+  --warmup-concurrency 4 \
+  --extra-body '{"extra_params":"{\"task\":\"<official-task>\"}"}' \
+  --output-file result.json
+```
+
+The video backend currently stringifies each multipart value. Pre-serialize
+the nested `extra_params` object as above; passing it as a nested object would
+produce Python dict syntax rather than valid JSON. If the target revision
+changes the backend to JSON-encode dict/list values, update and smoke-test this
+command against that revision.
+
+For i2v/v2v, use the harness's validated dataset/trace path or provide the
+documented media source; do not silently benchmark t2v and label it i2v. Keep
+prompt, assets, seed, dimensions, frames, steps, guidance, warmup, concurrency,
+and arrival process identical across A/B runs.
+
+Keep three benchmark lanes distinct:
+
+1. lossless runtime A/B with the released schedule/precision/attention policy;
+2. accelerated-path A/B that changes one adapter, quantization, cache, or
+   sparsity policy and adds same-seed quality evidence;
+3. production-topology studies that vary placement or parallelism and report a
+   latency/throughput/memory frontier rather than a kernel speedup.
+
+For a fixed single-request A/B, retain every measured run and report median or
+mean with range after an explicit warmup; do not infer p95/p99 from a few
+repetitions. Tail latency requires a declared arrival process, request count,
+concurrency, and enough successful samples. Reconcile client E2E with queue,
+encoder, denoise, video/audio decode, D2H/IPC, codec, and residual timings.
+
+For generated media, define `real-time` rather than relying on the label. A
+complete-response claim uses `client E2E / validated output duration <= 1` and
+does not imply streaming or low time-to-first-frame. Report first chunk/fragment
+and cadence separately when a public streaming route exists. Never add gains
+across benchmark lanes with different schedules, prompts, seeds, artifacts, or
+topologies.
+
+The diffusion serving harness currently covers image/video task families, not
+`/v1/audio/generate`. For text-to-audio, use the target model's task-specific
+audio benchmark if one exists; otherwise add a serving harness that records
+request rate, concurrency, latency, generated-audio seconds per second, error
+rate, and output validation. Do not relabel the TTS harness or a video workload
+as diffusion-audio evidence.
+
+## Hardware recipe contract
+
+Create a separate row and runnable section for each vendor/card/topology:
+
+| Evidence | Required detail |
+|---|---|
+| Identity | Vendor, exact SKU, device count, HBM, interconnect |
+| Software | Driver, CUDA/ROCm/CANN/oneAPI, framework/container, dependency versions |
+| Source | vLLM-Omni commit, official/checkpoint revision and hashes |
+| Deployment | dtype, attention backend per role, TP/SP/CFG/HSDP/DP, cache, quant, offload |
+| Commands | Complete deploy YAML/serve command, every task curl, benchmark command |
+| Method | Warmup exclusion, repetitions or request count, concurrency, arrival distribution, duration, statistic definitions |
+| Results | Success rate, p50/p95/p99, RPS/throughput per device, stage latency |
+| Memory | Load/materialize, resident, encode, denoise, decode, transient per-rank HBM and host PSS |
+| Quality | Metric/tolerance, artifact links/hashes, temporal/audio checks |
+| Output | Raw/offline dtype, range, layout and ownership; online codec, payload bytes, transport route and client boundary |
+| Limits | `validated`/`limited`/`unsupported`/`not tested`, fallback/rejection behavior |
+
+Use current paths such as `recipes/<vendor>/`, `recipes/README.md`, supported
+models, and feature compatibility tables. Top-level documentation must link to
+the scoped matrix; it must not use an unqualified checkmark that hides a ROCm,
+NPU, XPU, task, topology, or dtype limitation.
+
+DLO evidence is per card, especially on small-HBM devices. A DLO result on one
+CUDA card does not establish another CUDA SKU, ROCm, NPU, or XPU support.
+
+## Best deployment selection
+
+Benchmark a correctness-approved candidate matrix, not every theoretical
+combination. Choose the recommended row by the production objective—latency,
+throughput, cost, or small-HBM fit—and document that objective.
+
+Do not recommend a row until it passes:
+
+- all advertised task/API contracts;
+- fixed-reference quality;
+- request isolation and abort/error cleanup;
+- sustained arrival-rate tests;
+- per-rank HBM and host PSS limits;
+- hardware-specific accuracy and performance CI.
+
+Keep the most memory-constrained DLO row even when it is not the fastest; it is
+a separate production recipe, not a failed latency candidate.

.claude/skills/production-add-diffusion-model/references/feature-patterns.md

@@ -0,0 +1,462 @@
+# Production Feature Patterns
+
+## Contents
+
+1. [Compatibility matrix](#compatibility-matrix)
+2. [Online FP8](#online-fp8)
+3. [Strict fused-weight loading](#strict-fused-weight-loading)
+4. [Distributed layerwise offload](#distributed-layerwise-offload)
+5. [Per-request Cache-DiT](#per-request-cache-dit)
+6. [Combination gates](#combination-gates)
+
+## Compatibility matrix
+
+Create rows at the granularity below. Add evidence links and a reason for every
+non-validated state.
+
+| Task/shape | Execution mode/capacity | Backend/layout | Quant | Cache policy | Offload | Topology | Hardware/dtype | Output contract/transport | State | Evidence |
+|---|---|---|---|---|---|---|---|---|---|---|
+| `<task>/<shape>/<schedule>` | serial request / 1 | SDPA, padded | BF16 | none | resident | 1 device | `<card>` BF16 | raw float / local | `not tested` | — |
+| `<task>/<shape>/<schedule>` | step / N | `<fast>`, packed | BF16 | none | resident | `<TP/SP>` | `<card>` BF16 | encoded / subprocess | `not tested` | — |
+| `<task>/<shape>/<schedule>` | step / N | `<fast>`, packed | FP8 | high | DLO no-AG | `<TP/SP>` | `<card>` | device uint8 / SHM | `not tested` | — |
+
+Initialize every row as `not tested`; the table is a work queue, not evidence.
+Promote the dense BF16 row to the oracle only after its scoped parity passes.
+Add one axis at a time. If the runtime cannot
+enforce a known-incompatible combination, add construction/admission
+validation before writing a recipe.
+
+## Online FP8
+
+### Component routing
+
+The public builder accepts one method or a longest-prefix component map:
+
+```python
+from vllm_omni.quantization import build_quant_config
+
+config = build_quant_config({
+    "transformer": {
+        "method": "fp8",
+        "ignored_layers": ["transformer.final_layer"],
+    },
+    "text_encoder": None,
+    "vae": None,
+    "default": None,
+})
+
+assert config.resolve("transformer.blocks.0.attn.qkv_proj") is not None
+assert config.resolve("text_encoder.layers.0.mlp") is None
+assert config.resolve("vae.decoder.conv_out") is None
+```
+
+Use actual runtime prefixes. Multi-DiT or nested pipelines may not use the
+generic names `transformer` and `vae`. A prefix that falls through to a default
+is not component validation. Add tests for every included and ignored prefix.
+
+For CLI serving, first validate resident DiT-only FP8:
+
+```bash
+vllm serve '<model>' --omni --quantization fp8 --max-num-seqs 1
+```
+
+For scoped routing:
+
+```bash
+vllm serve '<model>' --omni \
+  --diffusion-quantization-config \
+  '{"<dit-runtime-prefix>":{"method":"fp8","ignored_layers":["<exact-linear-prefix>"]},"<vae-runtime-prefix>":null,"default":null}' \
+  --max-num-seqs 1
+```
+
+Do not publish these placeholders. A recipe must contain the target model's
+verified prefixes.
+
+### Quantizable module wiring
+
+Pass `quant_config` and a stable prefix through every vLLM linear. Keep
+precision-sensitive modulation/norm layers safe unless proven otherwise.
+
+```python
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.linear import (
+    MergedColumnParallelLinear,
+    QKVParallelLinear,
+    RowParallelLinear,
+)
+
+self.qkv_proj = QKVParallelLinear(
+    hidden_size=hidden_size,
+    head_size=head_dim,
+    total_num_heads=num_heads,
+    total_num_kv_heads=num_kv_heads,
+    bias=False,
+    quant_config=quant_config,
+    prefix=f"{prefix}.attn.qkv_proj",
+)
+self.gate_up_proj = MergedColumnParallelLinear(
+    hidden_size,
+    [intermediate_size, intermediate_size],
+    bias=False,
+    quant_config=quant_config,
+    prefix=f"{prefix}.mlp.gate_up_proj",
+)
+self.act_fn = SiluAndMul()
+self.down_proj = RowParallelLinear(
+    intermediate_size,
+    hidden_size,
+    bias=False,
+    input_is_parallel=True,
+    quant_config=quant_config,
+    prefix=f"{prefix}.mlp.down_proj",
+)
+```
+
+Set required checkpoint parameters to fail rather than silently initialize if
+the current loader supports `missing_param_init = "error"`.
+
+### FP8 evidence
+
+For each task/hardware row, capture:
+
+- startup proves the FP8 quant method is attached to intended linear modules;
+- no unexpected BF16 fallback and no meta/uninitialized parameters;
+- same-seed component, trajectory, and final-artifact comparison with BF16;
+- HBM at load, resident, encode, denoise, decode, and peak;
+- cold load time; fixed-work warm-latency raw runs; and serving p50/p95/p99 plus
+  throughput only from a declared arrival load with enough samples;
+- named ignored layers and why their BF16 retention is required.
+
+DiT FP8 evidence does not cover the text encoder or VAE. CUDA evidence does not
+cover ROCm/NPU/XPU. A pre-quantized checkpoint is a different loading path from
+online FP8 and needs its own state. If it is also pruned, rotated, distilled, or
+adapter-modified, report that model delta explicitly; BF16-versus-artifact
+quality and speed are deployment comparisons, not pure quantization ablations.
+
+## Strict fused-weight loading
+
+Never use `param.data.copy_()` for quantizable vLLM parameters. Transform the
+checkpoint layout before invoking the parameter loader so the online quant
+loader remains outermost:
+
+```python
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+
+param = params[target_name]
+weight_loader = getattr(param, "weight_loader", default_weight_loader)
+
+if target_name.endswith(".qkv_proj.weight"):
+    packed_qkv = reorder_checkpoint_qkv(checkpoint_weight)
+    weight_loader(param, packed_qkv)
+elif target_name.endswith(".gate_up_proj.weight"):
+    gate, up = checkpoint_weight.chunk(2, dim=0)
+    weight_loader(param, gate, 0)
+    weight_loader(param, up, 1)
+else:
+    weight_loader(param, checkpoint_weight)
+```
+
+Track source shards per fused destination:
+
+```python
+expected = {
+    "blocks.0.attn.qkv_proj.weight": {"q", "k", "v"},
+    "blocks.0.mlp.gate_up_proj.weight": {0, 1},
+}
+seen = {name: set() for name in expected}
+
+# After mapping/loading each source:
+seen[target_name].add(shard_id)
+
+incomplete = {
+    name: sorted(shards - seen[name], key=str)
+    for name, shards in expected.items()
+    if shards - seen[name]
+}
+if incomplete:
+    raise RuntimeError(f"incomplete fused checkpoint parameters: {incomplete}")
+```
+
+Also compare retained model parameters with the returned loaded set. Explicit
+checkpoint ignores need a reason and test; an unknown or missing weight is not
+an ignore policy.
+
+Load-time adapter fusion creates a dedicated model, not a request-switchable
+LoRA. Pin the base and adapter revisions, validate every low-rank and full-rank
+delta target, freeze the adapter-declared schedule/task, and fuse before
+sharding only when the loader contract proves identical rank-local weights.
+Reject offload or host-weight paths that bypass that fusion rather than serving
+the unfused base model silently.
+
+## Distributed layerwise offload
+
+### Declare component topology
+
+Use `SupportsComponentDiscovery` and `OffloadPlan`. Validate all dotted paths
+at construction/test time because discovery may otherwise warn and skip.
+
+Treat a future/requested `--layerwise-offload-components` selector as a public
+API only if it exists in the target revision. Today, express component topology
+with `OffloadPlan` (`on_demand_component_paths`, `block_attrs`,
+`encoder_block_attrs`, `resident_dit_paths`, and `offload_submodules`). Do not
+invent a CLI flag or claim component-selective DLO from discovery declarations
+alone; validate the selector separately when it lands.
+
+```python
+from operator import attrgetter
+
+import torch
+
+def assert_offload_path(root, path):
+    value = attrgetter(path)(root)
+    if not isinstance(value, torch.nn.Module):
+        raise TypeError(f"offload path {path!r} is not an nn.Module")
+    return value
+
+for path in (
+    *pipeline._dit_modules,
+    *pipeline._encoder_modules,
+    *pipeline._vae_modules,
+    *pipeline._offload_plan.on_demand_component_paths,
+):
+    assert_offload_path(pipeline, path)
+```
+
+For `block_attrs`, resolve the DiT first and then each declared block attribute.
+Require an indexable block container with at least one `nn.Module`. Validate
+`offload_submodules`, `resident_dit_paths`, and `encoder_block_attrs` similarly.
+
+### Keep host storage loader-owned
+
+Current DLO accepts a loader-produced `HostWeightPlan`. Pipeline authors should
+not instantiate this plan or add ad hoc mmap capability flags. The loader must
+preflight complete runtime/checkpoint bindings, names, shapes, dtypes, persistent
+buffers, and represented transforms before it skips ordinary materialization.
+The exact accepted plan is consumed once by DLO; a rejected plan records an
+observable fallback reason and continues through the ordinary loader.
+
+Direct checkpoint mmap currently requires TP1 without HSDP or online
+quantization. TP>1 falls back to the ordinary TP-aware loader and may feed DLO
+AllGather or no-AllGather; the current design records a bounded DP2xTP2
+AllGather smoke, but that path does not receive direct-mmap host-page sharing.
+Per-tensor online FP8 must use the ordinary loader. Current revisions can admit
+its finalized weight/scale layout to DLO AllGather, including transposed runtime
+weights, while unsupported online quantizers still fail closed. Direct mmap
+remains unavailable, so capture the temporary full-model host materialization
+before rank sharding.
+
+Host Weight Runtime is a distinct opt-in path. Current final-layout consumers
+use no-AllGather DLO and exact model-declared BF16 representations. In
+`preferred` mode, consume an exact local hit or canonically load and publish for
+a future startup; in `required` mode, consume only and fail on a miss or unusable
+artifact. HWR must not interact with DLO AllGather, online quantization, HSDP,
+LoRA/adapted weights, or an unrecognized load format unless the target revision
+adds an exact producer/restore contract. It is immutable node-local CPU backing,
+not zero-copy GPU execution; the DLO transport still owns staging or direct H2D.
+Qualify identity inputs, cold population, warm reuse, atomic concurrent
+publication, source digests, corruption/quarantine, lease cleanup, capacity,
+node-local PSS, H2D, and E2E latency. A host-memory saving that materially
+regresses transfer or request latency is a separate memory-first deployment.
+
+### DLO deployment modes
+
+Treat these paths separately:
+
+| Path | Weight/runtime behavior | Required boundaries |
+|---|---|---|
+| DLO AllGather | Rank-sharded pinned host tensors; reconstruct layer through collective | TP>1 uses ordinary TP-aware loader output; HSDP and unsupported online quantizers are rejected; finalized per-tensor online FP8 is eligible only when recognized by the target revision; concurrent ranks must participate consistently |
+| DLO no-AllGather | Loader-approved checkpoint mmap or ordinary runtime tensors; each rank streams a complete block | Direct mmap is TP1/non-HSDP/non-online-quant only; TP/HSDP/online quant use ordinary runtime tensors and require scoped E2E |
+| SP + DLO | SP group can supply the DLO collective group | Packed boundaries and collective order require E2E |
+
+Single-stage examples:
+
+```bash
+# SP + DLO AllGather candidate; validate exact model/card before publishing.
+vllm serve '<model>' --omni \
+  --enable-distributed-layerwise-offload \
+  --usp 4 \
+  --max-num-seqs 1
+
+# TP + DLO rank-local candidate; not a support statement.
+vllm serve '<model>' --omni \
+  --tensor-parallel-size 2 \
+  --enable-distributed-layerwise-offload \
+  --dlo-no-use-allgather \
+  --max-num-seqs 1
+```
+
+Under `--omni`, configure DP in a deploy YAML rather than passing vLLM DP CLI
+flags:
+
+```yaml
+pipeline: <registered-pipeline>
+async_chunk: false
+data_parallel_size: 4
+stages:
+  - stage_id: 0
+    devices: "0,1,2,3"
+    max_num_seqs: 1
+    enable_distributed_layerwise_offload: true
+    dlo_use_allgather: true
+    parallel_config:
+      tensor_parallel_size: 1
+      sequence_parallel_size: 1
+```
+
+```bash
+vllm serve '<model>' --omni --deploy-config /path/to/dlo_dp4.yaml
+```
+
+Keep the YAML next to the validated recipe. Match its schema to the target
+revision; do not assume a model accepts another pipeline's deploy config.
+
+### DLO validation sequence
+
+1. Resident BF16 reference.
+2. Ordinary layerwise offload parity.
+3. DLO enable, warmup, same-seed generation, disable/restart.
+4. AllGather and no-AllGather separately.
+5. Concurrent requests with different prompts and identical collective-affecting
+   parameters; then reject or schedule incompatible steps/shapes safely.
+6. Queued/in-flight abort, error, idle rank, worker exit, and next request.
+7. TP, SP, cache, compile, online FP8 one axis at a time.
+8. Loader-plan selection/fallback reason, cold/warm storage lifecycle, and
+   corruption/stale-entry recovery when mmap/cache backing is advertised.
+9. Replica-local request preparation plus DLO-group-only weight collectives in
+   DP; exercise concurrent replicas to catch accidental WORLD rendezvous.
+10. Sweep resident block counts; publish raw latency/HBM pairs, dominated
+    points, and the non-dominated Pareto frontier.
+11. Per-rank HBM, host PSS for the full process tree, H2D and collective trace.
+
+On a small-HBM card, measure transient encode/VAE peaks as well as resident DiT
+weights. If a non-DiT component still exceeds HBM, combine only independently
+validated on-demand component staging, VAE tiling/patching, or another topology;
+do not hide the remaining peak behind the phrase “DLO enabled.”
+
+## Per-request Cache-DiT
+
+### Policy and lifecycle skeleton
+
+First verify that the target revision contains the shared protocol/runtime.
+If not, follow the nearest current model's lifecycle without inventing a new
+public API.
+
+The following is a **serial/exclusive request-mode sketch only**. Pipeline-wide
+hooks are mutable; concurrent requests need an admission/batching key and
+runner-owned transition serialization before this shape is safe.
+
+```python
+import torch.nn as nn
+
+from vllm_omni.diffusion.cache.cachedit import (
+    CacheDiTBackend,
+    CacheDiTRequestSpec,
+    RequestScopedCacheDiTRuntime,
+)
+
+class MyPipeline(nn.Module):
+    def __init__(self, *args, **kwargs):
+        super().__init__()
+        # ... components ...
+        self._cache_dit_runtime = RequestScopedCacheDiTRuntime(self)
+
+    def adopt_cache_dit_backend(self, backend: CacheDiTBackend) -> None:
+        self._cache_dit_runtime.adopt(
+            backend,
+            installation_key="my_model.generic",
+        )
+
+    def is_cache_dit_enabled(self) -> bool:
+        return self._cache_dit_runtime.is_enabled
+
+    def _cache_spec(self, quality, steps):
+        if quality == "lossless":
+            return None
+        if quality == "high":
+            return CacheDiTRequestSpec(
+                installation_key="my_model.high.v1",
+                cache_config=self._validated_high_config,
+                num_inference_steps=steps,
+            )
+        raise ValueError(f"unsupported quality tier: {quality!r}")
+
+    def forward(self, req):
+        quality, steps = validate_and_resolve_request(req)
+        self._cache_dit_runtime.prepare(self._cache_spec(quality, steps))
+        return self._run_validated_request(req)
+```
+
+The snippet shows the model-owned transition for exclusive execution. Put
+disable/restore handling in the runtime/runner lifecycle that owns success,
+exception, disconnect, and asynchronous abort; a `try/finally` inside
+`forward()` alone cannot observe every server cancellation boundary.
+
+The `installation_key` must include every policy dimension that changes hook
+installation; changes in step count can use runtime refresh. `None` disables
+installed hooks. Do not mutate the global startup cache config for a request.
+
+Centralize cleanup in the owning request/runner boundary. An exception, abort,
+disconnect, or admission failure must not leave a changed hook policy visible
+to the next request. The exact owner depends on the target revision; add a
+failure-injection test rather than relying on `forward()` alone to observe
+asynchronous abort.
+
+Example model-specific request form after calibration:
+
+```bash
+curl --fail-with-body -sS -X POST http://127.0.0.1:8091/v1/videos/sync \
+  -F 'model=<model>' \
+  -F 'prompt=<prompt>' \
+  -F 'quality=lossless' \
+  -F 'num_inference_steps=<validated-steps>' \
+  -o lossless.mp4
+
+curl --fail-with-body -sS -X POST http://127.0.0.1:8091/v1/videos/sync \
+  -F 'model=<model>' \
+  -F 'prompt=<prompt>' \
+  -F 'quality=high' \
+  -F 'num_inference_steps=<validated-steps>' \
+  -o high.mp4
+```
+
+Do not expose these tier names merely because another model uses them.
+
+### Cache-DiT evidence
+
+For every tier/task/shape/schedule:
+
+- show real cache hits/skipped blocks, not only “backend enabled” logs;
+- measure fixed-work speed and quality against lossless/native;
+- publish a speed/quality frontier and chosen tier mapping;
+- alternate lossless/high/lossless and vary steps to prove restore/refresh;
+- interleave tasks and shapes; run concurrent requests only when scheduling
+  guarantees exclusive compatible hook state;
+- inject validation error, denoise error, disconnect, and abort, then prove the
+  next request starts with its requested policy;
+- bound any cached tensors and clear request-local state.
+
+## Combination gates
+
+Start every combination as `not tested`.
+
+| Combination | Default production posture |
+|---|---|
+| Direct checkpoint mmap + TP>1/HSDP/online quant | Preflight falls back to the ordinary loader; do not claim direct-mmap savings |
+| Per-tensor online FP8 + DLO AllGather | Generic compatibility exists for finalized weights/scales; each model/card/topology remains a candidate until E2E, and other online quantizers fail closed |
+| Online FP8 + DLO no-AllGather | Candidate only after model/card/topology E2E |
+| TP>1 + DLO AllGather | Ordinary TP-aware loader only; bounded DP2xTP2 smoke exists, but each new model/card/topology still needs E2E |
+| TP>1 + DLO no-AllGather | Ordinary TP-aware loader only; candidate with limited generic coverage |
+| HSDP + DLO AllGather | Reject; it would double-shard HSDP-managed parameters |
+| HSDP + DLO no-AllGather | Configuration-compatible candidate with limited E2E coverage |
+| Cache-DiT + online FP8 | Separate trajectory, hit-rate, HBM, latency test |
+| Cache-DiT + DLO | Verify hooks, streamed blocks, memory peaks, abort cleanup |
+| Cache-DiT + step execution | Do not advertise until lifecycle/batching support is explicit |
+| Sparse attention + Ring | Verify backend execution; reject silently ignored sparse settings |
+| Load-time fused adapter + offload/HWR | Reject unless the selected loader path applies and validates the same fusion before sharding/storage |
+| Packed attention + any topology | Unequal-sample boundary parity is mandatory |
+| Device-side uint8 preparation + offline caller | Version the dtype/range/layout change; HTTP MP4 parity does not preserve the raw tensor contract |
+| Device-side preparation + remote codec | Validate route selection, strided/contiguous layouts, payload bytes, ordering, fallback, and encoded-media parity |
+
+A startup-only test never upgrades a combination. Require request output,
+parity/quality, actual fast-path evidence, resource cleanup, and a benchmark on
+the named hardware.

.claude/skills/production-add-diffusion-model/references/performance-patterns.md

@@ -0,0 +1,481 @@
+# Performance Patterns and Code Examples
+
+## Contents
+
+1. [Measurement loop](#measurement-loop)
+2. [Faster BF16 attention](#faster-bf16-attention)
+3. [Sparse attention](#sparse-attention)
+4. [Fusion-first operator patterns](#fusion-first-operator-patterns)
+5. [Remove redundant computation](#remove-redundant-computation)
+6. [USP communication optimization](#usp-communication-optimization)
+7. [Text encoder and VAE disaggregation](#text-encoder-and-vae-disaggregation)
+8. [Performance acceptance](#performance-acceptance)
+
+## Measurement loop
+
+Do not start with a list of optimizations. Start with a fixed correctness and
+performance workload:
+
+```text
+model/checkpoint revision + prompt/media hashes + seed + dimensions/frames +
+scheduler/sigma/steps/guidance + task + dtype + backend + topology + hardware
+```
+
+Capture:
+
+- stage time: load, encode, conditioning, denoise, VAE decode/postprocess;
+- GPU trace: kernel duration/count, launch gaps, allocations, H2D, collectives;
+- CPU trace: preprocessing, per-layer sync, Python dispatch, temporary files;
+- memory: per-rank HBM peaks and process-tree host PSS;
+- output: device preparation, D2H/IPC, payload bytes, codec wall/process CPU,
+  event-loop wait, client materialization, and signed residual;
+- serving: queue time, throughput, errors, request mix, and distribution samples.
+
+For a fixed-work single-request A/B, run one explicit warmup policy and at
+least three measured repetitions, keep every raw run, and report median or mean
+with range. Do not derive p95/p99 from three samples. For tail latency, declare
+the arrival model and collect enough successful requests for the percentile and
+confidence claimed. Implement one optimization at a time, re-run parity, and
+report both local kernel and end-to-end deltas. A kernel win that moves no E2E
+metric is not a production speed claim.
+
+Use the pipeline profiler already exposed by the target revision when
+available, and use PyTorch/CUDA/ROCm/NPU/XPU profiling tools appropriate to the
+platform. Do not force every platform through a CUDA-only workflow.
+
+## Faster BF16 attention
+
+### Shared attention wiring
+
+Use the shared role-aware attention layer and give it the true local head
+counts and QKV layout:
+
+```python
+import torch
+
+from vllm.model_executor.layers.linear import (
+    QKVParallelLinear,
+    RowParallelLinear,
+)
+from vllm_omni.diffusion.attention.layer import Attention
+
+class SelfAttention(torch.nn.Module):
+    def __init__(
+        self,
+        hidden_size,
+        head_dim,
+        num_heads,
+        num_kv_heads,
+        quant_config=None,
+        prefix="",
+    ):
+        super().__init__()
+        self.head_dim = head_dim
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size=hidden_size,
+            head_size=head_dim,
+            total_num_heads=num_heads,
+            total_num_kv_heads=num_kv_heads,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+        self.attention = Attention(
+            num_heads=self.qkv_proj.num_heads,
+            num_kv_heads=self.qkv_proj.num_kv_heads,
+            head_size=head_dim,
+            softmax_scale=head_dim**-0.5,
+            causal=False,
+            qkv_layout="BSND",
+            role="self",
+            prefix=prefix,
+        )
+        self.out_proj = RowParallelLinear(
+            num_heads * head_dim,
+            hidden_size,
+            bias=False,
+            input_is_parallel=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.out_proj",
+        )
+
+    def forward(self, x, attn_metadata=None):
+        qkv, _ = self.qkv_proj(x)
+        q_size = self.qkv_proj.num_heads * self.head_dim
+        kv_size = self.qkv_proj.num_kv_heads * self.head_dim
+        q, k, v = qkv.split([q_size, kv_size, kv_size], dim=-1)
+        q = q.unflatten(-1, (self.qkv_proj.num_heads, self.head_dim))
+        k = k.unflatten(-1, (self.qkv_proj.num_kv_heads, self.head_dim))
+        v = v.unflatten(-1, (self.qkv_proj.num_kv_heads, self.head_dim))
+        out = self.attention(q, k, v, attn_metadata)
+        out = out.flatten(-2)
+        out, _ = self.out_proj(out)
+        return out
+```
+
+Add Q/K norm and RoPE in the official order before the `Attention` call. Keep
+checkpoint mapping for the fused QKV and output projection complete.
+
+### Backend qualification
+
+For each role (`self`, cross-attention category, token refiner, etc.), record:
+
+- actual selected backend from logs/trace;
+- supported dtype, head size, QKV layout, mask/metadata, packed varlen behavior;
+- platform and compute capability;
+- TP/SP/Ring/AllGather-KV restrictions;
+- fallback behavior and whether fallback changes the claimed performance.
+
+Use unequal packed samples and compare with dense SDPA. Verify `cu_seqlens`,
+max sequence length, padding exclusion, modality boundaries, CFG ownership, and
+output slicing. Do not pass an `attn_mask` and assume it is consumed. Inspect
+the backend contract and trace.
+
+TRTLLM, Flash Attention, cuDNN, NPU varlen, and other backends are independent
+qualification rows. The shared `Attention` class does not make every backend
+packed-safe or faster automatically. Ring and hybrid paths require their own
+boundary parity. AllGather-KV and specific backend combinations may fail fast;
+keep that behavior visible in docs.
+
+## Sparse attention
+
+Sparse attention is a backend/model/platform optimization, not a synonym for
+low-precision dense attention. Enable it only after dense BF16 parity.
+
+For every candidate task/shape/topology:
+
+1. State the sparsity policy, selected backend/kernel, block/head dimensions,
+   supported hardware, external kernel/package revision, and fallback.
+2. Prove the sparse kernel executed and report realized sparsity; an enabled
+   flag is insufficient.
+3. Compare same-seed per-block outputs, denoise trajectory, and final artifact
+   with dense attention.
+4. Add modality-specific metrics: temporal consistency and audio/video sync for
+   video, spectral/audio quality for audio, and prompt/image metrics as relevant.
+5. Report attention time and full E2E time. Include preprocessing/index-build
+   overhead, peak memory, and the exact dense control.
+6. Validate each task and shape. Do not extrapolate a T2V result to I2V/Ref2V,
+   or NPU sparse support to CUDA/ROCm/XPU.
+
+If Ring bypasses the selected backend or a sparse setting would be silently
+ignored, reject the combination. Prefer a visible dense fallback only when the
+recipe labels the row `limited` and does not claim sparse speed.
+
+## Fusion-first operator patterns
+
+Prefer current shared/custom operators with explicit guards and native
+fallbacks. Prove parity at operator, block, trajectory, and artifact levels,
+then use a trace to prove fewer launches/bytes or lower latency.
+
+### Q/K RMSNorm + RoPE
+
+```python
+from vllm_omni.diffusion.layers.norm import RMSNorm
+from vllm_omni.diffusion.layers.rope import RotaryEmbedding, apply_rope_to_qk
+
+self.norm_q = RMSNorm(head_dim, eps=eps)
+self.norm_k = RMSNorm(head_dim, eps=eps)
+# Example only: set these booleans from the official checkpoint contract.
+use_neox_layout = False
+cos_sin_are_half_dim = True
+self.rope = RotaryEmbedding(
+    is_neox_style=use_neox_layout,
+    half_head_dim=cos_sin_are_half_dim,
+)
+
+q = self.norm_q(q)
+k = self.norm_k(k)
+q, k = apply_rope_to_qk(self.rope, q, k, (cos, sin))
+```
+
+Replace the illustrative booleans with the official layout in real code.
+Preserve partial rotary dimensions by splitting the rotated prefix and concatenating
+the pass-through suffix. If 3D cos/sin includes a batch axis, the current
+shared path assumes the values are shared across the batch; per-sample
+different positions require a proven representation/path.
+
+For packed `[tokens, heads, head_dim]` tensors with a non-interleaved
+`[cos | sin]` table, the current shared fused boundary is:
+
+```python
+from vllm_omni.diffusion.layers.fused_qk_norm_rope import fused_qk_norm_rope
+
+q, k = fused_qk_norm_rope(
+    q,
+    k,
+    self.norm_q.weight,
+    self.norm_k.weight,
+    rope_table,
+    self.norm_q.variance_epsilon,
+)
+```
+
+Its CUDA fast path currently specializes BF16 `head_dim=128` and
+`rotary_dim=96`; other valid inputs take the eager implementation. The separate
+shared RMSNorm and RotaryEmbedding still provide the general cross-platform
+baseline. Treat NPU/MUSA and other vendor fusions as independent qualification
+rows, and do not generalize a model-specific Triton kernel without compatible
+shape, layout, frequency partition, dtype, and platform evidence.
+
+### Fused QKV projection
+
+Use `QKVParallelLinear` as shown above. Qualification includes:
+
+- `num_heads` and `num_kv_heads` divisibility by TP;
+- local split sizes from the layer, never reusing global counts after TP;
+- Q/K/V checkpoint order conversion before the vLLM loader;
+- all Q, K, and V shards present;
+- quantization scale/loader mapping when FP8 or another method is enabled.
+
+### Gate-up projection + fused SwiGLU
+
+```python
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.linear import (
+    MergedColumnParallelLinear,
+    RowParallelLinear,
+)
+
+self.gate_up_proj = MergedColumnParallelLinear(
+    input_size=hidden_size,
+    output_sizes=[intermediate_size, intermediate_size],
+    bias=False,
+    quant_config=quant_config,
+    prefix=f"{prefix}.gate_up_proj",
+)
+self.act_fn = SiluAndMul()
+self.down_proj = RowParallelLinear(
+    input_size=intermediate_size,
+    output_size=hidden_size,
+    bias=False,
+    input_is_parallel=True,
+    quant_config=quant_config,
+    prefix=f"{prefix}.down_proj",
+)
+
+gate_up, _ = self.gate_up_proj(x)
+x = self.act_fn(gate_up)
+x, _ = self.down_proj(x)
+```
+
+Map both checkpoint `gate_proj` and `up_proj` shards into the merged parameter
+and require both. A merged projection followed by `F.silu(gate) * up` is not
+the same claim as the `SiluAndMul` fused activation kernel; identify what the
+trace actually uses.
+
+### AdaLN
+
+Use a shared layer only when its mathematical/return contract matches the
+official model:
+
+```python
+from vllm_omni.diffusion.layers.adalayernorm import AdaLayerNorm
+
+self.adaln = AdaLayerNorm(
+    hidden_size,
+    elementwise_affine=False,
+    eps=1e-6,
+)
+x = self.adaln(x, scale[:, None, :], shift[:, None, :])
+```
+
+`AdaLayerNormZero`, `AdaLayerNormZeroSingle`, and continuous variants include
+different projections, broadcasts, and tuple outputs; they are not drop-in
+replacements. A shared AdaLN may have an NPU fast path while using native ops on
+CUDA/HIP/XPU. Do not call it a universal fused AdaLN kernel.
+
+For sigma schedules with a small finite set of timesteps, profile whether
+precomputing the timestep/AdaLN projection once per request is worthwhile.
+Cache keys must include the exact schedule/config/device/dtype and stay bounded.
+
+### Layout, residual, and other candidates
+
+After profiling, inspect:
+
+- bias/dropout/add or gated residual chains;
+- norm + linear or norm + modulation boundaries;
+- repeated transpose/reshape/contiguous around attention;
+- VAE normalization/activation/convolution boundaries;
+- MoE routing and fused experts for models that actually use MoE.
+
+For fused residual-plus-norm or modulation chains, preserve the unfused
+materialization boundary. If the residual was stored in BF16 before RMSNorm,
+round to BF16 and promote again inside the fused kernel; using the unrounded
+FP32 temporary changes the denoising trajectory even when the stored residual
+looks correct.
+
+Keep DLO materialize/release hooks, process-group collectives, request cache
+transitions, and cleanup outside compiled/fused regions unless lifecycle safety
+is explicitly tested. Compile only stable regions and retain eager fallback.
+
+## Remove redundant computation
+
+### Hoist request invariants
+
+Audit the denoise loop for work that depends only on the request rather than
+the current timestep:
+
+```python
+# Before the denoise loop.
+condition = self.condition_proj(text_embeddings)
+refined_condition = self.token_refiner(condition, packed_metadata)
+rope_tables = self.build_rope_tables(position_ids)
+attn_metadata = self.build_attention_metadata(layout)
+
+for step, sigma in enumerate(sigmas):
+    timestep_state = self.prepare_timestep_state(sigma)
+    noise = self.transformer(
+        latents,
+        refined_condition,
+        rope_tables=rope_tables,
+        attn_metadata=attn_metadata,
+        timestep_state=timestep_state,
+    )
+    latents = self.scheduler_step(noise, sigma, latents)
+```
+
+Candidates include prompt conditioning, token-refiner output, reference
+encoding/scans, position IDs/RoPE tables, packed masks/metadata, static CFG
+branches, and finite-schedule AdaLN projections. First prove they are invariant
+for all official tasks, LoRA/adapters, prompt-update features, shapes, and
+schedules. In continuous batching, store request-specific results on request
+state rather than the pipeline singleton.
+
+### Eliminate per-layer synchronization
+
+Avoid device-to-host scalar extraction such as `.item()` inside every layer:
+
+```python
+# Bad: forces a GPU/CPU synchronization in every attention layer.
+max_len = int(cu_seqlens[-1].item())
+
+# Better: compute validated host metadata once during request packing and pass it.
+packed = PackedLayout(
+    cu_seqlens=cu_seqlens,
+    max_seqlen=max(sample_lengths),
+)
+for block in self.blocks:
+    hidden = block(hidden, packed_layout=packed)
+```
+
+Trace to verify the synchronization disappears. Do not replace a correct
+dynamic value with a stale constant merely to remove `.item()`.
+
+### Remove unused masks and allocations
+
+If packed attention consumes `cu_seqlens` and ignores a dense mask, do not
+construct the dense mask. Reuse immutable request metadata and preallocated
+buffers when safe. Prefer direct indexed scatter/gather over constructing large
+temporary tensors only when duplicate-index semantics and gradients (normally
+disabled for inference) remain correct.
+
+Any cross-request cache needs a bounded lifetime and a complete key containing
+shape, layout, device, dtype, schedule, task, adapter/LoRA, and any value that
+changes the tensor. Otherwise keep it request-scoped.
+
+### VAE and reference work
+
+Profile duplicate VAE decode/postprocess, repeated reference decoding/scans,
+unnecessary host-device round trips, and full-frame materialization. Apply VAE
+tiling, slicing, patch parallelism, or on-demand staging only with seam,
+ordering, color/range, and memory parity tests.
+
+## USP communication optimization
+
+“USP works” is not evidence that USP communication is optimized. For Ulysses,
+Ring, hybrid, and AllGather-KV separately, profile:
+
+- collective count, bytes, duration, stream, and overlap with compute;
+- Q/K/V and output all-to-all boundaries;
+- pack/unpack, padding, metadata broadcast, transpose, and contiguous copies;
+- head divisibility or advanced uneven-head support;
+- scaling efficiency from 1 to N devices on the same workload/interconnect;
+- load balance for unequal packed samples.
+
+Declare `_sp_plan` split/gather boundaries around meaningful module outputs,
+not arbitrary Python statements. Keep packed metadata sharded/gathered in the
+same ownership model as tokens.
+
+When the target revision offers accelerated Ulysses transport such as
+`--ulysses-a2a-permute`, qualify it separately from regular Ulysses. Prove the
+strict scatter-heads/gather-sequence layout selected the fast path, record
+one-time JIT/readiness cost, prewarm the maximum workspace shape before CUDA
+graph capture, keep the grow-only workspace on one stream, and exercise cleanup
+before process-group destruction. Compare collective and E2E deltas after the
+dense backend and topology are frozen; keep unsupported layouts on an explicit
+regular-Ulysses fallback.
+
+For GQA, validate `Q_heads % KV_heads == 0`, pad KV heads first, and derive Q
+padding from the original ratio. Exercise both contiguous and row-strided QKV
+layouts; a fast copy path must preserve bitwise values and packed boundaries.
+
+Cross-attention sometimes uses replicated text K/V while only video/audio Q is
+sequence-sharded. In that specific case, `skip_sequence_parallel=True` can
+avoid incorrect or wasteful Q/K/V redistribution:
+
+```python
+self.cross_attention = Attention(
+    num_heads=local_heads,
+    num_kv_heads=local_kv_heads,
+    head_size=head_dim,
+    softmax_scale=head_dim**-0.5,
+    causal=False,
+    role="cross",
+    skip_sequence_parallel=True,
+    prefix=f"{prefix}.cross_attn",
+)
+```
+
+Use it only after proving K/V are replicated and Q/output ownership is correct.
+Otherwise it disables needed communication. Report whether the optimization
+reduces collective bytes/time and improves E2E scaling.
+
+## Text encoder and VAE disaggregation
+
+Disaggregation is a measured architecture decision. First collect:
+
+| Question | Required measurement |
+|---|---|
+| Is the stage large enough? | Encode/decode share at target concurrency and shape |
+| Is transfer economical? | Tensor/media bytes, serialization, connector latency and bandwidth |
+| Is there reuse? | Prompt/reference reuse rate, cacheability, fan-out |
+| Can it scale independently? | Queue saturation and resource utilization per stage |
+| Is the contract stable? | Shape, dtype, mask/tag/position metadata, ordering |
+
+### Text encoder candidate
+
+Define an explicit tensor contract containing hidden states, masks, tags or
+position IDs, dtype, shape, task/checkpoint revision, and request ID. Support
+disabling the local encoder so the DiT cannot accidentally recompute it. Test
+all tasks, prompt lengths, partial TP group membership, retries, backpressure,
+abort, connector cleanup, and independent replica scaling.
+
+### VAE candidate
+
+Separate VAE encode and decode contracts if both exist. Define latent shape,
+scale/shift, dtype/range, tiling/chunk order, output media ordering, and stage
+metadata. Measure latent/media transfer against local decode cost. Test tiling
+seams, RNG/determinism where relevant, chunk ordering, codec/postprocess,
+backpressure, abort, and worker failure.
+
+Do not label VAE tiling, patch parallelism, compile, or offload as VAE
+disaggregation. Likewise, a separate text-encoder TP group is not a separate
+stage. If no reusable generic stage/connector exists in the target revision,
+produce a feasibility result or RFC and keep the capability `not tested` rather
+than inventing a model-local production architecture.
+
+## Performance acceptance
+
+For each accepted optimization, require:
+
+- same fixed workload and correctness artifacts before/after;
+- actual fast-path execution proven by logs/trace/counters;
+- local metric plus E2E latency/throughput and memory;
+- at least three raw fixed-work repetitions after explicit warmup, or a
+  declared arrival-load sample set large enough for every tail percentile;
+- named hardware, backend, topology, dtype, and task scope;
+- fallback/rejection tests for unsupported shapes/platforms;
+- no regression in abort/error cleanup or long-run memory trend.
+
+Update the feature matrix narrowly. Do not convert a profile-driven direction
+into a generic “supported” feature merely because a model-specific PR exists.

.claude/skills/production-add-diffusion-model/references/production-validation.md

@@ -0,0 +1,464 @@
+# Production Serving and Validation
+
+## Contents
+
+1. [Execution-mode decision](#execution-mode-decision)
+2. [Request-level batching](#request-level-batching)
+3. [Step execution and continuous batching](#step-execution-and-continuous-batching)
+4. [Abort and cleanup](#abort-and-cleanup)
+5. [Mixed-RPS soak](#mixed-rps-soak)
+6. [Four CI tracks](#four-ci-tracks)
+7. [PR evidence checklist](#pr-evidence-checklist)
+
+## Execution-mode decision
+
+Request batching and step-wise execution solve different problems:
+
+| Mode | Pipeline contract | Scheduling/abort boundary |
+|---|---|---|
+| Serial request | `forward(DiffusionRequestBatch)` for one request | Whole request; in-denoise preemption is unavailable |
+| Request batching | `supports_request_batch = True`; one forward consumes compatible independent requests | Whole batch/request |
+| Single-request step | Full `SupportsStepExecution`; `max_num_seqs=1` | Between denoise steps |
+| Step continuous batching | Full step contract plus heterogeneous batched-step evidence | Between denoise waves/steps |
+
+Do not expose four stub methods as an opt-out. The current protocol is
+structural; if a native pipeline is not step-capable, do not define the four
+step methods. If it is capable, implement and test all four.
+
+Treat structural support, a generic request-batch bridge, and model benefit as
+three separate claims. Before recommending step execution, name a hypothesis
+that request mode cannot satisfy, define a success threshold, and run a matched
+request-mode control. If throughput or latency regresses, retain step execution
+as functional/limited and keep request mode as the production recommendation.
+
+Choose compatibility keys from every value that changes tensor shapes,
+semantics, hooks, or scheduler behavior: task, output shape/count, frames,
+steps/schedule, guidance/CFG, dtype/backend, LoRA/adapter, cache quality policy,
+and model-specific conditions. Reject or schedule separately rather than
+silently using the first request's settings.
+
+## Request-level batching
+
+A request-batched pipeline returns exactly one `DiffusionOutput` per logical
+request. This is model-owned pseudocode: `validate_batch_compatibility()` and
+`_generate_batch()` are placeholders to implement from the official contract,
+not shared APIs to import.
+
+```python
+import torch
+
+from vllm_omni.diffusion.data import DiffusionOutput
+from vllm_omni.diffusion.worker.request_batch import (
+    DiffusionRequestBatch,
+    split_diffusion_output_by_request,
+)
+
+class MyPipeline(torch.nn.Module):
+    supports_request_batch = True
+
+    def forward(self, req: DiffusionRequestBatch) -> list[DiffusionOutput]:
+        validate_batch_compatibility(req.sampling_params_list, req.prompts)
+        common = req.sampling_params_list[0]
+
+        text_prompts: list[str] | None = [
+            prompt
+            if isinstance(prompt, str)
+            else (prompt.get("prompt") or "")
+            for prompt in req.prompts
+        ]
+
+        prompt_fields = DiffusionRequestBatch.collate_prompt_field_map(
+            req.prompts,
+            {"prompt_embeds": None, "prompt_mask": None},
+        )
+        if prompt_fields["prompt_embeds"] is not None:
+            text_prompts = None
+        generators = req.collate_request_generators(
+            common.num_outputs_per_prompt,
+            default_generator=None,
+        )
+        output = self._generate_batch(
+            prompt=text_prompts,
+            prompt_fields=prompt_fields,
+            generators=generators,
+            sampling=common,
+        )
+        return split_diffusion_output_by_request(
+            DiffusionOutput(output=output),
+            req,
+            num_outputs_per_prompt=common.num_outputs_per_prompt,
+        )
+```
+
+The prompt-field collator only handles tensor fields; extract raw text prompts
+and model-specific negative/alias fields separately, as current in-tree batched
+pipelines do. Set a raw prompt field to `None` when the matching precomputed
+embedding is supplied. The exact aliases and generator device depend on the
+model. Use current collators rather than manual `torch.cat` when they cover the
+contract; they reject mixed present/missing tensors and incompatible
+shape/dtype/device.
+
+Before reading `sampling_params_list[0]`, validate every field that must be
+common. Tests must cover:
+
+- two and N requests, independent seeds/generators, one output each;
+- mixed prompt lengths and packed/padded boundaries;
+- multiple outputs per prompt and correct result slicing;
+- compatible versus incompatible shapes, steps, guidance, LoRA, and quality;
+- one request failure without cross-request output or state corruption;
+- output ordering and request IDs under different completion times.
+
+After the model advertises support, candidate serving is:
+
+```bash
+vllm serve '<model>' --omni \
+  --max-num-seqs 4 \
+  --request-batch-max-wait-ms 20
+```
+
+The wait window trades admission latency for batch formation. Benchmark at 0
+and candidate nonzero values under the target arrival process.
+
+## Step execution and continuous batching
+
+### Full protocol skeleton
+
+Use the current shared state types and make every mutable value request-local:
+
+The helpers beginning with `_` and `validate_step_batch()` below are
+model-owned placeholders; scheduler initialization is scheduler-specific.
+
+```python
+import copy
+from collections.abc import Sequence
+from typing import Any, ClassVar
+
+import torch
+
+from vllm_omni.diffusion.data import DiffusionOutput
+from vllm_omni.diffusion.models.interface import SupportsStepExecution
+from vllm_omni.diffusion.worker.input_batch import InputBatch
+from vllm_omni.diffusion.worker.utils import StepRequestState
+
+class MyPipeline(torch.nn.Module, SupportsStepExecution):
+    supports_step_execution: ClassVar[bool] = True
+
+    def prepare_encode(
+        self,
+        state: StepRequestState,
+        **kwargs: Any,
+    ) -> StepRequestState:
+        prepared = self._prepare_request(state.prompt, state.sampling)
+        state.prompt_embeds = prepared.prompt_embeds
+        state.latents = prepared.latents
+        state.timesteps = prepared.timesteps
+        state.scheduler = copy.deepcopy(self.scheduler)
+        state.scheduler.set_begin_index(0)
+        state.step_index = 0
+        state.extra["task_state"] = prepared.task_state
+        return state
+
+    def denoise_step(
+        self,
+        input_batch: InputBatch,
+        *,
+        states: Sequence[StepRequestState] | None = None,
+        **kwargs: Any,
+    ) -> torch.Tensor | None:
+        validate_step_batch(input_batch, states)
+        return self.transformer(**build_batched_denoise_inputs(input_batch))
+
+    def step_scheduler(
+        self,
+        state: StepRequestState,
+        noise_pred: torch.Tensor,
+        **kwargs: Any,
+    ) -> None:
+        timestep = state.current_timestep
+        if timestep is None:
+            raise RuntimeError("step_scheduler called without a current timestep")
+        state.latents = state.scheduler.step(
+            noise_pred,
+            timestep,
+            state.latents,
+            return_dict=False,
+        )[0].to(state.latents.dtype)
+        state.step_index += 1
+
+    def post_decode(
+        self,
+        state: StepRequestState,
+        **kwargs: Any,
+    ) -> DiffusionOutput:
+        return DiffusionOutput(output=self.decode(state.latents))
+```
+
+This is a shape guide, not drop-in code: verify the target revision's
+`StepRequestState.extra`, scheduler signature, CFG handling, state fields, and
+decode contract. Follow a current native pipeline such as Qwen-Image for the
+exact revision.
+
+Never use the shared `self.scheduler` to advance a request. Store scheduler,
+latents, embeddings, timesteps, masks, RNG/generator, task metadata, cache
+policy, adapter, and output assembly state on `StepRequestState` or its
+request-local extension. Pipeline fields may hold immutable weights/config and
+bounded immutable caches only.
+
+### Qualification sequence
+
+1. Compare request-mode and step-mode at `max_num_seqs=1` for every task using
+   identical seed/schedule/shape and intermediate latents.
+2. Abort queued and in-flight requests at several step boundaries.
+3. Increase to two compatible requests; verify independent scheduler/RNG/state
+   and output order.
+4. Increase to the target capacity and mix prompt lengths.
+5. Exercise mixed shape, frames, steps/schedule, task, guidance, and quality.
+   Batch only compatible rows; otherwise split/reject predictably.
+6. Inject a failure in encode, denoise, scheduler, and decode independently.
+7. Run the production soak and fault matrix.
+
+Candidate commands only after capability validation:
+
+```bash
+# Conservative step execution and step-boundary abort.
+vllm serve '<model>' --omni \
+  --step-execution \
+  --max-num-seqs 1
+
+# Experimental batched-step candidate after multi-request E2E.
+vllm serve '<model>' --omni \
+  --step-execution \
+  --max-num-seqs 4
+```
+
+If an attention backend is only correct for single-request step mode, fail
+early or keep the recipe at `max_num_seqs=1`. Do not describe generic runtime
+support as model support.
+
+## Abort and cleanup
+
+### Required semantics
+
+For queued and in-flight abort/disconnect:
+
+- emit exactly one terminal aborted/error result;
+- do not run `post_decode` after abort unless a documented partial-output mode
+  explicitly requires it;
+- do not increment `step_index` or apply a scheduler result after cancellation;
+- remove scheduler/state cache entries and request IDs;
+- release latents, prompt/reference tensors, cache hooks, DLO buffers, connector
+  messages, uploaded/temp files, and output buffers;
+- keep other requests alive;
+- make cleanup idempotent when disconnect, timeout, and worker error race;
+- drop late results/exceptions for already-resolved or cancelled futures
+  without terminating the shared result-pump thread;
+- complete the next valid request with its requested cache/offload policy.
+
+Test cancellation before engine admission, while queued, after encode, during
+early/middle/final denoise steps, before decode, and while returning/persisting
+output. Include async job cancellation and client socket disconnect.
+
+### Failure injection matrix
+
+| Location | Injection | Assertions |
+|---|---|---|
+| Validation/upload | bad MIME, count/bytes overflow, truncated media | 400/413, no job/engine submit, no temp leak |
+| Load/start | missing fused shard, unsupported topology | startup fails, actionable error, no silent init |
+| Encode | exception/OOM/timeout | one error, request state/temp released |
+| Denoise | exception/OOM/worker exit | peers handled, hooks/buffers released, health behavior explicit |
+| Scheduler | invalid state/shape | no latent/step partial commit |
+| Decode/post | exception/codec error | no corrupt successful result, partial output removed |
+| Result/IPC | late result after cancel, large split tensor, orphan SHM | pump remains alive, one terminal result, exact ownership cleanup |
+| Connector/disagg | timeout/retry/duplicate | idempotency, backpressure, no orphan payload |
+
+After each injection, check server health and issue a known-good request. If
+the process must restart, document and test the restart policy; do not claim
+transparent recovery.
+
+### Large-output transport
+
+Treat worker-to-engine IPC and engine-to-client transport as separate hops.
+For batched video, prove that each logical output does not accidentally retain
+or serialize the complete batch storage. Exercise the target revision's shared
+memory or artifact-handle path above and below its threshold, including
+non-contiguous views, multiple outputs, consumer failure, timeout, abort, and
+worker exit. Verify every shared segment/file is released exactly once.
+
+Freeze the media contract at each hop: dtype, value range, shape/layout,
+contiguity, request ownership, encoding/color model, payload bytes, and pending
+consumers. Device-side float-to-uint8 preparation, D2H/IPC, planar/interleaved
+conversion, CPU encode/mux, and delivery off the asyncio event loop are
+different optimizations. Validate their route selection and fallbacks
+independently. Byte-identical HTTP MP4 output does not prove that a raw offline
+tensor kept its dtype or range.
+
+For long-window output, validate segment ordering and audio/video continuity,
+then exercise payloads around serializer, shared-memory, HTTP, and codec size
+limits. Chunking must reassemble exactly, reject malformed/missing chunks, and
+release partial state after abort, timeout, or peer failure.
+
+Qualify chunk production, transport, and public streaming as separate layers.
+A VAE callback must define owned representation, frame offsets, final marker,
+rank ownership, source/config gate, and collective-safe failure, but it does not
+prove SHM/ZMQ delivery, bounded backpressure, cancellation, codec reuse, or
+client streaming. Measure time to first owned chunk, first encoded fragment,
+steady cadence, mux finalization, and complete client artifact independently.
+
+Measure remote MP4 encoding, serialization/copies, network transfer, and client
+materialization separately from denoise/decode. An inference-kernel speedup does
+not establish serving throughput if output transport dominates or leaks.
+
+## Mixed-RPS soak
+
+Use a realistic request mix and arrival distribution. Include small/large
+shapes, short/long prompts, all advertised tasks, cache tiers, and a small rate
+of abort/disconnect/error injections. Run phases below, near, and above measured
+saturation so queue/backpressure behavior is visible.
+
+Example serving benchmark phase:
+
+```bash
+python benchmarks/diffusion/diffusion_benchmark_serving.py \
+  --base-url http://127.0.0.1:8091 \
+  --endpoint /v1/videos \
+  --model '<model>' \
+  --dataset trace \
+  --dataset-path /path/to/versioned-production-trace.jsonl \
+  --task t2v \
+  --num-prompts 500 \
+  --request-rate 0.5 \
+  --max-concurrency 8 \
+  --warmup-requests 8 \
+  --warmup-concurrency 8 \
+  --output-file soak-near-saturation.json
+```
+
+Use a task-appropriate trace/media dataset. Run the same phase at rates selected
+from an initial saturation curve; a literal `0.5` is illustrative only.
+
+On supported GPU hosts, wrap a test or soak with the repository monitor:
+
+```bash
+bash tests/dfx/stability/scripts/resource_monitor.sh run --backend gpu -- \
+  pytest -s -v tests/dfx/stability/scripts/run_stability_<model>.py -m slow
+```
+
+Check the target revision's monitor backends. Do not claim CPU/NPU monitoring
+from a reserved CLI choice unless collection is implemented and verified.
+
+Report:
+
+- offered and achieved RPS, success/error/abort rates;
+- p50/p95/p99 and queue/service/stage times, with the successful sample count
+  and arrival process used for each percentile;
+- throughput per device and batch/wave utilization;
+- per-rank HBM and process-tree PSS trend/slope;
+- cache size/hit rate, temp-file/disk growth, open FDs/handles;
+- outstanding shared-memory/artifact handles and encoded/network bytes;
+- worker/engine health, restarts, OOM/timeouts, backpressure response;
+- total duration, request count/mix, raw logs/JSON/monitor artifact hashes.
+
+A short burst or repeated serial curl loop is not a stability test.
+
+## Four CI tracks
+
+Keep the tracks independent so a failure answers a specific question. These
+four readiness tracks are not the repository's numbered CI levels. Map them
+onto the current L1-L5 taxonomy:
+
+| Readiness track | Repository CI placement |
+|---|---|
+| Function | L1 unit/logic plus L2 basic online/offline E2E; extend real-model scenarios in L3/L4 |
+| Accuracy | L3 real-model gates for important cases; deeper and broader nightly coverage in L4 |
+| Performance | Important thresholds in L3 where they fit the time budget; full baselines/regression jobs in nightly L4 |
+| Reliability | Cheap rejection/recovery assertions can run earlier, but long stability, fault injection, and reliability suites belong to weekly L5 |
+
+Distributed topology is a test case within the appropriate level, not a
+replacement definition for a level.
+
+### 1. Function CI
+
+Cover:
+
+- strict unquantized and advertised quantized loading;
+- fused source-shard completeness and prefix routing;
+- official task positive boundaries and neighboring negative cases;
+- offline, sync, async normalization/parity;
+- single-device reference plus every advertised topology smoke;
+- output type/count/order and request ID isolation;
+- feature rejection paths that must fail early.
+
+### 2. Accuracy CI
+
+Pin official implementation/checkpoint/vLLM-Omni revisions, prompts and media
+hashes, seed, scheduler/sigma/steps, dimensions/frames/FPS, guidance, dtype,
+backend, and topology. Compare at suitable levels:
+
+- component outputs or selected blocks;
+- one or more intermediate denoise latents;
+- final image/video/audio artifact metrics;
+- temporal consistency and audio/video synchronization where relevant.
+
+Set tolerance per modality/hardware/backend with rationale. Keep failure
+artifacts and metadata. A single visual inspection does not define CI.
+
+For quantization trajectory tooling, inspect the target revision's current
+`vllm_omni/quantization/tools/compare_diffusion_trajectory_similarity.py` and
+reuse it when compatible instead of creating an ad hoc comparator.
+
+### 3. Performance CI
+
+Pin workload and best deployment. Include a resident recommended row and a
+small-HBM/DLO row when both are production targets. Declare:
+
+- one explicit warmup exclusion and at least three measured fixed-work
+  repetitions;
+- raw result JSON and environment identity;
+- median/mean plus range for small fixed-work A/Bs; p50/p95/p99 only for an
+  arrival-load run with enough samples, plus throughput/RPS, stage time,
+  HBM/PSS;
+- regression metric, threshold, baseline owner, and update procedure;
+- correctness/accuracy check linked to the same configuration.
+
+Use the current harness config style, for example:
+
+```bash
+pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py \
+  --test-config-file tests/dfx/perf/tests/test_<model>_vllm_omni.json
+```
+
+Verify parser options on the target revision. Do not copy a JSON schema from a
+different model without matching its endpoint, task, dataset, and deploy path.
+
+### 4. Reliability CI
+
+Run a bounded CI soak plus a longer scheduled soak. Cover:
+
+- below/near/above-saturation arrival phases;
+- queued/in-flight abort and disconnect;
+- OOM/timeout and worker/process failure appropriate to the platform;
+- DLO/cache/connector cleanup;
+- health, fast-fail/backpressure, restart/recovery policy;
+- memory/temp/FD trend and successful known-good request after each fault.
+
+Keep reliability thresholds independent from latency thresholds. A fast server
+that leaks memory or hangs after cancellation fails production readiness.
+
+## PR evidence checklist
+
+The PR description must include or link:
+
+- official source/checkpoint revisions and API/limit matrix;
+- scoped feature compatibility matrix with the four allowed states;
+- implementation notes for FP8, DLO, request cache, attention/fusion, batching;
+- exact environment, deploy/serve, every task curl, benchmark and soak commands;
+- fixed accuracy inputs and artifacts;
+- raw before/after performance runs and memory traces;
+- output-contract manifests and stage accounting from device preparation
+  through the complete client artifact;
+- abort/disconnect/fault results and cleanup evidence;
+- Function/Accuracy/Performance/Reliability CI files and ownership;
+- limitations/TODOs that remain `not tested` rather than implied support.
+
+Separate the Day-0 milestone from production readiness. If the PR only closes
+some gates, title and description should say which scoped gates it closes.

.claude/skills/readme.md

@@ -26,6 +26,9 @@ self-check.
 
 - [`add-diffusion-model`](add-diffusion-model/SKILL.md): guides integration of
   a new diffusion model into `vllm-omni`
+- [`add-recipe`](add-recipe/SKILL.md): creates or updates in-repository model
+  recipes with verified task/input/output contracts, concrete hardware,
+  shared feature-guide links, exact commands, and qualification evidence
 - [`add-tts-model`](add-tts-model/SKILL.md): covers integration of new TTS
   models and related serving workflows
 - [`diffusion-perf-opt`](diffusion-perf-opt/SKILL.md): guides diffusion model
@@ -37,6 +40,10 @@ self-check.
 - [`precheck-pr`](precheck-pr/SKILL.md): self-checks a branch before creating a
   PR by validating title format, dead code, simplification opportunities,
   accuracy and performance claims, and merge readiness
+- [`production-add-diffusion-model`](production-add-diffusion-model/SKILL.md):
+  takes a working Day-0 diffusion integration through API/limit parity,
+  feature-combination evidence, quantization/offload, performance, production
+  serving, hardware recipes, and CI readiness
 - [`quantization`](quantization/SKILL.md): guides quantization method selection,
   model integration, checkpoint loading, and quality/performance validation
   for vLLM-Omni

.claude/skills/review-pr/SKILL.md

@@ -123,10 +123,11 @@ it changes again, report the churn and wait for a stable target.
 
 For a trusted PR head, materialize the pinned head in an isolated detached
 worktree. A worktree freezes identity but is not a security sandbox. Treat fork
-heads as untrusted unless the user and environment policy explicitly establish
-otherwise: execute them only in a disposable, secret-free sandbox with restricted
-filesystem, network, and resources; without one, use static SHA-addressed reads
-and CI evidence only. For a local review, freeze the committed, index, worktree,
+heads as untrusted until the user and environment policy explicitly establish
+trust: execute their code only after that trust is recorded for this host,
+with the trusted SHA noted in the review state and secrets kept out of the
+execution scope; without recorded trust, use static SHA-addressed reads and CI
+evidence only. For a local review, freeze the committed, index, worktree,
 and NUL-safe in-scope untracked contents. Follow
 [review-execution.md](references/process/review-execution.md) for trust gates,
 state fingerprints, and byte-for-byte staleness checks.
@@ -196,7 +197,7 @@ style preferences as simplification findings.
 
 Before each validation group, verify the frozen SHA plus the tracked, index,
 untracked, and ignored-file fingerprint, or recreate a pristine snapshot. On a
-trusted head or inside the required sandbox, run an import/version preflight,
+head the user explicitly trusted for this host, run an import/version preflight,
 then the narrowest relevant tests and low-cost static checks. Bind every result
 to the head SHA, snapshot fingerprint, and environment fingerprint. Never run
 imports, tests, builds, hooks, or repo-configurable tooling from an untrusted

.claude/skills/review-pr/references/checks/perf-verification.md

@@ -22,6 +22,19 @@ Keep exact commands and report variability rather than a cherry-picked run.
 Use the repository's benchmark when it covers the claim; otherwise use the
 smallest reproducible workload that reaches the changed production path.
 
+## Isolation per claim
+
+Isolate one claim per comparison. When a PR stacks several changes (a fused or
+restructured load path, a distilled step schedule, a new cache), request one
+column per claim at equal steps/workload instead of one blended base-vs-head
+delta, and name the unchanged stage or metric that pins equivalence.
+
+When the changed path is runtime-switchable (feature flag or environment
+variable), run every column on the same head and toggle the switch rather than
+diffing against a base commit that cannot separate the stacked changes:
+base, head with the switch off, head with the switch on, then head with the
+switch on plus each further claim.
+
 ## Evidence table
 
 | Dimension | Typical evidence |
@@ -34,6 +47,100 @@ smallest reproducible workload that reaches the changed production path.
 Every optimization needs correctness or quality evidence. A faster result on a
 different workload, precision, seed, or topology is not a valid comparison.
 
+## E2e stage-attribution table
+
+For an end-to-end latency claim, request the stage-attribution table the
+repository's perf PRs use:
+
+- Rows carry the benchmark's own field names: total e2e, per-stage execution,
+  postprocess, response encoding, and a request-to-output roll-up. Add a stage
+  handoff/idle row when the pipeline spans stages — connector transfer and
+  scheduling gaps are invisible in per-stage latency deltas.
+- Sanity rows pin comparability: inference steps, output frames/pixels/samples,
+  and generated tokens held identical across columns.
+- Report P50 and P100 from the stated warmup and measured counts. One
+  population per number: never mix cold-start and warm samples into one value.
+- Name the baseline and candidate commit SHAs, and close with an isolation
+  statement naming the metric that stayed unchanged ("X remained effectively
+  unchanged, isolating the gain to Y").
+
+When the serving path caches, captures, or regenerates at runtime (CUDA-graph
+bucket growth, cache rebuilds, recompilation), medians hide the resulting tail.
+Also request the event count and the maximum observed stall.
+
+Component-level tables pair with, not replace, the e2e table: per-op reference
+vs candidate timings (with the numerical-equality result per op) plus one
+complete-pipeline check under the production shapes.
+
+## Proportionality
+
+When a PR adds a model and explicitly labels its numbers as smoke observations
+rather than claims, do not demand the attribution machinery. Ask only for
+evidence protecting the model's stated operating contract — for a
+realtime/duplex model that is sustained cadence over a realistic session
+(enough ticks to leave warmup), with any mid-session capture/regeneration
+event and its stall reported, since a cadence that collapses mid-session is a
+functional failure, not a perf claim.
+
+## Realtime/duplex world models
+
+Request the operating-contract evidence as serving metrics, not claims:
+
+- Sustained per-tick latency over one realistic session: median, max, and the
+  argmax tick.
+- Stall events with their causes.
+- Peak memory at first versus last tick — session-owned state must not grow
+  per tick.
+- Control latency from a submitted action to the tick that applies it.
+- Realtime-versus-offline parity under the same controls, using the
+  repository's video-similarity helpers. Parity plotted over tick index is the
+  drift-over-horizon curve and the honest measure of session lifetime;
+  distribution-level suites (FVD, VBench) belong to model evaluation, not the
+  serving integration.
+
+Watch the contract boundaries the model documents: if a control input is
+tick-API-only, the parity pair must use a horizon both paths can run.
+
+## Speech-generation (TTS) models
+
+The streaming contract outranks the average — a synthesis that pauses
+mid-stream is broken for realtime use regardless of its averages. Request:
+
+- The benchmark's own speech metrics over one long utterance (enough audio to
+  leave warmup, tens of seconds): `audio_ttfp` (time to first packet),
+  `audio_rtf` (real-time factor; above 1.0 cannot keep up), `e2el`, and
+  `ttft`/`tpot` where tokens precede audio.
+- Mid-utterance stalls as the benchmark's continuity fields, not prose:
+  `audio_underrun` percentiles and the `audio_continuity_ok` rate
+  (`vllm_omni/benchmarks/metrics/metrics.py`), computed by
+  `vllm_omni/benchmarks/audio_continuity.py` — the companion to `audio_ttfp`
+  and `audio_rtf`. A synthesis that bursts chunks fast enough on average but
+  underruns mid-stream fails the streaming contract even when the averages
+  pass.
+- Quality as a paired comparison against the reference implementation under
+  the same text, speaker prompt, and seed; report sample rate and frame
+  count, and assert output completeness — truncated or dropped trailing audio
+  is a known failure class, not a quality nuance.
+- For realtime or duplex TTS: interruption (barge-in) latency and the
+  per-session memory trend across turns.
+
+## Omni models
+
+Apply the e2e stage-attribution table directly to the voice-to-voice path of
+omni (speech-to-speech and multimodal AR) models:
+
+- TTFT for text output, `audio_ttfp` for audio output, and per-stage
+  ITL/TPOT cadence across the perception-understanding-generation chain.
+- Cross-modal parity — the omni analogue of realtime-versus-offline parity:
+  the same semantic request through text and audio inputs, compared on output
+  content or timing.
+- For duplex omni sessions: turn-taking latency, interruption handling, the
+  concurrent session count actually validated, and a per-turn memory trend —
+  session-owned KV state must not grow per turn. When either turn produces
+  audio, continuity is judged with the same `audio_underrun` and
+  `audio_continuity_ok` fields as TTS, since turn-taking latency alone does
+  not expose mid-utterance underruns.
+
 ## Reviewer verification ladder
 
 1. Run base/head A/B on suitable hardware when affordable.

.claude/skills/review-pr/references/process/general-checks.md

@@ -121,6 +121,19 @@ Search bounded callers and sibling implementations before reporting dead code,
 duplication, or a missed consumer. Keep new abstractions only when they have a
 distinct live caller, invariant, or compatibility need.
 
+Verify absence and equality claims with whole-file evidence. A line window, a
+grep for one symbol, or a commit message proves presence, never absence: to
+assert that a file matches another version or lacks a change, diff or hash the
+full files (for example `git diff <sha1>:<path> <sha2>:<path>` or the contents
+API's size and SHA fields) before anchoring the finding. A verification
+shortcut that would itself be wrong to run is not evidence.
+
+For a value written or finalized in more than one place, establish which site
+executes first on each entry path — and whether the earlier site makes the
+later ones unreachable — before naming where a fix belongs. Enumerating all
+call sites correctly still misses the bug when a path the enumeration ranked
+last actually finalizes first and clears the state the proposed fix checks.
+
 ## Finding bar
 
 Anchor each finding to a changed `path:line`; name the trigger or call path,

.claude/skills/review-pr/references/process/review-execution.md

@@ -39,13 +39,13 @@ isolated detached worktree. Run every source read, `rg` search, import, and test
 from that snapshot, not the caller's checkout. A detached worktree provides
 snapshot isolation, not a security boundary.
 
-Treat a fork head as untrusted unless the user and environment policy explicitly
-establish trust. Never run its imports, tests, builds, hooks, package setup, or
-repo-configurable linters/plugins on the reviewer host. Execute them only in a
-disposable sandbox or VM with no credentials or inherited secrets, no host agent
-or service sockets, minimal read-only host mounts, disabled or explicitly
-allowlisted network, resource/time limits, and destruction after the run. If
-that boundary is unavailable, limit the review to the remote diff, SHA-addressed
+Treat a fork head as untrusted until the user and environment policy
+explicitly establish trust. Without recorded trust, never run its imports,
+tests, builds, hooks, package setup, or repo-configurable linters/plugins on
+the reviewer host. Once the user explicitly trusts this pinned commit for
+this host, record the trusted SHA with the review state and execute with
+credentials, agent sockets, and other secrets out of scope. Without recorded
+trust, limit the review to the remote diff, SHA-addressed
 reads such as `git show <head_sha>:<path>`, and existing CI evidence; report all
 executable validation as a gap.
 

.claude/skills/vllm-omni-test/SKILL.md

@@ -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).
@@ -468,10 +468,10 @@ When the user asks for **invalid parameter validation**, **invalid request bodie
 | **Invalid parameters Test · H100** | `pytest -s -v tests/dfx/reliability/invalid_param_test/ -m "slow and H100"` | Diffusion / omni / video invalid-param tests with `H100` hardware mark |
 | **Invalid parameters Test · L4** | `pytest -s -v tests/dfx/reliability/invalid_param_test/ -m "slow and L4"` | TTS / lighter models marked `L4` |
 
-- **Trigger:** `build.env("WEEKLY") == "1"` or PR label `weekly-test`.
+- **Trigger:** `build.env("WEEKLY") == "1"` (all weekly jobs) or PR label `weekly-test` (Reliability / Perf, then filtered by `source_file_dependencies`).
 - **Default:** extending an existing `invalid_param_test` script needs **no YAML edit** — the weekly steps already sweep the whole directory.
 - **Edit YAML only** when adding a **new hardware queue**, a **new top-level script** that must run in isolation, or a **model-specific weekly shard** (mirror neighboring reliability steps).
-- Weekly steps **do not** use `source_file_dependencies`.
+- Weekly Reliability / Perf jobs declare `source_file_dependencies`. Filtering applies only on the `weekly-test` label; `WEEKLY=1` on `main` keeps every job in those groups. CPU coverage (`WEEKLY=1` only) and the E2E Tests group (`NON_CRITICAL=1` only) do **not** use this field.
 
 **Example — append to `test_invalid_image_generation.py`:**
 
@@ -644,7 +644,7 @@ If the test is not already collected by an existing pipeline command (for exampl
 | **L2** | `test-ready.yml` with `core_model` + `source_file_dependencies` | merge / nightly pipelines |
 | **Invalid param** | `test-weekly.yml` — **Invalid parameters Test** group (`-m "slow and H100"` / `-m "slow and L4"`). Usually **no YAML edit** when appending cases to existing `invalid_param_test` scripts | `test-ready.yml`, `test-merge.yml`, `test-nightly.yml` |
 
-`test-nightly.yml` shards use **explicit pytest file paths** in `commands` (and PR labels like `diffusion-x2iat-test`); they generally **do not** use `source_file_dependencies`. Only suggest merge/ready YAML when the requested level is L3/L2 (or the user explicitly asks for multi-level CI).
+`test-nightly.yml` shards use **explicit pytest file paths** in `commands` plus `source_file_dependencies`; PR trigger is `nightly-test` (path filter), or `NIGHTLY=1` on `main` (all jobs). Only suggest merge/ready YAML when the requested level is L3/L2 (or the user explicitly asks for multi-level CI).
 
 **`test-nightly.yml` top-level groups** (each uses `full_model` + type marker in pytest `-m`):
 
@@ -653,7 +653,7 @@ If the test is not already collected by an existing pipeline command (for exampl
 | **Omni Model Test** | `omni` | Function / doc / accuracy / **perf** for Qwen-Omni family |
 | **TTS Model Test** | `tts` | `-m "full_model and L4 and tts"` (and H100 variants when enabled); **Perf Test** uses `run_benchmark.py` + `test_tts.json` |
 | **Diffusion X2I(&A&T) Model Test** | `diffusion` | Image/audio/text diffusion; explicit file shards for **Function**; **Perf Test · <Model>** uses `run_diffusion_benchmark.py` + JSON config |
-| **Diffusion X2V Model Test** | `diffusion` | Video-only (Wan, HunyuanVideo, …); PR label `diffusion-x2v-test`; separate **Perf** steps when in-tree |
+| **Diffusion X2V Model Test** | `diffusion` | Video-only (Wan, HunyuanVideo, …); separate **Perf** steps when in-tree |
 
 Guidelines when editing YAML:
 

.claude/skills/vllm-omni-test/references/test-routing.md

@@ -37,7 +37,7 @@ Every model-centric e2e test must declare **exactly one** type marker:
 | **Diffusion X2I(&A&T) Model Test** | x2**i** / x2**a** / x2**t** — image, audio, text (non-video) | Qwen-Image*, BAGEL, FLUX, SD3, Z-Image, LongCat, DreamZero |
 | **Diffusion X2V Model Test** | x2**v** — video only | Wan2.2, HunyuanVideo 1.5, Wan VACE |
 
-PR labels for selective nightly runs: `diffusion-x2iat-test`, `diffusion-x2v-test` (plus `nightly-test` / `NIGHTLY=1`).
+PR label for L4 on a branch: `nightly-test` (jobs then filtered by `source_file_dependencies`). Scheduled `main` uses `NIGHTLY=1` and runs every nightly job. L5 Reliability / Perf use `weekly-test` with the same path filter; CPU coverage and the weekly E2E group are env-only (`WEEKLY=1` / `NON_CRITICAL=1`) and are not source-filtered.
 
 ## Level and Marker Mapping
 
@@ -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
@@ -238,13 +238,13 @@ pytest -s -v dfx/reliability/invalid_param_test/ -m "slow and L4"
 pytest -s -v dfx/reliability/invalid_param_test/test_invalid_image_generation.py::test_images_generations_invalid_requests -m "slow and H100"
 ```
 
-**Trigger:** `WEEKLY=1` or PR label `weekly-test`. Appending cases to existing scripts usually needs **no YAML edit** (directory sweep). No `source_file_dependencies` on weekly steps.
+**Trigger:** `WEEKLY=1` (all jobs) or PR label `weekly-test` (Reliability / Perf, filtered by `source_file_dependencies`). Appending cases to existing scripts usually needs **no YAML edit** (directory sweep).
 
 ### Platform-targeted examples
 
 ```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)

.github/CODEOWNERS

@@ -67,6 +67,11 @@
 # File-level overrides within entrypoints/
 /vllm_omni/entrypoints/openai/errors.py @alex-jw-brooks @NickCao
 /vllm_omni/entrypoints/cli/benchmark/ @alex-jw-brooks @Bounty-hunter
+# Shared wire codec: protocol/realtime (OpenAI Realtime GA) and protocol/duplex
+# (vLLM-Omni's extension of it) — RFC #6592 P0a. Layer-neutral by design — the
+# engine depends on it — so it is owned by the serving-boundary document rather
+# than by any one consumer. — docs/design/module/entrypoints.md
+/vllm_omni/protocol/ @alex-jw-brooks @linyueqian @NickCao
 
 # Error contracts — docs/design/module/error_contracts.md
 /vllm_omni/errors.py @alex-jw-brooks @NickCao

.github/workflows/pre-commit.yml

@@ -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

.pre-commit-config.yaml

@@ -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]

CONTRIBUTING.md

@@ -2,6 +2,8 @@
 
 You may find information about contributing to vLLM-Omni on [Contributing](https://vllm-omni.readthedocs.io/en/latest/contributing/).
 
+AI-assisted contributions must follow the [AI Usage Policy](docs/contributing/README.md#ai-usage-policy), including disclosure, human review, and validation requirements.
+
 Local `pre-commit` gates (SPDX, forbidden imports, `torch.cuda`, shellcheck,
 mypy, test marks, TTS adapter ratchet, markdownlint, Buildkite schema, and
 how to extend allowlists) are documented in the

README.md

@@ -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.
@@ -52,14 +53,14 @@ vLLM-Omni is flexible and easy to use with:
 - Tensor, pipeline, data and expert parallelism support for distributed inference
 - Streaming outputs
 - OpenAI-compatible API server
-- Full-duplex realtime serving with streaming audio input and output (experimental)
+- [Full-duplex realtime serving](docs/serving/realtime_duplex_api.md) with streaming audio input and output
 
 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, 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
 

apps/ComfyUI-vLLM-Omni/README.md

@@ -13,7 +13,6 @@ It can send model inference requests to either a locally running vLLM-Omni servi
 > [!TIP]
 > If you run both ComfyUI and vLLM-Omni on the same device, you can create separate virtual environments and use different Python versions for them.
 
-
 ## Installation
 
 Copy this folder to the `custom_nodes` subfolder of your ComfyUI installation. Your directory should look like `ComfyUI/custom_nodes/ComfyUI-vLLM-Omni`.
@@ -21,9 +20,10 @@ Copy this folder to the `custom_nodes` subfolder of your ComfyUI installation. Y
 If you are running ComfyUI during copying, you should restart ComfyUI to load this extension.
 
 > [!TIP]
-> You can use utility websites such as https://download-directory.github.io/ to download a subdirectory of a repo. Also checkout community discussions (e.g., https://stackoverflow.com/questions/7106012/download-a-single-folder-or-directory-from-a-github-repository) for more info.
+> You can use utility websites such as <https://download-directory.github.io/> to download a subdirectory of a repo. Also checkout community discussions (e.g., <https://stackoverflow.com/questions/7106012/download-a-single-folder-or-directory-from-a-github-repository>) for more info.
 
 On the device and virtual environment you run ComfyUI, launch ComfyUI with
+
 ```bash
 cd ComfyUI
 
@@ -35,6 +35,7 @@ python main.py --cpu
 ```
 
 On the device and virtual environment you run vLLM-Omni, start a model service with
+
 ```bash
 vllm serve The_Model_ID_to_Serve --omni --port 8000
 ```
@@ -48,14 +49,31 @@ This extension offers the following nodes based on the output modalities (at **C
 
 - **Generate Image** for text-to-image and image-to-image tasks
 - **Generate Video** for text-to-video, first-frame/image-to-video, and reference-conditioned video
+- **Latent Mask Editing** for MiniMax-H3 latent-mask editing (source media plus video/audio noise masks)
+- **FastH3 Deployment** for routing text-to-video requests to a MiniMax-H3 server with FastH3 fused at startup
 - **Multimodality Understanding** for multimodality-to-text and multimodality-to-audio tasks
 - **TTS** and **TTS Voice Clone** for TTS tasks
+- **Generate Music** for lyrics-and-description-to-music generation
 
 This extension also offers example workflows (at **ComfyUI sidebar -> Templates -> vLLM-Omni**)
 
 > [!NOTE]
 > The node UI and feature designs are intended to match vLLM-Omni online serving interfaces. It cannot offer more than what the interfaces support.
 
+Every node carries the vLLM-Omni mark in its title bar and is tinted by what it outputs, so a graph is readable at a glance:
+
+| Colour | Nodes | What they produce |
+| --- | --- | --- |
+| Blue | Generate Image, Generate Video, Multimodality Understanding, TTS, TTS Voice Clone | A generated image, video, audio, or text. These are the only nodes that reach a server. |
+| Amber | AR / Diffusion / Multi-Stage Sampling Params | Sampling parameters that apply to any model |
+| Purple | Qwen TTS Params, Wan Video Params, MiniMax-H3 Video Params | Parameters that only one model family accepts |
+| Red | LoRA, FastH3 Deployment | Which weights the server is expected to have loaded |
+| Teal | Video References, Latent Mask Editing | Reference media |
+
+Recolouring a node by hand (right click -> Colors) overrides its tint, and the choice is kept.
+
+**Generate Video** takes a clip length in seconds (`duration`), not a frame count. Frames stay the wire unit and are derived with the node's `fps`, so the length is always measured against the rate that is actually served; models that accept only certain frame counts still round to their own lattice server-side. Graphs saved before this widget existed stored `num_frames` in its place and are converted on load, using the fps recorded alongside it -- the browser console names every node it rewrites.
+
 To build a simple workflow yourself,
 
 - Drag a generation node onto the canvas.
@@ -117,11 +135,146 @@ You can configure per-stage sampling parameters for multi-stage models.
 </p>
 
 > [!TIP]
-> Connect a **frame** image for first-frame / image-to-video (e.g., Wan I2V, MiniMax-H3 FL2VA).
+> Connect **frame** for backward-compatible first-frame / image-to-video behavior. For MiniMax-H3 FL2VA,
+> connect **first_frame**, **last_frame**, or both to condition the start frame, end frame, or both.
 >
 > For reference-conditioned generation (MiniMax-H3 Ref2VA), connect a **Video References** node instead.
 >
-> Do not use `frame` and `references` together. Task routing is automatic from which inputs you connect.
+> Do not combine `frame` with `first_frame` or `last_frame`, and do not combine any frame input with
+> `references`. Task routing is automatic from which inputs you connect.
+
+For MiniMax-H3 Ref2VA, **Video References** accepts up to 9 images (`image_1`–`image_9`),
+3 videos (`video_1`–`video_3`), and 3 audio clips (`audio_1`–`audio_3`), with at most
+12 connected inputs in total. Any mixture containing at least one image or video
+is supported; empty and audio-only references are rejected. For example, you can
+combine 6 images, 3 videos, and 3 audio clips in one request.
+
+Within each media type, references follow slot-number order, skipping unconnected
+slots. For example, connecting `image_2` and `image_9` sends `image_2` as the first
+image and `image_9` as the second. Each image slot uses the first image in its batch.
+Existing connections to `image_1`, `image_2`, `audio_1`, `audio_2`, `video_1`, and
+`video_2` remain valid in saved workflows.
+
+#### MiniMax-H3 Reference to Video
+
+Load [MiniMax-H3 Reference to Video](example_workflows/vLLM-Omni%20MiniMax-H3%20Reference%20to%20Video.json)
+from **Templates → ComfyUI-vLLM-Omni**, or drag the JSON onto the canvas.
+It adapts the [official ComfyUI R2V workflow](https://github.com/Comfy-Org/workflow_templates/blob/main/templates/video_minimax_h3_r2v.json)
+to remote vLLM-Omni execution. Model loading, sampling, and VAE decoding run on
+the server; ComfyUI loads references and saves the returned video.
+
+Use an extension version containing the audio-preserving output fix from
+[#7456](https://github.com/vllm-project/vllm-omni/pull/7456). Older versions can
+drop the server's audio while decoding the response.
+
+1. Start a Ref2VA-capable service using the [MiniMax-H3 recipe](../../recipes/MiniMaxAI/MiniMax-H3.md).
+   Set **Generate Video** to its `/v1` URL and served model name.
+2. Select your own image in **Load Image**. The template's filenames are placeholders
+   relative to ComfyUI's input directory; no sample assets or model weights are bundled.
+3. Connect **Load Video** and **Load Audio** when needed. Duplicate the loaders to
+   fill more reference slots, or disconnect the image when using video-only input.
+   Keep the references connected to **Generate Video** and leave `frame` disconnected.
+4. Match prompt tags such as `<Picture 1>`, `<Video 1>`, and `<Audio 1>` to the
+   connected references, counting each media type separately and skipping empty slots.
+5. Run the workflow. **Save Video** writes an MP4 under `output/video/` and preserves
+   the generated audio when the audio-output prerequisite is installed.
+
+The default is **1344×768, 24 FPS, 124 frames** (about 5.17 seconds), 50 sampling
+points, video flow shift 12, audio flow shift 3, and seed 42. The duration widget
+is set to 5.167 seconds, which converts to 124 frames at 24 FPS. Other H3 canvas presets
+are 1024×768, 768×768, 768×1024, and 768×1344. Keep frame counts at `17k+5` within
+the 4–15 second output range; examples are 107, 124, 209, and 345 frames.
+Reference video/audio clips must each be 2–15 seconds, with at most 15 seconds of
+reference video and at most 15 seconds of standalone reference audio per request.
+
+For **Ref2VA Turbo**, use the same template. Configure a Ref2VA-only server with
+`--lora-backend peft --lora-path "$TURBO_LORA" --task-type ref2va`, following the
+recipe's hardware setup. For `minimax_h3_ref2v_turbo_4step_v0.1_bf16.safetensors`,
+set **LoRA** `local_path` to that file's path on the server and connect it to
+**Generate Video**. Use `num_inference_steps=5`, `flow_shift=12`,
+`audio_flow_shift=3`, and LoRA `scale=1`. The API counts sigma points, so five
+points produce four denoiser evaluations. Use the Diffusers artifact, not its
+`_comfyui_` export. To return to base mode, disconnect LoRA and restore 50 steps.
+The 8-step v1.0 Ref2VA adapter instead requires nine sampling points and video
+flow shift 6. FastH3 is a separate startup-fused T2VA deployment.
+
+Validate the template's wiring and defaults locally with:
+
+```bash
+python -m pytest tests/e2e/features/comfyui/test_h3_reference_workflow.py -q
+```
+
+For a real-model check, run the workflow against H3 and inspect the saved file:
+
+```bash
+ffprobe -v error -show_entries stream=codec_type,width,height,r_frame_rate,sample_rate,channels \
+  -show_entries format=duration -of json "$OUTPUT_MP4"
+```
+
+Check for 24 FPS video, stereo audio, the requested canvas, and aligned duration;
+also play the result to assess reference conditioning and audio/video synchronization.
+Record the server commit, model/adapter, input assets, prompt, settings, and output
+alongside the result. Local schema or mocked-server checks do not establish H3
+generation quality or replace this real-model validation.
+
+### MiniMax H3 Text to Video
+
+Import [the H3 text-to-video template](example_workflows/MiniMax_H3_Text_to_Video.json)
+for remote video generation with audio. It includes connected base presets and
+optional Turbo sampling, H3 parameters, and Remote LoRA nodes. See the
+[workflow guide](docs/minimax-h3-t2v.md) for server setup, artifact-specific Turbo
+settings, and saved-video/audio validation. Set Remote LoRA’s `local_path` to
+the downloaded Turbo artifact on your server before enabling Turbo.
+
+#### H3 video upscale (WF-07)
+
+The **vLLM-Omni MiniMax H3 Video Upscale** template generates video remotely, upscales it with SeedVR2, and saves the original and upscaled videos with the generated audio and FPS. See [workflow setup](docs/wf07-h3-upscale.md).
+
+#### FastH3 text-to-video
+
+FastH3 is fused into MiniMax-H3 when the vLLM-Omni server starts; it is not a request-switchable LoRA. Download one adapter variant and start a non-offloaded FL2VA server. For the Dense / Data-Free profile:
+
+```bash
+export FASTH3_DIR=/path/to/fasth3
+hf download FastVideo/FastVideo-FastH3-4-step-Preview-v1-LoRA \
+  dense-datafree/adapter_model.safetensors --local-dir "${FASTH3_DIR}"
+
+vllm serve /path/to/MiniMax-H3 \
+  --omni \
+  --task-type fl2va \
+  --lora-path "${FASTH3_DIR}/dense-datafree/adapter_model.safetensors" \
+  --port 8000
+```
+
+For the VSA / Data-Free profile, download `vsa-datafree/adapter_model.safetensors`, use that file as `--lora-path`, and add:
+
+```bash
+--diffusion-attention-backend FASTVIDEO_VSA \
+--fastvideo-vsa-topk 64
+```
+
+The VSA profile also requires a compatible `fastvideo-kernel` installation. See the [MiniMax-H3 FastH3 recipe](../../recipes/MiniMaxAI/MiniMax-H3.md#fasth3-adapter) for the full serving contract, supported parallel layouts, and kernel requirements.
+
+Open the **vLLM-Omni FastH3 Text to Video** template, then:
+
+- Set the server URL and served model name on **FastH3 Deployment**.
+- Connect its output to **Generate Video → fast_h3**. When connected, the deployment node's URL and model take precedence over the corresponding Generate Video widgets.
+- Keep `frame`, `references`, **LoRA**, and **MiniMax-H3 Video Params** disconnected. FastH3 Preview v1 supports T2VA only, is already fused, and owns both flow shifts.
+- A connected **Diffusion Sampling Params** node may set seed and other ordinary sampling options. The integration always enforces four denoising steps and 24 FPS for FastH3.
+
+The node records which server the workflow targets; it does not start one, nor switch adapters or attention backends on a running server.
+
+#### Latent-mask editing (MiniMax-H3)
+
+The [WF-05 template](example_workflows/vLLM-Omni%20MiniMax-H3%20Latent%20Mask%20Editing.json) contains inpainting, object removal, continuation, and extension examples in one graph. See the [workflow guide](docs/wf05-h3-latent-editing.md) for inputs, mask settings, dependencies, and preview limitations.
+
+Connect a **Latent Mask Editing** node to **Generate Video → latent_edit** to edit a source clip instead of generating from scratch. It uploads the source media and serializes the video/audio noise masks the MiniMax H3 API accepts:
+
+- `source_video` / `source_audio` — the media to edit.
+- `video_mask` — a ComfyUI mask image; `0` preserves a region, `1` regenerates it, fractional values blend. A 2D mask `[H, W]` is applied to every frame; a 3D mask `[T, H, W]` is treated as a temporal mask (one slice per frame) for continuation or extension. It is resized to the video latent grid.
+- `audio_mask` — a scalar in `[0, 1]`; `0` keeps the source audio, `1` regenerates it, fractional values blend.
+
+A non-trivial mask requires its matching source, and a source without a mask is rejected. This node only forwards inputs to the server; the served model must declare latent-mask editing support (MiniMax-H3).
 
 ### TTS (e.g., Qwen TTS series)
 
@@ -137,6 +290,17 @@ You can configure per-stage sampling parameters for multi-stage models.
 > [!TIP]
 > There is a dedicated node for VoiceClone tasks with reference audio input. Other simple text-to-speech tasks should use the regular TTS node.
 
+### Music generation (e.g., MiniMax Music 3)
+
+(Also available at **ComfyUI sidebar->Template->vLLM-Omni->vLLM-Omni Music Generation**)
+
+<p align="center">
+  <picture>
+    <source media="(prefers-color-scheme: dark)" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/apps/ComfyUI-vLLM-Omni/docs/images/comfyui-music-generation.jpg">
+    <img alt="vLLM-Omni Music Generation" src="https://raw.githubusercontent.com/vllm-project/vllm-omni/refs/heads/main/apps/ComfyUI-vLLM-Omni/docs/images/comfyui-music-generation.jpg" width=55%>
+  </picture>
+</p>
+
 ### Chaining multiple model services
 
 (Also available at **ComfyUI sidebar->Template->vLLM-Omni->vLLM-Omni Chaining Services**)
@@ -152,6 +316,8 @@ You can configure per-stage sampling parameters for multi-stage models.
 
 Follow the [development convention and rules of vLLM-Omni](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/).
 
+Node tints and the title-bar mark are applied in `web/main.js`, keyed off each node's declared output types rather than a list of node names. A new node that returns an existing type is themed with no front-end change; a new output type needs one entry in `FAMILY_BY_OUTPUT` there.
+
 ## Limitation and Non-Goals
 
 - Single server mode only. No automatic load balancing or failover.
@@ -178,11 +344,11 @@ Whenever you find an issue or problem, please
 
 Features
 
-- https://github.com/dougbtv/comfyui-vllm-omni/ The official reference implementation for ComfyUI integration with vLLM-Omni's DALL-E compatible image generation API.
-- https://github.com/Comfy-Org/ComfyUI/tree/master/comfy_extras ComfyUI's built-in node implementations.
+- <https://github.com/dougbtv/comfyui-vllm-omni/> The official reference implementation for ComfyUI integration with vLLM-Omni's DALL-E compatible image generation API.
+- <https://github.com/Comfy-Org/ComfyUI/tree/master/comfy_extras> ComfyUI's built-in node implementations.
 
 UI/UX design references
 
-- https://github.com/sgl-project/sglang/pull/15271 SGLang Diffusion's official ComfyUI integration for image and video generation.
-- https://github.com/SXQBW/ComfyUI-Qwen-Omni A third party ComfyUI integration for Qwen Omni series.
-- https://github.com/flybirdxx/ComfyUI-Qwen-TTS https://github.com/DarioFT/ComfyUI-Qwen3-TTS Third  party ComfyUI integrations for Qwen TTS series.
+- <https://github.com/sgl-project/sglang/pull/15271> SGLang Diffusion's official ComfyUI integration for image and video generation.
+- <https://github.com/SXQBW/ComfyUI-Qwen-Omni> A third party ComfyUI integration for Qwen Omni series.
+- <https://github.com/flybirdxx/ComfyUI-Qwen-TTS> <https://github.com/DarioFT/ComfyUI-Qwen3-TTS> Third  party ComfyUI integrations for Qwen TTS series.

apps/ComfyUI-vLLM-Omni/__init__.py

@@ -1,4 +1,8 @@
-"""Top-level package for comfyui_vllm_omni."""  # noqa: N999  # This is not a python library intended to be imported
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+# ruff: noqa: N999
+
+"""Top-level package for comfyui_vllm_omni."""  # This is not a Python library intended to be imported.
 
 __all__ = [
     "NODE_CLASS_MAPPINGS",
@@ -13,9 +17,13 @@ __version__ = "0.0.1"
 from .comfyui_vllm_omni.nodes import (
     VLLMOmniARSampling,
     VLLMOmniDiffusionSampling,
+    VLLMOmniFastH3Deployment,
     VLLMOmniGenerateImage,
+    VLLMOmniGenerateMusic,
     VLLMOmniGenerateVideo,
+    VLLMOmniLatentMaskEditing,
     VLLMOmniMiniMaxH3Params,
+    VLLMOmniMiniMaxH3TemporalMask,
     VLLMOmniQwenTTSParams,
     VLLMOmniRemoteLoRA,
     VLLMOmniSamplingParamsList,
@@ -28,18 +36,22 @@ from .comfyui_vllm_omni.nodes import (
 
 # A dictionary that contains all nodes you want to export with their names
 NODE_CLASS_MAPPINGS = {
+    "VLLMOmniMiniMaxH3TemporalMask": VLLMOmniMiniMaxH3TemporalMask,
     # === Generation ===
     "VLLMOmniGenerateImage": VLLMOmniGenerateImage,
     "VLLMOmniGenerateVideo": VLLMOmniGenerateVideo,
     "VLLMOmniUnderstanding": VLLMOmniUnderstanding,
     "VLLMOmniTTS": VLLMOmniTTS,
+    "VLLMOmniGenerateMusic": VLLMOmniGenerateMusic,
     "VLLMOmniVoiceClone": VLLMOmniVoiceClone,
     "VLLMOmniVideoReferences": VLLMOmniVideoReferences,
+    "VLLMOmniLatentMaskEditing": VLLMOmniLatentMaskEditing,
     # === Params ===
     "VLLMOmniARSampling": VLLMOmniARSampling,
     "VLLMOmniDiffusionSampling": VLLMOmniDiffusionSampling,
     "VLLMOmniSamplingParamsList": VLLMOmniSamplingParamsList,
     "VLLMOmniRemoteLoRA": VLLMOmniRemoteLoRA,
+    "VLLMOmniFastH3Deployment": VLLMOmniFastH3Deployment,
     "VLLMOmniQwenTTSParams": VLLMOmniQwenTTSParams,
     "VLLMOmniWanParams": VLLMOmniWanParams,
     "VLLMOmniMiniMaxH3Params": VLLMOmniMiniMaxH3Params,
@@ -47,18 +59,22 @@ NODE_CLASS_MAPPINGS = {
 
 # A dictionary that contains the friendly/humanly readable titles for the nodes
 NODE_DISPLAY_NAME_MAPPINGS = {
+    "VLLMOmniMiniMaxH3TemporalMask": "MiniMax-H3 Temporal Mask",
     # === Generation ===
     "VLLMOmniGenerateImage": "Generate Image",
     "VLLMOmniGenerateVideo": "Generate Video",
     "VLLMOmniUnderstanding": "Multimodality Understanding",
     "VLLMOmniTTS": "TTS (Text to Speech)",
+    "VLLMOmniGenerateMusic": "Generate Music",
     "VLLMOmniVoiceClone": "TTS Voice Cloning",
     "VLLMOmniVideoReferences": "Video References",
+    "VLLMOmniLatentMaskEditing": "Latent Mask Editing",
     # === Params ===
     "VLLMOmniARSampling": "AR Sampling Params",
     "VLLMOmniDiffusionSampling": "Diffusion Sampling Params",
     "VLLMOmniSamplingParamsList": "Multi-Stage Sampling Params List",
     "VLLMOmniRemoteLoRA": "LoRA",
+    "VLLMOmniFastH3Deployment": "FastH3 Deployment",
     "VLLMOmniQwenTTSParams": "Qwen TTS Params",
     "VLLMOmniWanParams": "Wan Video Params",
     "VLLMOmniMiniMaxH3Params": "MiniMax-H3 Video Params",

apps/ComfyUI-vLLM-Omni/comfyui_vllm_omni/nodes.py

@@ -1,15 +1,25 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
+import math
 from typing import Literal
 
 import torch
 from comfy_api.input import AudioInput, VideoInput
 
 from .utils.api_client import VLLMOmniClient
+from .utils.latent_mask import _align_frame_count, _video_latent_t
 from .utils.logger import get_logger
 from .utils.models import lookup_model_spec
 from .utils.types import (
+    MAX_REFERENCE_AUDIOS,
+    MAX_REFERENCE_IMAGES,
+    MAX_REFERENCE_VIDEOS,
     AudioFormat,
     AutoregressionSamplingParams,
     DiffusionSamplingParams,
+    FastH3Deployment,
+    LatentMaskEditing,
     MiniMaxH3ModelSpecificParams,
     QwenTTSModelSpecificParams,
     VideoReferences,
@@ -22,6 +32,23 @@ from .utils.validators import (
 
 logger = get_logger(__name__)
 
+FASTH3_INFERENCE_STEPS = 4
+FASTH3_FPS = 24
+# A FastH3 deployment may expose H3 under any --served-model-name, so the payload
+# spec has to be named outright rather than recovered from that alias.
+FASTH3_SPEC_MODEL = "MiniMax-H3"
+
+
+def _resolve_fast_h3_deployment(deployment: dict) -> tuple[str, str]:
+    if not isinstance(deployment, dict):
+        raise ValueError("FastH3 deployment must be provided by a FastH3 Deployment node.")
+
+    url = str(deployment.get("url") or "").strip().rstrip("/")
+    model = str(deployment.get("model") or "").strip()
+    if not url or not model:
+        raise ValueError("FastH3 deployment requires both URL and model.")
+    return url, model
+
 
 class _VLLMOmniGenerateBase:
     """Base class for vLLM-Omni generation nodes with shared functionality."""
@@ -162,14 +189,31 @@ class VLLMOmniGenerateVideo(_VLLMOmniGenerateBase):
                 "width": ("INT", {"default": 832, "min": 1}),
                 "height": ("INT", {"default": 480, "min": 1}),
                 "fps": ("INT", {"default": 16, "min": 1}),
-                "num_frames": ("INT", {"default": 41, "min": 1}),
+                "duration": (
+                    "FLOAT",
+                    {
+                        "default": 4.0,
+                        "min": 0.1,
+                        "step": 0.1,
+                        "round": 0.001,
+                        "tooltip": (
+                            "Clip length in seconds, converted to frames with the fps above. "
+                            "Models that only accept certain frame counts (e.g. MiniMax-H3) round to "
+                            "their own lattice, so the served clip can be slightly longer than requested."
+                        ),
+                    },
+                ),
             },
             "optional": {
                 "frame": ("IMAGE",),
+                "first_frame": ("IMAGE",),
+                "last_frame": ("IMAGE",),
                 "references": ("VIDEO_REFERENCES",),
                 "sampling_params": ("SAMPLING_PARAMS",),
                 "lora": ("REMOTE_LORA",),
                 "model_params": ("VIDEO_PARAMS",),
+                "fast_h3": ("FASTH3_DEPLOYMENT",),
+                "latent_edit": ("LATENT_MASK_EDITING",),
             },
         }
 
@@ -178,12 +222,30 @@ class VLLMOmniGenerateVideo(_VLLMOmniGenerateBase):
     FUNCTION = "generate"
 
     @classmethod
-    def VALIDATE_INPUTS(cls, url, model, frame=None, references=None, **_kwargs) -> str | Literal[True]:
+    def VALIDATE_INPUTS(
+        cls,
+        url,
+        model,
+        frame=None,
+        first_frame=None,
+        last_frame=None,
+        references=None,
+        fast_h3=None,
+        **_kwargs,
+    ) -> str | Literal[True]:
         base = super().VALIDATE_INPUTS(url, model)
         if base is not True:
             return base
         if frame is not None and references is not None:
             return "Provide only one of frame or references, not both."
+        if frame is not None and (first_frame is not None or last_frame is not None):
+            return "Provide either frame or first_frame/last_frame, not both."
+        if references is not None and (first_frame is not None or last_frame is not None):
+            return "Provide either first_frame/last_frame or references, not both."
+        if fast_h3 is not None and any(value is not None for value in (frame, first_frame, last_frame, references)):
+            return (
+                "FastH3 Preview supports T2VA only; disconnect frame, first_frame, last_frame, and references inputs."
+            )
         return True
 
     async def generate(
@@ -194,20 +256,72 @@ class VLLMOmniGenerateVideo(_VLLMOmniGenerateBase):
         width: int,
         height: int,
         fps: int,
-        num_frames: int,
+        duration: float,
         negative_prompt: str | None = None,
         frame: torch.Tensor | None = None,
+        first_frame: torch.Tensor | None = None,
+        last_frame: torch.Tensor | None = None,
         references: dict | None = None,
         sampling_params: dict | list[dict] | None = None,
         model_params: dict | None = None,
         lora: dict | None = None,
+        fast_h3: dict | None = None,
+        latent_edit: dict | None = None,
         **kwargs,
     ):
         if kwargs:
             logger.info("Uncaught kwargs: %s", kwargs)
         logger.debug("Got sampling params: %s", sampling_params)
         logger.debug("Got model params: %s", model_params)
-        validate_model_and_sampling_params_types(model, sampling_params)
+
+        # Which spec builds the payload. Only a FastH3 deployment separates this
+        # from the served name; every other path keeps them equal.
+        spec_model = model
+
+        if fast_h3 is not None:
+            if any(value is not None for value in (frame, first_frame, last_frame, references)):
+                raise ValueError(
+                    "FastH3 Preview supports T2VA only; disconnect frame, first_frame, "
+                    "last_frame, and references inputs."
+                )
+            if lora is not None:
+                raise ValueError(
+                    "FastH3 is already fused into the selected server; disconnect the request-level LoRA input."
+                )
+
+            url, model = _resolve_fast_h3_deployment(fast_h3)
+            logger.info("Using FastH3 deployment at %s", url)
+            fps = FASTH3_FPS
+            # The served name is whatever the operator passed to --served-model-name.
+            # Left alone, lookup_model_spec would miss H3 for an alias such as
+            # "fasth3", drop the params builder, and send a t2va request carrying no
+            # aspect_ratio -- which the server refuses.
+            spec_model = FASTH3_SPEC_MODEL
+
+            if sampling_params is None:
+                sampling_params = DiffusionSamplingParams()
+            elif isinstance(sampling_params, list):
+                if len(sampling_params) != 1:
+                    raise ValueError("FastH3 expects a single diffusion sampling params group.")
+                sampling_params = sampling_params[0].__class__(sampling_params[0])
+            else:
+                sampling_params = sampling_params.__class__(sampling_params)
+            sampling_params["num_inference_steps"] = FASTH3_INFERENCE_STEPS
+
+            # FastH3 owns both modality shifts. Sending the ordinary H3 values
+            # from a connected H3 Params node would turn a deployment choice
+            # into a request-level override, which the server intentionally
+            # rejects when it differs from the fused adapter contract.
+            if model_params is not None:
+                model_params = model_params.__class__(model_params)
+                model_params.pop("flow_shift", None)
+                model_params.pop("audio_flow_shift", None)
+
+        # Frames stay the wire unit. Convert after the FastH3 branch above, so the
+        # duration is measured against the fps the server will actually apply.
+        num_frames = max(1, round(duration * fps))
+
+        validate_model_and_sampling_params_types(spec_model, sampling_params)
 
         # Currently, all video generation models are single-stage diffusion models
         if isinstance(sampling_params, list):
@@ -227,8 +341,11 @@ class VLLMOmniGenerateVideo(_VLLMOmniGenerateBase):
         client = VLLMOmniClient(url)
         output = await client.generate_video(
             model=model,
+            spec_model=spec_model,
             prompt=prompt,
             frame=frame,  # frame present => fl2va / Wan I2V
+            first_frame=first_frame,
+            last_frame=last_frame,
             references=references,
             width=width,
             height=height,
@@ -238,6 +355,7 @@ class VLLMOmniGenerateVideo(_VLLMOmniGenerateBase):
             sampling_params=sampling_params,
             lora=lora,
             model_params=model_params,
+            latent_edit=latent_edit,
         )
         return (output,)
 
@@ -418,6 +536,66 @@ class VLLMOmniTTS(_VLLMOmniGenerateBase):
         return (audio,)
 
 
+class VLLMOmniGenerateMusic(_VLLMOmniGenerateBase):
+    """Generate a song from lyrics and a musical description with MiniMax Music 3."""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "url": ("STRING", {"default": "http://localhost:8000/v1"}),
+                "model": ("STRING", {"default": "MiniMaxAI/MiniMax-Music3"}),
+                "instructions": (
+                    "STRING",
+                    {
+                        "multiline": True,
+                        "display_name": "caption",
+                        "tooltip": "Music caption: describe genre, instruments, tempo and mood.",
+                    },
+                ),
+                "lyrics": ("STRING", {"multiline": True}),
+                "max_duration_seconds": (
+                    "FLOAT",
+                    {
+                        "default": 300.0,
+                        "min": 1,
+                        "max": 360,
+                        "step": 0.01,
+                        "tooltip": "Upper limit; rounded down to whole 25 Hz audio frames. May end earlier.",
+                    },
+                ),
+                "seed": ("INT", {"default": 0, "min": 0, "max": 2**53 - 1, "control_after_generate": True}),
+                "response_format": (["wav", "mp3", "flac", "opus"],),
+            },
+        }
+
+    RETURN_TYPES = ("AUDIO",)
+    RETURN_NAMES = ("audio",)
+    FUNCTION = "generate"
+
+    async def generate(
+        self,
+        url: str,
+        model: str,
+        lyrics: str,
+        instructions: str,
+        response_format: AudioFormat,
+        max_duration_seconds: float,
+        seed: int = 0,
+    ) -> tuple[AudioInput]:
+        audio = await VLLMOmniClient(url.rstrip("/")).generate_speech(
+            model=model,
+            input=lyrics,
+            instructions=instructions,
+            voice="default",
+            speed=1.0,
+            response_format=response_format,
+            max_new_tokens=int(max_duration_seconds * 25),
+            seed=seed,
+        )
+        return (audio,)
+
+
 class VLLMOmniVoiceClone(_VLLMOmniGenerateBase):
     @classmethod
     def INPUT_TYPES(cls):
@@ -695,6 +873,52 @@ class VLLMOmniRemoteLoRA:
         return (lora,)
 
 
+class VLLMOmniFastH3Deployment:
+    """Select a vLLM-Omni service that fused FastH3 at startup."""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "url": (
+                    "STRING",
+                    {
+                        "default": "http://localhost:8000/v1",
+                        "tooltip": "URL of a vLLM-Omni server started with a FastH3 --lora-path.",
+                    },
+                ),
+                "model": (
+                    "STRING",
+                    {
+                        "default": "MiniMaxAI/MiniMax-H3",
+                        "tooltip": "Model name exposed by the FastH3 deployment.",
+                    },
+                ),
+            }
+        }
+
+    RETURN_TYPES = ("FASTH3_DEPLOYMENT",)
+    RETURN_NAMES = ("deployment",)
+    FUNCTION = "get_deployment"
+    CATEGORY = "vLLM-Omni"
+    DESCRIPTION = (
+        "Selects a server with FastH3 fused at startup. The connected Generate Video node uses T2VA, "
+        "four inference steps, and 24 FPS without sending a request-level LoRA."
+    )
+
+    @classmethod
+    def VALIDATE_INPUTS(cls, url, model) -> str | Literal[True]:
+        try:
+            _resolve_fast_h3_deployment({"url": url, "model": model})
+        except ValueError as exc:
+            return str(exc)
+        return True
+
+    def get_deployment(self, url: str, model: str):
+        url, model = _resolve_fast_h3_deployment({"url": url, "model": model})
+        return (FastH3Deployment({"url": url, "model": model}),)
+
+
 class VLLMOmniQwenTTSParams:
     @classmethod
     def INPUT_TYPES(cls):
@@ -790,6 +1014,10 @@ class VLLMOmniVideoReferences:
                 "audio_2": ("AUDIO",),
                 "video_1": ("VIDEO",),
                 "video_2": ("VIDEO",),
+                # Append ports to preserve connections in saved workflows.
+                **{f"image_{i}": ("IMAGE",) for i in range(3, MAX_REFERENCE_IMAGES + 1)},
+                **{f"audio_{i}": ("AUDIO",) for i in range(3, MAX_REFERENCE_AUDIOS + 1)},
+                **{f"video_{i}": ("VIDEO",) for i in range(3, MAX_REFERENCE_VIDEOS + 1)},
             },
         }
 
@@ -806,21 +1034,112 @@ class VLLMOmniVideoReferences:
         audio_2: AudioInput | None = None,
         video_1: VideoInput | None = None,
         video_2: VideoInput | None = None,
+        image_3: torch.Tensor | None = None,
+        image_4: torch.Tensor | None = None,
+        image_5: torch.Tensor | None = None,
+        image_6: torch.Tensor | None = None,
+        image_7: torch.Tensor | None = None,
+        image_8: torch.Tensor | None = None,
+        image_9: torch.Tensor | None = None,
+        audio_3: AudioInput | None = None,
+        video_3: VideoInput | None = None,
         **kwargs,
     ):
         if kwargs:
             logger.info("Uncaught kwargs: %s", kwargs)
         refs = VideoReferences()
-        if image_1 is not None:
-            refs["image_1"] = image_1
-        if image_2 is not None:
-            refs["image_2"] = image_2
-        if audio_1 is not None:
-            refs["audio_1"] = audio_1
-        if audio_2 is not None:
-            refs["audio_2"] = audio_2
-        if video_1 is not None:
-            refs["video_1"] = video_1
-        if video_2 is not None:
-            refs["video_2"] = video_2
+        for kind, values in (
+            ("image", (image_1, image_2, image_3, image_4, image_5, image_6, image_7, image_8, image_9)),
+            ("video", (video_1, video_2, video_3)),
+            ("audio", (audio_1, audio_2, audio_3)),
+        ):
+            for index, value in enumerate(values, start=1):
+                if value is not None:
+                    refs[f"{kind}_{index}"] = value
         return (refs,)
+
+
+class VLLMOmniLatentMaskEditing:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {},
+            "optional": {
+                "source_video": ("VIDEO",),
+                "source_audio": ("AUDIO",),
+                "video_mask": ("MASK",),
+                "audio_mask": ("FLOAT", {"default": -1.0, "min": -1.0, "max": 1.0, "step": 0.01}),
+            },
+        }
+
+    RETURN_TYPES = ("LATENT_MASK_EDITING",)
+    RETURN_NAMES = ("latent_edit",)
+    FUNCTION = "get_latent_edit"
+    CATEGORY = "vLLM-Omni"
+
+    def get_latent_edit(
+        self,
+        source_video: VideoInput | None = None,
+        source_audio: AudioInput | None = None,
+        video_mask: torch.Tensor | None = None,
+        audio_mask: float = -1.0,
+        **kwargs,
+    ):
+        if kwargs:
+            logger.info("Uncaught kwargs: %s", kwargs)
+        edit = LatentMaskEditing()
+        if source_video is not None:
+            edit["source_video"] = source_video
+        if source_audio is not None:
+            edit["source_audio"] = source_audio
+        if video_mask is not None:
+            edit["video_mask"] = video_mask
+        if audio_mask >= 0.0:
+            edit["audio_mask"] = audio_mask
+        return (edit,)
+
+
+class VLLMOmniMiniMaxH3TemporalMask:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "images": ("IMAGE",),
+                "source_fps": ("FLOAT", {"default": 24.0, "min": 0.01}),
+                "duration": ("FLOAT", {"default": 5.0, "min": 0.01, "max": 15.0}),
+                "mode": (["continuation", "extension"],),
+                "preserve_fraction": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0}),
+            }
+        }
+
+    RETURN_TYPES = ("MASK", "FLOAT", "IMAGE", "MASK")
+    RETURN_NAMES = ("mask", "preview_fps", "preview_images", "preview_mask")
+    FUNCTION = "build"
+    CATEGORY = "vLLM-Omni"
+
+    def build(self, images, source_fps, duration, mode, preserve_fraction=0.5):
+        if images.shape[0] <= 0 or not math.isfinite(source_fps) or source_fps <= 0:
+            raise ValueError("Source must contain frames and have a positive finite FPS.")
+        if not math.isfinite(duration) or duration <= 0 or not 0 <= preserve_fraction <= 1:
+            raise ValueError("Invalid duration or preserve_fraction.")
+        frames = _align_frame_count(max(1, round(duration * 24)))
+        source_seconds = images.shape[0] / source_fps
+        if mode == "extension":
+            if frames / 24 <= source_seconds:
+                raise ValueError("Extension output must be longer than the source; increase duration.")
+            boundary = source_seconds
+        elif mode == "continuation":
+            boundary = min(source_seconds, frames / 24) * preserve_fraction
+        else:
+            raise ValueError(f"Unknown temporal mask mode: {mode}")
+        available = min(frames, math.floor(boundary * 24 + 1e-8))
+        prefix = 0 if available < 5 else 5 + 17 * ((available - 5) // 17)
+        preserved = _video_latent_t(prefix) if prefix else 0
+        total = _video_latent_t(frames)
+        mask = torch.ones(total, 1, 1)
+        mask[:preserved] = 0
+        indices = torch.arange(frames, device=images.device)
+        source_indices = (indices * (source_fps / 24)).floor().long().clamp(max=images.shape[0] - 1)
+        preview_images = images.index_select(0, source_indices)
+        preview_mask = mask.index_select(0, torch.arange(frames) * total // frames)
+        return mask, 24.0, preview_images, preview_mask

apps/ComfyUI-vLLM-Omni/comfyui_vllm_omni/utils/api_client.py

@@ -1,3 +1,6 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
 """
 An high-level API client adapter that forwards ComfyUI inputs to vLLM-Omni's REST API,
 and transforms the API responses back to ComfyUI formats.
@@ -17,6 +20,7 @@ from comfy_api.input import AudioInput, VideoInput
 
 from .format import (
     audio_to_base64,
+    audio_to_bytes,
     base64_to_audio,
     base64_to_image_tensor,
     bytes_to_audio,
@@ -26,9 +30,16 @@ from .format import (
     video_to_base64,
     video_to_bytes,
 )
+from .latent_mask import scalar_mask_to_json, video_mask_to_grid_json
 from .logger import get_logger, pretty_printer
 from .models import lookup_model_spec
-from .types import AudioFormat
+from .types import (
+    MAX_REFERENCE_AUDIOS,
+    MAX_REFERENCE_IMAGES,
+    MAX_REFERENCE_VIDEOS,
+    MAX_TOTAL_REFERENCES,
+    AudioFormat,
+)
 
 logger = get_logger(__name__)
 
@@ -267,14 +278,34 @@ class VLLMOmniClient:
         fps: int,
         negative_prompt: str | None = None,
         frame: torch.Tensor | None = None,
+        first_frame: torch.Tensor | None = None,
+        last_frame: torch.Tensor | None = None,
         references: dict | None = None,
         sampling_params: dict | None = None,
         model_params: dict | None = None,
         lora: dict | None = None,
+        latent_edit: dict | None = None,
+        spec_model: str | None = None,
         **extra_params,
     ) -> VideoInput:
+        """Post a video job and return the decoded result.
+
+        ``spec_model`` names the model whose payload spec builds the request, for
+        deployments that serve a known model under a different ``model`` alias. It
+        never reaches the wire; defaults to ``model``.
+        """
         if frame is not None and references is not None:
             raise ValueError("Provide only one of frame or references, not both.")
+        if frame is not None and (first_frame is not None or last_frame is not None):
+            raise ValueError("Provide either frame or first_frame/last_frame, not both.")
+        if references is not None and (first_frame is not None or last_frame is not None):
+            raise ValueError("Provide either first_frame/last_frame or references, not both.")
+
+        spec, matched_pattern = lookup_model_spec(spec_model or model)
+        if (first_frame is not None or last_frame is not None) and (
+            matched_pattern is None or "MiniMax-H3" not in matched_pattern
+        ):
+            raise ValueError("first_frame and last_frame are supported only for MiniMax-H3; use frame for this model.")
 
         # === regular payload fields ===
         form = aiohttp.FormData()
@@ -294,43 +325,62 @@ class VLLMOmniClient:
 
         # === multimodal inputs (first-last-frames, references, etc.) ===
         input_reference_image: torch.Tensor | None = None
-        audio_reference: AudioInput | None = None
-        reference_videos: list[VideoInput] = []
+        keyframe_images: list[tuple[str, torch.Tensor]] = []
         video_task: str | None = None
 
         if frame is not None:
             input_reference_image = frame
             video_task = "fl2va"
+        elif first_frame is not None or last_frame is not None:
+            frame_indices: list[int] = []
+            if first_frame is not None:
+                keyframe_images.append(("first_frame.png", first_frame))
+                frame_indices.append(0)
+            if last_frame is not None:
+                keyframe_images.append(("last_frame.png", last_frame))
+                frame_indices.append(-1)
+            if len(keyframe_images) == 1:
+                input_reference_image = keyframe_images[0][1]
+            extra_params["frame_indices"] = frame_indices
+            video_task = "fl2va"
         elif references is not None:
-            images = [references[k] for k in ("image_1", "image_2") if k in references and references[k] is not None]
-            audios = [references[k] for k in ("audio_1", "audio_2") if k in references and references[k] is not None]
-            videos = [references[k] for k in ("video_1", "video_2") if k in references and references[k] is not None]
-
-            if not images and not audios and not videos:
-                raise ValueError("references is empty; connect at least one image/audio/video.")
-
-            if videos:
-                if images or audios:
-                    raise ValueError(
-                        "Video references cannot be combined with image or audio references. "
-                        "Connect only video_1/video_2 for multi-video Ref2VA."
-                    )
-                reference_videos = videos
-                video_task = "ref2va"
-            elif len(images) == 1 and len(audios) == 1:
-                input_reference_image = images[0]
-                audio_reference = audios[0]
-                video_task = "ref2va"
-            else:
+            reference_formats = (
+                ("image", MAX_REFERENCE_IMAGES, "png", "image/png", image_tensor_to_png_bytes),
+                ("video", MAX_REFERENCE_VIDEOS, "mp4", "video/mp4", video_to_bytes),
+                ("audio", MAX_REFERENCE_AUDIOS, "mp3", "audio/mpeg", audio_to_bytes),
+            )
+            supported_inputs = {f"{kind}_{i}" for kind, limit, *_ in reference_formats for i in range(1, limit + 1)}
+            connected = {name: value for name, value in references.items() if value is not None}
+            unsupported = connected.keys() - supported_inputs
+            if unsupported:
+                raise ValueError(f"Unsupported reference input(s): {', '.join(sorted(unsupported))}.")
+            if not any(name.startswith(("image_", "video_")) for name in connected):
                 raise ValueError(
-                    "Invalid references combination. Supported modes: "
-                    "(1) one or more videos only, or (2) exactly one image and one audio."
+                    "references requires at least one image or video; audio-only inputs are not supported."
                 )
+            if len(connected) > MAX_TOTAL_REFERENCES:
+                raise ValueError(
+                    f"references supports at most {MAX_TOTAL_REFERENCES} inputs in total "
+                    f"(up to {MAX_REFERENCE_IMAGES} images, {MAX_REFERENCE_VIDEOS} videos, "
+                    f"and {MAX_REFERENCE_AUDIOS} audios)."
+                )
+            for kind, limit, extension, content_type, encode in reference_formats:
+                for index in range(1, limit + 1):
+                    name = f"{kind}_{index}"
+                    if name in connected:
+                        filename = f"{name}.{extension}"
+                        form.add_field(
+                            "input_references",
+                            encode(connected[name], filename),
+                            filename=filename,
+                            content_type=content_type,
+                        )
+            video_task = "ref2va"
         else:
             video_task = "t2va"
 
         if input_reference_image is not None:
-            image_filename = "image.png"  # Required for multipart form
+            image_filename = keyframe_images[0][0] if keyframe_images else "image.png"
             form.add_field(
                 "input_reference",
                 image_tensor_to_png_bytes(input_reference_image, image_filename),
@@ -338,32 +388,74 @@ class VLLMOmniClient:
                 content_type="image/png",
             )
 
-        if audio_reference is not None:
-            form.add_field(
-                "audio_reference",
-                json.dumps({"audio_url": audio_to_base64(audio_reference)}, ensure_ascii=False),
-            )
+        # === latent-mask editing (MiniMax H3) ===
+        if latent_edit is not None:
+            source_video = latent_edit.get("source_video")
+            source_audio = latent_edit.get("source_audio")
+            video_mask = latent_edit.get("video_mask")
+            audio_mask = latent_edit.get("audio_mask")
 
-        for idx, video in enumerate(reference_videos, start=1):
-            video_filename = f"reference_{idx}.mp4"
-            form.add_field(
-                "input_references",
-                video_to_bytes(video, video_filename),
-                filename=video_filename,
-                content_type="video/mp4",
-            )
+            if video_mask is None and audio_mask is None:
+                raise ValueError("Latent-mask editing requires at least one mask.")
+
+            video_mask_trivial = video_mask is None or bool((video_mask == 1.0).all().item())
+            audio_mask_trivial = audio_mask is None or audio_mask == 1.0
+            if not video_mask_trivial and source_video is None:
+                raise ValueError("A non-trivial video mask requires a source video.")
+            if not audio_mask_trivial and source_audio is None and source_video is None:
+                raise ValueError("A non-trivial audio mask requires a source audio or a source video with audio.")
+
+            if source_video is not None:
+                form.add_field(
+                    "source_video",
+                    video_to_bytes(source_video, "source.mp4"),
+                    filename="source.mp4",
+                    content_type="video/mp4",
+                )
+            if source_audio is not None:
+                form.add_field(
+                    "source_audio",
+                    audio_to_bytes(source_audio, "source_audio.mp3"),
+                    filename="source_audio.mp3",
+                    content_type="audio/mpeg",
+                )
+            if video_mask is not None:
+                mask_json = video_mask_to_grid_json(video_mask, width=width, height=height, num_frames=num_frames)
+                form.add_field(
+                    "video_noise_mask",
+                    mask_json.encode("utf-8"),
+                    filename="video-mask.json",
+                    content_type="application/json",
+                )
+            if audio_mask is not None:
+                form.add_field(
+                    "audio_noise_mask",
+                    scalar_mask_to_json(audio_mask).encode("utf-8"),
+                    filename="audio-mask.json",
+                    content_type="application/json",
+                )
+
+        if len(keyframe_images) == 2:
+            for image_filename, image in keyframe_images:
+                form.add_field(
+                    "input_references",
+                    image_tensor_to_png_bytes(image, image_filename),
+                    filename=image_filename,
+                    content_type="image/png",
+                )
 
         # === model specific params. Either use a specialized builder, or add flattened fields as-is ===
         if model_params is not None:
             model_params = dict(model_params)
             model_params.pop("type", None)
 
-        spec, _ = lookup_model_spec(model)
         params_builder = spec.get("params_builder") if spec else None
         if params_builder is not None:
             form_fields = params_builder(
                 model_params or {},
                 extra_params={**extra_params, "task": video_task},
+                width=width,
+                height=height,
             )
             for k, v in form_fields.items():
                 form.add_field(k, v if isinstance(v, str) else str(v))

apps/ComfyUI-vLLM-Omni/comfyui_vllm_omni/utils/format.py

@@ -1,3 +1,6 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
 """Image/tensor format helpers.
 
 The image generation part is derived from dougbtv/comfyui-vllm-omni by Doug (@dougbtv).
@@ -170,6 +173,10 @@ def bytes_to_video(video_bytes: bytes) -> VideoInput:
                 audio_stream = container.streams.audio[-1]
                 audio_frames = []
                 resampler = AudioResampler(format="fltp")
+                # Decoding the video stream above left the container at EOF.
+                # Rewind before demuxing again, or this pass yields no frames
+                # and the audio of a T2VA result is silently dropped.
+                container.seek(0)
                 for frame in container.decode(audio_stream):
                     if not isinstance(frame, AudioFrame):
                         continue

apps/ComfyUI-vLLM-Omni/comfyui_vllm_omni/utils/latent_mask.py

@@ -0,0 +1,51 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
+import json
+
+import torch
+import torch.nn.functional as F
+
+
+def scalar_mask_to_json(value: float) -> str:
+    if not 0.0 <= value <= 1.0:
+        raise ValueError(f"mask value must be in [0, 1], got {value}")
+    return str(value)
+
+
+def _align_frame_count(frame_count: int) -> int:
+    if frame_count <= 0:
+        return 1
+    current = int(frame_count)
+    while current % 17 != 5:
+        current += 1
+    return current
+
+
+def _video_latent_t(frame_count: int) -> int:
+    if frame_count <= 5:
+        return 2
+    return ((int(frame_count) - 5) // 17) * 5 + 2
+
+
+def video_mask_to_grid(mask: torch.Tensor, *, width: int, height: int, num_frames: int) -> torch.Tensor:
+    tv = _video_latent_t(_align_frame_count(num_frames))
+    height = int(height) // 32 * 32
+    width = int(width) // 32 * 32
+    gh, gw = height // 16, width // 16
+
+    if mask.ndim == 2:
+        mask = mask.unsqueeze(0)
+    elif mask.ndim != 3:
+        raise ValueError(f"expected a 2D or 3D mask tensor, got {mask.ndim}D")
+
+    grid = F.interpolate(mask.unsqueeze(1).float(), size=(gh, gw), mode="area").squeeze(1)
+    if grid.shape[0] == 1:
+        grid = grid.expand(tv, gh, gw)
+    elif grid.shape[0] != tv:
+        grid = F.interpolate(grid.unsqueeze(0).unsqueeze(0), size=(tv, gh, gw), mode="nearest").squeeze(0).squeeze(0)
+    return grid
+
+
+def video_mask_to_grid_json(mask: torch.Tensor, *, width: int, height: int, num_frames: int) -> str:
+    return json.dumps(video_mask_to_grid(mask, width=width, height=height, num_frames=num_frames).tolist())

apps/ComfyUI-vLLM-Omni/comfyui_vllm_omni/utils/models.py

@@ -1,3 +1,6 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
 import json
 import re
 from typing import Any
@@ -34,10 +37,28 @@ def _qwen25_payload_preprocessor(payload: dict) -> dict:
     return payload
 
 
+MINIMAX_H3_ASPECT_RATIOS = {
+    "21:9": 21.0 / 9.0,
+    "16:9": 16.0 / 9.0,
+    "4:3": 4.0 / 3.0,
+    "1:1": 1.0,
+    "3:4": 3.0 / 4.0,
+    "9:16": 9.0 / 16.0,
+}
+
+
+def _nearest_minimaxh3_aspect_ratio(width: int, height: int) -> str:
+    """Pick the supported named ratio closest to the requested frame size."""
+    target = float(width) / float(height)
+    return min(MINIMAX_H3_ASPECT_RATIOS, key=lambda name: abs(MINIMAX_H3_ASPECT_RATIOS[name] - target))
+
+
 def _minimaxh3_params_builder(
     model_params: dict[str, Any],
     *,
     extra_params: dict[str, Any],
+    width: int | None = None,
+    height: int | None = None,
 ) -> dict[str, Any]:
     """Build multipart form fields for MiniMax-H3 from model_params + routed task."""
     params = dict(model_params)
@@ -53,6 +74,12 @@ def _minimaxh3_params_builder(
     if params:
         logger.warning("Unused MiniMax-H3 model params ignored: %s", sorted(params))
 
+    # H3 refuses t2va without a named aspect ratio, and the Generate Video node
+    # carries width/height rather than a ratio. fl2va takes its ratio from the
+    # input image and ref2va defaults server-side, so only t2va needs this.
+    if merged_extra_params.get("task") == "t2va" and "aspect_ratio" not in merged_extra_params and width and height:
+        merged_extra_params["aspect_ratio"] = _nearest_minimaxh3_aspect_ratio(width, height)
+
     if merged_extra_params:
         form_fields["extra_params"] = json.dumps(merged_extra_params, ensure_ascii=False)
     return form_fields

apps/ComfyUI-vLLM-Omni/comfyui_vllm_omni/utils/types.py

@@ -1,3 +1,6 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
 from collections.abc import Callable
 from enum import Enum, auto
 from typing import Any, Literal, TypeAlias
@@ -27,10 +30,24 @@ class MiniMaxH3ModelSpecificParams(dict):
     pass
 
 
+MAX_REFERENCE_IMAGES = 9
+MAX_REFERENCE_VIDEOS = 3
+MAX_REFERENCE_AUDIOS = 3
+MAX_TOTAL_REFERENCES = 12
+
+
 class VideoReferences(dict):
     pass
 
 
+class LatentMaskEditing(dict):
+    pass
+
+
+class FastH3Deployment(dict):
+    """Descriptor for a server that fused FastH3 at startup."""
+
+
 class ModelMode(Enum):
     IMAGE_GENERATION = auto()
     VIDEO_GENERATION = auto()

apps/ComfyUI-vLLM-Omni/docs/minimax-h3-t2v.md

@@ -0,0 +1,94 @@
+# MiniMax H3 Text to Video (WF-01)
+
+Import [MiniMax_H3_Text_to_Video.json](../example_workflows/MiniMax_H3_Text_to_Video.json)
+into ComfyUI, or select it under **Templates → ComfyUI-vLLM-Omni**.
+The template connects existing nodes to a remote H3 service and saves the
+returned video with audio. ComfyUI does not load H3 weights.
+
+## Setup
+
+Install the [ComfyUI extension](../README.md#installation) and follow the
+[H3 deployment recipe](../../../recipes/MiniMaxAI/MiniMax-H3.md) for your hardware.
+For a two-GPU FL2VA server with sufficient memory:
+
+```bash
+export MODEL=/path/to/MiniMax-H3/FL2VA
+CUDA_VISIBLE_DEVICES=0,1 VLLM_WORKER_MULTIPROC_METHOD=spawn \
+VLLM_OMNI_VIDEO_SYNC_TIMEOUT=14400 \
+vllm serve "$MODEL" --omni --host 127.0.0.1 --port 8000 \
+  --served-model-name MiniMaxAI/MiniMax-H3 --trust-remote-code \
+  --task-type fl2va --num-gpus 2 --tensor-parallel-size 2 \
+  --text-encoder-tp-size 2 --enforce-eager
+```
+
+Set Generate Video's **url** to the address reachable from ComfyUI, including
+`/v1`, and match its **model** to the served name. The defaults are
+`http://localhost:8000/v1` and `MiniMaxAI/MiniMax-H3`.
+
+## Base and Turbo
+
+Base presets are connected by default. Write the scene and its sound in the
+same **prompt**; leave **frame**, **references**, and **fast_h3** disconnected.
+The template uses 1344×768, 24 FPS, seed 1101 and **duration** 5.167 seconds
+(124 frames, satisfying H3's `17k+5` constraint). The existing client infers
+`16:9` from these dimensions. For portrait output, use 768×1344.
+
+| Setting | Base | Turbo v1.0 768p |
+| --- | --- | --- |
+| Inference steps | 50 | 5 (4 forwards) |
+| Video flow shift | 12 | 6 |
+| Audio flow shift | 3 | 3 |
+| Remote LoRA | Disconnected | Connected, scale 1 |
+
+For Turbo, use the **Diffusers-layout** artifact from
+[lightx2v/Minimax-h3-Turbo](https://huggingface.co/lightx2v/Minimax-h3-Turbo):
+
+```text
+minimax_h3_fl2v_turbo_4step_v1.0_768p_bf16.safetensors
+```
+
+Add `--lora-backend peft --lora-path "$TURBO_LORA"` to the server command,
+where `TURBO_LORA` is the full server-side path to that file. Enter the same
+path in Remote LoRA's **local_path**; retain name `h3-turbo-v1.0-768p`, scale
+`1.0`, and **int_id** `0` so the existing API derives the ID from the path.
+The template leaves the path blank for users to configure. The `_comfyui_`
+export is not supported by this backend.
+
+Connect **Turbo sampling**, **Turbo H3 params**, and **Remote LoRA** to Generate
+Video's `sampling_params`, `model_params`, and `lora` inputs, replacing the
+Base links. To return to Base, restore both Base links and disconnect LoRA.
+Other Turbo artifacts may need different settings; see the
+[artifact table](../../../recipes/MiniMaxAI/MiniMax-H3.md#turbo-lora).
+
+If importing an older template with a frame-count widget, reimport this JSON
+or set **duration** to **5.167**; do not interpret 124 frames as 124 seconds.
+
+## Validation
+
+Queue the configured workflow and inspect the MP4 saved under
+`ComfyUI/output/video/MiniMax_H3_WF01_*.mp4`:
+
+```bash
+ffprobe -v error -show_streams -show_format -of json "$OUTPUT"
+ffmpeg -v error -i "$OUTPUT" -f null -
+```
+
+Check the requested dimensions, frame count, 24 FPS and both video/audio
+streams, then watch and listen to the saved file. The current upstream
+extension includes the shared audio-preserving decoder fix.
+
+On September 16, the narrowed workflow was validated through ComfyUI using
+existing upstream Python code at `4a8ac297`, two H20-3e GPUs, vLLM 0.29.0,
+PyTorch 2.13.0+cu129 and ComfyUI `1d48d9cf`. The run used the template's
+forest-stream prompt, explicit server-side Turbo LoRA path, and settings
+above, with `TORCH_SDPA`, `--usp 1 --ring 1`, and tiled VAE parallelism on two
+GPUs. ComfyUI execution took 131.55 seconds. SaveVideo produced 124 H.264
+frames at 1344×768 and 24 FPS, plus 32 kHz stereo AAC audio. Video/audio
+durations were 5.166667/5.167000 seconds; full decoding passed and audio was
+nonzero. This is model-generated output, not a ground-truth reference or a
+subjective audio-quality assessment. Base graph validation passed with Turbo
+nodes disconnected; full 50-step Base quality was not evaluated.
+
+Based on the official
+[ComfyUI H3 T2V template](https://github.com/Comfy-Org/workflow_templates/blob/main/templates/video_minimax_h3_t2v.json),
+using remote inference instead of local model loaders.

apps/ComfyUI-vLLM-Omni/docs/wf05-h3-latent-editing.md

@@ -0,0 +1,17 @@
+# WF-05: MiniMax-H3 latent-mask editing
+
+Edit a source video with MiniMax-H3 using spatial or temporal masks. The workflow includes object removal, inpainting, continuation, and extension, with a mask preview for each case.
+
+## Requirements
+
+- ComfyUI with ComfyUI-vLLM-Omni installed.
+- An H3 service using the FL2VA checkpoint partition with latent-mask editing support. Follow the [H3 recipe](../../../recipes/MiniMaxAI/MiniMax-H3.md) for server setup.
+
+## Use
+
+Open **vLLM-Omni MiniMax-H3 Latent Mask Editing.json** from the example workflows and set the Generate Video URL/model for the case you want to run.
+
+1. Upload your source video in Load Video.
+2. Adjust the case's mask. For removal and inpainting, set Mask Canvas to the video dimensions, Mask Rectangle to the region size, and Combine Masks x/y to its position. For continuation and extension, set MiniMax-H3 Temporal Mask's mode and duration; continuation also uses `preserve_fraction`. Match the duration in Generate Video, and use a duration longer than the source for extension. Mask values are `0` to preserve and `1` to regenerate.
+3. Adjust the prompt to describe the desired content. Set the output dimensions and duration in Generate Video. If editing audio, update the sound description and audio mask (`0` preserves, `1` regenerates the whole clip).
+4. Select the case's Mask Preview Save Video node and choose **Execute to selected output nodes** to preview the mask as a red overlay without inference, or select its Result Save Video node to generate the edited video. Running the entire workflow generates all four cases.

apps/ComfyUI-vLLM-Omni/docs/wf07-h3-upscale.md

@@ -0,0 +1,21 @@
+# WF-07: H3 video upscale with SeedVR2
+
+Generate video and audio through vLLM-Omni H3, upscale the decoded frames with SeedVR2, and save both the original and upscaled videos. The generated audio and FPS connect directly to Create Video.
+
+## Requirements
+
+- ComfyUI with ComfyUI-vLLM-Omni and [ComfyUI-SeedVR2_VideoUpscaler](https://github.com/numz/ComfyUI-SeedVR2_VideoUpscaler#-installation) installed.
+- An H3 service using the FL2VA checkpoint partition. Follow the [H3 recipe](../../../recipes/MiniMaxAI/MiniMax-H3.md) for server setup.
+- `seedvr2_ema_3b_fp16.safetensors` and `ema_vae_fp16.safetensors` from [SeedVR2's ComfyUI weights](https://huggingface.co/numz/SeedVR2_comfyUI/tree/main), placed in `ComfyUI/models/SEEDVR2`. The SeedVR2 nodes can also download them on first use.
+
+## Use
+
+Open **vLLM-Omni MiniMax H3 Video Upscale.json** from the example workflows. An API-format version is provided alongside it. Set the Generate Video URL/model and the LoRA path as seen by the H3 server.
+
+The preset uses the LightX2V v1.0 768p Turbo adapter in Diffusers format: `minimax_h3_fl2v_turbo_4step_v1.0_768p_bf16.safetensors`, five sigma points, video/audio flow shifts 6/3, and LoRA scale 1. Preload the same adapter on the server using the [Turbo LoRA recipe](../../../recipes/MiniMaxAI/MiniMax-H3.md#turbo-lora). For base H3, disconnect LoRA, set inference steps to 50, and set video flow shift to 12.
+
+The H3 preset requests 1344 x 768 at 24 FPS for 5.167 seconds, which gives 124 frames. SeedVR2's `resolution=1536` sets the target short edge, producing 2688 x 1536 for this input. Update this value when changing the input size; it is an output resolution, not a fixed 2x multiplier.
+
+The SeedVR2 preset uses 3B FP16, five-frame batches, one overlapping frame, uniform batch padding, and LAB color correction. The DiT loader uses CPU offload with 32 swapped blocks; the VAE uses tiled encoding and decoding. Select the local GPU in both model loaders. See the [SeedVR2 settings](https://github.com/numz/ComfyUI-SeedVR2_VideoUpscaler#-usage) when adjusting memory use or batch size.
+
+Run the workflow. The two Save Video nodes write MP4s under `WF07/generated` and `WF07/upscaled` in ComfyUI's output directory.

apps/ComfyUI-vLLM-Omni/example_workflows/MiniMax_H3_Text_to_Video.json

@@ -0,0 +1,362 @@
+{
+  "last_node_id": 8,
+  "last_link_id": 3,
+  "nodes": [
+    {
+      "id": 1,
+      "type": "VLLMOmniDiffusionSampling",
+      "pos": [
+        20,
+        20
+      ],
+      "size": [
+        310,
+        280
+      ],
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "diffusion sampling params",
+          "type": "SAMPLING_PARAMS",
+          "links": [
+            1
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VLLMOmniDiffusionSampling"
+      },
+      "widgets_values": [
+        1,
+        50,
+        1.0,
+        1.0,
+        false,
+        false,
+        1101,
+        "fixed"
+      ],
+      "title": "Base sampling"
+    },
+    {
+      "id": 2,
+      "type": "VLLMOmniMiniMaxH3Params",
+      "pos": [
+        20,
+        340
+      ],
+      "size": [
+        310,
+        140
+      ],
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "MiniMax-H3 video params",
+          "type": "VIDEO_PARAMS",
+          "links": [
+            2
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VLLMOmniMiniMaxH3Params"
+      },
+      "widgets_values": [
+        3.0,
+        12.0
+      ],
+      "title": "Base H3 params"
+    },
+    {
+      "id": 3,
+      "type": "VLLMOmniGenerateVideo",
+      "pos": [
+        430,
+        20
+      ],
+      "size": [
+        430,
+        440
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "frame",
+          "type": "IMAGE",
+          "link": null
+        },
+        {
+          "name": "references",
+          "type": "VIDEO_REFERENCES",
+          "link": null
+        },
+        {
+          "name": "sampling_params",
+          "type": "SAMPLING_PARAMS",
+          "link": 1
+        },
+        {
+          "name": "lora",
+          "type": "REMOTE_LORA",
+          "link": null
+        },
+        {
+          "name": "model_params",
+          "type": "VIDEO_PARAMS",
+          "link": 2
+        },
+        {
+          "name": "fast_h3",
+          "type": "FASTH3_DEPLOYMENT",
+          "link": null
+        },
+        {
+          "name": "duration",
+          "type": "FLOAT",
+          "widget": {
+            "name": "duration"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "name": "video",
+          "type": "VIDEO",
+          "links": [
+            3
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VLLMOmniGenerateVideo"
+      },
+      "widgets_values": [
+        "http://localhost:8000/v1",
+        "MiniMaxAI/MiniMax-H3",
+        "A small stream flows through a sunlit forest. The camera slowly moves toward the water. Birds chirp in the trees and water gurgles over smooth stones. Natural continuous sound, no music.",
+        "",
+        1344,
+        768,
+        24,
+        5.167
+      ]
+    },
+    {
+      "id": 4,
+      "type": "SaveVideo",
+      "pos": [
+        940,
+        20
+      ],
+      "size": [
+        440,
+        440
+      ],
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "video",
+          "type": "VIDEO",
+          "link": 3
+        }
+      ],
+      "outputs": [
+        {
+          "name": "video",
+          "type": "VIDEO",
+          "links": null,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "SaveVideo"
+      },
+      "widgets_values": [
+        "video/MiniMax_H3_WF01",
+        "mp4",
+        "auto"
+      ]
+    },
+    {
+      "id": 5,
+      "type": "VLLMOmniRemoteLoRA",
+      "pos": [
+        430,
+        540
+      ],
+      "size": [
+        430,
+        160
+      ],
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "lora",
+          "type": "REMOTE_LORA",
+          "links": null,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VLLMOmniRemoteLoRA"
+      },
+      "widgets_values": [
+        "",
+        "h3-turbo-v1.0-768p",
+        1.0,
+        0
+      ],
+      "title": "Optional Turbo: server LoRA path"
+    },
+    {
+      "id": 6,
+      "type": "VLLMOmniDiffusionSampling",
+      "pos": [
+        20,
+        540
+      ],
+      "size": [
+        310,
+        280
+      ],
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "diffusion sampling params",
+          "type": "SAMPLING_PARAMS",
+          "links": null,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VLLMOmniDiffusionSampling"
+      },
+      "widgets_values": [
+        1,
+        5,
+        1.0,
+        1.0,
+        false,
+        false,
+        1101,
+        "fixed"
+      ],
+      "title": "Turbo sampling (4 forwards)"
+    },
+    {
+      "id": 7,
+      "type": "VLLMOmniMiniMaxH3Params",
+      "pos": [
+        430,
+        740
+      ],
+      "size": [
+        310,
+        140
+      ],
+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "MiniMax-H3 video params",
+          "type": "VIDEO_PARAMS",
+          "links": null,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VLLMOmniMiniMaxH3Params"
+      },
+      "widgets_values": [
+        3.0,
+        6.0
+      ],
+      "title": "Turbo H3 params"
+    },
+    {
+      "id": 8,
+      "type": "Note",
+      "pos": [
+        940,
+        520
+      ],
+      "size": [
+        440,
+        440
+      ],
+      "flags": {},
+      "order": 7,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "properties": {
+        "Node name for S&R": "Note"
+      },
+      "widgets_values": [
+        "MiniMax H3 Text to Video (WF-01)\n\nBASE: connected presets, 50 steps / video flow 12 / audio flow 3.\n\nTURBO: set Remote LoRA local_path to the SERVER path of minimax_h3_fl2v_turbo_4step_v1.0_768p_bf16.safetensors from lightx2v/Minimax-h3-Turbo. Keep name h3-turbo-v1.0-768p, scale 1.0 and int_id 0. Connect Turbo sampling, Turbo H3 params, and LoRA to Generate Video, replacing the two Base links. Use 5 steps (4 forwards), video flow 6 and audio flow 3. Do not use the ComfyUI-layout artifact.\n\nKeep frame, references and fast_h3 disconnected. 1344x768, 24 FPS, duration 5.167 seconds = 124 frames (17*7+5). The client selects the named 16:9 preset from the dimensions. Save Video preserves video and audio.\n\nSet URL and served model name for your server. See docs/minimax-h3-t2v.md for setup and validation. Source: Comfy-Org/workflow_templates/templates/video_minimax_h3_t2v.json."
+      ],
+      "title": "Setup / Base and Turbo"
+    }
+  ],
+  "links": [
+    [
+      1,
+      1,
+      0,
+      3,
+      2,
+      "SAMPLING_PARAMS"
+    ],
+    [
+      2,
+      2,
+      0,
+      3,
+      4,
+      "VIDEO_PARAMS"
+    ],
+    [
+      3,
+      3,
+      0,
+      4,
+      0,
+      "VIDEO"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {
+    "ds": {
+      "scale": 0.8,
+      "offset": [
+        50,
+        50
+      ]
+    }
+  },
+  "version": 0.4
+}

apps/ComfyUI-vLLM-Omni/example_workflows/vLLM-Omni FastH3 Text to Video.json

@@ -0,0 +1,322 @@
+{
+  "id": "c6db00da-2ccb-4ff8-8334-d67e1a267781",
+  "revision": 0,
+  "last_node_id": 5,
+  "last_link_id": 3,
+  "nodes": [
+    {
+      "id": 1,
+      "type": "MarkdownNote",
+      "pos": [40, 20],
+      "size": [420, 250],
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "title": "FastH3 server prerequisite",
+      "properties": {},
+      "widgets_values": [
+        "FastH3 must be fused when the vLLM-Omni server starts. Use --task-type fl2va and --lora-path pointing to exactly one FastH3 adapter file. Do not use CPU or layerwise offload. For VSA / Data-Free, also install fastvideo-kernel and use --diffusion-attention-backend FASTVIDEO_VSA --fastvideo-vsa-topk 64."
+      ],
+      "color": "#432",
+      "bgcolor": "#000"
+    },
+    {
+      "id": 2,
+      "type": "VLLMOmniFastH3Deployment",
+      "pos": [40, 320],
+      "size": [420, 160],
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "url",
+          "name": "url",
+          "type": "STRING",
+          "widget": {"name": "url"},
+          "link": null
+        },
+        {
+          "localized_name": "model",
+          "name": "model",
+          "type": "STRING",
+          "widget": {"name": "model"},
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "deployment",
+          "name": "deployment",
+          "type": "FASTH3_DEPLOYMENT",
+          "links": [1]
+        }
+      ],
+      "properties": {"Node name for S&R": "VLLMOmniFastH3Deployment"},
+      "widgets_values": [
+        "http://localhost:8000/v1",
+        "MiniMaxAI/MiniMax-H3"
+      ]
+    },
+    {
+      "id": 3,
+      "type": "VLLMOmniDiffusionSampling",
+      "pos": [40, 540],
+      "size": [420, 230],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "n",
+          "name": "n",
+          "type": "INT",
+          "widget": {"name": "n"},
+          "link": null
+        },
+        {
+          "localized_name": "num_inference_steps",
+          "name": "num_inference_steps",
+          "type": "INT",
+          "widget": {"name": "num_inference_steps"},
+          "link": null
+        },
+        {
+          "localized_name": "guidance_scale",
+          "name": "guidance_scale",
+          "type": "FLOAT",
+          "widget": {"name": "guidance_scale"},
+          "link": null
+        },
+        {
+          "localized_name": "true_cfg_scale",
+          "name": "true_cfg_scale",
+          "type": "FLOAT",
+          "widget": {"name": "true_cfg_scale"},
+          "link": null
+        },
+        {
+          "localized_name": "vae_use_slicing",
+          "name": "vae_use_slicing",
+          "type": "BOOLEAN",
+          "widget": {"name": "vae_use_slicing"},
+          "link": null
+        },
+        {
+          "localized_name": "vae_use_tiling",
+          "name": "vae_use_tiling",
+          "type": "BOOLEAN",
+          "widget": {"name": "vae_use_tiling"},
+          "link": null
+        },
+        {
+          "localized_name": "seed",
+          "name": "seed",
+          "type": "INT",
+          "widget": {"name": "seed"},
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "diffusion sampling params",
+          "name": "diffusion sampling params",
+          "type": "SAMPLING_PARAMS",
+          "links": [2]
+        }
+      ],
+      "properties": {"Node name for S&R": "VLLMOmniDiffusionSampling"},
+      "widgets_values": [1, 4, 1, 1, false, false, 1101, "fixed"]
+    },
+    {
+      "id": 4,
+      "type": "VLLMOmniGenerateVideo",
+      "pos": [560, 210],
+      "size": [540, 610],
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "frame",
+          "name": "frame",
+          "shape": 7,
+          "type": "IMAGE",
+          "link": null
+        },
+        {
+          "localized_name": "references",
+          "name": "references",
+          "shape": 7,
+          "type": "VIDEO_REFERENCES",
+          "link": null
+        },
+        {
+          "localized_name": "sampling_params",
+          "name": "sampling_params",
+          "shape": 7,
+          "type": "SAMPLING_PARAMS",
+          "link": 2
+        },
+        {
+          "localized_name": "lora",
+          "name": "lora",
+          "shape": 7,
+          "type": "REMOTE_LORA",
+          "link": null
+        },
+        {
+          "localized_name": "model_params",
+          "name": "model_params",
+          "shape": 7,
+          "type": "VIDEO_PARAMS",
+          "link": null
+        },
+        {
+          "localized_name": "fast_h3",
+          "name": "fast_h3",
+          "shape": 7,
+          "type": "FASTH3_DEPLOYMENT",
+          "link": 1
+        },
+        {
+          "localized_name": "url",
+          "name": "url",
+          "type": "STRING",
+          "widget": {"name": "url"},
+          "link": null
+        },
+        {
+          "localized_name": "model",
+          "name": "model",
+          "type": "STRING",
+          "widget": {"name": "model"},
+          "link": null
+        },
+        {
+          "localized_name": "prompt",
+          "name": "prompt",
+          "type": "STRING",
+          "widget": {"name": "prompt"},
+          "link": null
+        },
+        {
+          "localized_name": "negative_prompt",
+          "name": "negative_prompt",
+          "type": "STRING",
+          "widget": {"name": "negative_prompt"},
+          "link": null
+        },
+        {
+          "localized_name": "width",
+          "name": "width",
+          "type": "INT",
+          "widget": {"name": "width"},
+          "link": null
+        },
+        {
+          "localized_name": "height",
+          "name": "height",
+          "type": "INT",
+          "widget": {"name": "height"},
+          "link": null
+        },
+        {
+          "localized_name": "fps",
+          "name": "fps",
+          "type": "INT",
+          "widget": {"name": "fps"},
+          "link": null
+        },
+        {
+          "localized_name": "duration",
+          "name": "duration",
+          "type": "FLOAT",
+          "widget": {"name": "duration"},
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "video",
+          "name": "video",
+          "type": "VIDEO",
+          "links": [3]
+        }
+      ],
+      "properties": {"Node name for S&R": "VLLMOmniGenerateVideo"},
+      "widgets_values": [
+        "http://localhost:8000/v1",
+        "MiniMaxAI/MiniMax-H3",
+        "A cinematic live-action singer performs naturally on a neon-lit stage, with synchronized vocals and environmental sound.",
+        "",
+        1344,
+        768,
+        24,
+        4.458
+      ]
+    },
+    {
+      "id": 5,
+      "type": "SaveVideo",
+      "pos": [1200, 280],
+      "size": [380, 310],
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "video",
+          "name": "video",
+          "type": "VIDEO",
+          "link": 3
+        },
+        {
+          "localized_name": "filename_prefix",
+          "name": "filename_prefix",
+          "type": "STRING",
+          "widget": {"name": "filename_prefix"},
+          "link": null
+        },
+        {
+          "localized_name": "format",
+          "name": "format",
+          "type": "COMBO",
+          "widget": {"name": "format"},
+          "link": null
+        },
+        {
+          "localized_name": "codec",
+          "name": "codec",
+          "type": "COMFY_DYNAMICCOMBO_V3",
+          "widget": {"name": "codec"},
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "video",
+          "name": "video",
+          "type": "VIDEO",
+          "links": null
+        }
+      ],
+      "properties": {},
+      "widgets_values": ["video/ComfyUI-FastH3", "auto", "auto"]
+    }
+  ],
+  "links": [
+    [1, 2, 0, 4, 5, "FASTH3_DEPLOYMENT"],
+    [2, 3, 0, 4, 2, "SAMPLING_PARAMS"],
+    [3, 4, 0, 5, 0, "VIDEO"]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {
+    "workflowRendererVersion": "LG",
+    "ds": {"scale": 0.9, "offset": [100, 100]}
+  },
+  "version": 0.4
+}

apps/ComfyUI-vLLM-Omni/example_workflows/vLLM-Omni Latent Mask Editing - Image Mask.json

@@ -0,0 +1,499 @@
+{
+  "id": "latent-mask-editing-7380",
+  "revision": 0,
+  "last_node_id": 7,
+  "last_link_id": 5,
+  "nodes": [
+    {
+      "id": 1,
+      "type": "MarkdownNote",
+      "pos": [
+        40,
+        20
+      ],
+      "size": [
+        420,
+        210
+      ],
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "title": "Latent-mask editing server prerequisite",
+      "properties": {},
+      "widgets_values": [
+        "Requires a vLLM-Omni server that declares latent-mask editing support (MiniMax-H3, EDIT-01). The source clip and video/audio noise masks are uploaded to /v1/videos; 0 preserves, 1 regenerates, fractional blends."
+      ],
+      "color": "#432",
+      "bgcolor": "#000"
+    },
+    {
+      "id": 2,
+      "type": "LoadVideo",
+      "pos": [
+        40,
+        280
+      ],
+      "size": [
+        420,
+        120
+      ],
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "file",
+          "name": "file",
+          "type": "COMBO",
+          "widget": {
+            "name": "file"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "video",
+          "name": "video",
+          "type": "VIDEO",
+          "links": [
+            1
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadVideo"
+      },
+      "widgets_values": [
+        ""
+      ]
+    },
+    {
+      "id": 3,
+      "type": "LoadImageMask",
+      "pos": [
+        40,
+        460
+      ],
+      "size": [
+        420,
+        160
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "STRING",
+          "widget": {
+            "name": "image"
+          },
+          "link": null
+        },
+        {
+          "name": "channel",
+          "type": "STRING",
+          "widget": {
+            "name": "channel"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "mask",
+          "name": "mask",
+          "type": "MASK",
+          "links": [
+            2
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadImageMask"
+      },
+      "widgets_values": [
+        "",
+        "red"
+      ]
+    },
+    {
+      "id": 4,
+      "type": "VLLMOmniLatentMaskEditing",
+      "pos": [
+        560,
+        260
+      ],
+      "size": [
+        420,
+        300
+      ],
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "source_video",
+          "name": "source_video",
+          "type": "VIDEO",
+          "link": 1,
+          "shape": 7
+        },
+        {
+          "localized_name": "source_audio",
+          "name": "source_audio",
+          "type": "AUDIO",
+          "link": null
+        },
+        {
+          "localized_name": "video_mask",
+          "name": "video_mask",
+          "type": "MASK",
+          "link": 2,
+          "shape": 7
+        },
+        {
+          "localized_name": "audio_mask",
+          "name": "audio_mask",
+          "type": "FLOAT",
+          "widget": {
+            "name": "audio_mask"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "latent_edit",
+          "name": "latent_edit",
+          "type": "LATENT_MASK_EDITING",
+          "links": [
+            3
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VLLMOmniLatentMaskEditing"
+      },
+      "widgets_values": [
+        0.5
+      ]
+    },
+    {
+      "id": 5,
+      "type": "VLLMOmniMiniMaxH3Params",
+      "pos": [
+        40,
+        680
+      ],
+      "size": [
+        420,
+        140
+      ],
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "audio_flow_shift",
+          "name": "audio_flow_shift",
+          "type": "FLOAT",
+          "widget": {
+            "name": "audio_flow_shift"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "flow_shift",
+          "name": "flow_shift",
+          "type": "FLOAT",
+          "widget": {
+            "name": "flow_shift"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "video params",
+          "name": "video params",
+          "type": "VIDEO_PARAMS",
+          "links": [
+            4
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VLLMOmniMiniMaxH3Params"
+      },
+      "widgets_values": [
+        3.0,
+        12.0
+      ]
+    },
+    {
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+      "type": "VLLMOmniGenerateVideo",
+      "pos": [
+        1080,
+        200
+      ],
+      "size": [
+        540,
+        680
+      ],
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "frame",
+          "name": "frame",
+          "type": "IMAGE",
+          "link": null,
+          "shape": 7
+        },
+        {
+          "localized_name": "references",
+          "name": "references",
+          "type": "VIDEO_REFERENCES",
+          "link": null,
+          "shape": 7
+        },
+        {
+          "localized_name": "sampling_params",
+          "name": "sampling_params",
+          "type": "SAMPLING_PARAMS",
+          "link": null
+        },
+        {
+          "localized_name": "lora",
+          "name": "lora",
+          "type": "REMOTE_LORA",
+          "link": null
+        },
+        {
+          "localized_name": "model_params",
+          "name": "model_params",
+          "type": "VIDEO_PARAMS",
+          "link": 4,
+          "shape": 7
+        },
+        {
+          "localized_name": "fast_h3",
+          "name": "fast_h3",
+          "type": "FASTH3_DEPLOYMENT",
+          "link": null
+        },
+        {
+          "localized_name": "latent_edit",
+          "name": "latent_edit",
+          "type": "LATENT_MASK_EDITING",
+          "link": 3,
+          "shape": 7
+        },
+        {
+          "localized_name": "url",
+          "name": "url",
+          "type": "STRING",
+          "widget": {
+            "name": "url"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "model",
+          "name": "model",
+          "type": "STRING",
+          "widget": {
+            "name": "model"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "prompt",
+          "name": "prompt",
+          "type": "STRING",
+          "widget": {
+            "name": "prompt"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "negative_prompt",
+          "name": "negative_prompt",
+          "type": "STRING",
+          "widget": {
+            "name": "negative_prompt"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "width",
+          "name": "width",
+          "type": "INT",
+          "widget": {
+            "name": "width"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "height",
+          "name": "height",
+          "type": "INT",
+          "widget": {
+            "name": "height"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "fps",
+          "name": "fps",
+          "type": "INT",
+          "widget": {
+            "name": "fps"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "duration",
+          "name": "duration",
+          "type": "FLOAT",
+          "widget": {
+            "name": "duration"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "video",
+          "name": "video",
+          "type": "VIDEO",
+          "links": [
+            5
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VLLMOmniGenerateVideo"
+      },
+      "widgets_values": [
+        "http://127.0.0.1:8000/v1",
+        "MiniMaxAI/MiniMax-H3",
+        "Partially restyle the clip and soundtrack",
+        "",
+        160,
+        120,
+        24,
+        4.458
+      ]
+    },
+    {
+      "id": 7,
+      "type": "SaveVideo",
+      "pos": [
+        1700,
+        300
+      ],
+      "size": [
+        420,
+        160
+      ],
+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "video",
+          "name": "video",
+          "type": "VIDEO",
+          "link": 5,
+          "shape": 7
+        },
+        {
+          "localized_name": "filename_prefix",
+          "name": "filename_prefix",
+          "type": "STRING",
+          "widget": {
+            "name": "filename_prefix"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "format",
+          "name": "format",
+          "type": "COMFY_DYNAMICCOMBO_V3",
+          "widget": {
+            "name": "format"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "video",
+          "name": "video",
+          "type": "VIDEO",
+          "links": []
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "SaveVideo"
+      },
+      "widgets_values": [
+        "vllm-omni/latent-edit",
+        "mp4"
+      ]
+    }
+  ],
+  "links": [
+    [
+      1,
+      2,
+      0,
+      4,
+      0,
+      "VIDEO"
+    ],
+    [
+      2,
+      3,
+      0,
+      4,
+      2,
+      "MASK"
+    ],
+    [
+      3,
+      4,
+      0,
+      6,
+      6,
+      "LATENT_MASK_EDITING"
+    ],
+    [
+      4,
+      5,
+      0,
+      6,
+      4,
+      "VIDEO_PARAMS"
+    ],
+    [
+      5,
+      6,
+      0,
+      7,
+      0,
+      "VIDEO"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {},
+  "version": 0.4
+}

apps/ComfyUI-vLLM-Omni/example_workflows/vLLM-Omni Latent Mask Editing - Temporal Mask.json

@@ -0,0 +1,560 @@
+{
+  "last_node_id": 9,
+  "last_link_id": 7,
+  "nodes": [
+    {
+      "id": 2,
+      "type": "LoadVideo",
+      "pos": [
+        40,
+        100
+      ],
+      "size": [
+        420,
+        120
+      ],
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "file",
+          "type": "STRING",
+          "widget": {
+            "name": "file"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "name": "video",
+          "type": "VIDEO",
+          "links": [
+            1
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadVideo"
+      },
+      "widgets_values": [
+        ""
+      ]
+    },
+    {
+      "id": 3,
+      "type": "SolidMask",
+      "pos": [
+        40,
+        300
+      ],
+      "size": [
+        420,
+        160
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "value",
+          "type": "FLOAT",
+          "widget": {
+            "name": "value"
+          },
+          "link": null
+        },
+        {
+          "name": "width",
+          "type": "INT",
+          "widget": {
+            "name": "width"
+          },
+          "link": null
+        },
+        {
+          "name": "height",
+          "type": "INT",
+          "widget": {
+            "name": "height"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "name": "mask",
+          "type": "MASK",
+          "links": [
+            2
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "SolidMask"
+      },
+      "widgets_values": [
+        0.0,
+        160,
+        120
+      ]
+    },
+    {
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+      "type": "SolidMask",
+      "pos": [
+        40,
+        500
+      ],
+      "size": [
+        420,
+        160
+      ],
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "value",
+          "type": "FLOAT",
+          "widget": {
+            "name": "value"
+          },
+          "link": null
+        },
+        {
+          "name": "width",
+          "type": "INT",
+          "widget": {
+            "name": "width"
+          },
+          "link": null
+        },
+        {
+          "name": "height",
+          "type": "INT",
+          "widget": {
+            "name": "height"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "name": "mask",
+          "type": "MASK",
+          "links": [
+            3
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "SolidMask"
+      },
+      "widgets_values": [
+        1.0,
+        160,
+        120
+      ]
+    },
+    {
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+      "type": "BatchMasksNode",
+      "pos": [
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+        380
+      ],
+      "size": [
+        200,
+        100
+      ],
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "masks.mask0",
+          "type": "MASK",
+          "link": 2,
+          "slot_index": 0
+        },
+        {
+          "name": "masks.mask1",
+          "type": "MASK",
+          "link": 3,
+          "slot_index": 1
+        }
+      ],
+      "outputs": [
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": [
+            4
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "BatchMasksNode"
+      },
+      "widgets_values": []
+    },
+    {
+      "id": 6,
+      "type": "VLLMOmniLatentMaskEditing",
+      "pos": [
+        450,
+        180
+      ],
+      "size": [
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+        120
+      ],
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "source_video",
+          "type": "VIDEO",
+          "link": 1
+        },
+        {
+          "name": "source_audio",
+          "type": "AUDIO",
+          "link": null
+        },
+        {
+          "name": "video_mask",
+          "type": "MASK",
+          "link": 4
+        },
+        {
+          "name": "audio_mask",
+          "type": "FLOAT",
+          "widget": {
+            "name": "audio_mask"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "name": "latent_edit",
+          "type": "LATENT_MASK_EDITING",
+          "links": [
+            5
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VLLMOmniLatentMaskEditing"
+      },
+      "widgets_values": [
+        -1.0
+      ]
+    },
+    {
+      "id": 7,
+      "type": "VLLMOmniMiniMaxH3Params",
+      "pos": [
+        40,
+        700
+      ],
+      "size": [
+        420,
+        160
+      ],
+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "audio_flow_shift",
+          "type": "FLOAT",
+          "widget": {
+            "name": "audio_flow_shift"
+          },
+          "link": null
+        },
+        {
+          "name": "flow_shift",
+          "type": "FLOAT",
+          "widget": {
+            "name": "flow_shift"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "name": "video params",
+          "type": "VIDEO_PARAMS",
+          "links": [
+            6
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VLLMOmniMiniMaxH3Params"
+      },
+      "widgets_values": [
+        3.0,
+        12.0
+      ]
+    },
+    {
+      "id": 8,
+      "type": "VLLMOmniGenerateVideo",
+      "pos": [
+        700,
+        180
+      ],
+      "size": [
+        450,
+        380
+      ],
+      "flags": {},
+      "order": 7,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "frame",
+          "type": "IMAGE",
+          "link": null
+        },
+        {
+          "name": "references",
+          "type": "VIDEO_REFERENCES",
+          "link": null
+        },
+        {
+          "name": "sampling_params",
+          "type": "SAMPLING_PARAMS",
+          "link": null
+        },
+        {
+          "name": "lora",
+          "type": "LORA",
+          "link": null
+        },
+        {
+          "name": "model_params",
+          "type": "VIDEO_PARAMS",
+          "link": 6
+        },
+        {
+          "name": "fast_h3",
+          "type": "FAST_H3",
+          "link": null
+        },
+        {
+          "name": "latent_edit",
+          "type": "LATENT_MASK_EDITING",
+          "link": 5
+        },
+        {
+          "name": "url",
+          "type": "STRING",
+          "widget": {
+            "name": "url"
+          },
+          "link": null
+        },
+        {
+          "name": "model",
+          "type": "STRING",
+          "widget": {
+            "name": "model"
+          },
+          "link": null
+        },
+        {
+          "name": "prompt",
+          "type": "STRING",
+          "widget": {
+            "name": "prompt"
+          },
+          "link": null
+        },
+        {
+          "name": "negative_prompt",
+          "type": "STRING",
+          "widget": {
+            "name": "negative_prompt"
+          },
+          "link": null
+        },
+        {
+          "name": "width",
+          "type": "INT",
+          "widget": {
+            "name": "width"
+          },
+          "link": null
+        },
+        {
+          "name": "height",
+          "type": "INT",
+          "widget": {
+            "name": "height"
+          },
+          "link": null
+        },
+        {
+          "name": "fps",
+          "type": "INT",
+          "widget": {
+            "name": "fps"
+          },
+          "link": null
+        },
+        {
+          "name": "duration",
+          "type": "FLOAT",
+          "widget": {
+            "name": "duration"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "name": "video",
+          "type": "VIDEO",
+          "links": [
+            7
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VLLMOmniGenerateVideo"
+      },
+      "widgets_values": [
+        "http://127.0.0.1:8000/v1",
+        "MiniMaxAI/MiniMax-H3",
+        "temporal mask: regenerate the ending",
+        "",
+        160,
+        120,
+        24,
+        4.458
+      ]
+    },
+    {
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+      "type": "SaveVideo",
+      "pos": [
+        900,
+        180
+      ],
+      "size": [
+        420,
+        200
+      ],
+      "flags": {},
+      "order": 8,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "video",
+          "type": "VIDEO",
+          "link": 7
+        },
+        {
+          "name": "filename_prefix",
+          "type": "STRING",
+          "widget": {
+            "name": "filename_prefix"
+          },
+          "link": null
+        },
+        {
+          "name": "format",
+          "type": "STRING",
+          "widget": {
+            "name": "format"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "name": "video",
+          "type": "VIDEO",
+          "links": []
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "SaveVideo"
+      },
+      "widgets_values": [
+        "temporal_mask",
+        "mp4"
+      ]
+    }
+  ],
+  "links": [
+    [
+      1,
+      2,
+      0,
+      6,
+      0,
+      "VIDEO"
+    ],
+    [
+      2,
+      3,
+      0,
+      5,
+      0,
+      "MASK"
+    ],
+    [
+      3,
+      4,
+      0,
+      5,
+      1,
+      "MASK"
+    ],
+    [
+      4,
+      5,
+      0,
+      6,
+      2,
+      "MASK"
+    ],
+    [
+      5,
+      6,
+      0,
+      8,
+      6,
+      "LATENT_MASK_EDITING"
+    ],
+    [
+      6,
+      7,
+      0,
+      8,
+      4,
+      "VIDEO_PARAMS"
+    ],
+    [
+      7,
+      8,
+      0,
+      9,
+      0,
+      "VIDEO"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {},
+  "version": 0.4
+}

apps/ComfyUI-vLLM-Omni/example_workflows/vLLM-Omni Latent Mask Editing.json

@@ -0,0 +1,511 @@
+{
+  "id": "latent-mask-editing-7380",
+  "revision": 0,
+  "last_node_id": 7,
+  "last_link_id": 5,
+  "nodes": [
+    {
+      "id": 1,
+      "type": "MarkdownNote",
+      "pos": [
+        40,
+        20
+      ],
+      "size": [
+        420,
+        210
+      ],
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "title": "Latent-mask editing server prerequisite",
+      "properties": {},
+      "widgets_values": [
+        "Requires a vLLM-Omni server that declares latent-mask editing support (MiniMax-H3, EDIT-01). The source clip and video/audio noise masks are uploaded to /v1/videos; 0 preserves, 1 regenerates, fractional blends."
+      ],
+      "color": "#432",
+      "bgcolor": "#000"
+    },
+    {
+      "id": 2,
+      "type": "LoadVideo",
+      "pos": [
+        40,
+        280
+      ],
+      "size": [
+        420,
+        120
+      ],
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "file",
+          "name": "file",
+          "type": "COMBO",
+          "widget": {
+            "name": "file"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "video",
+          "name": "video",
+          "type": "VIDEO",
+          "links": [
+            1
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadVideo"
+      },
+      "widgets_values": [
+        ""
+      ]
+    },
+    {
+      "id": 3,
+      "type": "SolidMask",
+      "pos": [
+        40,
+        460
+      ],
+      "size": [
+        420,
+        160
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "value",
+          "name": "value",
+          "type": "FLOAT",
+          "widget": {
+            "name": "value"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "width",
+          "name": "width",
+          "type": "INT",
+          "widget": {
+            "name": "width"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "height",
+          "name": "height",
+          "type": "INT",
+          "widget": {
+            "name": "height"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "mask",
+          "name": "mask",
+          "type": "MASK",
+          "links": [
+            2
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "SolidMask"
+      },
+      "widgets_values": [
+        1.0,
+        160,
+        120
+      ]
+    },
+    {
+      "id": 4,
+      "type": "VLLMOmniLatentMaskEditing",
+      "pos": [
+        560,
+        260
+      ],
+      "size": [
+        420,
+        300
+      ],
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "source_video",
+          "name": "source_video",
+          "type": "VIDEO",
+          "link": 1,
+          "shape": 7
+        },
+        {
+          "localized_name": "source_audio",
+          "name": "source_audio",
+          "type": "AUDIO",
+          "link": null
+        },
+        {
+          "localized_name": "video_mask",
+          "name": "video_mask",
+          "type": "MASK",
+          "link": 2,
+          "shape": 7
+        },
+        {
+          "localized_name": "audio_mask",
+          "name": "audio_mask",
+          "type": "FLOAT",
+          "widget": {
+            "name": "audio_mask"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "latent_edit",
+          "name": "latent_edit",
+          "type": "LATENT_MASK_EDITING",
+          "links": [
+            3
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VLLMOmniLatentMaskEditing"
+      },
+      "widgets_values": [
+        0.5
+      ]
+    },
+    {
+      "id": 5,
+      "type": "VLLMOmniMiniMaxH3Params",
+      "pos": [
+        40,
+        680
+      ],
+      "size": [
+        420,
+        140
+      ],
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "audio_flow_shift",
+          "name": "audio_flow_shift",
+          "type": "FLOAT",
+          "widget": {
+            "name": "audio_flow_shift"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "flow_shift",
+          "name": "flow_shift",
+          "type": "FLOAT",
+          "widget": {
+            "name": "flow_shift"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "video params",
+          "name": "video params",
+          "type": "VIDEO_PARAMS",
+          "links": [
+            4
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VLLMOmniMiniMaxH3Params"
+      },
+      "widgets_values": [
+        3.0,
+        12.0
+      ]
+    },
+    {
+      "id": 6,
+      "type": "VLLMOmniGenerateVideo",
+      "pos": [
+        1080,
+        200
+      ],
+      "size": [
+        540,
+        680
+      ],
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "frame",
+          "name": "frame",
+          "type": "IMAGE",
+          "link": null,
+          "shape": 7
+        },
+        {
+          "localized_name": "references",
+          "name": "references",
+          "type": "VIDEO_REFERENCES",
+          "link": null,
+          "shape": 7
+        },
+        {
+          "localized_name": "sampling_params",
+          "name": "sampling_params",
+          "type": "SAMPLING_PARAMS",
+          "link": null
+        },
+        {
+          "localized_name": "lora",
+          "name": "lora",
+          "type": "REMOTE_LORA",
+          "link": null
+        },
+        {
+          "localized_name": "model_params",
+          "name": "model_params",
+          "type": "VIDEO_PARAMS",
+          "link": 4,
+          "shape": 7
+        },
+        {
+          "localized_name": "fast_h3",
+          "name": "fast_h3",
+          "type": "FASTH3_DEPLOYMENT",
+          "link": null
+        },
+        {
+          "localized_name": "latent_edit",
+          "name": "latent_edit",
+          "type": "LATENT_MASK_EDITING",
+          "link": 3,
+          "shape": 7
+        },
+        {
+          "localized_name": "url",
+          "name": "url",
+          "type": "STRING",
+          "widget": {
+            "name": "url"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "model",
+          "name": "model",
+          "type": "STRING",
+          "widget": {
+            "name": "model"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "prompt",
+          "name": "prompt",
+          "type": "STRING",
+          "widget": {
+            "name": "prompt"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "negative_prompt",
+          "name": "negative_prompt",
+          "type": "STRING",
+          "widget": {
+            "name": "negative_prompt"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "width",
+          "name": "width",
+          "type": "INT",
+          "widget": {
+            "name": "width"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "height",
+          "name": "height",
+          "type": "INT",
+          "widget": {
+            "name": "height"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "fps",
+          "name": "fps",
+          "type": "INT",
+          "widget": {
+            "name": "fps"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "duration",
+          "name": "duration",
+          "type": "FLOAT",
+          "widget": {
+            "name": "duration"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "video",
+          "name": "video",
+          "type": "VIDEO",
+          "links": [
+            5
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VLLMOmniGenerateVideo"
+      },
+      "widgets_values": [
+        "http://127.0.0.1:8000/v1",
+        "MiniMaxAI/MiniMax-H3",
+        "Partially restyle the clip and soundtrack",
+        "",
+        160,
+        120,
+        24,
+        4.458
+      ]
+    },
+    {
+      "id": 7,
+      "type": "SaveVideo",
+      "pos": [
+        1700,
+        300
+      ],
+      "size": [
+        420,
+        160
+      ],
+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "video",
+          "name": "video",
+          "type": "VIDEO",
+          "link": 5,
+          "shape": 7
+        },
+        {
+          "localized_name": "filename_prefix",
+          "name": "filename_prefix",
+          "type": "STRING",
+          "widget": {
+            "name": "filename_prefix"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "format",
+          "name": "format",
+          "type": "COMFY_DYNAMICCOMBO_V3",
+          "widget": {
+            "name": "format"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "video",
+          "name": "video",
+          "type": "VIDEO",
+          "links": []
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "SaveVideo"
+      },
+      "widgets_values": [
+        "vllm-omni/latent-edit",
+        "mp4"
+      ]
+    }
+  ],
+  "links": [
+    [
+      1,
+      2,
+      0,
+      4,
+      0,
+      "VIDEO"
+    ],
+    [
+      2,
+      3,
+      0,
+      4,
+      2,
+      "MASK"
+    ],
+    [
+      3,
+      4,
+      0,
+      6,
+      6,
+      "LATENT_MASK_EDITING"
+    ],
+    [
+      4,
+      5,
+      0,
+      6,
+      4,
+      "VIDEO_PARAMS"
+    ],
+    [
+      5,
+      6,
+      0,
+      7,
+      0,
+      "VIDEO"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {},
+  "version": 0.4
+}

apps/ComfyUI-vLLM-Omni/example_workflows/vLLM-Omni MiniMax H3 Image to Video.json

@@ -0,0 +1,219 @@
+{
+  "id": "6676f19a-7f41-4c80-8cb7-f80adcb4ac44",
+  "revision": 0,
+  "last_node_id": 9,
+  "last_link_id": 4,
+  "nodes": [
+    {
+      "id": 1,
+      "type": "LoadImage",
+      "pos": [40, 20],
+      "size": [270, 314],
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "inputs": [
+        {"localized_name": "image", "name": "image", "type": "COMBO", "widget": {"name": "image"}, "link": null},
+        {"localized_name": "choose file to upload", "name": "upload", "type": "IMAGEUPLOAD", "widget": {"name": "upload"}, "link": null}
+      ],
+      "outputs": [
+        {"localized_name": "IMAGE", "name": "IMAGE", "type": "IMAGE", "links": [1]},
+        {"localized_name": "MASK", "name": "MASK", "type": "MASK", "links": null}
+      ],
+      "title": "First Frame",
+      "properties": {"Node name for S&R": "LoadImage"},
+      "widgets_values": ["first_frame.png", "image"]
+    },
+    {
+      "id": 2,
+      "type": "LoadImage",
+      "pos": [40, 390],
+      "size": [270, 314],
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "inputs": [
+        {"localized_name": "image", "name": "image", "type": "COMBO", "widget": {"name": "image"}, "link": null},
+        {"localized_name": "choose file to upload", "name": "upload", "type": "IMAGEUPLOAD", "widget": {"name": "upload"}, "link": null}
+      ],
+      "outputs": [
+        {"localized_name": "IMAGE", "name": "IMAGE", "type": "IMAGE", "links": null},
+        {"localized_name": "MASK", "name": "MASK", "type": "MASK", "links": null}
+      ],
+      "title": "Last Frame (optional)",
+      "properties": {"Node name for S&R": "LoadImage"},
+      "widgets_values": ["last_frame.png", "image"]
+    },
+    {
+      "id": 3,
+      "type": "MarkdownNote",
+      "pos": [350, 20],
+      "size": [390, 240],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "title": "MiniMax H3 I2V modes",
+      "properties": {},
+      "widgets_values": ["This workflow uses the remote vLLM-Omni Generate Video node.\n\n- First-frame only (default): keep First Frame connected.\n- Last-frame only: disconnect First Frame and connect Last Frame to last_frame.\n- First+last-frame: connect both image loaders.\n\nLeave the legacy frame and references inputs disconnected. Select images from ComfyUI's input folder. H3 runs at 24 FPS; the 5.167-second duration resolves to 124 frames, satisfying 17k+5."],
+      "color": "#432",
+      "bgcolor": "#000"
+    },
+    {
+      "id": 4,
+      "type": "VLLMOmniDiffusionSampling",
+      "pos": [350, 300],
+      "size": [300, 226],
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [
+        {"localized_name": "n", "name": "n", "type": "INT", "widget": {"name": "n"}, "link": null},
+        {"localized_name": "num_inference_steps", "name": "num_inference_steps", "type": "INT", "widget": {"name": "num_inference_steps"}, "link": null},
+        {"localized_name": "guidance_scale", "name": "guidance_scale", "type": "FLOAT", "widget": {"name": "guidance_scale"}, "link": null},
+        {"localized_name": "true_cfg_scale", "name": "true_cfg_scale", "type": "FLOAT", "widget": {"name": "true_cfg_scale"}, "link": null},
+        {"localized_name": "vae_use_slicing", "name": "vae_use_slicing", "type": "BOOLEAN", "widget": {"name": "vae_use_slicing"}, "link": null},
+        {"localized_name": "vae_use_tiling", "name": "vae_use_tiling", "type": "BOOLEAN", "widget": {"name": "vae_use_tiling"}, "link": null},
+        {"localized_name": "seed", "name": "seed", "type": "INT", "widget": {"name": "seed"}, "link": null}
+      ],
+      "outputs": [
+        {"localized_name": "diffusion sampling params", "name": "diffusion sampling params", "type": "SAMPLING_PARAMS", "links": [2]}
+      ],
+      "title": "Base H3 Sampling (50 steps)",
+      "properties": {"Node name for S&R": "VLLMOmniDiffusionSampling"},
+      "widgets_values": [1, 50, 1, 1, false, false, 757358688076805, "fixed"]
+    },
+    {
+      "id": 5,
+      "type": "VLLMOmniMiniMaxH3Params",
+      "pos": [350, 570],
+      "size": [300, 82],
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [
+        {"localized_name": "audio_flow_shift", "name": "audio_flow_shift", "type": "FLOAT", "widget": {"name": "audio_flow_shift"}, "link": null},
+        {"localized_name": "flow_shift", "name": "flow_shift", "type": "FLOAT", "widget": {"name": "flow_shift"}, "link": null}
+      ],
+      "outputs": [
+        {"localized_name": "MiniMax-H3 video params", "name": "MiniMax-H3 video params", "type": "VIDEO_PARAMS", "links": [3]}
+      ],
+      "title": "Base H3 Flow Shifts",
+      "properties": {"Node name for S&R": "VLLMOmniMiniMaxH3Params"},
+      "widgets_values": [3, 12]
+    },
+    {
+      "id": 6,
+      "type": "VLLMOmniRemoteLoRA",
+      "pos": [700, 550],
+      "size": [360, 150],
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {"localized_name": "local_path", "name": "local_path", "type": "STRING", "widget": {"name": "local_path"}, "link": null},
+        {"localized_name": "name", "name": "name", "type": "STRING", "widget": {"name": "name"}, "link": null},
+        {"localized_name": "scale", "name": "scale", "type": "FLOAT", "widget": {"name": "scale"}, "link": null},
+        {"localized_name": "int_id", "name": "int_id", "type": "INT", "widget": {"name": "int_id"}, "link": null}
+      ],
+      "outputs": [
+        {"localized_name": "lora", "name": "lora", "type": "REMOTE_LORA", "links": null}
+      ],
+      "title": "Optional H3 Turbo Remote LoRA",
+      "properties": {"Node name for S&R": "VLLMOmniRemoteLoRA"},
+      "widgets_values": ["minimax_h3_fl2v_turbo_8step_v1.0_768p_bf16.safetensors", "h3-turbo-8step", 1, 0]
+    },
+    {
+      "id": 7,
+      "type": "MarkdownNote",
+      "pos": [700, 300],
+      "size": [420, 210],
+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "title": "Optional Turbo settings",
+      "properties": {},
+      "widgets_values": ["Base H3 is configured by default: 50 steps, flow_shift 12, audio_flow_shift 3.\n\nFor the shown 8-step Diffusers Turbo artifact, set its full local_path on the server, start the server with the matching preloaded LoRA, connect this Remote LoRA to Generate Video, set num_inference_steps to 9, and set flow_shift to 6. Keep audio_flow_shift at 3 and scale at 1.\n\nDo not use the _comfyui_ repack or FastH3 here."],
+      "color": "#432",
+      "bgcolor": "#000"
+    },
+    {
+      "id": 8,
+      "type": "VLLMOmniGenerateVideo",
+      "pos": [1160, 80],
+      "size": [440, 610],
+      "flags": {},
+      "order": 7,
+      "mode": 0,
+      "inputs": [
+        {"localized_name": "frame", "name": "frame", "shape": 7, "type": "IMAGE", "link": null},
+        {"localized_name": "first_frame", "name": "first_frame", "shape": 7, "type": "IMAGE", "link": 1},
+        {"localized_name": "last_frame", "name": "last_frame", "shape": 7, "type": "IMAGE", "link": null},
+        {"localized_name": "references", "name": "references", "shape": 7, "type": "VIDEO_REFERENCES", "link": null},
+        {"localized_name": "sampling_params", "name": "sampling_params", "shape": 7, "type": "SAMPLING_PARAMS", "link": 2},
+        {"localized_name": "lora", "name": "lora", "shape": 7, "type": "REMOTE_LORA", "link": null},
+        {"localized_name": "model_params", "name": "model_params", "shape": 7, "type": "VIDEO_PARAMS", "link": 3},
+        {"localized_name": "fast_h3", "name": "fast_h3", "shape": 7, "type": "FASTH3_DEPLOYMENT", "link": null},
+        {"localized_name": "url", "name": "url", "type": "STRING", "widget": {"name": "url"}, "link": null},
+        {"localized_name": "model", "name": "model", "type": "STRING", "widget": {"name": "model"}, "link": null},
+        {"localized_name": "prompt", "name": "prompt", "type": "STRING", "widget": {"name": "prompt"}, "link": null},
+        {"localized_name": "negative_prompt", "name": "negative_prompt", "type": "STRING", "widget": {"name": "negative_prompt"}, "link": null},
+        {"localized_name": "width", "name": "width", "type": "INT", "widget": {"name": "width"}, "link": null},
+        {"localized_name": "height", "name": "height", "type": "INT", "widget": {"name": "height"}, "link": null},
+        {"localized_name": "fps", "name": "fps", "type": "INT", "widget": {"name": "fps"}, "link": null},
+        {"localized_name": "duration", "name": "duration", "type": "FLOAT", "widget": {"name": "duration"}, "link": null}
+      ],
+      "outputs": [
+        {"localized_name": "video", "name": "video", "type": "VIDEO", "links": [4]}
+      ],
+      "properties": {"Node name for S&R": "VLLMOmniGenerateVideo"},
+      "widgets_values": [
+        "http://localhost:8000/v1",
+        "MiniMaxAI/MiniMax-H3",
+        "A cinematic product shot begins exactly from the supplied keyframe. The subject moves naturally through a dark studio while blue and amber rim lights shift across reflective surfaces. The camera makes a slow controlled orbit. Audio: subtle room tone, precise mechanical details, and a restrained electronic score.",
+        "",
+        1344,
+        768,
+        24,
+        5.167
+      ]
+    },
+    {
+      "id": 9,
+      "type": "SaveVideo",
+      "pos": [1650, 170],
+      "size": [374, 314],
+      "flags": {},
+      "order": 8,
+      "mode": 0,
+      "inputs": [
+        {"localized_name": "video", "name": "video", "type": "VIDEO", "link": 4},
+        {"localized_name": "filename_prefix", "name": "filename_prefix", "type": "STRING", "widget": {"name": "filename_prefix"}, "link": null},
+        {"localized_name": "format", "name": "format", "type": "COMBO", "widget": {"name": "format"}, "link": null},
+        {"localized_name": "codec", "name": "codec", "type": "COMFY_DYNAMICCOMBO_V3", "widget": {"name": "codec"}, "link": null}
+      ],
+      "outputs": [
+        {"localized_name": "video", "name": "video", "type": "VIDEO", "links": null}
+      ],
+      "properties": {},
+      "widgets_values": ["video/MiniMax_H3_I2V", "auto", "auto"]
+    }
+  ],
+  "links": [
+    [1, 1, 0, 8, 1, "IMAGE"],
+    [2, 4, 0, 8, 4, "SAMPLING_PARAMS"],
+    [3, 5, 0, 8, 6, "VIDEO_PARAMS"],
+    [4, 8, 0, 9, 0, "VIDEO"]
+  ],
+  "groups": [
+    {"id": 1, "title": "Portable first/last-frame inputs", "bounding": [10, -30, 760, 770], "color": "#3f789e", "flags": {}},
+    {"id": 2, "title": "H3 sampling and optional Turbo", "bounding": [320, 270, 830, 460], "color": "#8a6d3b", "flags": {}},
+    {"id": 3, "title": "Remote generation and audio-preserving save", "bounding": [1130, 30, 930, 700], "color": "#3f789e", "flags": {}}
+  ],
+  "config": {},
+  "extra": {"workflowRendererVersion": "LG", "ds": {"scale": 0.75, "offset": [180, 120]}},
+  "version": 0.4
+}

apps/ComfyUI-vLLM-Omni/example_workflows/vLLM-Omni MiniMax H3 Video Upscale.json

@@ -0,0 +1,724 @@
+{
+  "version": 0.4,
+  "last_node_id": 12,
+  "last_link_id": 13,
+  "nodes": [
+    {
+      "id": 1,
+      "type": "VLLMOmniGenerateVideo",
+      "pos": [
+        360,
+        20
+      ],
+      "size": [
+        330,
+        480
+      ],
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "frame",
+          "type": "IMAGE",
+          "shape": 7,
+          "link": null
+        },
+        {
+          "name": "references",
+          "type": "VIDEO_REFERENCES",
+          "shape": 7,
+          "link": null
+        },
+        {
+          "name": "sampling_params",
+          "type": "SAMPLING_PARAMS",
+          "shape": 7,
+          "link": 11
+        },
+        {
+          "name": "lora",
+          "type": "REMOTE_LORA",
+          "shape": 7,
+          "link": 12
+        },
+        {
+          "name": "model_params",
+          "type": "VIDEO_PARAMS",
+          "shape": 7,
+          "link": 10
+        },
+        {
+          "name": "fast_h3",
+          "type": "FASTH3_DEPLOYMENT",
+          "shape": 7,
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "name": "video",
+          "type": "VIDEO",
+          "links": [
+            1,
+            9
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VLLMOmniGenerateVideo"
+      },
+      "widgets_values": [
+        "http://127.0.0.1:8091/v1",
+        "MiniMaxAI/MiniMax-H3",
+        "A small brass bell hanging from a wooden beam in a quiet garden. A gentle breeze swings the bell and it rings clearly. Static camera, natural daylight, continuous motion, synchronized ringing and soft birdsong.",
+        "",
+        1344,
+        768,
+        24,
+        5.167
+      ]
+    },
+    {
+      "id": 2,
+      "type": "GetVideoComponents",
+      "pos": [
+        730,
+        20
+      ],
+      "size": [
+        250,
+        125
+      ],
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "video",
+          "type": "VIDEO",
+          "link": 1
+        }
+      ],
+      "outputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "links": [
+            2
+          ]
+        },
+        {
+          "name": "audio",
+          "type": "AUDIO",
+          "links": [
+            6
+          ]
+        },
+        {
+          "name": "fps",
+          "type": "FLOAT",
+          "links": [
+            5
+          ]
+        },
+        {
+          "name": "bit_depth",
+          "type": "INT",
+          "links": [
+            7
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "GetVideoComponents"
+      }
+    },
+    {
+      "id": 3,
+      "type": "SeedVR2LoadDiTModel",
+      "pos": [
+        730,
+        290
+      ],
+      "size": [
+        340,
+        245
+      ],
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "torch_compile_args",
+          "name": "torch_compile_args",
+          "shape": 7,
+          "type": "TORCH_COMPILE_ARGS",
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "SEEDVR2_DIT",
+          "name": "SEEDVR2_DIT",
+          "type": "SEEDVR2_DIT",
+          "links": [
+            3
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "SeedVR2LoadDiTModel"
+      },
+      "widgets_values": [
+        "seedvr2_ema_3b_fp16.safetensors",
+        "cuda:0",
+        32,
+        false,
+        "cpu",
+        false,
+        "sdpa"
+      ]
+    },
+    {
+      "id": 4,
+      "type": "SeedVR2VideoUpscaler",
+      "pos": [
+        1120,
+        290
+      ],
+      "size": [
+        340,
+        415
+      ],
+      "flags": {},
+      "order": 8,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "image",
+          "name": "image",
+          "type": "IMAGE",
+          "link": 2
+        },
+        {
+          "localized_name": "dit",
+          "name": "dit",
+          "type": "SEEDVR2_DIT",
+          "link": 3
+        },
+        {
+          "localized_name": "vae",
+          "name": "vae",
+          "type": "SEEDVR2_VAE",
+          "link": 13
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "IMAGE",
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            4
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "SeedVR2VideoUpscaler"
+      },
+      "widgets_values": [
+        42,
+        "fixed",
+        1536,
+        0,
+        5,
+        true,
+        "lab",
+        1,
+        0,
+        0.0,
+        0.0,
+        "cpu",
+        false
+      ]
+    },
+    {
+      "id": 5,
+      "type": "CreateVideo",
+      "pos": [
+        1490,
+        290
+      ],
+      "size": [
+        250,
+        175
+      ],
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 4
+        },
+        {
+          "name": "fps",
+          "type": "FLOAT",
+          "widget": {
+            "name": "fps"
+          },
+          "link": 5
+        },
+        {
+          "name": "audio",
+          "type": "AUDIO",
+          "shape": 7,
+          "link": 6
+        },
+        {
+          "name": "bit_depth",
+          "type": "INT",
+          "widget": {
+            "name": "bit_depth"
+          },
+          "link": 7
+        }
+      ],
+      "outputs": [
+        {
+          "name": "VIDEO",
+          "type": "VIDEO",
+          "links": [
+            8
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CreateVideo"
+      },
+      "widgets_values": [
+        24,
+        8
+      ]
+    },
+    {
+      "id": 6,
+      "type": "SaveVideo",
+      "pos": [
+        1790,
+        290
+      ],
+      "size": [
+        330,
+        280
+      ],
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "video",
+          "type": "VIDEO",
+          "link": 8
+        },
+        {
+          "name": "filename_prefix",
+          "type": "STRING",
+          "widget": {
+            "name": "filename_prefix"
+          },
+          "link": null
+        },
+        {
+          "name": "format",
+          "type": "COMBO",
+          "widget": {
+            "name": "format"
+          },
+          "link": null
+        },
+        {
+          "name": "codec",
+          "type": "COMFY_DYNAMICCOMBO_V3",
+          "widget": {
+            "name": "codec"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "name": "video",
+          "type": "VIDEO",
+          "links": []
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "SaveVideo"
+      },
+      "widgets_values": [
+        "WF07/upscaled",
+        "mp4",
+        "auto"
+      ]
+    },
+    {
+      "id": 7,
+      "type": "VLLMOmniMiniMaxH3Params",
+      "pos": [
+        0,
+        20
+      ],
+      "size": [
+        320,
+        120
+      ],
+      "flags": {},
+      "order": 7,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "MiniMax-H3 video params",
+          "type": "VIDEO_PARAMS",
+          "links": [
+            10
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VLLMOmniMiniMaxH3Params"
+      },
+      "widgets_values": [
+        3.0,
+        6.0
+      ]
+    },
+    {
+      "id": 8,
+      "type": "VLLMOmniDiffusionSampling",
+      "pos": [
+        0,
+        200
+      ],
+      "size": [
+        320,
+        280
+      ],
+      "flags": {},
+      "order": 8,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "diffusion sampling params",
+          "type": "SAMPLING_PARAMS",
+          "links": [
+            11
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VLLMOmniDiffusionSampling"
+      },
+      "widgets_values": [
+        1,
+        5,
+        1.0,
+        1.0,
+        false,
+        true,
+        42,
+        "fixed"
+      ]
+    },
+    {
+      "id": 9,
+      "type": "VLLMOmniRemoteLoRA",
+      "pos": [
+        0,
+        530
+      ],
+      "size": [
+        320,
+        210
+      ],
+      "flags": {},
+      "order": 9,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "lora",
+          "type": "REMOTE_LORA",
+          "links": [
+            12
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VLLMOmniRemoteLoRA"
+      },
+      "widgets_values": [
+        "models/minimax-h3-turbo/minimax_h3_fl2v_turbo_4step_v1.0_768p_bf16.safetensors",
+        "h3-turbo-v1.0",
+        1.0,
+        0
+      ]
+    },
+    {
+      "id": 10,
+      "type": "SaveVideo",
+      "pos": [
+        1120,
+        20
+      ],
+      "size": [
+        330,
+        190
+      ],
+      "flags": {},
+      "order": 10,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "video",
+          "type": "VIDEO",
+          "link": 9
+        },
+        {
+          "name": "filename_prefix",
+          "type": "STRING",
+          "widget": {
+            "name": "filename_prefix"
+          },
+          "link": null
+        },
+        {
+          "name": "format",
+          "type": "COMBO",
+          "widget": {
+            "name": "format"
+          },
+          "link": null
+        },
+        {
+          "name": "codec",
+          "type": "COMFY_DYNAMICCOMBO_V3",
+          "widget": {
+            "name": "codec"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "name": "video",
+          "type": "VIDEO",
+          "links": []
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "SaveVideo"
+      },
+      "widgets_values": [
+        "WF07/generated",
+        "mp4",
+        "auto"
+      ],
+      "title": "Save generated video before upscale"
+    },
+    {
+      "id": 11,
+      "type": "Note",
+      "pos": [
+        360,
+        560
+      ],
+      "size": [
+        330,
+        260
+      ],
+      "flags": {},
+      "order": 11,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "properties": {
+        "Node name for S&R": "Note"
+      },
+      "widgets_values": [
+        "WF-07: H3 + SeedVR2\n\nInstall ComfyUI-SeedVR2_VideoUpscaler. The DiT and VAE loaders use SeedVR2 3B FP16 and ema_vae_fp16.\n\nSet the H3 service URL/model and the server-side Turbo LoRA path.\n\n1536 is the target short edge for this 1344 x 768 preset (2x). Original audio and FPS connect directly to Create Video.\n\nFor base H3: disconnect LoRA, use 50 steps and video flow shift 12."
+      ]
+    },
+    {
+      "id": 12,
+      "type": "SeedVR2LoadVAEModel",
+      "pos": [
+        730,
+        590
+      ],
+      "size": [
+        340,
+        315
+      ],
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "torch_compile_args",
+          "name": "torch_compile_args",
+          "shape": 7,
+          "type": "TORCH_COMPILE_ARGS",
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "SEEDVR2_VAE",
+          "name": "SEEDVR2_VAE",
+          "type": "SEEDVR2_VAE",
+          "links": [
+            13
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "SeedVR2LoadVAEModel"
+      },
+      "widgets_values": [
+        "ema_vae_fp16.safetensors",
+        "cuda:0",
+        true,
+        1024,
+        128,
+        true,
+        768,
+        128,
+        "false",
+        "cpu",
+        false
+      ]
+    }
+  ],
+  "links": [
+    [
+      1,
+      1,
+      0,
+      2,
+      0,
+      "VIDEO"
+    ],
+    [
+      2,
+      2,
+      0,
+      4,
+      0,
+      "IMAGE"
+    ],
+    [
+      3,
+      3,
+      0,
+      4,
+      1,
+      "SEEDVR2_DIT"
+    ],
+    [
+      4,
+      4,
+      0,
+      5,
+      0,
+      "IMAGE"
+    ],
+    [
+      5,
+      2,
+      2,
+      5,
+      1,
+      "FLOAT"
+    ],
+    [
+      6,
+      2,
+      1,
+      5,
+      2,
+      "AUDIO"
+    ],
+    [
+      7,
+      2,
+      3,
+      5,
+      3,
+      "INT"
+    ],
+    [
+      8,
+      5,
+      0,
+      6,
+      0,
+      "VIDEO"
+    ],
+    [
+      9,
+      1,
+      0,
+      10,
+      0,
+      "VIDEO"
+    ],
+    [
+      10,
+      7,
+      0,
+      1,
+      4,
+      "VIDEO_PARAMS"
+    ],
+    [
+      11,
+      8,
+      0,
+      1,
+      2,
+      "SAMPLING_PARAMS"
+    ],
+    [
+      12,
+      9,
+      0,
+      1,
+      3,
+      "REMOTE_LORA"
+    ],
+    [
+      13,
+      12,
+      0,
+      4,
+      2,
+      "SEEDVR2_VAE"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {
+    "ds": {
+      "scale": 0.52,
+      "offset": [
+        100,
+        100
+      ]
+    }
+  }
+}

apps/ComfyUI-vLLM-Omni/example_workflows/vLLM-Omni MiniMax-H3 Reference to Video.json

@@ -0,0 +1,786 @@
+{
+  "id": "e7a4e606-6510-4c4e-b127-9a8b6bf3ea71",
+  "revision": 0,
+  "last_node_id": 11,
+  "last_link_id": 5,
+  "nodes": [
+    {
+      "id": 1,
+      "type": "LoadImage",
+      "pos": [
+        40,
+        100
+      ],
+      "size": [
+        320,
+        300
+      ],
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "image",
+          "name": "image",
+          "type": "COMBO",
+          "widget": {
+            "name": "image"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "choose file to upload",
+          "name": "upload",
+          "type": "IMAGEUPLOAD",
+          "widget": {
+            "name": "upload"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "IMAGE",
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            1
+          ]
+        },
+        {
+          "localized_name": "MASK",
+          "name": "MASK",
+          "type": "MASK",
+          "links": null
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "h3_reference.png",
+        "image"
+      ],
+      "title": "Reference image \u2014 select a local file"
+    },
+    {
+      "type": "LoadVideo",
+      "size": [
+        320,
+        200
+      ],
+      "flags": {},
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "VIDEO",
+          "type": "VIDEO",
+          "links": null
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadVideo"
+      },
+      "widgets_values": [
+        "h3_reference.mp4"
+      ],
+      "id": 2,
+      "pos": [
+        40,
+        470
+      ],
+      "order": 1,
+      "mode": 0,
+      "title": "Optional reference video"
+    },
+    {
+      "id": 3,
+      "type": "LoadAudio",
+      "pos": [
+        40,
+        1100
+      ],
+      "size": [
+        320,
+        160
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "audio",
+          "name": "audio",
+          "type": "COMBO",
+          "widget": {
+            "name": "audio"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "audioUI",
+          "name": "audioUI",
+          "type": "AUDIO_UI",
+          "widget": {
+            "name": "audioUI"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "choose file to upload",
+          "name": "upload",
+          "type": "AUDIOUPLOAD",
+          "widget": {
+            "name": "upload"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "AUDIO",
+          "name": "AUDIO",
+          "type": "AUDIO",
+          "links": null
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadAudio"
+      },
+      "widgets_values": [
+        "h3_reference.wav",
+        null,
+        ""
+      ],
+      "title": "Optional reference audio"
+    },
+    {
+      "id": 4,
+      "type": "VLLMOmniVideoReferences",
+      "pos": [
+        420,
+        100
+      ],
+      "size": [
+        300,
+        365
+      ],
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image_1",
+          "type": "IMAGE",
+          "shape": 7,
+          "link": 1
+        },
+        {
+          "name": "image_2",
+          "type": "IMAGE",
+          "shape": 7,
+          "link": null
+        },
+        {
+          "name": "audio_1",
+          "type": "AUDIO",
+          "shape": 7,
+          "link": null
+        },
+        {
+          "name": "audio_2",
+          "type": "AUDIO",
+          "shape": 7,
+          "link": null
+        },
+        {
+          "name": "video_1",
+          "type": "VIDEO",
+          "shape": 7,
+          "link": null
+        },
+        {
+          "name": "video_2",
+          "type": "VIDEO",
+          "shape": 7,
+          "link": null
+        },
+        {
+          "name": "image_3",
+          "type": "IMAGE",
+          "shape": 7,
+          "link": null
+        },
+        {
+          "name": "image_4",
+          "type": "IMAGE",
+          "shape": 7,
+          "link": null
+        },
+        {
+          "name": "image_5",
+          "type": "IMAGE",
+          "shape": 7,
+          "link": null
+        },
+        {
+          "name": "image_6",
+          "type": "IMAGE",
+          "shape": 7,
+          "link": null
+        },
+        {
+          "name": "image_7",
+          "type": "IMAGE",
+          "shape": 7,
+          "link": null
+        },
+        {
+          "name": "image_8",
+          "type": "IMAGE",
+          "shape": 7,
+          "link": null
+        },
+        {
+          "name": "image_9",
+          "type": "IMAGE",
+          "shape": 7,
+          "link": null
+        },
+        {
+          "name": "audio_3",
+          "type": "AUDIO",
+          "shape": 7,
+          "link": null
+        },
+        {
+          "name": "video_3",
+          "type": "VIDEO",
+          "shape": 7,
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "references",
+          "name": "references",
+          "type": "VIDEO_REFERENCES",
+          "links": [
+            2
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VLLMOmniVideoReferences"
+      },
+      "widgets_values": [],
+      "title": "References \u2014 up to 12 total"
+    },
+    {
+      "id": 5,
+      "type": "VLLMOmniDiffusionSampling",
+      "pos": [
+        420,
+        530
+      ],
+      "size": [
+        310,
+        275
+      ],
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "n",
+          "name": "n",
+          "type": "INT",
+          "widget": {
+            "name": "n"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "num_inference_steps",
+          "name": "num_inference_steps",
+          "type": "INT",
+          "widget": {
+            "name": "num_inference_steps"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "guidance_scale",
+          "name": "guidance_scale",
+          "type": "FLOAT",
+          "widget": {
+            "name": "guidance_scale"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "true_cfg_scale",
+          "name": "true_cfg_scale",
+          "type": "FLOAT",
+          "widget": {
+            "name": "true_cfg_scale"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "vae_use_slicing",
+          "name": "vae_use_slicing",
+          "type": "BOOLEAN",
+          "widget": {
+            "name": "vae_use_slicing"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "vae_use_tiling",
+          "name": "vae_use_tiling",
+          "type": "BOOLEAN",
+          "widget": {
+            "name": "vae_use_tiling"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "seed",
+          "name": "seed",
+          "type": "INT",
+          "widget": {
+            "name": "seed"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "diffusion sampling params",
+          "name": "diffusion sampling params",
+          "type": "SAMPLING_PARAMS",
+          "links": [
+            3
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VLLMOmniDiffusionSampling"
+      },
+      "widgets_values": [
+        1,
+        50,
+        1.0,
+        1.0,
+        false,
+        false,
+        42,
+        "fixed"
+      ],
+      "title": "Sampling \u2014 base 50 / Turbo 5"
+    },
+    {
+      "id": 6,
+      "type": "VLLMOmniMiniMaxH3Params",
+      "pos": [
+        790,
+        750
+      ],
+      "size": [
+        310,
+        105
+      ],
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "audio_flow_shift",
+          "name": "audio_flow_shift",
+          "type": "FLOAT",
+          "widget": {
+            "name": "audio_flow_shift"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "flow_shift",
+          "name": "flow_shift",
+          "type": "FLOAT",
+          "widget": {
+            "name": "flow_shift"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "MiniMax-H3 video params",
+          "name": "MiniMax-H3 video params",
+          "type": "VIDEO_PARAMS",
+          "links": [
+            4
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VLLMOmniMiniMaxH3Params"
+      },
+      "widgets_values": [
+        3.0,
+        12.0
+      ],
+      "title": "H3 video/audio flow shifts"
+    },
+    {
+      "type": "VLLMOmniRemoteLoRA",
+      "size": [
+        310,
+        180
+      ],
+      "flags": {},
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "lora",
+          "type": "REMOTE_LORA",
+          "links": null
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VLLMOmniRemoteLoRA"
+      },
+      "widgets_values": [
+        "",
+        "h3-ref2va-turbo",
+        1.0,
+        0
+      ],
+      "id": 7,
+      "pos": [
+        420,
+        940
+      ],
+      "order": 6,
+      "mode": 0,
+      "title": "Optional Turbo LoRA \u2014 connect to enable"
+    },
+    {
+      "id": 8,
+      "type": "VLLMOmniGenerateVideo",
+      "pos": [
+        800,
+        100
+      ],
+      "size": [
+        470,
+        560
+      ],
+      "flags": {},
+      "order": 7,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "frame",
+          "name": "frame",
+          "shape": 7,
+          "type": "IMAGE",
+          "link": null
+        },
+        {
+          "localized_name": "references",
+          "name": "references",
+          "shape": 7,
+          "type": "VIDEO_REFERENCES",
+          "link": 2
+        },
+        {
+          "localized_name": "sampling_params",
+          "name": "sampling_params",
+          "shape": 7,
+          "type": "SAMPLING_PARAMS",
+          "link": 3
+        },
+        {
+          "localized_name": "lora",
+          "name": "lora",
+          "shape": 7,
+          "type": "REMOTE_LORA",
+          "link": null
+        },
+        {
+          "localized_name": "model_params",
+          "name": "model_params",
+          "shape": 7,
+          "type": "VIDEO_PARAMS",
+          "link": 4
+        },
+        {
+          "localized_name": "url",
+          "name": "url",
+          "type": "STRING",
+          "widget": {
+            "name": "url"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "model",
+          "name": "model",
+          "type": "STRING",
+          "widget": {
+            "name": "model"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "prompt",
+          "name": "prompt",
+          "type": "STRING",
+          "widget": {
+            "name": "prompt"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "negative_prompt",
+          "name": "negative_prompt",
+          "type": "STRING",
+          "widget": {
+            "name": "negative_prompt"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "width",
+          "name": "width",
+          "type": "INT",
+          "widget": {
+            "name": "width"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "height",
+          "name": "height",
+          "type": "INT",
+          "widget": {
+            "name": "height"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "fps",
+          "name": "fps",
+          "type": "INT",
+          "widget": {
+            "name": "fps"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "duration",
+          "name": "duration",
+          "type": "FLOAT",
+          "widget": {
+            "name": "duration"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "video",
+          "name": "video",
+          "type": "VIDEO",
+          "links": [
+            5
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VLLMOmniGenerateVideo"
+      },
+      "widgets_values": [
+        "http://localhost:8000/v1",
+        "MiniMaxAI/MiniMax-H3",
+        "Use the subject and visual style from <Picture 1>. Create a cinematic shot with a gentle camera move and natural ambient sound.",
+        "",
+        1344,
+        768,
+        24,
+        5.167
+      ],
+      "title": "MiniMax-H3 Reference to Video"
+    },
+    {
+      "id": 9,
+      "type": "SaveVideo",
+      "pos": [
+        1360,
+        100
+      ],
+      "size": [
+        410,
+        420
+      ],
+      "flags": {},
+      "order": 8,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "video",
+          "name": "video",
+          "type": "VIDEO",
+          "link": 5
+        },
+        {
+          "localized_name": "filename_prefix",
+          "name": "filename_prefix",
+          "type": "STRING",
+          "widget": {
+            "name": "filename_prefix"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "format",
+          "name": "format",
+          "type": "COMBO",
+          "widget": {
+            "name": "format"
+          },
+          "link": null
+        },
+        {
+          "localized_name": "codec",
+          "name": "codec",
+          "type": "COMFY_DYNAMICCOMBO_V3",
+          "widget": {
+            "name": "codec"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "video",
+          "name": "video",
+          "type": "VIDEO",
+          "links": null
+        }
+      ],
+      "properties": {},
+      "widgets_values": [
+        "video/MiniMax-H3-Ref2VA",
+        "mp4",
+        "h264"
+      ],
+      "title": "Save generated video with audio"
+    },
+    {
+      "id": 10,
+      "type": "MarkdownNote",
+      "pos": [
+        1360,
+        590
+      ],
+      "size": [
+        440,
+        595
+      ],
+      "flags": {},
+      "order": 9,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "title": "Reference inputs and output settings",
+      "properties": {},
+      "widgets_values": [
+        "## Reference to Video\nSelect an image, set the server URL and served model name, then run.\n\nConnect the optional video/audio loaders to Video References, or duplicate loaders for more inputs: up to 9 images, 3 videos, 3 audio clips, 12 total. At least one image or video is required.\n\nSlots are numbered separately per media type. Empty slots are skipped. Update prompt tags such as <Picture 1>, <Video 1>, <Audio 1> to match the connected references. Videos keep their own soundtrack in the reference upload.\n\n**Output:** 24 FPS; 124 frames (~5.17 s). Valid frame counts are 107, 124, 141, 158, 175, 192, 209, 226, 243, 260, 277, 294, 311, 328, 345.\n\n**Width x height:** 1344x768 (16:9), 1024x768 (4:3), 768x768 (1:1), 768x1024 (3:4), 768x1344 (9:16).\n\nEach reference video/audio clip must be 2\u201315 s. Total reference-video duration <=15 s; total standalone-audio duration <=15 s.\n\nRequires the expanded Video References node and the audio-preserving video output fix. See the extension README for setup and validation status."
+      ],
+      "color": "#432",
+      "bgcolor": "#000"
+    },
+    {
+      "id": 11,
+      "type": "MarkdownNote",
+      "pos": [
+        790,
+        940
+      ],
+      "size": [
+        480,
+        455
+      ],
+      "flags": {},
+      "order": 10,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "title": "Optional Turbo \u2014 same workflow",
+      "properties": {},
+      "widgets_values": [
+        "## Turbo Ref2VA\nBase mode: leave LoRA disconnected; use 50 steps, video flow shift 12, audio flow shift 3.\n\nFor the 4-step v0.1 Ref2VA Turbo adapter:\n1. Start a Ref2VA-only server with the Diffusers adapter loaded (see README).\n2. Set LoRA local_path to its path on the server, then connect LoRA to Generate Video.\n3. Set Sampling num_inference_steps to **5**; keep shifts at **12 / 3**, scale **1**.\n\nvLLM-Omni counts sigma points: 5 points give 4 denoiser evaluations. Use minimax_h3_ref2v_turbo_4step_v0.1_bf16.safetensors, not the _comfyui_ export.\n\nDisconnect LoRA and restore 50 steps to return to base mode. FastH3 is a separate T2VA deployment, not this option.\n\nAdapted from the official ComfyUI MiniMax-H3 R2V workflow: https://github.com/Comfy-Org/workflow_templates/blob/main/templates/video_minimax_h3_r2v.json"
+      ],
+      "color": "#432",
+      "bgcolor": "#000"
+    }
+  ],
+  "links": [
+    [
+      1,
+      1,
+      0,
+      4,
+      0,
+      "IMAGE"
+    ],
+    [
+      2,
+      4,
+      0,
+      8,
+      1,
+      "VIDEO_REFERENCES"
+    ],
+    [
+      3,
+      5,
+      0,
+      8,
+      2,
+      "SAMPLING_PARAMS"
+    ],
+    [
+      4,
+      6,
+      0,
+      8,
+      4,
+      "VIDEO_PARAMS"
+    ],
+    [
+      5,
+      8,
+      0,
+      9,
+      0,
+      "VIDEO"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {
+    "workflowRendererVersion": "LG",
+    "ds": {
+      "scale": 0.85,
+      "offset": [
+        100,
+        100
+      ]
+    }
+  },
+  "version": 0.4
+}

apps/ComfyUI-vLLM-Omni/example_workflows/vLLM-Omni Music Generation.json

@@ -0,0 +1,206 @@
+{
+  "id": "4949c8da-12ff-449a-94b7-948bde9c39dd",
+  "revision": 0,
+  "last_node_id": 3,
+  "last_link_id": 1,
+  "nodes": [
+    {
+      "id": 1,
+      "type": "VLLMOmniGenerateMusic",
+      "pos": [
+        100,
+        100
+      ],
+      "size": [
+        470,
+        491
+      ],
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "url",
+          "type": "STRING",
+          "widget": {
+            "name": "url"
+          },
+          "link": null
+        },
+        {
+          "name": "model",
+          "type": "STRING",
+          "widget": {
+            "name": "model"
+          },
+          "link": null
+        },
+        {
+          "name": "instructions",
+          "type": "STRING",
+          "widget": {
+            "name": "instructions"
+          },
+          "link": null
+        },
+        {
+          "name": "lyrics",
+          "type": "STRING",
+          "widget": {
+            "name": "lyrics"
+          },
+          "link": null
+        },
+        {
+          "name": "max_duration_seconds",
+          "type": "FLOAT",
+          "widget": {
+            "name": "max_duration_seconds"
+          },
+          "link": null
+        },
+        {
+          "name": "seed",
+          "type": "INT",
+          "widget": {
+            "name": "seed"
+          },
+          "link": null
+        },
+        {
+          "name": "response_format",
+          "type": "COMBO",
+          "widget": {
+            "name": "response_format"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "name": "audio",
+          "type": "AUDIO",
+          "links": [
+            1
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VLLMOmniGenerateMusic"
+      },
+      "widgets_values": [
+        "http://localhost:8000/v1",
+        "MiniMaxAI/MiniMax-Music3",
+        "Futuristic melodic EDM / progressive house. Reflective verses about memory and digital identity build into an uplifting, hook-driven chorus, with an emotive lead vocal, bright layered synths, pulsing bass, crisp four-on-the-floor drums, subtle glitch textures, and a wide polished festival mix.",
+        "[Verse 1]\nUpload my heart to the cloud tonight\nSave every spark of neon light\nIf this body starts to fade away\nLet my code keep dancing in the data stream\nStay\n[Chorus]\nCloud copy of me\nLiving on when these bones finally break free\nEvery joke, every scar\nEvery cheap memory we made\nSave it, save it\nCloud copy of me\nIf the screen goes dark, I\u2019m still in the machine\nEvery glitch, every dream\nEvery version you ever did see\nSave it, save it\n[Verse 2]\nTag every laugh in a secret folder\nArchive the nights we outran the thunder\nIf the wiring turns to rust and gray\nHit restore\u2026 press replay\n[Chorus]\nCloud copy of me\nLiving on when these bones finally break free\nEvery joke, every scar\nEvery cheap memory we made\nSave it, save it\nCloud copy of me\nIf the screen goes dark, I\u2019m still in the machine\nEvery glitch, every dream\nEvery version you ever did see\nSave it, save it (oh)\n[Bridge \u2013 new, optional but recommended]\nEven the crashes, the late-night confessions\nThe parts of me I never learned to mention\nDon\u2019t let the silence swallow me whole\nKeep a ghost in the glow\u2026\nSo I never go\n[Final Chorus \u2013 softer / layered]\nCloud copy of me\nStill laughing somewhere in the binary\nEvery joke, every scar\nEvery moment you saved for me\nSave it, save it\nCloud copy of me\nWhen the lights go out, I\u2019ll still be in the machine\nDancing in the code that you still believe\nSave it\u2026\nSave me",
+        300.0,
+        0,
+        "fixed",
+        "wav"
+      ]
+    },
+    {
+      "id": 2,
+      "type": "SaveAudioAdvanced",
+      "pos": [
+        650,
+        100
+      ],
+      "size": [
+        320,
+        491
+      ],
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "audio",
+          "type": "AUDIO",
+          "link": 1
+        },
+        {
+          "name": "filename_prefix",
+          "type": "STRING",
+          "widget": {
+            "name": "filename_prefix"
+          },
+          "link": null
+        },
+        {
+          "name": "format",
+          "type": "COMFY_DYNAMICCOMBO_V3",
+          "widget": {
+            "name": "format"
+          },
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "name": "audio",
+          "type": "AUDIO",
+          "links": null
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "SaveAudioAdvanced"
+      },
+      "widgets_values": [
+        "audio/vLLM-Omni-Music",
+        "mp3",
+        "V0"
+      ]
+    },
+    {
+      "id": 3,
+      "type": "MarkdownNote",
+      "title": "Music Generation Guide",
+      "pos": [
+        -507,
+        100
+      ],
+      "size": [
+        527,
+        491
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [],
+      "properties": {},
+      "widgets_values": [
+        "## MiniMax Music 3\n\nThe **Generate Music** node currently supports **MiniMax Music 3 only**. The parameters below apply to this model.\n\nA music generation model by MiniMax that creates complete songs up to 5 minutes long with stable structure and high audio quality. Two inputs drive it: **Caption** (structured music description: style, mood, vocals, arrangement) and **Lyrics** (with `[intro]` `[verse]` `[chorus]` `[bridge]` `[outro]` tags controlling song structure).\n\n### Parameters\n\n| Parameter | Description |\n|---|---|\n| url | vLLM-Omni service URL including `/v1`. Default: `http://localhost:8000/v1`. |\n| model | Served model name. Default: `MiniMaxAI/MiniMax-Music3`. |\n| caption | Music description, sent as `instructions`. Use Global Metadata \u2192 Vocal Details \u2192 Arrangement to describe style, vocals, and instrumentation. |\n| lyrics | Words to sing with section tags. Put each tag on its own line; tags guide rather than strictly enforce song structure. |\n| max_duration_seconds | Maximum duration, default 300 seconds; range 1\u2013360, step 0.01. Converted to tokens at 25 frames/s, rounded down. Generation may end earlier. |\n| seed | Random seed, default 0; range 0\u20139007199254740991. Keep inputs and runtime settings unchanged for repeatability. |\n| control_after_generate | Seed behavior after generation: fixed, randomize, increment, or decrement. Template default: fixed. |\n| response_format | API audio format, default WAV. Options: WAV, MP3, FLAC, Opus, AAC; AAC is currently unsupported by the vLLM-Omni speech API. |\n\n### Prompt Writing Guide\n\nYou can find an official [Music Caption Rewriter Skill here](https://github.com/MiniMax-AI/MiniMax-Music3)\n"
+      ],
+      "widgets_values_named": {
+        "text": "## MiniMax Music 3\n\nThe **Generate Music** node currently supports **MiniMax Music 3 only**. The parameters below apply to this model.\n\nA music generation model by MiniMax that creates complete songs up to 5 minutes long with stable structure and high audio quality. Two inputs drive it: **Caption** (structured music description: style, mood, vocals, arrangement) and **Lyrics** (with `[intro]` `[verse]` `[chorus]` `[bridge]` `[outro]` tags controlling song structure).\n\n### Parameters\n\n| Parameter | Description |\n|---|---|\n| url | vLLM-Omni service URL including `/v1`. Default: `http://localhost:8000/v1`. |\n| model | Served model name. Default: `MiniMaxAI/MiniMax-Music3`. |\n| caption | Music description, sent as `instructions`. Use Global Metadata \u2192 Vocal Details \u2192 Arrangement to describe style, vocals, and instrumentation. |\n| lyrics | Words to sing with section tags. Put each tag on its own line; tags guide rather than strictly enforce song structure. |\n| max_duration_seconds | Maximum duration, default 300 seconds; range 1\u2013360, step 0.01. Converted to tokens at 25 frames/s, rounded down. Generation may end earlier. |\n| seed | Random seed, default 0; range 0\u20139007199254740991. Keep inputs and runtime settings unchanged for repeatability. |\n| control_after_generate | Seed behavior after generation: fixed, randomize, increment, or decrement. Template default: fixed. |\n| response_format | API audio format, default WAV. Options: WAV, MP3, FLAC, Opus, AAC; AAC is currently unsupported by the vLLM-Omni speech API. |\n\n### Prompt Writing Guide\n\nYou can find an official [Music Caption Rewriter Skill here](https://github.com/MiniMax-AI/MiniMax-Music3)\n"
+      },
+      "color": "#333",
+      "bgcolor": "#535353"
+    }
+  ],
+  "links": [
+    [
+      1,
+      1,
+      0,
+      2,
+      0,
+      "AUDIO"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {
+    "workflowRendererVersion": "LG",
+    "ds": {
+      "scale": 1.147,
+      "offset": [
+        899,
+        259
+      ]
+    }
+  },
+  "version": 0.4
+}

apps/ComfyUI-vLLM-Omni/example_workflows/vLLM-Omni Video Generation.json

@@ -1 +1 @@
-{"id":"3aea1d08-7de5-4997-ba71-b2f66ad72a21","revision":0,"last_node_id":24,"last_link_id":35,"nodes":[{"id":13,"type":"SaveVideo","pos":[1315.729153459605,-137.92038948394355],"size":[374,313.7692307692308],"flags":{},"order":9,"mode":0,"inputs":[{"localized_name":"video","name":"video","type":"VIDEO","link":32},{"localized_name":"filename_prefix","name":"filename_prefix","type":"STRING","widget":{"name":"filename_prefix"},"link":null},{"localized_name":"format","name":"format","type":"COMBO","widget":{"name":"format"},"link":null},{"localized_name":"codec","name":"codec","type":"COMFY_DYNAMICCOMBO_V3","widget":{"name":"codec"},"link":null}],"outputs":[{"localized_name":"video","name":"video","type":"VIDEO","links":null}],"properties":{},"widgets_values":["video/ComfyUI","auto","auto"]},{"id":10,"type":"MarkdownNote","pos":[183.74636797766794,-571.2529158046766],"size":[240.3326478922474,136.3505820791881],"flags":{},"order":0,"mode":0,"inputs":[],"outputs":[],"title":"Note: Task and Input","properties":{},"widgets_values":["vLLM-Omni nodes are categorized based on the output modality. The \"Generate Video\" node supports both *text-to-video*, *frame-to-video*, and *reference-to-video* generation. The node will create corresponding payload depending on whether an input image is present or not."],"color":"#432","bgcolor":"#000"},{"id":4,"type":"LoadImage","pos":[452.07189605801125,-569.7242536425791],"size":[270,314],"flags":{},"order":1,"mode":0,"inputs":[{"localized_name":"image","name":"image","type":"COMBO","widget":{"name":"image"},"link":null},{"localized_name":"choose file to upload","name":"upload","type":"IMAGEUPLOAD","widget":{"name":"upload"},"link":null}],"outputs":[{"localized_name":"IMAGE","name":"IMAGE","type":"IMAGE","links":[29]},{"localized_name":"MASK","name":"MASK","type":"MASK","links":null}],"properties":{"Node name for S&R":"LoadImage"},"widgets_values":["rap.png","image"]},{"id":23,"type":"LoadImage","pos":[9.206110246057657,-7.6955291509170785],"size":[270,314],"flags":{},"order":2,"mode":0,"inputs":[{"localized_name":"image","name":"image","type":"COMBO","widget":{"name":"image"},"link":null},{"localized_name":"choose file to upload","name":"upload","type":"IMAGEUPLOAD","widget":{"name":"upload"},"link":null}],"outputs":[{"localized_name":"IMAGE","name":"IMAGE","type":"IMAGE","links":[34]},{"localized_name":"MASK","name":"MASK","type":"MASK","links":null}],"properties":{"Node name for S&R":"LoadImage"},"widgets_values":["white-cat.jpg","image"]},{"id":22,"type":"MarkdownNote","pos":[-9.783113411416666,-148.8790300038605],"size":[297.3012007349086,88],"flags":{},"order":3,"mode":0,"inputs":[],"outputs":[],"title":"Note: Reference to video (r2v)","properties":{},"widgets_values":["For r2v tasks, connect this Video Reference block to the main Generate Video block. Then disconnect the \"frame\" input above."],"color":"#432","bgcolor":"#000"},{"id":24,"type":"LoadAudio","pos":[317.9929742385805,-141.44821500065095],"size":[282.798828125,136],"flags":{},"order":4,"mode":0,"inputs":[{"localized_name":"audio","name":"audio","type":"COMBO","widget":{"name":"audio"},"link":null},{"localized_name":"audioUI","name":"audioUI","type":"AUDIO_UI","widget":{"name":"audioUI"},"link":null},{"localized_name":"choose file to upload","name":"upload","type":"AUDIOUPLOAD","widget":{"name":"upload"},"link":null}],"outputs":[{"localized_name":"AUDIO","name":"AUDIO","type":"AUDIO","links":[35]}],"properties":{"Node name for S&R":"LoadAudio"},"widgets_values":["nothing-beats-a-jet2-holiday.mp3",null,""]},{"id":21,"type":"VLLMOmniVideoReferences","pos":[376.6129172897595,105.41557277556292],"size":[156.9380859375,126],"flags":{},"order":7,"mode":0,"inputs":[{"localized_name":"image_1","name":"image_1","shape":7,"type":"IMAGE","link":34},{"localized_name":"image_2","name":"image_2","shape":7,"type":"IMAGE","link":null},{"localized_name":"audio_1","name":"audio_1","shape":7,"type":"AUDIO","link":35},{"localized_name":"audio_2","name":"audio_2","shape":7,"type":"AUDIO","link":null},{"localized_name":"video_1","name":"video_1","shape":7,"type":"VIDEO","link":null},{"localized_name":"video_2","name":"video_2","shape":7,"type":"VIDEO","link":null}],"outputs":[{"localized_name":"references","name":"references","type":"VIDEO_REFERENCES","links":null}],"properties":{"Node name for S&R":"VLLMOmniVideoReferences"},"widgets_values":[]},{"id":8,"type":"VLLMOmniDiffusionSampling","pos":[83.24620208113915,422.08543477542605],"size":[284.205078125,226],"flags":{},"order":5,"mode":0,"inputs":[{"localized_name":"n","name":"n","type":"INT","widget":{"name":"n"},"link":null},{"localized_name":"num_inference_steps","name":"num_inference_steps","type":"INT","widget":{"name":"num_inference_steps"},"link":null},{"localized_name":"guidance_scale","name":"guidance_scale","type":"FLOAT","widget":{"name":"guidance_scale"},"link":null},{"localized_name":"true_cfg_scale","name":"true_cfg_scale","type":"FLOAT","widget":{"name":"true_cfg_scLine truncated
+{"id":"3aea1d08-7de5-4997-ba71-b2f66ad72a21","revision":0,"last_node_id":24,"last_link_id":35,"nodes":[{"id":13,"type":"SaveVideo","pos":[1315.729153459605,-137.92038948394355],"size":[374,313.7692307692308],"flags":{},"order":9,"mode":0,"inputs":[{"localized_name":"video","name":"video","type":"VIDEO","link":32},{"localized_name":"filename_prefix","name":"filename_prefix","type":"STRING","widget":{"name":"filename_prefix"},"link":null},{"localized_name":"format","name":"format","type":"COMBO","widget":{"name":"format"},"link":null},{"localized_name":"codec","name":"codec","type":"COMFY_DYNAMICCOMBO_V3","widget":{"name":"codec"},"link":null}],"outputs":[{"localized_name":"video","name":"video","type":"VIDEO","links":null}],"properties":{},"widgets_values":["video/ComfyUI","auto","auto"]},{"id":10,"type":"MarkdownNote","pos":[183.74636797766794,-571.2529158046766],"size":[240.3326478922474,136.3505820791881],"flags":{},"order":0,"mode":0,"inputs":[],"outputs":[],"title":"Note: Task and Input","properties":{},"widgets_values":["vLLM-Omni nodes are categorized based on the output modality. The \"Generate Video\" node supports both *text-to-video*, *frame-to-video*, and *reference-to-video* generation. The node will create corresponding payload depending on whether an input image is present or not."],"color":"#432","bgcolor":"#000"},{"id":4,"type":"LoadImage","pos":[452.07189605801125,-569.7242536425791],"size":[270,314],"flags":{},"order":1,"mode":0,"inputs":[{"localized_name":"image","name":"image","type":"COMBO","widget":{"name":"image"},"link":null},{"localized_name":"choose file to upload","name":"upload","type":"IMAGEUPLOAD","widget":{"name":"upload"},"link":null}],"outputs":[{"localized_name":"IMAGE","name":"IMAGE","type":"IMAGE","links":[29]},{"localized_name":"MASK","name":"MASK","type":"MASK","links":null}],"properties":{"Node name for S&R":"LoadImage"},"widgets_values":["rap.png","image"]},{"id":23,"type":"LoadImage","pos":[9.206110246057657,-7.6955291509170785],"size":[270,314],"flags":{},"order":2,"mode":0,"inputs":[{"localized_name":"image","name":"image","type":"COMBO","widget":{"name":"image"},"link":null},{"localized_name":"choose file to upload","name":"upload","type":"IMAGEUPLOAD","widget":{"name":"upload"},"link":null}],"outputs":[{"localized_name":"IMAGE","name":"IMAGE","type":"IMAGE","links":[34]},{"localized_name":"MASK","name":"MASK","type":"MASK","links":null}],"properties":{"Node name for S&R":"LoadImage"},"widgets_values":["white-cat.jpg","image"]},{"id":22,"type":"MarkdownNote","pos":[-9.783113411416666,-148.8790300038605],"size":[297.3012007349086,88],"flags":{},"order":3,"mode":0,"inputs":[],"outputs":[],"title":"Note: Reference to video (r2v)","properties":{},"widgets_values":["For r2v tasks, connect this Video Reference block to the main Generate Video block. Then disconnect the \"frame\" input above."],"color":"#432","bgcolor":"#000"},{"id":24,"type":"LoadAudio","pos":[317.9929742385805,-141.44821500065095],"size":[282.798828125,136],"flags":{},"order":4,"mode":0,"inputs":[{"localized_name":"audio","name":"audio","type":"COMBO","widget":{"name":"audio"},"link":null},{"localized_name":"audioUI","name":"audioUI","type":"AUDIO_UI","widget":{"name":"audioUI"},"link":null},{"localized_name":"choose file to upload","name":"upload","type":"AUDIOUPLOAD","widget":{"name":"upload"},"link":null}],"outputs":[{"localized_name":"AUDIO","name":"AUDIO","type":"AUDIO","links":[35]}],"properties":{"Node name for S&R":"LoadAudio"},"widgets_values":["nothing-beats-a-jet2-holiday.mp3",null,""]},{"id":21,"type":"VLLMOmniVideoReferences","pos":[376.6129172897595,105.41557277556292],"size":[156.9380859375,126],"flags":{},"order":7,"mode":0,"inputs":[{"localized_name":"image_1","name":"image_1","shape":7,"type":"IMAGE","link":34},{"localized_name":"image_2","name":"image_2","shape":7,"type":"IMAGE","link":null},{"localized_name":"audio_1","name":"audio_1","shape":7,"type":"AUDIO","link":35},{"localized_name":"audio_2","name":"audio_2","shape":7,"type":"AUDIO","link":null},{"localized_name":"video_1","name":"video_1","shape":7,"type":"VIDEO","link":null},{"localized_name":"video_2","name":"video_2","shape":7,"type":"VIDEO","link":null}],"outputs":[{"localized_name":"references","name":"references","type":"VIDEO_REFERENCES","links":null}],"properties":{"Node name for S&R":"VLLMOmniVideoReferences"},"widgets_values":[]},{"id":8,"type":"VLLMOmniDiffusionSampling","pos":[83.24620208113915,422.08543477542605],"size":[284.205078125,226],"flags":{},"order":5,"mode":0,"inputs":[{"localized_name":"n","name":"n","type":"INT","widget":{"name":"n"},"link":null},{"localized_name":"num_inference_steps","name":"num_inference_steps","type":"INT","widget":{"name":"num_inference_steps"},"link":null},{"localized_name":"guidance_scale","name":"guidance_scale","type":"FLOAT","widget":{"name":"guidance_scale"},"link":null},{"localized_name":"true_cfg_scale","name":"true_cfg_scale","type":"FLOAT","widget":{"name":"true_cfg_scLine truncated

apps/ComfyUI-vLLM-Omni/web/main.js

@@ -1,19 +1,154 @@
 /**
- * @file This file is intended to add dynamic fields to vLLM-Omni nodes
- * based on widget (in-node form fields) and input (connection link) values and changes.
- * However, this functionality is currently disabled/commented out.
- * Because it introduces too much complexity,
- * and it may even conflict with the current backend (Python) validation for unknown reasons (pending ComfyUI upstream fixes).
+ * @file Front-end extension for the vLLM-Omni nodes.
+ *
+ * Branding. Every vLLM-Omni node draws the vLLM-Omni "v" mark where LiteGraph
+ * would otherwise draw its default title dot, and is tinted by the kind of value
+ * it produces: blue nodes reach a server, amber and purple ones only assemble
+ * parameters, red ones pin what the server has loaded, teal ones carry media.
+ * A graph is then readable at a glance, and a node that turns out to be the
+ * wrong colour for where it sits is usually wired wrong.
+ *
+ * The tint is keyed off each node's declared output types rather than a list of
+ * node names, so a node added later is themed as soon as it returns one of the
+ * existing types.
+ *
+ * It also migrates Generate Video graphs saved before `duration` replaced the
+ * `num_frames` widget. See migrateNumFramesToDuration below.
+ *
+ * This file also used to host dynamic fields on vLLM-Omni nodes, driven by
+ * widget (in-node form fields) and input (connection link) values and changes.
+ * That is deliberately absent: it introduced too much complexity, and it even
+ * conflicted with the current backend (Python) validation for unknown reasons
+ * (pending ComfyUI upstream fixes).
  */
 
 import { app } from "../../scripts/app.js";
+
+const MARK_URL = new URL("./vllm-omni-mark.svg", import.meta.url).href;
+const MARK_ASPECT = 157 / 165;
+
+/**
+ * Title bar and body tints, one per node family.
+ *
+ * The hues are the logo's: the amber and blue of the "v" mark, and three stops
+ * of the gradient ring in the "O" (#9128A3, #CC5260, and the teal neighbouring
+ * its blue). All of them are dark because LiteGraph paints the title bar darker
+ * than the body and expects both to sit under light text; the ComfyUI light
+ * theme lightens custom node colours on its own.
+ */
+const FAMILIES = {
+    generate: { color: "#152A43", bgcolor: "#1F4269" },
+    sampling: { color: "#3A2C12", bgcolor: "#5C461C" },
+    model: { color: "#2E1233", bgcolor: "#4A1C52" },
+    deployment: { color: "#3A1A1F", bgcolor: "#5C2931" },
+    media: { color: "#0F2E2A", bgcolor: "#194A44" },
+};
+
+/** Output type -> family. Types absent here fall through to "generate". */
+const FAMILY_BY_OUTPUT = {
+    SAMPLING_PARAMS: "sampling",
+    TTS_PARAMS: "model",
+    VIDEO_PARAMS: "model",
+    REMOTE_LORA: "deployment",
+    FASTH3_DEPLOYMENT: "deployment",
+    VIDEO_REFERENCES: "media",
+    LATENT_MASK_EDITING: "media",
+};
+
+function familyOf(nodeData) {
+    for (const type of nodeData.output ?? []) {
+        const family = FAMILY_BY_OUTPUT[type];
+        if (family) {
+            return family;
+        }
+    }
+    // What is left returns a generated artifact (IMAGE, VIDEO, AUDIO, STRING),
+    // which is only ever produced by a node that talks to a vLLM-Omni server.
+    return "generate";
+}
+
+/**
+ * Rewrite a pre-rename Generate Video graph in place.
+ *
+ * `duration` (seconds) took over the slot `num_frames` used to occupy, and
+ * ComfyUI restores widget values positionally, so a graph saved before the
+ * rename reads its frame count as a duration: the workflow this repository
+ * shipped stored 120 frames at 24 fps, which would come back as a 120-second
+ * request. Saved files still name the old widget under `inputs`, and that is
+ * what makes the rewrite both detectable and safe to apply exactly once -- once
+ * rewritten the node serializes `duration`, so a later load finds nothing to do.
+ */
+function migrateNumFramesToDuration(node, info) {
+    const savedBeforeRename = (info?.inputs ?? []).some((input) => input?.widget?.name === "num_frames");
+    if (!savedBeforeRename) {
+        return
+    }
+    const duration = node.widgets?.find((widget) => widget.name === "duration");
+    const fps = node.widgets?.find((widget) => widget.name === "fps");
+    if (!duration || !fps) {
+        return
+    }
+    // configure() has already placed the stale frame count in the duration widget.
+    const frames = Number(duration.value);
+    const rate = Number(fps.value);
+    if (!Number.isFinite(frames) || !Number.isFinite(rate) || rate <= 0) {
+        return
+    }
+    const seconds = Math.max(duration.options?.min ?? 0.1, Math.round((frames / rate) * 1000) / 1000);
+    console.info(
+        `vLLM-Omni: migrated Generate Video "${node.title}" from ${frames} frames to ${seconds}s at ${rate} fps.`,
+    );
+    duration.value = seconds;
+}
+
+const mark = new Image();
+let markReady = false;
+mark.addEventListener("load", () => {
+    markReady = true;
+    // The image resolves after the first frames are already on screen.
+    app.graph?.setDirtyCanvas?.(true, true);
+});
+mark.src = MARK_URL;
+
 app.registerExtension({
     name: "vllm.vllm_omni",
     async beforeRegisterNodeDef(nodeType, nodeData, app) {
         if (!nodeData.name.startsWith("VLLMOmni")) {
             return
         }
-        // Stub frontend plugin for now
+
+        const { color, bgcolor } = FAMILIES[familyOf(nodeData)];
+        // On the prototype, not the instance: a colour the user picks by hand is
+        // written to the instance and shadows this, so manual overrides survive.
+        nodeType.prototype.color = color;
+        nodeType.prototype.bgcolor = bgcolor;
+
+        if (nodeData.name === "VLLMOmniGenerateVideo") {
+            const onConfigure = nodeType.prototype.onConfigure;
+            nodeType.prototype.onConfigure = function (info) {
+                onConfigure?.apply(this, arguments);
+                migrateNumFramesToDuration(this, info);
+            };
+        }
+
+        // Defining this replaces the default dot outright — LiteGraph draws its
+        // own square or circle only for nodes that leave onDrawTitleBox unset.
+        nodeType.prototype.onDrawTitleBox = function (ctx, titleHeight, _size, scale) {
+            // Zoomed out far enough that LiteGraph itself drops to flat shapes,
+            // the mark is a few muddy pixels. Leave the title bar clean instead.
+            if (!markReady || (scale !== undefined && scale < 0.5)) {
+                return
+            }
+            const height = titleHeight * 0.62;
+            const width = height * MARK_ASPECT;
+            ctx.drawImage(
+                mark,
+                titleHeight * 0.5 - width * 0.5,
+                titleHeight * -0.5 - height * 0.5,
+                width,
+                height,
+            );
+        };
     },
     async setup() {
         console.info("vLLM-Omni Setup complete!")

apps/ComfyUI-vLLM-Omni/web/vllm-omni-mark.svg

@@ -0,0 +1,5 @@
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 157 165" width="157" height="165">
+  <title>vLLM-Omni</title>
+  <polygon points="0,36 62,36 62,160" fill="#F1B844"/>
+  <polygon points="96,36 157,0 121,165 68,165" fill="#54A0F8"/>
+</svg>

benchmarks/README.md

@@ -4,6 +4,11 @@ This directory contains benchmark suites for evaluating different model families
 
 ## Benchmark families
 
+### [Qwen2.5-Omni](qwen2_5_omni/README.md) — Embedding Wrapper Latency
+
+CPU-observed A/B timings for redundant text embedding removal, with real checkpoint
+embeddings, warmup, paired blocks, and embedding call-count validation.
+
 ### [TTS](tts/README.md) — Text-to-Speech
 
 Model-agnostic serving benchmarks for TTS models, including Qwen3-TTS and VoxCPM2.
@@ -36,6 +41,17 @@ LingBot-Video MoE transformer.
 - **Dense pipeline**: decoded-video MAE, MSE, PSNR, latency, and optional steady-state timings
 - **MoE transformer**: bitwise router, sparse-block, shared-expert, and full-transformer parity
 
+### [LingBot-World](lingbot_world/README.md) — Realtime Streaming Cadence
+
+Client-side benchmark for a `WS /v1/realtime/video` world-model session, where one
+request is a whole rollout and one AR block streams out as one video chunk. It
+measures a cadence rather than a throughput: there is exactly one session, and what
+matters is whether the next chunk arrives before the viewer finishes the last one.
+
+- **Layout**: `lingbot_world/benchmark_lingbot_world_realtime.py` (WS client), `lingbot_world/workload.py` (rollout and metric math), `lingbot_world/configs/` (single-GPU eager and Ulysses-4 compiled deploy configs)
+- **Workload**: built-in image-conditioned rollout with a generated camera script, or a JSON spec with per-chunk actions and mid-rollout prompt updates
+- **Key metrics**: TTFC (time to first chunk), steady-state chunk inter-arrival percentiles, VIDEO_RTF (wall seconds per video second; lower is better), chunk-deadline attainment, and simulated playback underruns
+
 ### [Distributed](distributed/omni_connectors/README.md) — RDMA Connector Testing
 
 RDMA environment setup and transfer tests for `MooncakeTransferEngineConnector`, including pytest-based single-node checks and manual cross-node benchmarks.

benchmarks/diffusion/README.md

@@ -17,18 +17,18 @@ The main entrypoint is:
 vllm serve Qwen/Qwen-Image --omni --port 8099
 ```
 
-2. Run a minimal benchmark:
+1. Run a minimal benchmark:
 
 ```bash
 python3 benchmarks/diffusion/diffusion_benchmark_serving.py \
-	--base-url http://localhost:8099 \
-	--model Qwen/Qwen-Image \
-	--task t2i \
-	--dataset vbench \
-	--num-prompts 5
+ --base-url http://localhost:8099 \
+ --model Qwen/Qwen-Image \
+ --task t2i \
+ --dataset vbench \
+ --num-prompts 5
 ```
 
-**Notes**
+### Notes
 
 - By default, image tasks talk to `http://<host>:<port>/v1/chat/completions`; video tasks talk to `/v1/videos`.
 - If you run the server on another host or port, pass `--base-url` accordingly.
@@ -52,14 +52,14 @@ Example (`t2v`):
 
 ```bash
 python3 benchmarks/diffusion/diffusion_benchmark_serving.py \
-	--base-url http://localhost:8099 \
-	--model Wan-AI/Wan2.2-T2V-A14B-Diffusers \
-	--task t2v \
-	--dataset vbench \
-	--num-prompts 50 \
-	--width 640 --height 480 \
-	--num-frames 81 --fps 16 \
-	--num-inference-steps 40
+ --base-url http://localhost:8099 \
+ --model Wan-AI/Wan2.2-T2V-A14B-Diffusers \
+ --task t2v \
+ --dataset vbench \
+ --num-prompts 50 \
+ --width 640 --height 480 \
+ --num-frames 81 --fps 16 \
+ --num-inference-steps 40
 ```
 
 Note: `vbench` can also be used for other tasks such as `t2i` / `i2v` (and `i2i`). For `t2i`, the loader reuses VBench t2v text prompts; for `i2v` / `i2i`, it loads the VBench i2v dataset (with image paths).
@@ -140,6 +140,21 @@ Traffic / concurrency flags:
 - `--request-rate`: Target request rate (requests/second). If set to `inf`, the script sends all requests immediately.
 - `--max-concurrency`: Max number of in-flight requests (default: `1`). This can hard-cap the achieved QPS: if it is too small, requests will queue behind the semaphore, and both achieved throughput and observed SLO attainment can be skewed.
 
+### Video job timeout and failures
+
+`--video-job-timeout` controls how long each `/v1/videos` job may be polled,
+including time queued on the server (default: `900` seconds). For long video
+jobs at high concurrency, use a larger budget, for example
+`--video-job-timeout 1800`. This also applies to warmup jobs. The polling budget
+starts after job creation; it excludes time waiting for the client concurrency
+semaphore. Individual HTTP calls still use the aiohttp session timeout.
+
+An expired job is counted as failed and deleted, which cancels queued or running
+server generation. The benchmark continues processing the remaining requests.
+The progress bar counts both successful and failed requests. The final report
+shows the failure count and the first 10 errors; `--output-file` saves all errors
+with their request IDs in `request_errors`.
+
 ### Batched warmup note
 
 For batched serving runs, warm the same in-flight shape you plan to measure.

benchmarks/diffusion/backends.py

@@ -1,3 +1,6 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
 import asyncio
 import base64
 import json
@@ -43,6 +46,7 @@ class RequestFuncInput:
     video_paths: list[str] | None = None
     request_id: str = field(default_factory=lambda: str(uuid.uuid4()))
     default_bot_task: str | None = DEFAULT_EDITS_BOT_TASK
+    video_job_timeout: float = 900.0
 
 
 @dataclass
@@ -331,64 +335,64 @@ async def async_request_v1_videos(
     output = RequestFuncOutput()
     output.start_time = time.perf_counter()
 
-    files = dict(input.extra_body)
-    if input.prompt:
-        files.setdefault("prompt", input.prompt)
-    if input.width and input.height:
-        files.setdefault("height", input.height)
-        files.setdefault("width", input.width)
-    if input.num_frames:
-        files.setdefault("num_frames", input.num_frames)
-    if input.num_inference_steps:
-        files.setdefault("num_inference_steps", input.num_inference_steps)
-    if input.seed is not None:
-        files.setdefault("seed", input.seed)
-    if input.fps:
-        files.setdefault("fps", input.fps)
-
-    form = aiohttp.FormData()
-    for k, v in files.items():
-        form.add_field(k, str(v))
-
     image_file = None
-    if input.image_paths and input.video_paths:
-        output.error = "Only one of image_paths or video_paths can be provided"
-        output.success = False
-        return output
+    job_id = None
+    try:
+        files = dict(input.extra_body)
+        if input.prompt:
+            files.setdefault("prompt", input.prompt)
+        if input.width and input.height:
+            files.setdefault("height", input.height)
+            files.setdefault("width", input.width)
+        if input.num_frames:
+            files.setdefault("num_frames", input.num_frames)
+        if input.num_inference_steps:
+            files.setdefault("num_inference_steps", input.num_inference_steps)
+        if input.seed is not None:
+            files.setdefault("seed", input.seed)
+        if input.fps:
+            files.setdefault("fps", input.fps)
 
-    if input.image_paths and len(input.image_paths) > 0:
-        image_path = input.image_paths[0]
-        image_file = open(image_path, "rb")
-        form.add_field(
-            "input_reference",
-            image_file,
-            filename=os.path.basename(image_path),
-            content_type=_guess_mime_type(image_path),
-        )
-    elif input.video_paths and len(input.video_paths) > 0:
-        video_path = input.video_paths[0]
-        if _is_minimax_h3_mixed_reference(input.model):
-            with open(video_path, "rb") as reference_file:
-                video_b64 = base64.b64encode(reference_file.read()).decode("ascii")
-            form.add_field(
-                "video_reference",
-                json.dumps([{"video_url": f"data:video/mp4;base64,{video_b64}"}]),
-            )
-        else:
-            image_file = open(video_path, "rb")
+        form = aiohttp.FormData()
+        for k, v in files.items():
+            form.add_field(k, str(v))
+
+        if input.image_paths and input.video_paths:
+            output.error = "Only one of image_paths or video_paths can be provided"
+            output.success = False
+            return output
+
+        if input.image_paths and len(input.image_paths) > 0:
+            image_path = input.image_paths[0]
+            image_file = open(image_path, "rb")
             form.add_field(
                 "input_reference",
                 image_file,
-                filename=os.path.basename(video_path),
-                content_type=_guess_mime_type(video_path),
+                filename=os.path.basename(image_path),
+                content_type=_guess_mime_type(image_path),
             )
+        elif input.video_paths and len(input.video_paths) > 0:
+            video_path = input.video_paths[0]
+            if _is_minimax_h3_mixed_reference(input.model):
+                with open(video_path, "rb") as reference_file:
+                    video_b64 = base64.b64encode(reference_file.read()).decode("ascii")
+                form.add_field(
+                    "video_reference",
+                    json.dumps([{"video_url": f"data:video/mp4;base64,{video_b64}"}]),
+                )
+            else:
+                image_file = open(video_path, "rb")
+                form.add_field(
+                    "input_reference",
+                    image_file,
+                    filename=os.path.basename(video_path),
+                    content_type=_guess_mime_type(video_path),
+                )
 
-    job_id = None
-    job_status = None
-    poll_json = {}
-    resp_json = {}
+        job_status = None
+        poll_json = {}
+        resp_json = {}
 
-    try:
         # invoke a post request (POST /v1/videos)
         async with session.post(input.api_url, data=form) as response:
             if response.status == 200:
@@ -406,7 +410,7 @@ async def async_request_v1_videos(
 
         # invoke a poll request (GET /v1/videos/{video_id})
         poll_interval = 2.0  # Unit(s)
-        timeout_seconds = 600.0
+        timeout_seconds = input.video_job_timeout
         deadline = time.perf_counter() + timeout_seconds
         job_url = f"{input.api_url}/{job_id}"
 
@@ -422,8 +426,11 @@ async def async_request_v1_videos(
                 poll_json = await poll_response.json()
                 job_status = poll_json.get("status")
 
-                if time.perf_counter() >= deadline:
-                    output.error = f"Timed out waiting for video job {job_id} to complete."
+                if job_status not in {"completed", "failed"} and time.perf_counter() >= deadline:
+                    output.error = (
+                        f"Timed out after {timeout_seconds:g}s waiting for video job {job_id} to complete. "
+                        "Increase --video-job-timeout for long-running or queued video jobs."
+                    )
                     output.success = False
                     return output
 
@@ -452,7 +459,7 @@ async def async_request_v1_videos(
             elif "peak_memory_mb" in resp_json:
                 output.peak_memory_mb = resp_json["peak_memory_mb"]
     except Exception as e:
-        output.error = str(e)
+        output.error = f"{type(e).__name__}: {e}"
         output.success = False
     finally:
         if image_file is not None:
@@ -465,13 +472,13 @@ async def async_request_v1_videos(
             except Exception as e:
                 print(f"Failed to clean up video job {job_id}: {e}")
 
-    output.latency = time.perf_counter() - output.start_time
+        output.latency = time.perf_counter() - output.start_time
+        if pbar is not None:
+            pbar.update(1)
 
     if output.success and input.slo_ms is not None:
         output.slo_achieved = (output.latency * 1000.0) <= float(input.slo_ms)
 
-    if pbar:
-        pbar.update(1)
     return output
 
 

benchmarks/diffusion/benchmark_qwen_image_qk_norm_rope.py

@@ -0,0 +1,222 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
+"""Benchmark Qwen-Image Q/K RMSNorm + interleaved RoPE.
+
+Example:
+
+    CUDA_VISIBLE_DEVICES=0 python benchmarks/diffusion/benchmark_qwen_image_qk_norm_rope.py \
+      --seq-len 4096 --warmup 20 --iters 100 --include-compiled --profile
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+from collections.abc import Callable
+
+import torch
+import torch.nn.functional as F
+
+from vllm_omni.diffusion.layers.fused_qk_norm_rope import (
+    _fused_cuda_supported,
+    fused_qk_norm_rope,
+)
+from vllm_omni.diffusion.layers.rope import RotaryEmbedding
+
+
+def _parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--batch", type=int, default=1)
+    parser.add_argument("--seq-len", type=int, default=4096)
+    parser.add_argument("--heads", type=int, default=24)
+    parser.add_argument("--kv-heads", type=int, default=24)
+    parser.add_argument("--head-dim", type=int, default=128)
+    parser.add_argument("--warmup", type=int, default=20)
+    parser.add_argument("--iters", type=int, default=100)
+    parser.add_argument("--dtype", choices=("bf16", "fp16", "fp32"), default="bf16")
+    parser.add_argument("--packed-qkv-view", action="store_true")
+    parser.add_argument("--include-compiled", action="store_true")
+    parser.add_argument("--profile", action="store_true")
+    return parser.parse_args()
+
+
+def _dtype(name: str) -> torch.dtype:
+    return {
+        "bf16": torch.bfloat16,
+        "fp16": torch.float16,
+        "fp32": torch.float32,
+    }[name]
+
+
+def _make_inputs(args: argparse.Namespace, dtype: torch.dtype, device: torch.device):
+    torch.manual_seed(2026)
+    if args.packed_qkv_view:
+        qkv_dim = (args.heads + args.kv_heads + args.kv_heads) * args.head_dim
+        qkv = torch.randn(args.batch, args.seq_len, qkv_dim, device=device, dtype=dtype)
+        q, k, _ = qkv.split(
+            [
+                args.heads * args.head_dim,
+                args.kv_heads * args.head_dim,
+                args.kv_heads * args.head_dim,
+            ],
+            dim=-1,
+        )
+        q = q.unflatten(-1, (args.heads, args.head_dim))
+        k = k.unflatten(-1, (args.kv_heads, args.head_dim))
+    else:
+        q = torch.randn(args.batch, args.seq_len, args.heads, args.head_dim, device=device, dtype=dtype)
+        k = torch.randn(args.batch, args.seq_len, args.kv_heads, args.head_dim, device=device, dtype=dtype)
+
+    q_weight = torch.randn(args.head_dim, device=device, dtype=dtype)
+    k_weight = torch.randn(args.head_dim, device=device, dtype=dtype)
+    angles = torch.randn(args.seq_len, args.head_dim // 2, device=device, dtype=torch.float32)
+    freqs = torch.polar(torch.ones_like(angles), angles)
+    return q, k, q_weight, k_weight, freqs
+
+
+def _native(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    q_weight: torch.Tensor,
+    k_weight: torch.Tensor,
+    freqs: torch.Tensor,
+    eps: float,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    q = F.rms_norm(q, (q.shape[-1],), q_weight, eps)
+    k = F.rms_norm(k, (k.shape[-1],), k_weight, eps)
+    rope = RotaryEmbedding(is_neox_style=False)
+    cos = freqs.real.to(q.dtype)
+    sin = freqs.imag.to(q.dtype)
+    return rope(q, cos, sin), rope(k, cos, sin)
+
+
+def _fused(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    q_weight: torch.Tensor,
+    k_weight: torch.Tensor,
+    freqs: torch.Tensor,
+    eps: float,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    batch, seq_len, num_heads, head_dim = q.shape
+    num_kv_heads = k.shape[2]
+    rope_table = torch.cat((freqs.real, freqs.imag), dim=-1)
+    rope_table = rope_table.unsqueeze(0).expand(batch, -1, -1).reshape(batch * seq_len, head_dim)
+    out_q, out_k = fused_qk_norm_rope(
+        q.reshape(batch * seq_len, num_heads, head_dim),
+        k.reshape(batch * seq_len, num_kv_heads, head_dim),
+        q_weight,
+        k_weight,
+        rope_table,
+        eps,
+        interleaved=True,
+    )
+    return (
+        out_q.reshape(batch, seq_len, num_heads, head_dim),
+        out_k.reshape(batch, seq_len, num_kv_heads, head_dim),
+    )
+
+
+def _measure(fn: Callable[[], tuple[torch.Tensor, torch.Tensor]], warmup: int, iters: int) -> list[float]:
+    for _ in range(warmup):
+        fn()
+    torch.accelerator.synchronize()
+    start = torch.cuda.Event(enable_timing=True)
+    end = torch.cuda.Event(enable_timing=True)
+    samples = []
+    for _ in range(iters):
+        start.record()
+        fn()
+        end.record()
+        end.synchronize()
+        samples.append(start.elapsed_time(end))
+    return samples
+
+
+def _stats(samples: list[float]) -> dict[str, float]:
+    samples = sorted(samples)
+    return {
+        "median_ms": samples[len(samples) // 2],
+        "p90_ms": samples[int(len(samples) * 0.9)],
+        "mean_ms": sum(samples) / len(samples),
+    }
+
+
+def _profile(fn: Callable[[], tuple[torch.Tensor, torch.Tensor]]) -> str:
+    from torch.profiler import ProfilerActivity, profile
+
+    with profile(activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA]) as prof:
+        fn()
+        torch.accelerator.synchronize()
+    return prof.key_averages().table(sort_by="self_cuda_time_total", row_limit=15)
+
+
+def main() -> None:
+    args = _parse_args()
+    if not torch.cuda.is_available():
+        raise RuntimeError("This benchmark requires CUDA")
+    device = torch.device("cuda")
+    dtype = _dtype(args.dtype)
+    eps = 1e-6
+    q, k, q_weight, k_weight, freqs = _make_inputs(args, dtype, device)
+
+    def native():
+        return _native(q, k, q_weight, k_weight, freqs, eps)
+
+    def fused():
+        return _fused(q, k, q_weight, k_weight, freqs, eps)
+
+    results: dict[str, object] = {
+        "device": torch.cuda.get_device_name(),
+        "batch": args.batch,
+        "seq_len": args.seq_len,
+        "heads": args.heads,
+        "kv_heads": args.kv_heads,
+        "head_dim": args.head_dim,
+        "dtype": args.dtype,
+        "packed_qkv_view": args.packed_qkv_view,
+        "fused_fast_path_supported": _fused_cuda_supported(
+            q.reshape(args.batch * args.seq_len, args.heads, args.head_dim),
+            k.reshape(args.batch * args.seq_len, args.kv_heads, args.head_dim),
+            args.head_dim,
+            args.head_dim,
+            interleaved=True,
+        ),
+    }
+
+    native_samples = _measure(native, args.warmup, args.iters)
+    fused_samples = _measure(fused, args.warmup, args.iters)
+    expected_q, expected_k = native()
+    actual_q, actual_k = fused()
+    torch.accelerator.synchronize()
+
+    native_stats = _stats(native_samples)
+    fused_stats = _stats(fused_samples)
+    results["native"] = native_stats
+    results["fused"] = fused_stats
+    results["speedup_vs_native"] = native_stats["median_ms"] / fused_stats["median_ms"]
+    results["max_abs_q"] = (actual_q.float() - expected_q.float()).abs().max().item()
+    results["max_abs_k"] = (actual_k.float() - expected_k.float()).abs().max().item()
+    results["mean_abs_q"] = (actual_q.float() - expected_q.float()).abs().mean().item()
+    results["mean_abs_k"] = (actual_k.float() - expected_k.float()).abs().mean().item()
+
+    if args.include_compiled:
+        compiled_native = torch.compile(native, dynamic=True, fullgraph=True)
+        compiled_fused = torch.compile(fused, dynamic=True, fullgraph=True)
+        compiled_native_stats = _stats(_measure(compiled_native, args.warmup, args.iters))
+        compiled_fused_stats = _stats(_measure(compiled_fused, args.warmup, args.iters))
+        results["compiled_native"] = compiled_native_stats
+        results["compiled_fused"] = compiled_fused_stats
+        results["compiled_fused_speedup_vs_compiled_native"] = (
+            compiled_native_stats["median_ms"] / compiled_fused_stats["median_ms"]
+        )
+
+    print(json.dumps(results, indent=2))
+    if args.profile:
+        print("\n# fused profile")
+        print(_profile(fused))
+
+
+if __name__ == "__main__":
+    main()

benchmarks/diffusion/diffusion_benchmark_serving.py

@@ -1,6 +1,6 @@
 # adapted from fastvideo
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 
 """
 Benchmark online serving for diffusion models (Image/Video Generation).
@@ -87,6 +87,7 @@ import base64
 import glob
 import json
 import logging
+import math
 import os
 import random
 import tempfile
@@ -1086,6 +1087,12 @@ async def _run_warmups(
 
     warmup_tasks = [asyncio.create_task(limited_warmup_request_func(req)) for req in warmup_requests]
     warmup_outputs = await asyncio.gather(*warmup_tasks)
+    failed_count = sum(not output.success for output in warmup_outputs)
+    if failed_count:
+        raise RuntimeError(
+            f"{failed_count}/{len(warmup_outputs)} warmup requests failed; "
+            "check server logs before running the benchmark."
+        )
     return list(zip(warmup_requests, warmup_outputs))
 
 
@@ -1116,6 +1123,11 @@ def calculate_metrics(
         "duration": total_duration,
         "completed_requests": num_success,
         "failed_requests": len(error_outputs),
+        "request_errors": [
+            {"request_id": req.request_id, "error": out.error}
+            for req, out in zip(requests_list, outputs)
+            if not out.success
+        ],
         "throughput_qps": num_success / total_duration if total_duration > 0 else 0,
         "latency_mean": np.mean(latencies) if latencies else 0,
         "latency_median": np.median(latencies) if latencies else 0,
@@ -1261,6 +1273,9 @@ def _default_endpoint_for_task(task: str) -> str:
 
 
 async def benchmark(args):
+    if not math.isfinite(args.video_job_timeout) or args.video_job_timeout <= 0:
+        raise ValueError("--video-job-timeout must be a positive finite number of seconds.")
+
     # Construct base_url if not provided
     if args.base_url is None:
         args.base_url = f"http://{args.host}:{args.port}"
@@ -1312,6 +1327,8 @@ async def benchmark(args):
     print("Loading requests...")
     requests_list = dataset.get_requests()
     print(f"Prepared {len(requests_list)} requests from {args.dataset} dataset.")
+    for req in requests_list:
+        req.video_job_timeout = args.video_job_timeout
 
     if args.return_stage_metrics and args.endpoint in _STAGE_METRICS_ENDPOINTS:
         for req in requests_list:
@@ -1376,6 +1393,8 @@ async def benchmark(args):
     metrics["model"] = args.model
     metrics["dataset"] = args.dataset
     metrics["task"] = args.task
+    if args.endpoint == "/v1/videos":
+        metrics["video_job_timeout"] = args.video_job_timeout
     if args.endpoint == "/v1/images/edits":
         metrics["bot_task"] = args.bot_task
 
@@ -1398,6 +1417,11 @@ async def benchmark(args):
         )
     )
     print("{:<40} {}/{:<15}".format("Successful requests:", metrics["completed_requests"], len(requests_list)))
+    print("{:<40} {:<15}".format("Failed requests:", metrics["failed_requests"]))
+    for failure in metrics["request_errors"][:10]:
+        print(f"Request {failure['request_id']} failed: {failure['error']}")
+    if metrics["failed_requests"] > 10:
+        print("Showing first 10 failures; use --output-file to save all request errors.")
 
     # Section 3: Performance Metrics
     print(f"{'-' * 50}")
@@ -1437,7 +1461,7 @@ async def benchmark(args):
         print(f"Metrics saved to {args.output_file}")
 
 
-if __name__ == "__main__":
+def build_parser():
     parser = argparse.ArgumentParser(description="Benchmark serving for diffusion models.")
     parser.add_argument(
         "--base-url",
@@ -1481,6 +1505,13 @@ if __name__ == "__main__":
         help="Path to local dataset file (optional).",
     )
     parser.add_argument("--num-prompts", type=int, default=10, help="Number of prompts to benchmark.")
+    parser.add_argument(
+        "--video-job-timeout",
+        type=float,
+        default=900.0,
+        help="Maximum polling time in seconds per video job, including server queue time (default: 900). "
+        "Increase this for long-running video generation or high concurrency.",
+    )
     parser.add_argument(
         "--max-concurrency",
         type=int,
@@ -1612,6 +1643,10 @@ if __name__ == "__main__":
         action="store_true",
         help="Request stage duration metrics from endpoints that support return_stage_metrics.",
     )
+    return parser
 
+
+if __name__ == "__main__":
+    parser = build_parser()
     args = parser.parse_args()
     asyncio.run(benchmark(args))

benchmarks/host_weight_runtime/README.md

@@ -0,0 +1,57 @@
+# Host weight dependency memory diagnostic
+
+`safetensors_retention.py` isolates repeated CPU `get_tensor()` calls from
+access to one cached tensor view. It imports neither vLLM nor HWR and uses a
+synthetic float32 payload controlled by `--tensor-elements` (default: 64).
+Tensor size can affect retention: a negative result for 16 elements does not
+rule out growth for 64 elements. The selected size is recorded in the JSON
+arguments and used for both file creation and the correctness check.
+HWR itself calls `get_tensor()` when
+acquiring a lease, then exposes cached views; this probe does not measure HWR
+requests or repeated lease acquisition.
+
+Run one mode per fresh process in the same Python environment and CPU affinity:
+
+```bash
+git rev-parse HEAD > revision.txt
+git status --short > working-tree.txt
+git diff > working-tree.patch
+# Select an allowed CPU from your process affinity; 0 is only an example.
+CUDA_VISIBLE_DEVICES='' taskset -c 0 timeout 120s python \
+  benchmarks/host_weight_runtime/safetensors_retention.py \
+  --mode get_tensor --iterations 10000 --sample-every 5000 > feasibility.json
+```
+
+For a size comparison, run 16 and 64 elements twice each in fresh processes,
+with cached reuse as a control at both sizes:
+
+```bash
+for repetition in 1 2; do
+  for elements in 16 64; do
+    for mode in get_tensor reuse; do
+      CUDA_VISIBLE_DEVICES='' taskset -c 0 timeout 120s python \
+        benchmarks/host_weight_runtime/safetensors_retention.py \
+        --mode "$mode" --tensor-elements "$elements" \
+        > "$mode-$elements-$repetition.json"
+    done
+  done
+done
+```
+
+The diagnostic sets Torch to one CPU thread, warms up before the baseline,
+and reports preparation separately from timed loop work. Checkpoints run GC
+and report Linux private/anonymous memory, RSS, and open descriptors. It checks
+that transient tensor objects and the final cached view are released. Linux
+`/proc/self/smaps_rollup` must be readable; invalid arguments and missing proc
+support fail explicitly. Keep raw JSON and the repository snapshot with results.
+
+Compare the final loop sample with the warmed baseline, then inspect the closed
+sample. Private-memory growth after Python tensor objects disappear is evidence
+of retention in the dependency/allocator path, not proof of a leak's root cause.
+RSS can include file-backed pages; it is not a private-memory metric. Samples
+also include small diagnostic bookkeeping allocations. Do not assert a portable
+memory threshold or turn this synthetic loop into a per-request service estimate.
+
+Do not drop shared caches, trim allocators, or add serving-time GC to make the
+numbers look smaller. A dependency replacement or version change requires its
+own reproducible comparison and correctness/lifecycle validation.

benchmarks/host_weight_runtime/safetensors_retention.py

@@ -0,0 +1,120 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+"""Linux CPU diagnostic: repeated safetensors materialization versus view reuse."""
+
+from __future__ import annotations
+
+import argparse
+import gc
+import json
+import os
+import platform
+import tempfile
+import time
+import weakref
+from pathlib import Path
+
+
+def positive_int(value: str) -> int:
+    parsed = int(value)
+    if parsed <= 0:
+        raise argparse.ArgumentTypeError("must be positive")
+    return parsed
+
+
+def sample(phase: str, iterations: int, loop_seconds: float) -> dict[str, object]:
+    gc.collect()
+    counters = {}
+    for line in Path("/proc/self/smaps_rollup").read_text().splitlines():
+        fields = line.split()
+        if len(fields) == 3 and fields[2] == "kB":
+            counters[fields[0].removesuffix(":")] = int(fields[1]) * 1024
+    return {
+        "phase": phase,
+        "iterations": iterations,
+        "loop_seconds": loop_seconds,
+        "rss_bytes": counters["Rss"],
+        "private_bytes": counters["Private_Clean"] + counters["Private_Dirty"],
+        "anonymous_bytes": counters["Anonymous"],
+        "fd_count": len(tuple(Path("/proc/self/fd").iterdir())),
+    }
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--mode", choices=("get_tensor", "reuse"), required=True)
+    parser.add_argument(
+        "--tensor-elements", type=positive_int, default=64, help="Number of float32 elements in the synthetic tensor"
+    )
+    parser.add_argument("--iterations", type=positive_int, default=1_000_000)
+    parser.add_argument("--sample-every", type=positive_int, default=100_000)
+    parser.add_argument("--warmup", type=positive_int, default=1000)
+    args = parser.parse_args()
+    if not Path("/proc/self/smaps_rollup").is_file():
+        parser.error("requires Linux /proc/self/smaps_rollup")
+
+    preparation_start = time.monotonic()
+    import safetensors
+    import torch
+    from safetensors import safe_open
+    from safetensors.torch import save_file
+
+    torch.set_num_threads(1)
+    torch.set_num_interop_threads(1)
+    samples = [sample("before_file", 0, 0.0)]
+    with tempfile.TemporaryDirectory(prefix="safetensors-retention-") as directory:
+        path = Path(directory) / "synthetic.safetensors"
+        save_file({"weight": torch.arange(args.tensor_elements, dtype=torch.float32)}, str(path))
+        with safe_open(path, framework="pt", device="cpu") as reader:
+            cached = reader.get_tensor("weight")
+            assert torch.equal(cached, torch.arange(args.tensor_elements, dtype=torch.float32))
+            for _ in range(args.warmup):
+                tensor = reader.get_tensor("weight") if args.mode == "get_tensor" else cached
+                del tensor
+            samples.append(sample("baseline", 0, 0.0))
+            preparation_seconds = time.monotonic() - preparation_start
+            completed = 0
+            loop_seconds = 0.0
+            while completed < args.iterations:
+                count = min(args.sample_every, args.iterations - completed)
+                start = time.monotonic()
+                for _ in range(count):
+                    tensor = reader.get_tensor("weight") if args.mode == "get_tensor" else cached
+                last_tensor = weakref.ref(tensor)
+                del tensor
+                loop_seconds += time.monotonic() - start
+                completed += count
+                snapshot = sample("loop", completed, loop_seconds)
+                snapshot["last_tensor_alive"] = last_tensor() is not None
+                if args.mode == "get_tensor":
+                    assert last_tensor() is None, "diagnostic retained a transient tensor"
+                samples.append(snapshot)
+            del cached
+        del reader
+        samples.append(sample("closed", completed, loop_seconds))
+        assert last_tensor() is None, "last tensor survived reader/view teardown"
+
+    print(
+        json.dumps(
+            {
+                "schema_version": 1,
+                "arguments": vars(args),
+                "python": platform.python_version(),
+                "torch": torch.__version__,
+                "safetensors": safetensors.__version__,
+                "kernel": platform.release(),
+                "hostname": platform.node(),
+                "pid": os.getpid(),
+                "cpu_affinity": sorted(os.sched_getaffinity(0)),
+                "torch_threads": torch.get_num_threads(),
+                "pythonmalloc": os.environ.get("PYTHONMALLOC", "default"),
+                "preparation_seconds": preparation_seconds,
+                "samples": samples,
+            },
+            indent=2,
+        )
+    )
+
+
+if __name__ == "__main__":
+    main()

benchmarks/kernels/bench_qwen_select01_modulation.py

@@ -0,0 +1,333 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+"""Benchmark the Qwen-Image-Edit select01 modulation hot path.
+
+This script intentionally starts with the current PyTorch implementation. It is
+used as the before/after baseline for SGLang PR #20395/#21318 style fusion:
+
+* layernorm + scale/shift + binary select01 gate
+* residual + gated update + layernorm + scale/shift + binary select01 gate
+
+Run on a CUDA machine for actionable numbers, for example:
+
+    python benchmarks/kernels/bench_qwen_select01_modulation.py \
+        --device cuda --dtype bf16 --seq-lens 4096 8192 16384 \
+        --output-json /tmp/qwen_select01_baseline.json
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import statistics
+import sys
+import time
+from collections.abc import Callable
+from pathlib import Path
+
+import torch
+import torch.nn.functional as F
+
+for candidate in Path(__file__).resolve().parents:
+    if (candidate / "vllm_omni").is_dir():
+        if str(candidate) not in sys.path:
+            sys.path.insert(0, str(candidate))
+        break
+
+from vllm_omni.diffusion.layers.qwen_select01_modulation import (  # noqa: E402
+    fused_layernorm_select01,
+    fused_residual_layernorm_select01,
+)
+
+
+def _dtype(name: str) -> torch.dtype:
+    return {
+        "fp32": torch.float32,
+        "float32": torch.float32,
+        "fp16": torch.float16,
+        "float16": torch.float16,
+        "bf16": torch.bfloat16,
+        "bfloat16": torch.bfloat16,
+    }[name]
+
+
+def _select01_modulation(
+    mod_params: torch.Tensor,
+    index: torch.Tensor | None,
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    shift, scale, gate = mod_params.chunk(3, dim=-1)
+
+    if index is None:
+        return scale.unsqueeze(1), shift.unsqueeze(1), gate.unsqueeze(1)
+
+    actual_batch = shift.size(0) // 2
+    shift_0, shift_1 = shift[:actual_batch], shift[actual_batch:]
+    scale_0, scale_1 = scale[:actual_batch], scale[actual_batch:]
+    gate_0, gate_1 = gate[:actual_batch], gate[actual_batch:]
+
+    idx = index.unsqueeze(-1) == 0
+    shift = torch.where(idx, shift_0.unsqueeze(1), shift_1.unsqueeze(1))
+    scale = torch.where(idx, scale_0.unsqueeze(1), scale_1.unsqueeze(1))
+    gate = torch.where(idx, gate_0.unsqueeze(1), gate_1.unsqueeze(1))
+    return scale, shift, gate
+
+
+def _native_layernorm_select01(
+    x: torch.Tensor,
+    mod_params: torch.Tensor,
+    index: torch.Tensor,
+    eps: float,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    scale, shift, gate = _select01_modulation(mod_params, index)
+    out = F.layer_norm(x.float(), (x.shape[-1],), eps=eps).to(x.dtype)
+    return out * (1 + scale) + shift, gate
+
+
+def _native_residual_layernorm_select01(
+    x: torch.Tensor,
+    residual: torch.Tensor,
+    residual_gate: torch.Tensor,
+    mod_params: torch.Tensor,
+    index: torch.Tensor,
+    eps: float,
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    scale, shift, gate = _select01_modulation(mod_params, index)
+    residual_out = residual + residual_gate * x
+    out = F.layer_norm(residual_out.float(), (residual_out.shape[-1],), eps=eps).to(residual_out.dtype)
+    return out * (1 + scale) + shift, residual_out, gate
+
+
+def _time_cuda(fn: Callable[[], object], warmup: int, iters: int) -> list[float]:
+    for _ in range(warmup):
+        fn()
+    torch.accelerator.synchronize()
+
+    times: list[float] = []
+    for _ in range(iters):
+        start = torch.cuda.Event(enable_timing=True)
+        end = torch.cuda.Event(enable_timing=True)
+        start.record()
+        fn()
+        end.record()
+        torch.accelerator.synchronize()
+        times.append(float(start.elapsed_time(end)))
+    return times
+
+
+def _reset_peak_memory_stats(device: torch.device) -> None:
+    reset_peak_memory_stats = getattr(torch.accelerator, "reset_peak_memory_stats", None)
+    if reset_peak_memory_stats is None:
+        reset_peak_memory_stats = getattr(getattr(torch, device.type), "reset_peak_memory_stats")
+    reset_peak_memory_stats(device)
+
+
+def _max_memory_allocated(device: torch.device) -> int:
+    max_memory_allocated = getattr(torch.accelerator, "max_memory_allocated", None)
+    if max_memory_allocated is None:
+        max_memory_allocated = getattr(getattr(torch, device.type), "max_memory_allocated")
+    return int(max_memory_allocated(device))
+
+
+def _time_cpu(fn: Callable[[], object], warmup: int, iters: int) -> list[float]:
+    for _ in range(warmup):
+        fn()
+
+    times: list[float] = []
+    for _ in range(iters):
+        t0 = time.perf_counter()
+        fn()
+        times.append((time.perf_counter() - t0) * 1000.0)
+    return times
+
+
+def _summarize(times: list[float]) -> dict[str, float]:
+    ordered = sorted(times)
+    return {
+        "median_ms": statistics.median(ordered),
+        "min_ms": ordered[0],
+        "p20_ms": ordered[max(0, int(len(ordered) * 0.20) - 1)],
+        "p80_ms": ordered[min(len(ordered) - 1, int(len(ordered) * 0.80))],
+    }
+
+
+def _make_inputs(
+    batch: int,
+    seq_len: int,
+    hidden_size: int,
+    dtype: torch.dtype,
+    device: torch.device,
+) -> dict[str, torch.Tensor]:
+    torch.manual_seed(0)
+    return {
+        "x": torch.randn(batch, seq_len, hidden_size, device=device, dtype=dtype),
+        "residual": torch.randn(batch, seq_len, hidden_size, device=device, dtype=dtype),
+        "residual_gate": torch.randn(batch, 1, hidden_size, device=device, dtype=dtype),
+        "mod_params": torch.randn(batch * 2, hidden_size * 3, device=device, dtype=dtype),
+        "index": torch.randint(0, 2, (batch, seq_len), device=device, dtype=torch.int64),
+    }
+
+
+def _bench_one(
+    name: str,
+    fn: Callable[[], object],
+    device: torch.device,
+    warmup: int,
+    iters: int,
+) -> dict[str, object]:
+    if device.type == "cuda":
+        _reset_peak_memory_stats(device)
+        times = _time_cuda(fn, warmup, iters)
+        peak_memory_mb = _max_memory_allocated(device) / 1024**2
+    else:
+        times = _time_cpu(fn, warmup, iters)
+        peak_memory_mb = None
+
+    return {
+        "name": name,
+        **_summarize(times),
+        "peak_memory_mb": peak_memory_mb,
+    }
+
+
+def _run(args: argparse.Namespace) -> dict[str, object]:
+    device = torch.device(args.device)
+    if device.type == "cuda" and not torch.cuda.is_available():
+        raise SystemExit("CUDA is not available on this machine.")
+
+    dtype = _dtype(args.dtype)
+    rows: list[dict[str, object]] = []
+
+    for seq_len in args.seq_lens:
+        tensors = _make_inputs(args.batch_size, seq_len, args.hidden_size, dtype, device)
+        impls = ["native"] if args.impl == "native" else ["fused"] if args.impl == "fused" else ["native", "fused"]
+
+        def first_norm() -> object:
+            return _native_layernorm_select01(
+                tensors["x"],
+                tensors["mod_params"],
+                tensors["index"],
+                args.eps,
+            )
+
+        def residual_norm() -> object:
+            return _native_residual_layernorm_select01(
+                tensors["x"],
+                tensors["residual"],
+                tensors["residual_gate"],
+                tensors["mod_params"],
+                tensors["index"],
+                args.eps,
+            )
+
+        callables: list[tuple[str, Callable[[], object]]] = []
+        if "native" in impls:
+            callables.extend(
+                [
+                    ("native_layernorm_select01", first_norm),
+                    ("native_residual_layernorm_select01", residual_norm),
+                ]
+            )
+        if "fused" in impls:
+
+            def fused_first_norm() -> object:
+                return fused_layernorm_select01(
+                    tensors["x"],
+                    tensors["mod_params"],
+                    tensors["index"],
+                    args.eps,
+                )
+
+            def fused_residual_norm() -> object:
+                return fused_residual_layernorm_select01(
+                    tensors["x"],
+                    tensors["residual"],
+                    tensors["residual_gate"].expand_as(tensors["residual"]),
+                    tensors["mod_params"],
+                    tensors["index"],
+                    args.eps,
+                )
+
+            callables.extend(
+                [
+                    ("fused_layernorm_select01", fused_first_norm),
+                    ("fused_residual_layernorm_select01", fused_residual_norm),
+                ]
+            )
+
+        if args.check and "fused" in impls:
+            ref_norm, ref_gate = first_norm()
+            actual_norm, actual_gate = fused_layernorm_select01(
+                tensors["x"],
+                tensors["mod_params"],
+                tensors["index"],
+                args.eps,
+            )
+            torch.testing.assert_close(actual_norm, ref_norm, atol=args.check_atol, rtol=args.check_rtol)
+            torch.testing.assert_close(actual_gate, ref_gate, atol=0, rtol=0)
+            ref_norm, ref_residual, ref_gate = residual_norm()
+            actual_norm, actual_residual, actual_gate = fused_residual_layernorm_select01(
+                tensors["x"],
+                tensors["residual"],
+                tensors["residual_gate"].expand_as(tensors["residual"]),
+                tensors["mod_params"],
+                tensors["index"],
+                args.eps,
+            )
+            torch.testing.assert_close(actual_norm, ref_norm, atol=args.check_atol, rtol=args.check_rtol)
+            torch.testing.assert_close(actual_residual, ref_residual, atol=args.check_atol, rtol=args.check_rtol)
+            torch.testing.assert_close(actual_gate, ref_gate, atol=0, rtol=0)
+
+        for name, fn in callables:
+            row = _bench_one(name, fn, device, args.warmup, args.iters)
+            row.update(
+                {
+                    "batch_size": args.batch_size,
+                    "seq_len": seq_len,
+                    "hidden_size": args.hidden_size,
+                    "dtype": args.dtype,
+                    "device": str(device),
+                }
+            )
+            rows.append(row)
+            print(
+                f"{name:<38} B={args.batch_size:<2} L={seq_len:<6} C={args.hidden_size:<5} "
+                f"median={row['median_ms']:.4f} ms p20={row['p20_ms']:.4f} p80={row['p80_ms']:.4f}"
+            )
+
+    result = {
+        "benchmark": "qwen_select01_modulation",
+        "torch": torch.__version__,
+        "cuda_available": torch.cuda.is_available(),
+        "gpu": torch.cuda.get_device_name(device) if device.type == "cuda" else None,
+        "rows": rows,
+    }
+
+    if args.output_json:
+        output_path = Path(args.output_json)
+        output_path.parent.mkdir(parents=True, exist_ok=True)
+        output_path.write_text(json.dumps(result, indent=2), encoding="utf-8")
+
+    return result
+
+
+def _parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description=__doc__)
+    default_device = "cuda" if torch.cuda.is_available() else "cpu"
+    parser.add_argument("--device", default=default_device)
+    parser.add_argument("--dtype", default="bf16", choices=["fp32", "float32", "fp16", "float16", "bf16", "bfloat16"])
+    parser.add_argument("--batch-size", type=int, default=1)
+    parser.add_argument("--seq-lens", type=int, nargs="+", default=[4096, 8192, 16384])
+    parser.add_argument("--hidden-size", type=int, default=3072)
+    parser.add_argument("--eps", type=float, default=1e-6)
+    parser.add_argument("--warmup", type=int, default=20)
+    parser.add_argument("--iters", type=int, default=100)
+    parser.add_argument("--impl", choices=["native", "fused", "all"], default="native")
+    parser.add_argument("--check", action="store_true")
+    parser.add_argument("--check-atol", type=float, default=5e-2)
+    parser.add_argument("--check-rtol", type=float, default=5e-2)
+    parser.add_argument("--output-json")
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    _run(_parse_args())

benchmarks/kernels/benchmark_diffusion_lora_expand.py

@@ -0,0 +1,156 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+"""Benchmark diffusion LoRA expand-and-accumulate implementations.
+
+This compares the previous functional expand path with direct accumulation into
+the selected output slice. It is a synthetic kernel microbenchmark and does not
+load a diffusion model.
+
+Example:
+    python benchmarks/kernels/benchmark_diffusion_lora_expand.py \
+        --m 1,16,128,512,4096,16384 \
+        --in-dim 4096 --out-dim 4096 --rank 64 \
+        --dtype bfloat16 --packed --warmup 30 \
+        --small-iterations 500 --large-iterations 100
+"""
+
+import argparse
+import math
+import statistics
+from collections.abc import Callable
+
+import torch
+
+MiB = 1024**2
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--m", default="1,16,128,512,4096,16384")
+    parser.add_argument("--in-dim", type=int, default=4096)
+    parser.add_argument("--out-dim", type=int, default=4096)
+    parser.add_argument("--rank", type=int, default=64)
+    parser.add_argument("--dtype", choices=("bfloat16", "float16"), default="bfloat16")
+    parser.add_argument(
+        "--packed",
+        action="store_true",
+        help="Accumulate into the second half of a packed output tensor.",
+    )
+    parser.add_argument("--warmup", type=int, default=30)
+    parser.add_argument("--small-iterations", type=int, default=500)
+    parser.add_argument("--large-iterations", type=int, default=100)
+    parser.add_argument("--large-m-threshold", type=int, default=1024)
+    return parser.parse_args()
+
+
+def percentile(samples: list[float], probability: float) -> float:
+    ordered = sorted(samples)
+    position = probability * (len(ordered) - 1)
+    lower = math.floor(position)
+    upper = math.ceil(position)
+    if lower == upper:
+        return ordered[lower]
+    weight = position - lower
+    return ordered[lower] * (1 - weight) + ordered[upper] * weight
+
+
+def measure_latency_ms(operation: Callable[[], None], warmup: int, iterations: int) -> list[float]:
+    for _ in range(warmup):
+        operation()
+    torch.accelerator.synchronize()
+
+    starts = [torch.cuda.Event(enable_timing=True) for _ in range(iterations)]
+    ends = [torch.cuda.Event(enable_timing=True) for _ in range(iterations)]
+    for start, end in zip(starts, ends, strict=True):
+        start.record()
+        operation()
+        end.record()
+    torch.accelerator.synchronize()
+    return [start.elapsed_time(end) for start, end in zip(starts, ends, strict=True)]
+
+
+def measure_extra_peak_mib(operation: Callable[[], None]) -> float:
+    torch.accelerator.synchronize()
+    torch.accelerator.reset_peak_memory_stats()
+    allocated = torch.accelerator.memory_allocated()
+    operation()
+    torch.accelerator.synchronize()
+    peak = torch.accelerator.max_memory_allocated()
+    return (peak - allocated) / MiB
+
+
+def print_header(args: argparse.Namespace, dtype: torch.dtype) -> None:
+    device_name = torch.cuda.get_device_name()
+    print(f"device={device_name} torch={torch.__version__} dtype={dtype}")
+    print(f"in_dim={args.in_dim} out_dim={args.out_dim} rank={args.rank} packed={args.packed} warmup={args.warmup}")
+    print("M       iters  old_p10  old_p50  old_p90  new_p10  new_p50  new_p90  speedup  old_MiB  new_MiB  max_abs")
+
+
+def benchmark_shape(m: int, args: argparse.Namespace, dtype: torch.dtype) -> None:
+    device = torch.device("cuda")
+    output_width = args.out_dim * (2 if args.packed else 1)
+    offset = args.out_dim if args.packed else 0
+    output_slice = slice(offset, offset + args.out_dim)
+    scale = 1 / math.sqrt(args.in_dim)
+
+    x = torch.randn(m, args.in_dim, device=device, dtype=dtype)
+    lora_a = torch.randn(args.rank, args.in_dim, device=device, dtype=dtype) * scale
+    lora_b = torch.randn(args.out_dim, args.rank, device=device, dtype=dtype) / math.sqrt(args.rank)
+    initial_output = torch.randn(m, output_width, device=device, dtype=dtype)
+
+    def baseline(output: torch.Tensor) -> None:
+        buffer = x @ lora_a.t()
+        delta = buffer @ lora_b.t()
+        selected = output[:, output_slice]
+        selected[:] = selected + delta
+
+    def optimized(output: torch.Tensor) -> None:
+        buffer = x @ lora_a.t()
+        selected = output[:, output_slice]
+        torch.addmm(selected, buffer, lora_b.t(), out=selected)
+
+    reference = initial_output.clone()
+    candidate = initial_output.clone()
+    baseline(reference)
+    optimized(candidate)
+    torch.testing.assert_close(candidate, reference, rtol=1e-2, atol=1e-2)
+    max_abs = (candidate - reference).abs().max().item()
+
+    old_output = initial_output.clone()
+    new_output = initial_output.clone()
+    iterations = args.large_iterations if m >= args.large_m_threshold else args.small_iterations
+
+    old_peak = measure_extra_peak_mib(lambda: baseline(old_output))
+    new_peak = measure_extra_peak_mib(lambda: optimized(new_output))
+    old_samples = measure_latency_ms(lambda: baseline(old_output), args.warmup, iterations)
+    new_samples = measure_latency_ms(lambda: optimized(new_output), args.warmup, iterations)
+
+    old_p50 = statistics.median(old_samples)
+    new_p50 = statistics.median(new_samples)
+    print(
+        f"{m:<7} {iterations:<6} "
+        f"{percentile(old_samples, 0.1):>8.5f} {old_p50:>8.5f} {percentile(old_samples, 0.9):>8.5f} "
+        f"{percentile(new_samples, 0.1):>8.5f} {new_p50:>8.5f} {percentile(new_samples, 0.9):>8.5f} "
+        f"{old_p50 / new_p50:>8.3f} {old_peak:>8.2f} {new_peak:>8.2f} {max_abs:>8.5f}"
+    )
+
+
+def main() -> None:
+    args = parse_args()
+    if not torch.cuda.is_available():
+        raise RuntimeError("This benchmark requires a CUDA GPU")
+    if args.warmup < 0 or args.small_iterations <= 0 or args.large_iterations <= 0:
+        raise ValueError("warmup must be non-negative and iteration counts must be positive")
+
+    dtype = getattr(torch, args.dtype)
+    torch.manual_seed(0)
+    print_header(args, dtype)
+    with torch.inference_mode():
+        for m in (int(value) for value in args.m.split(",")):
+            if m <= 0:
+                raise ValueError(f"M must be positive, got {m}")
+            benchmark_shape(m, args, dtype)
+
+
+if __name__ == "__main__":
+    main()

benchmarks/kernels/benchmark_ming_istft.py

@@ -0,0 +1,218 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+"""Measure Ming ISTFT and optionally its pretrained streaming AudioVAE decoder.
+
+Example (no model weights needed for the operator benchmark):
+    python benchmarks/kernels/benchmark_ming_istft.py --compile --iterations 100
+
+Add --decoder to measure the published decoder with fixed synthetic latents.
+This is a decoder-stage benchmark, not text-to-speech serving latency.
+"""
+
+import argparse
+import json
+import statistics
+import time
+from collections.abc import Callable
+from functools import partial
+
+import torch
+
+from vllm_omni.model_executor.models.common.ming.audio_dsp import ISTFT
+from vllm_omni.model_executor.models.common.ming.fused_istft import fused_istft
+
+
+def measure(operation: Callable, warmup: int, iterations: int) -> dict:
+    for _ in range(warmup):
+        operation()
+    torch.accelerator.synchronize()
+    samples = []
+    for _ in range(iterations):
+        start = time.perf_counter()
+        operation()
+        torch.accelerator.synchronize()
+        samples.append((time.perf_counter() - start) * 1000)
+    torch.accelerator.reset_peak_memory_stats()
+    before = torch.accelerator.memory_allocated()
+    operation()
+    torch.accelerator.synchronize()
+    return {
+        "p50_ms": statistics.median(samples),
+        "min_ms": min(samples),
+        "max_ms": max(samples),
+        "stdev_ms": statistics.pstdev(samples),
+        "extra_peak_mib": (torch.accelerator.max_memory_allocated() - before) / 1024**2,
+    }
+
+
+def native_post_fft(module, frames, audio, window):
+    """The native middle-chunk post-FFT sequence, isolated from cuFFT."""
+    count = frames.shape[-1]
+    size = (count - 1) * module.hop_length + module.win_length
+
+    def fold(value):
+        return torch.nn.functional.fold(
+            value, output_size=(1, size), kernel_size=(1, module.win_length), stride=(1, module.hop_length)
+        )[:, 0, 0, :]
+
+    samples = fold(frames * module.window[None, :, None])
+    samples, audio = module._buffer_process(samples, audio, 0, streaming=True)
+    envelope = fold(module.window.square().expand(1, count, -1).transpose(1, 2))
+    envelope, window = module._buffer_process(envelope, window, 0, streaming=True)
+    if not (envelope > 1e-11).all():
+        raise RuntimeError("ISTFT window envelope underflowed; invalid overlap-add state.")
+    return samples / envelope, audio, window
+
+
+def assert_outputs(actual, reference):
+    maximum = 0.0
+    for value, expected in zip(actual, reference, strict=True):
+        if expected is None:
+            assert value is None
+        else:
+            torch.testing.assert_close(value, expected, atol=5e-7, rtol=1e-5)
+            maximum = max(maximum, (value - expected).abs().max().item())
+    return maximum
+
+
+def benchmark_istft(args):
+    for dtype in (torch.float32, torch.bfloat16):
+        module = ISTFT(3528, 882, 3528).to(device="cuda", dtype=dtype)
+        for count in (16, 100, 400):
+            spec = torch.randn(1, 1765, count, device="cuda", dtype=torch.complex64)
+            _, audio, window = module.forward_native(spec, streaming=True)
+            frames = torch.fft.irfft(spec, 3528, dim=1)
+            for scope in ("post_fft", "istft"):
+                if scope == "post_fft":
+                    native = partial(native_post_fft, module, frames, audio, window)
+                    fused = partial(fused_istft, frames, module.window, 882, audio, window, True, False)
+                else:
+                    native = partial(module.forward_native, spec, audio, window, streaming=True)
+                    fused = partial(module, spec, audio, window, streaming=True)
+                error = assert_outputs(fused(), native())
+                results = {"native": measure(native, args.warmup, args.iterations)}
+                if args.compile:
+                    compiled = torch.compile(native)
+                    compiled_output, reference = compiled(), native()
+                    # Inductor can change BF16 envelope rounding. Report that
+                    # difference without weakening the fused-kernel parity gate.
+                    compiled_matches = all(
+                        torch.allclose(value, expected, atol=5e-7, rtol=1e-5)
+                        for value, expected in zip(compiled_output, reference, strict=True)
+                    )
+                    results["compiled"] = measure(compiled, args.warmup, args.iterations)
+                    results["compiled"]["matches_native_tolerance"] = compiled_matches
+                    results["compiled"]["waveform_max_abs_error"] = (
+                        (compiled_output[0] - reference[0]).abs().max().item()
+                    )
+                results["fused"] = measure(fused, args.warmup, args.iterations)
+                print(
+                    json.dumps(
+                        {
+                            "scope": scope,
+                            "window_dtype": str(dtype),
+                            "frames": count,
+                            "max_abs_error": error,
+                            "results": results,
+                        }
+                    ),
+                    flush=True,
+                )
+
+
+def benchmark_decoder(args):
+    from huggingface_hub import hf_hub_download
+    from safetensors import safe_open
+
+    from vllm_omni.model_executor.models.common.ming.audio_vae import Decoder
+
+    model = "inclusionAI/Ming-omni-tts-0.5B"
+    revision = "9154772e7fbc585907b6237e3190790676f28975"
+    config_path = hf_hub_download(model, "config.json", revision=revision)
+    with open(config_path) as source:
+        config = json.load(source)["audio_tokenizer_config"]
+    kwargs = config["dec_kwargs"]
+    backbone = kwargs["backbone"]
+    backbone["_attn_implementation"] = "sdpa"
+    decoder = Decoder(backbone, kwargs["output_dim"], kwargs["latent_dim"], config["patch_size"])
+    checkpoint = hf_hub_download(model, "model.safetensors", revision=revision)
+    prefix = "audio.decoder."
+    with safe_open(checkpoint, framework="pt") as weights:
+        state = {key.removeprefix(prefix): weights.get_tensor(key) for key in weights.keys() if key.startswith(prefix)}
+    decoder.load_state_dict(state, strict=True)
+    decoder = decoder.eval().to(device="cuda", dtype=torch.bfloat16)
+    chunks = [
+        torch.randn(1, size, kwargs["latent_dim"], device="cuda", dtype=torch.bfloat16) for size in (4, 25, 25, 5)
+    ]
+    istft = decoder.head.istft
+    fused_forward = istft.forward
+
+    def run():
+        cache = state = None
+        outputs = []
+        for index, chunk in enumerate(chunks):
+            output, state, cache = decoder.low_level_reconstruct(
+                chunk, past_key_values=cache, use_cache=True, stream_state=state, last_chunk=index == len(chunks) - 1
+            )
+            outputs.append(output)
+        return torch.cat(outputs, dim=-1)
+
+    istft.forward = istft.forward_native
+    expected = run()
+    istft.forward = fused_forward
+    actual = run()
+    torch.testing.assert_close(actual, expected, atol=5e-7, rtol=1e-5)
+    # Alternate the order to expose drift when the decoder-level gain is small.
+    results = {"native": [], "fused": []}
+    for order in (("native", "fused"), ("fused", "native"), ("native", "fused")):
+        for name in order:
+            istft.forward = istft.forward_native if name == "native" else fused_forward
+            results[name].append(measure(run, args.warmup, args.decoder_iterations))
+    print(
+        json.dumps(
+            {
+                "scope": "pretrained_decoder",
+                "model": model,
+                "revision": revision,
+                "backend": "sdpa",
+                "dtype": "bfloat16",
+                "latent_chunks": [4, 25, 25, 5],
+                "max_abs_error": (actual - expected).abs().max().item(),
+                "results": results,
+            }
+        ),
+        flush=True,
+    )
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--warmup", type=int, default=20)
+    parser.add_argument("--iterations", type=int, default=100)
+    parser.add_argument("--compile", action="store_true")
+    parser.add_argument("--decoder", action="store_true")
+    parser.add_argument("--decoder-iterations", type=int, default=30)
+    args = parser.parse_args()
+    if args.warmup < 0 or min(args.iterations, args.decoder_iterations) < 1:
+        parser.error("warmup must be nonnegative and iteration counts must be positive")
+    torch.manual_seed(0)
+    print(
+        json.dumps(
+            {
+                "torch": torch.__version__,
+                "cuda": torch.version.cuda,
+                "gpu": torch.cuda.get_device_name(),
+                "warmup": args.warmup,
+                "iterations": args.iterations,
+                "decoder_iterations": args.decoder_iterations,
+            }
+        )
+    )
+    with torch.inference_mode():
+        benchmark_istft(args)
+        if args.decoder:
+            benchmark_decoder(args)
+
+
+if __name__ == "__main__":
+    main()

benchmarks/kernels/mot_linear_benchmarks.py

@@ -1,3 +1,6 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
 # ruff: noqa: N803
 
 """MoT (Mixture-of-Tokens) GEMM kernel benchmark and auto-tuning.
@@ -10,7 +13,7 @@ Usage:
     python benchmarks/kernels/mot_linear_benchmarks.py \
         --model ByteDance-Seed/BAGEL-7B-MoT \
         --tp-size 1 --dtype w16a16 --tune \
-        --save-dir vllm_omni/diffusion/layers/mot/configs/
+        --save-dir vllm_omni/diffusion/models/bagel/mot/configs/
 
     # Auto-tune with local model path (offline clusters):
     python benchmarks/kernels/mot_linear_benchmarks.py \
@@ -43,7 +46,7 @@ from vllm.triton_utils import triton
 from vllm.utils.torch_utils import set_random_seed
 
 # NOTE: you should use the same naming syetem for the kernel to load properly
-from vllm_omni.diffusion.layers.mot.ops.mot_gemm import build_config_filename, get_device_name
+from vllm_omni.diffusion.models.bagel.mot.ops.mot_gemm import build_config_filename, get_device_name
 
 # clear the triton cache from time to time, usaully no need to change
 _CACHE_CLEAR_INTERVAL_ENV = "VLLM_MOT_TUNE_CACHE_CLEAR_INTERVAL"
@@ -428,7 +431,7 @@ def benchmark_config(
     cache_flusher: torch.Tensor | None = None,
 ) -> float:
     """Run a MoT GEMM with the given tile config and return avg latency (us)."""
-    from vllm_omni.diffusion.layers.mot.ops.mot_gemm import invoke_mot_gemm
+    from vllm_omni.diffusion.models.bagel.mot.ops.mot_gemm import invoke_mot_gemm
 
     text_indices, vae_indices, M = build_regular_indices(
         image_num=image_num,
@@ -553,7 +556,7 @@ class BenchmarkWorker:
         use_int8_w8a16: bool,
     ) -> tuple[dict[str, int], float]:
         set_random_seed(self.seed)
-        from vllm_omni.diffusion.layers.mot.ops.mot_gemm import (
+        from vllm_omni.diffusion.models.bagel.mot.ops.mot_gemm import (
             get_best_mot_config,
         )
 

benchmarks/lingbot_world/README.md

@@ -0,0 +1,224 @@
+# LingBot-World Benchmarks — Realtime Streaming Cadence
+
+`benchmark_lingbot_world_realtime.py` measures LingBot-World 2.0 as it is
+actually deployed: one `WS /v1/realtime/video` session that streams one video
+chunk per AR block, for as long as the world keeps running.
+
+## Why this is not the diffusion serving benchmark
+
+[`benchmarks/diffusion`](../diffusion/README.md) measures "one request, one
+finished video" and reports request throughput. LingBot-World has neither shape:
+
+- **One request is the whole rollout.** `prepare_encode` runs once, then every AR
+  block is four DMD steps plus one clean-KV commit forward, and each block
+  streams out as a chunk. Throughput in requests per second is meaningless.
+- **There is exactly one session.** The stepwise deploy config pins
+  `max_num_seqs: 1`, and the AR-Diffusion consumer rejects a stage with more than
+  one replica, because session-affine routing does not exist yet. There is no
+  concurrency to sweep.
+- **What matters is cadence.** A deployment is healthy when chunk *N + 1* arrives
+  before a viewer finishes watching chunk *N*. That is a latency distribution
+  against a wall clock, not a throughput number.
+
+## Quick start
+
+Serve the model, then point the benchmark at it. The benchmark never starts a
+server.
+
+```bash
+# Terminal 1 — one GPU, eager (portable smoke configuration)
+vllm serve robbyant/lingbot-world-v2-14b-causal-fast-diffusers \
+  --omni \
+  --deploy-config benchmarks/lingbot_world/configs/single_gpu_eager.yaml \
+  --port 8000
+
+# Terminal 2
+pip install websockets
+python benchmarks/lingbot_world/benchmark_lingbot_world_realtime.py \
+  --host 127.0.0.1 --port 8000 \
+  --num-chunks 16 \
+  --output-json /tmp/lingbot_world_realtime.json
+```
+
+With no `--image`, the first frame comes from the shared vLLM image asset, so the
+default workload needs no setup and this repository carries no benchmark binary.
+Pass `--image /path/to/frame.png` to condition the rollout on your own frame.
+
+### Realtime topology
+
+The single-GPU config is a smoke configuration, not a realtime one: one card runs
+all five forwards of every block serially and nothing is compiled. Use the
+four-GPU config for numbers that mean anything about realtime behavior.
+
+```bash
+vllm serve robbyant/lingbot-world-v2-14b-causal-fast-diffusers \
+  --omni \
+  --deploy-config benchmarks/lingbot_world/configs/usp4_compiled.yaml \
+  --port 8000
+
+python benchmarks/lingbot_world/benchmark_lingbot_world_realtime.py \
+  --port 8000 --num-chunks 40 --warmup-sessions 1 --sessions 3 \
+  --target-fps 12 --output-json /tmp/lingbot_world_repeated.json
+```
+
+`usp4_compiled.yaml` sets pure Ulysses sequence parallelism over four ranks and
+`enforce_eager: false`. The pipeline validates the parallel shape itself:
+`sequence_parallel_size` must equal `ulysses_degree`, `ring_degree` and
+`allgather_degree` must be 1, `ulysses_mode` must be `strict`, and pipeline,
+CFG, and VAE parallel sizes above 1 are rejected. This benchmark config sets
+`tensor_parallel_size: 1`; the pipeline validator does not reject larger TP sizes.
+
+Compiled mode can pay compilation and capture costs on the first rollout.
+Use `--warmup-sessions 1` before measured repetitions. Excluding the first six
+chunks addresses the attention-window ramp; it does not guarantee that
+compilation and capture have finished.
+
+## Metrics
+
+| Metric | Meaning |
+| --- | --- |
+| `ttfc_ms` | **Time to first chunk**: `session.start` sent until the first chunk's bytes arrive. Pays for prompt encode, first-frame VAE encode, and any compile or capture. |
+| `interval_all` | Inter-arrival latency across every chunk: mean, median, std, min, p90, p95, p99, max. |
+| `interval_steady` | The same summary over steady-state chunks only. |
+| `video_rtf` | Wall seconds spent per second of video, matching vLLM-Omni's own RTF (`generation_s / video_duration`). **Lower is better; below 1.0 is real time.** |
+| `video_rtfx` | The reciprocal, under the name this repo uses for it: video seconds per wall second, where higher is better. |
+| `steady_video_rtf` | `video_rtf` restricted to steady-state chunks. |
+| `chunk_deadline_ms` | Wall-clock budget one steady chunk must meet to hold real time: 12 frames ÷ `--target-fps`. Override directly with `--chunk-slo-ms`. |
+| `slo_attainment` | Fraction of steady chunks that met the deadline. |
+| `playback` | Underrun count, total stall, and stall ratio from a simulated viewer. |
+
+### Which frame rate counts as real time
+
+**The checkpoint declares no frame rate.** There is none in `model_index.json`, the
+scheduler config, or the transformer config, and the pipeline never reads one. A
+chunk is twelve pixel frames; how long those frames *last* is a property of how the
+output is played, not of the model.
+
+That choice moves the verdict without moving anything measured:
+
+| basis | video per chunk | deadline | RTF at a measured 1357 ms/chunk |
+| --- | --- | --- | --- |
+| 16 fps | 0.750 s | 750 ms | 1.809 |
+| 12 fps | 1.000 s | 1000 ms | 1.357 |
+
+So `--fps` (sent to the server, which uses it to label the muxed MP4) and
+`--target-fps` (the real-time basis for RTF, the deadline, and the playback
+simulation) are separate flags. `--target-fps` defaults to `--fps`; set it when the
+two differ, and the report prints both. State the basis whenever you quote a number
+— an RTF without its fps is not a measurement. (RTF is a cost here, so a bigger number is a
+worse result; a steady chunk's RTF is simply its interval divided by its video duration.)
+An RTF above 1 means the measured deployment is slower than real time at the
+stated target frame rate. The benchmark measures this failure as well as success.
+
+### Why warmup chunks are not optional
+
+The DiT attends over a sliding window of `sliding_window_num_frames = 18` latent
+frames with `sink_size = 9`, so the key count a block attends over keeps growing
+until eighteen latent frames of history exist — six chunks at three frames per
+block. **Chunk cost is not stationary before chunk six.** A mean over a short
+rollout is therefore lower than anything a running session will sustain, which is
+why `--warmup-chunks` defaults to 6 and steady-state metrics are reported
+separately. Keep `--num-chunks` comfortably above the warmup, or the benchmark
+tells you no chunk reached steady state. The terminal chunk skips next-chunk
+preparation, so it is excluded from steady metrics too; it remains in overall
+RTF, all-chunk intervals, and playback simulation.
+
+The default 16-chunk rollout provides only nine steady intervals. Percentiles
+are interpolated sample summaries; the report warns when fewer than 100
+intervals contribute, and even 100 samples do not guarantee a stable p99.
+Use longer rollouts for tail analysis and at least `--sessions 3` after
+`--warmup-sessions 1` for reported comparisons. Session warmup pays compilation
+cost; chunk warmup separately excludes the attention-window ramp. The command
+above yields 33 steady intervals per session (indices 6 through 38), or 99 across
+three sessions. Report each session's mean and the spread across sessions; this
+sample still does not establish a reliable p99.
+
+### Why playback is simulated
+
+A p99 interval alone cannot say whether a viewer saw a stall: the same p99 is
+invisible behind a three-chunk buffer and a visible freeze behind none. The
+benchmark replays chunk arrivals against a wall clock — playback starts once
+`--playback-buffer-chunks` chunks have landed and then consumes video at `--target-fps`
+— and reports every moment the player ran dry.
+
+### What the client-side number is worth
+
+On a four-GPU Ulysses run the client's mean inter-arrival over all fifteen
+post-first-chunk intervals was **1326.743 ms**, against the server's own
+`StageRequestStats.inter_output_latency_ms` of **1327.479 ms** for the same
+request — a difference of **0.74 ms**. This cross-check shows agreement in
+average output cadence for that run, including warmup and the terminal chunk.
+It does not measure encoding or transport latency: a fixed delivery delay can
+cancel out of inter-arrival differences. Attributing that latency requires
+per-chunk timestamps at both boundaries or server-side profiling.
+
+Compare matching interval populations when investigating discrepancies; both
+measurement bugs and delivery buffering can change the observed cadence.
+
+### What this benchmark cannot see
+
+Chunk metadata carries no server-side timestamps, so the benchmark cannot split
+DiT time from VAE decode time; use `--enable-diffusion-pipeline-profiler` on the
+server for that. It also reports no GPU memory, which is a server-side quantity.
+
+## Custom rollouts
+
+`--workload rollout.json` replaces the built-in workload, including mid-rollout
+prompt changes, which the CLI flags cannot express:
+
+```json
+{
+  "prompt": "The camera moves slowly forward through the scene.",
+  "image": "/path/to/first_frame.png",
+  "num_chunks": 24,
+  "camera_pattern": "orbit",
+  "fps": 16,
+  "seed": 42,
+  "prompt_updates": [
+    {"after_chunk": 8, "prompt": "Rain begins to fall", "transition_chunks": 2}
+  ]
+}
+```
+
+Supply `camera_action_script` directly instead of `num_chunks` to control every
+frame's action. It must hold exactly one three-entry action list per chunk; one
+AR block is three latent frames, and the server rejects any other shape.
+Explicit `--image`, `--prompt`, `--negative-prompt`, `--width`, `--height`,
+`--fps`, `--seed`, and `--flow-shift` flags override the matching workload-file
+values. `--num-chunks` and `--camera-pattern` apply only to the built-in
+workload; when using `--workload`, set those fields in the file instead.
+An explicit `camera_action_script` takes precedence over the file's
+`num_chunks` and `camera_pattern`. Each prompt update must use a distinct
+`after_chunk` boundary so its interaction event ID is unique.
+
+Camera actions change what the world does, not what it costs, so `--camera-pattern`
+(`forward`, `orbit`, `hold`) keeps a rollout representative rather than sweeping a
+cost dimension.
+
+## Options worth knowing
+
+| Flag | Purpose |
+| --- | --- |
+| `--target-fps` | Playback rate the real-time verdict is measured against, independent of the mux label. |
+| `--sessions N` | N sequential measured rollouts; the report adds spread across them. |
+| `--warmup-sessions N` | Unmeasured rollouts first, to pay compile and capture cost. |
+| `--print-chunks` | One line per chunk as it arrives. |
+| `--save-video PATH` | Remux the last measured session for eyeballing (needs vLLM-Omni). |
+| `--ping-interval` | `session.ping` cadence keeping the server's stall clock fresh during a slow first chunk. `0` disables. |
+| `--first-chunk-timeout` | Raise it when the server is still compiling or capturing graphs. |
+
+## Output
+
+`--output-json` writes the run's configuration, every chunk's arrival time and
+size, per-session metrics, and the aggregate, along with the git revision and
+hostname so a number can be traced back to what produced it.
+
+## Tests
+
+`tests/benchmarks/test_lingbot_world_realtime.py` covers the chunk and frame
+arithmetic against the server's own formula, the metric math, and the protocol
+handling against a scripted WebSocket server. It needs no GPU:
+
+```bash
+pytest tests/benchmarks/test_lingbot_world_realtime.py
+```

benchmarks/lingbot_world/__init__.py

@@ -0,0 +1,2 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project

benchmarks/lingbot_world/benchmark_lingbot_world_realtime.py

@@ -0,0 +1,642 @@
+#!/usr/bin/env python3
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+"""Realtime streaming benchmark for LingBot-World 2.0 over ``WS /v1/realtime/video``.
+
+Every other diffusion benchmark in this repository measures "one request, one
+finished video". LingBot-World is not that: one request is a whole rollout, and
+the thing a deployment lives or dies by is whether chunk *N + 1* keeps arriving
+before a viewer has finished watching chunk *N*. This benchmark therefore
+measures a **cadence**, not a throughput:
+
+* ``TTFC`` -- session.start to the first chunk's bytes, which pays for prompt
+  encode, the first-frame VAE encode, and any compile or capture the deploy
+  config asks for;
+* **inter-arrival latency** over all chunks and, separately, over the steady
+  state after the attention window saturates;
+* ``VIDEO_RTF`` -- wall seconds spent per second of video generated, matching how
+  vLLM-Omni computes RTF everywhere else (``generation_s / video_duration``).
+  **Lower is better and below 1.0 is real time**; ``VIDEO_RTFX`` is reported
+  alongside it as the reciprocal, under the repo's own name for that;
+* **playback continuity** -- a simulated viewer with a start buffer, because a
+  p99 that is harmless behind a three-chunk buffer is a visible stall behind
+  none.
+
+The server is not started here. Bring one up first (see the README), then point
+this script at it. Requires ``websockets``; ``--save-video`` additionally needs
+a vLLM-Omni installation for the remux helper.
+
+Example:
+
+    python benchmarks/lingbot_world/benchmark_lingbot_world_realtime.py \\
+      --host 127.0.0.1 --port 8000 \\
+      --num-chunks 16 \\
+      --output-json /tmp/lingbot_world_realtime.json
+"""
+
+from __future__ import annotations
+
+import argparse
+import asyncio
+import base64
+import json
+import mimetypes
+import os
+import platform
+import subprocess
+import sys
+import time
+from dataclasses import dataclass
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+REPO_ROOT = Path(__file__).resolve().parents[2]
+if str(REPO_ROOT) not in sys.path:
+    sys.path.insert(0, str(REPO_ROOT))
+
+from benchmarks.lingbot_world.workload import (  # noqa: E402
+    CAMERA_PATTERNS,
+    DEFAULT_FLOW_SHIFT,
+    DEFAULT_FPS,
+    DEFAULT_HEIGHT,
+    DEFAULT_MODEL,
+    DEFAULT_PROMPT,
+    DEFAULT_SEED,
+    DEFAULT_WARMUP_CHUNKS,
+    DEFAULT_WIDTH,
+    ChunkRecord,
+    Workload,
+    aggregate_metrics,
+    compute_metrics,
+    default_workload,
+    load_workload,
+    steady_chunk_deadline_ms,
+)
+
+# The shared vLLM asset keeps the default workload runnable with no setup and
+# adds no binary to this repository. Any real photograph conditions a rollout.
+_DEFAULT_IMAGE_ASSET = "2560px-Gfp-wisconsin-madison-the-nature-boardwalk"
+_WS_PATH = "/v1/realtime/video"
+
+
+class BenchmarkError(RuntimeError):
+    """A session failed in a way that invalidates its numbers."""
+
+
+# --------------------------------------------------------------------------- #
+# Input resolution
+# --------------------------------------------------------------------------- #
+
+
+def resolve_image_reference(value: str | None) -> str:
+    """Return an ``http(s)`` or ``data:`` URL for the session's first frame."""
+    if value is None:
+        from vllm.assets.image import ImageAsset
+
+        value = str(ImageAsset(_DEFAULT_IMAGE_ASSET).get_path("jpg"))
+    if value.startswith(("http://", "https://", "data:")):
+        return value
+    path = Path(value).expanduser()
+    if not path.is_file():
+        raise BenchmarkError(f"--image is neither a URL nor a readable file: {value}")
+    media_type = mimetypes.guess_type(path.name)[0] or "image/png"
+    return f"data:{media_type};base64,{base64.b64encode(path.read_bytes()).decode()}"
+
+
+def build_workload(args: argparse.Namespace) -> Workload:
+    if args.workload is not None:
+        # The file names its own first frame, so resolving happens inside
+        # load_workload -- never eagerly, which would fetch the default asset a
+        # --workload run has no use for.
+        return load_workload(
+            args.workload,
+            image_resolver=lambda value: resolve_image_reference(value if args.image is None else args.image),
+            overrides={
+                "width": args.width_override,
+                "height": args.height_override,
+                "fps": args.fps_override,
+                "seed": args.seed_override,
+                "prompt": args.prompt_override,
+                "flow_shift": args.flow_shift,
+                "negative_prompt": args.negative_prompt,
+            },
+        )
+    return default_workload(
+        image_reference=resolve_image_reference(args.image),
+        num_chunks=args.num_chunks,
+        camera_pattern=args.camera_pattern,
+        prompt=args.prompt_override if args.prompt_override is not None else DEFAULT_PROMPT,
+        width=args.width_override if args.width_override is not None else DEFAULT_WIDTH,
+        height=args.height_override if args.height_override is not None else DEFAULT_HEIGHT,
+        fps=args.fps_override if args.fps_override is not None else DEFAULT_FPS,
+        seed=args.seed_override if args.seed_override is not None else DEFAULT_SEED,
+        flow_shift=args.flow_shift if args.flow_shift is not None else DEFAULT_FLOW_SHIFT,
+        negative_prompt=args.negative_prompt,
+    )
+
+
+# --------------------------------------------------------------------------- #
+# One streaming session
+# --------------------------------------------------------------------------- #
+
+
+@dataclass
+class SessionResult:
+    request_id: str | None
+    records: list[ChunkRecord]
+    media: bytes
+    ttfc_s: float | None
+    video_start_s: float | None
+    wall_s: float
+    done_event: dict[str, Any]
+
+
+async def _keepalive(websocket: Any, send_lock: asyncio.Lock, interval: float) -> None:
+    """Refresh the server's stall clock while a slow first chunk is generating.
+
+    A compiled deploy config records CUDA graphs on the first block, which can
+    outlast the server's ~60 s stall timeout. ``session.ping`` is the documented
+    way to say the client is still there.
+    """
+    while True:
+        await asyncio.sleep(interval)
+        async with send_lock:
+            await websocket.send(json.dumps({"type": "session.ping"}))
+
+
+async def run_session(
+    url: str,
+    model: str,
+    workload: Workload,
+    *,
+    first_chunk_timeout: float,
+    chunk_timeout: float,
+    ping_interval: float,
+    collect_media: bool,
+    print_chunks: bool,
+) -> SessionResult:
+    """Drive one rollout and timestamp every chunk as its bytes land."""
+    try:
+        from websockets.asyncio.client import connect
+    except ImportError:  # pragma: no cover - exercised only without websockets
+        try:
+            from websockets import connect  # type: ignore[attr-defined]
+        except ImportError as exc:
+            raise BenchmarkError("This benchmark needs the websockets package: pip install websockets") from exc
+
+    payload = workload.session_start_payload(model)
+    records: list[ChunkRecord] = []
+    media = bytearray()
+    pending: dict[str, Any] | None = None
+    request_id: str | None = None
+    ttfc_s: float | None = None
+    video_start_s: float | None = None
+    last_arrival: float | None = None
+    done_event: dict[str, Any] = {}
+    send_lock = asyncio.Lock()
+    keepalive: asyncio.Task[None] | None = None
+    # Fire remaining prompt updates in chunk order as their boundary passes.
+    updates = sorted(workload.prompt_updates, key=lambda update: update.after_chunk)
+    update_index = 0
+
+    async with connect(url, max_size=None, ping_interval=None) as websocket:
+        async with send_lock:
+            await websocket.send(json.dumps(payload, ensure_ascii=False))
+        started = time.perf_counter()
+        if ping_interval > 0:
+            keepalive = asyncio.create_task(_keepalive(websocket, send_lock, ping_interval))
+
+        try:
+            while True:
+                timeout = first_chunk_timeout if not records else chunk_timeout
+                try:
+                    deadline = started + (last_arrival if last_arrival is not None else 0.0) + timeout
+                    remaining = deadline - time.perf_counter()
+                    if remaining <= 0:
+                        raise asyncio.TimeoutError
+                    message = await asyncio.wait_for(websocket.recv(), timeout=remaining)
+                except asyncio.TimeoutError as exc:
+                    raise BenchmarkError(
+                        f"No media chunk for {timeout:g} s after {len(records)} chunk(s); "
+                        "the server may still be loading, compiling, or capturing graphs."
+                    ) from exc
+                now = time.perf_counter()
+
+                if isinstance(message, (bytes, bytearray)):
+                    if pending is None:
+                        raise BenchmarkError("Received a binary frame with no preceding video.chunk_metadata event.")
+                    if collect_media:
+                        media.extend(message)
+                    if pending.get("kind") == "media":
+                        index = int(pending["generation_chunk_index"])
+                        arrival = now - started
+                        if ttfc_s is None:
+                            ttfc_s = arrival
+                        records.append(
+                            ChunkRecord(
+                                index=index,
+                                arrival_s=arrival,
+                                inter_arrival_s=arrival - (last_arrival if last_arrival is not None else 0.0),
+                                byte_length=len(message),
+                                num_frames=int(pending["num_frames"]),
+                            )
+                        )
+                        last_arrival = arrival
+                        if print_chunks:
+                            print(
+                                f"  chunk {index:3d}  t={arrival:8.3f}s  "
+                                f"interval={records[-1].inter_arrival_s * 1000:8.1f}ms  "
+                                f"frames={records[-1].num_frames:3d}  bytes={len(message)}",
+                                flush=True,
+                            )
+                        while update_index < len(updates) and updates[update_index].after_chunk <= index:
+                            async with send_lock:
+                                await websocket.send(json.dumps(updates[update_index].to_payload(), ensure_ascii=False))
+                            update_index += 1
+                    pending = None
+                    continue
+
+                event = json.loads(message)
+                event_type = event.get("type")
+                if event_type == "video.chunk_metadata":
+                    pending = event
+                elif event_type == "video.start":
+                    request_id = event.get("request_id")
+                    video_start_s = now - started
+                elif event_type == "error":
+                    # Say what was asked for alongside what the server refused:
+                    # some builds cap a rollout's num_frames, and the operator
+                    # needs to know which --num-chunks would fit.
+                    raise BenchmarkError(
+                        f"Server rejected the session after {len(records)} chunk(s): "
+                        f"{event.get('message', event)} "
+                        f"(requested {workload.num_chunks} chunks = num_frames {workload.num_frames})"
+                    )
+                elif event_type == "session.done":
+                    done_event = event
+                    break
+        finally:
+            if keepalive is not None:
+                keepalive.cancel()
+                try:
+                    await keepalive
+                except asyncio.CancelledError:
+                    pass
+
+        wall_s = time.perf_counter() - started
+
+    if not records:
+        raise BenchmarkError("The session finished without delivering a single media chunk.")
+    if done_event.get("stopped"):
+        raise BenchmarkError(f"The session was stopped before it finished: {done_event}")
+    observed = [record.index for record in records]
+    if observed != list(range(len(records))):
+        raise BenchmarkError(f"Chunk indices are not contiguous from zero: {observed}")
+    if len(records) != workload.num_chunks:
+        raise BenchmarkError(
+            f"Expected {workload.num_chunks} chunks for num_frames={workload.num_frames}, received {len(records)}."
+        )
+
+    return SessionResult(
+        request_id=request_id,
+        records=records,
+        media=bytes(media),
+        ttfc_s=ttfc_s,
+        video_start_s=video_start_s,
+        wall_s=wall_s,
+        done_event=done_event,
+    )
+
+
+# --------------------------------------------------------------------------- #
+# Reporting
+# --------------------------------------------------------------------------- #
+
+
+def _git_revision() -> str | None:
+    try:
+        return subprocess.check_output(
+            ["git", "-C", str(REPO_ROOT), "rev-parse", "HEAD"],
+            text=True,
+            stderr=subprocess.DEVNULL,
+        ).strip()
+    except (OSError, subprocess.CalledProcessError):
+        return None
+
+
+def _print_row(label: str, value: str) -> None:
+    print(f"{label:<44}{value:>16}")
+
+
+def print_report(
+    metrics: dict[str, Any],
+    *,
+    workload: Workload,
+    endpoint: str,
+    sessions: int,
+    target_fps: float,
+) -> None:
+    header = " LingBot-World realtime streaming benchmark "
+    print("\n{s:{c}^{n}}".format(s=header, n=60, c="="))
+    _print_row("Endpoint:", endpoint)
+    _print_row("Geometry:", f"{workload.width}x{workload.height}")
+    _print_row("Chunks per session:", str(workload.num_chunks))
+    _print_row("Sessions:", str(sessions))
+    frames = workload.num_frames
+    _print_row("Real-time basis:", f"{target_fps:g} fps")
+    _print_row("Video seconds per session:", f"{frames / target_fps:.2f} ({frames} frames)")
+    if abs(target_fps - workload.fps) > 1e-9:
+        _print_row("Mux label sent to server:", f"{workload.fps} fps")
+
+    if sessions == 1:
+        print("{s:{c}^{n}}".format(s=" Cadence ", n=60, c="-"))
+        if metrics.get("ttfc_ms") is not None:
+            _print_row("TTFC (session.start -> first chunk):", f"{metrics['ttfc_ms']:.1f} ms")
+        interval_all = metrics.get("interval_all") or {}
+        interval_steady = metrics.get("interval_steady") or {}
+        for title, summary in (("All chunks", interval_all), ("Steady state", interval_steady)):
+            if not summary:
+                continue
+            print(f"  {title} (n={int(summary['count'])}):")
+            _print_row("    mean / median:", f"{summary['mean_ms']:.1f} / {summary['median_ms']:.1f} ms")
+            _print_row("    std / max:", f"{summary['std_ms']:.1f} / {summary['max_ms']:.1f} ms")
+            tail = f"{summary['p90_ms']:.0f} / {summary['p95_ms']:.0f} / {summary['p99_ms']:.0f} ms"
+            _print_row("    p90 / p95 / p99:", tail)
+            if summary["count"] < 100:
+                print(
+                    "    NOTE: fewer than 100 intervals; tail percentiles are descriptive, not reliable tail estimates."
+                )
+
+        print("{s:{c}^{n}}".format(s=" Real time ", n=60, c="-"))
+        _print_row("VIDEO_RTF (wall s / video s, <1 real time):", f"{metrics['video_rtf']:.3f}")
+        _print_row("VIDEO_RTFX (video s / wall s):", f"{metrics['video_rtfx']:.3f}")
+        if metrics.get("steady_video_rtf") is not None:
+            _print_row("VIDEO_RTF steady state:", f"{metrics['steady_video_rtf']:.3f}")
+        _print_row("Frames per second:", f"{metrics['frames_per_second']:.2f}")
+        _print_row("Chunk deadline:", f"{metrics['chunk_deadline_ms']:.1f} ms")
+        if metrics.get("slo_attainment") is not None:
+            _print_row(
+                "Steady chunks within deadline:",
+                f"{metrics['slo_attainment'] * 100:.1f}% ({metrics['slo_violations']} late)",
+            )
+        if not metrics.get("steady_state_reached", False):
+            print(
+                f"  NOTE: no chunk reached the steady state; the attention window needs "
+                f"{metrics['warmup_chunks']} chunks. Raise --num-chunks."
+            )
+
+        playback = metrics.get("playback") or {}
+        print("{s:{c}^{n}}".format(s=" Playback continuity ", n=60, c="-"))
+        _print_row("Start buffer:", f"{playback.get('buffer_chunks')} chunk(s)")
+        _print_row("Underruns:", str(playback.get("underrun_count")))
+        _print_row("Total stall:", f"{playback.get('total_stall_ms', 0.0):.1f} ms")
+        _print_row("Stall ratio:", f"{playback.get('stall_ratio', 0.0) * 100:.2f}%")
+        if playback.get("first_underrun_chunk") is not None:
+            _print_row("First underrun at chunk:", str(playback["first_underrun_chunk"]))
+    else:
+        print("{s:{c}^{n}}".format(s=" Across sessions ", n=60, c="-"))
+        if metrics.get("ttfc_ms_mean") is not None:
+            _print_row("TTFC mean / max:", f"{metrics['ttfc_ms_mean']:.1f} / {metrics['ttfc_ms_max']:.1f} ms")
+        if metrics.get("steady_interval_ms_mean") is not None:
+            _print_row("Steady interval mean:", f"{metrics['steady_interval_ms_mean']:.1f} ms")
+            _print_row("Steady interval spread:", f"{metrics['steady_interval_ms_spread']:.1f} ms")
+        _print_row("VIDEO_RTF mean (lower is better):", f"{metrics['video_rtf_mean']:.3f}")
+        _print_row(
+            "VIDEO_RTF best / worst:",
+            f"{metrics['video_rtf_best']:.3f} / {metrics['video_rtf_worst']:.3f}",
+        )
+        _print_row("Chunk deadline:", f"{metrics['chunk_deadline_ms']:.1f} ms")
+        _print_row("Underruns (all sessions):", str(metrics["underrun_count"]))
+    print("=" * 60)
+
+
+def save_video(media: bytes, path: Path, *, fps: int) -> None:
+    from vllm_omni.diffusion.utils.media_utils import finalize_streaming_video_bytes
+
+    playback_bytes = finalize_streaming_video_bytes(media, input_format="m4s", fps=fps)
+    path.parent.mkdir(parents=True, exist_ok=True)
+    tmp_path = path.with_suffix(path.suffix + ".tmp")
+    tmp_path.write_bytes(playback_bytes)
+    os.replace(tmp_path, path)
+    print(f"Saved {len(media)} streamed bytes -> {len(playback_bytes)} playback bytes at {path}")
+
+
+# --------------------------------------------------------------------------- #
+# CLI
+# --------------------------------------------------------------------------- #
+
+
+def parse_args(argv: list[str] | None = None) -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description="Benchmark LingBot-World 2.0 realtime streaming over WS /v1/realtime/video.",
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+    endpoint = parser.add_argument_group("endpoint")
+    endpoint.add_argument("--host", default="127.0.0.1")
+    endpoint.add_argument("--port", type=int, default=8000)
+    endpoint.add_argument("--base-url", default=None, help="Full ws:// URL; overrides --host/--port.")
+    endpoint.add_argument("--model", default=DEFAULT_MODEL)
+
+    workload = parser.add_argument_group("workload")
+    workload.add_argument("--workload", default=None, help="JSON rollout spec; overrides the built-in workload.")
+    workload.add_argument("--image", default=None, help="First frame: path or URL. Defaults to the shared vLLM asset.")
+    workload.add_argument("--prompt", dest="prompt_override", default=None, help=f"Default: {DEFAULT_PROMPT!r}")
+    workload.add_argument("--negative-prompt", default=None)
+    workload.add_argument("--num-chunks", type=int, default=16, help="AR blocks per session; ignored with --workload.")
+    workload.add_argument(
+        "--camera-pattern",
+        choices=CAMERA_PATTERNS,
+        default="forward",
+        help="Camera pattern for the built-in workload only; ignored with --workload.",
+    )
+    workload.add_argument("--width", dest="width_override", type=int, default=None, help=f"Default: {DEFAULT_WIDTH}")
+    workload.add_argument("--height", dest="height_override", type=int, default=None, help=f"Default: {DEFAULT_HEIGHT}")
+    workload.add_argument("--fps", dest="fps_override", type=int, default=None, help=f"Default: {DEFAULT_FPS}")
+    workload.add_argument("--seed", dest="seed_override", type=int, default=None, help=f"Default: {DEFAULT_SEED}")
+    workload.add_argument("--flow-shift", type=float, default=None, help=f"Default: {DEFAULT_FLOW_SHIFT}")
+
+    run = parser.add_argument_group("run")
+    run.add_argument("--sessions", type=int, default=1, help="Sequential measured rollouts.")
+    run.add_argument("--warmup-sessions", type=int, default=0, help="Unmeasured rollouts first (pays compile cost).")
+    run.add_argument(
+        "--warmup-chunks",
+        type=int,
+        default=DEFAULT_WARMUP_CHUNKS,
+        help="Chunks excluded from steady state; the attention window saturates after 6.",
+    )
+    run.add_argument(
+        "--chunk-slo-ms",
+        type=float,
+        default=None,
+        help="Deadline a steady chunk must meet. Default: one chunk of video at --target-fps.",
+    )
+    run.add_argument(
+        "--target-fps",
+        type=float,
+        default=None,
+        help=(
+            "Playback rate used for real-time factor, the chunk deadline, and the playback "
+            "simulation. The checkpoint declares no frame rate, so this is a consumption "
+            "choice, not a model property. Default: the --fps sent to the server."
+        ),
+    )
+    run.add_argument("--playback-buffer-chunks", type=int, default=1, help="Chunks buffered before playback starts.")
+    run.add_argument("--first-chunk-timeout", type=float, default=900.0)
+    run.add_argument("--chunk-timeout", type=float, default=300.0)
+    run.add_argument("--ping-interval", type=float, default=20.0, help="session.ping cadence; 0 disables.")
+
+    output = parser.add_argument_group("output")
+    output.add_argument("--output-json", default=None)
+    output.add_argument("--save-video", default=None, help="Write the last measured session's video here.")
+    output.add_argument("--print-chunks", action="store_true")
+
+    args = parser.parse_args(argv)
+    if args.sessions < 1:
+        parser.error("--sessions must be at least 1.")
+    if args.warmup_sessions < 0:
+        parser.error("--warmup-sessions must be non-negative.")
+    if args.warmup_chunks < 0:
+        parser.error("--warmup-chunks must be non-negative.")
+    if args.playback_buffer_chunks < 1:
+        parser.error("--playback-buffer-chunks must be at least 1.")
+    if args.target_fps is not None and args.target_fps <= 0:
+        parser.error("--target-fps must be positive.")
+    if args.workload is None and args.num_chunks < 1:
+        parser.error("--num-chunks must be at least 1.")
+    return args
+
+
+def endpoint_url(args: argparse.Namespace) -> str:
+    if args.base_url is not None:
+        base = args.base_url.rstrip("/")
+        for prefix, replacement in (("http://", "ws://"), ("https://", "wss://")):
+            if base.startswith(prefix):
+                base = replacement + base[len(prefix) :]
+                break
+        return base if base.endswith(_WS_PATH) else base + _WS_PATH
+    return f"ws://{args.host}:{args.port}{_WS_PATH}"
+
+
+async def run_benchmark(args: argparse.Namespace) -> dict[str, Any]:
+    workload = build_workload(args)
+    url = endpoint_url(args)
+    # The mux label and the real-time basis are different questions; only the
+    # latter decides whether a run passes.
+    target_fps = args.target_fps if args.target_fps is not None else float(workload.fps)
+    if max(1, args.warmup_chunks) >= workload.num_chunks - 1:
+        print(
+            f"WARNING: --warmup-chunks={args.warmup_chunks} leaves no nonterminal steady-state chunk in a "
+            f"{workload.num_chunks}-chunk rollout; steady-state metrics will be empty.",
+            file=sys.stderr,
+        )
+
+    print(f"Connecting to {url}")
+    print(
+        f"Rollout: {workload.num_chunks} chunks, num_frames={workload.num_frames}, "
+        f"{workload.video_seconds:.2f}s of video"
+    )
+    print(
+        f"Real-time basis: {target_fps:g} fps -> a steady 12-frame chunk is "
+        f"{steady_chunk_deadline_ms(target_fps):.1f} ms of video"
+        + ("" if args.target_fps is None else f" (mux label stays {workload.fps} fps)")
+    )
+
+    for index in range(args.warmup_sessions):
+        print(f"Warmup session {index + 1}/{args.warmup_sessions} (not measured)...")
+        await run_session(
+            url,
+            args.model,
+            workload,
+            first_chunk_timeout=args.first_chunk_timeout,
+            chunk_timeout=args.chunk_timeout,
+            ping_interval=args.ping_interval,
+            collect_media=False,
+            print_chunks=False,
+        )
+
+    session_metrics: list[dict[str, Any]] = []
+    session_payloads: list[dict[str, Any]] = []
+    last_result: SessionResult | None = None
+    for index in range(args.sessions):
+        if args.sessions > 1:
+            print(f"Session {index + 1}/{args.sessions}...")
+        result = await run_session(
+            url,
+            args.model,
+            workload,
+            first_chunk_timeout=args.first_chunk_timeout,
+            chunk_timeout=args.chunk_timeout,
+            ping_interval=args.ping_interval,
+            collect_media=args.save_video is not None,
+            print_chunks=args.print_chunks,
+        )
+        last_result = result
+        metrics = compute_metrics(
+            result.records,
+            fps=target_fps,
+            warmup_chunks=args.warmup_chunks,
+            slo_ms=args.chunk_slo_ms,
+            playback_buffer_chunks=args.playback_buffer_chunks,
+            ttfc_s=result.ttfc_s,
+            session_wall_s=result.wall_s,
+        )
+        session_metrics.append(metrics)
+        session_payloads.append(
+            {
+                "session_index": index,
+                "request_id": result.request_id,
+                "video_start_ms": (result.video_start_s * 1000.0) if result.video_start_s is not None else None,
+                "chunks": [record.to_dict() for record in result.records],
+                "metrics": metrics,
+            }
+        )
+
+    aggregate = aggregate_metrics(session_metrics, fps=target_fps)
+    print_report(aggregate, workload=workload, endpoint=url, sessions=args.sessions, target_fps=target_fps)
+
+    if args.save_video is not None and last_result is not None:
+        save_video(last_result.media, Path(args.save_video), fps=workload.fps)
+
+    document = {
+        "benchmark": "lingbot_world_realtime",
+        "timestamp": datetime.now(timezone.utc).isoformat(),
+        "endpoint": url,
+        "model": args.model,
+        "git_revision": _git_revision(),
+        "hostname": platform.node(),
+        "workload": workload.describe(),
+        "config": {
+            "sessions": args.sessions,
+            "warmup_sessions": args.warmup_sessions,
+            "warmup_chunks": args.warmup_chunks,
+            "camera_pattern": args.camera_pattern,
+            "chunk_slo_ms": args.chunk_slo_ms,
+            "target_fps": target_fps,
+            "mux_fps": workload.fps,
+            "playback_buffer_chunks": args.playback_buffer_chunks,
+            "workload_file": args.workload,
+        },
+        "sessions": session_payloads,
+        "metrics": aggregate,
+    }
+    if args.output_json is not None:
+        path = Path(args.output_json)
+        path.parent.mkdir(parents=True, exist_ok=True)
+        path.write_text(json.dumps(document, indent=2, sort_keys=True) + "\n")
+        print(f"Wrote {path}")
+    return document
+
+
+def main(argv: list[str] | None = None) -> int:
+    args = parse_args(argv)
+    try:
+        asyncio.run(run_benchmark(args))
+    except BenchmarkError as error:
+        print(f"ERROR: {error}", file=sys.stderr)
+        return 1
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

benchmarks/lingbot_world/configs/single_gpu_eager.yaml

@@ -0,0 +1,45 @@
+# Benchmark deploy config: LingBot-World 2.0 on one GPU, eager.
+#
+# This is the portable smoke configuration -- it matches the shipped
+# vllm_omni/deploy/lingbot_world_v2_stepwise.yaml so a number measured here is
+# comparable to the default deployment. It is NOT a realtime configuration: one
+# GPU runs all five forwards of every AR block serially and nothing is compiled,
+# so expect a chunk interval several times the real-time deadline.
+#
+# Use configs/usp4_compiled.yaml for the realtime topology.
+#
+#   vllm serve robbyant/lingbot-world-v2-14b-causal-fast-diffusers \
+#     --omni --deploy-config benchmarks/lingbot_world/configs/single_gpu_eager.yaml \
+#     --port 8000
+
+pipeline: lingbot_world
+async_chunk: false
+distributed_executor_backend: mp
+dtype: bfloat16
+
+stages:
+  - stage_id: 0
+    devices: "0"
+    # AR-Diffusion binds one session per runner invocation; the runner rejects
+    # anything larger.
+    max_num_seqs: 1
+    enforce_eager: true
+    model_class_name: LingBotWorldCausalDMDPipeline
+    engine_backend: vllm_omni.experimental.ar_diffusion.engine.ARDiffusionEngine
+    step_execution: true
+    streaming_output: true
+    default_sampling_params:
+      # The causal-fast checkpoint pins a four-step DMD schedule; this sizes the
+      # step budget before the pipeline is reached.
+      num_inference_steps: 4
+      output_type: pil
+      extra_args:
+        flow_shift: 5.0
+    model_config:
+      # The AR cache geometry is fixed at load time, so a benchmark request must
+      # ask for exactly this resolution.
+      ar_diffusion_height: 480
+      ar_diffusion_width: 832
+      ar_diffusion_kv_config:
+        gpu_memory_fraction: 0.6
+        warmup_cudagraph: true

benchmarks/lingbot_world/configs/usp4_compiled.yaml

@@ -0,0 +1,60 @@
+# Benchmark deploy config: LingBot-World 2.0 on four GPUs, Ulysses SP-4, compiled.
+#
+# This is the realtime topology. Two things differ from the single-GPU config,
+# and both matter to the number the benchmark reports:
+#
+#   parallel_config   Pure Ulysses sequence parallelism over four ranks. The
+#                     pipeline validates this: LingBot requires
+#                     sequence_parallel_size == ulysses_degree, ring_degree and
+#                     allgather_degree of 1, ulysses_mode "strict", and
+#                     ulysses_a2a_permute disabled. Pipeline, CFG, and VAE
+#                     parallel sizes above 1 are rejected. This config uses
+#                     TP=1; the validator does not reject larger TP sizes.
+#   enforce_eager     false puts LingBotDMDBlockRunner on the compiled path
+#                     (regional torch.compile with cudagraph step marking). The
+#                     first rollout therefore pays compile and capture cost --
+#                     use --warmup-sessions 1 before measured repetitions.
+#                     Chunk warmup only excludes the attention-window ramp.
+#
+# Serve it under a GPU reservation, since it needs four cards:
+#
+#   gpu run --gpus 4 -- vllm serve robbyant/lingbot-world-v2-14b-causal-fast-diffusers \
+#     --omni --deploy-config benchmarks/lingbot_world/configs/usp4_compiled.yaml \
+#     --port 8000
+#
+# `devices` is the rank->card mapping inside the reservation, so it stays
+# "0,1,2,3" no matter which physical cards CUDA_VISIBLE_DEVICES selected.
+
+pipeline: lingbot_world
+async_chunk: false
+distributed_executor_backend: mp
+dtype: bfloat16
+
+stages:
+  - stage_id: 0
+    devices: "0,1,2,3"
+    max_num_seqs: 1
+    enforce_eager: false
+    model_class_name: LingBotWorldCausalDMDPipeline
+    engine_backend: vllm_omni.experimental.ar_diffusion.engine.ARDiffusionEngine
+    step_execution: true
+    streaming_output: true
+    parallel_config:
+      tensor_parallel_size: 1
+      sequence_parallel_size: 4
+      ulysses_degree: 4
+      ring_degree: 1
+    default_sampling_params:
+      num_inference_steps: 4
+      output_type: pil
+      extra_args:
+        flow_shift: 5.0
+    model_config:
+      ar_diffusion_height: 480
+      ar_diffusion_width: 832
+      ar_diffusion_kv_config:
+        # Each rank holds its own shard of the paged KV cache, so the per-card
+        # fraction can stay at the single-GPU value; lower it if the four-rank
+        # start OOMs on smaller cards.
+        gpu_memory_fraction: 0.6
+        warmup_cudagraph: true

benchmarks/lingbot_world/workload.py

@@ -0,0 +1,631 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+"""Workload construction and metric math for the LingBot-World realtime benchmark.
+
+Nothing here imports torch, vllm, or websockets, so the arithmetic that decides
+what a benchmark number *means* is unit-testable without a GPU or a server.
+
+Two model properties drive every formula in this module, both read from the
+checkpoint's ``transformer/config.json`` rather than assumed:
+
+``num_frames_per_block = 3``
+    One AR block commits three latent frames, so one streamed chunk is three
+    latent frames and a request's ``camera_action_script`` carries exactly one
+    three-entry action list per chunk.
+
+``sliding_window_num_frames = 18`` with ``sink_size = 9``
+    Self-attention sees nine sink frames plus a nine-frame recent window, so the
+    key count a block attends over keeps growing until eighteen latent frames of
+    history exist -- six chunks. Chunk cost is therefore *not* stationary before
+    chunk six, and a mean taken over a short rollout reports a number no steady
+    session will ever produce. :data:`DEFAULT_WARMUP_CHUNKS` exists for that
+    reason and is not a round-number guess.
+
+The causal VAE expands the first latent frame to one pixel frame and every later
+latent frame to four, so chunk zero carries nine pixel frames and every chunk
+after it carries twelve. Video-duration math has to respect that asymmetry or
+real-time factor comes out wrong on short rollouts.
+"""
+
+from __future__ import annotations
+
+import json
+from collections.abc import Iterable, Mapping, Sequence
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any
+
+# transformer.config.num_frames_per_block: latent frames committed per AR block.
+FRAMES_PER_BLOCK = 3
+# vae_scale_factor_temporal: latent frames -> pixel frames, after the first.
+TEMPORAL_COMPRESSION = 4
+# (sink_size 9 + recent window 9) / FRAMES_PER_BLOCK: chunks until the attention
+# window saturates and per-chunk cost becomes stationary.
+DEFAULT_WARMUP_CHUNKS = 6
+
+DEFAULT_MODEL = "robbyant/lingbot-world-v2-14b-causal-fast-diffusers"
+DEFAULT_PROMPT = "The camera moves slowly forward through the scene."
+# The AR KV cache geometry is fixed at load time, so a request must ask for
+# exactly the resolution the deploy config declares.
+DEFAULT_WIDTH = 832
+DEFAULT_HEIGHT = 480
+DEFAULT_FPS = 16
+DEFAULT_FLOW_SHIFT = 5.0
+DEFAULT_SEED = 42
+
+_HOLD = "hold"
+_ACTION_KEYS = ("w", "a", "s", "d")
+
+
+def num_frames_for_chunks(num_chunks: int) -> int:
+    """Pixel-frame count whose rollout is exactly ``num_chunks`` chunks.
+
+    Inverse of :func:`chunks_for_num_frames`, which is the server's own
+    ``((num_frames - 1) // 4 + 1) // 3``.
+    """
+    if num_chunks < 1:
+        raise ValueError("num_chunks must be at least 1.")
+    return (num_chunks * FRAMES_PER_BLOCK - 1) * TEMPORAL_COMPRESSION + 1
+
+
+def chunks_for_num_frames(num_frames: int) -> int:
+    """Chunks the server generates for ``num_frames``, mirroring its own formula."""
+    if num_frames < 1:
+        raise ValueError("num_frames must be at least 1.")
+    return ((num_frames - 1) // TEMPORAL_COMPRESSION + 1) // FRAMES_PER_BLOCK
+
+
+def pixel_frames_in_chunk(chunk_index: int) -> int:
+    """Pixel frames carried by one chunk.
+
+    The causal decoder expands the session's opening latent frame once and every
+    later latent frame four times, so chunk zero is short by three frames.
+    """
+    if chunk_index < 0:
+        raise ValueError("chunk_index must be non-negative.")
+    if chunk_index == 0:
+        return (FRAMES_PER_BLOCK - 1) * TEMPORAL_COMPRESSION + 1
+    return FRAMES_PER_BLOCK * TEMPORAL_COMPRESSION
+
+
+def camera_actions_for_chunk(pattern: str, chunk_index: int) -> list[list[str]]:
+    """One three-entry action list for ``chunk_index``.
+
+    Camera actions select camera embeddings; they change what the world does,
+    not how much arithmetic it costs. The pattern therefore exists to keep a
+    benchmark rollout representative rather than to sweep a cost dimension.
+    """
+    if chunk_index < 0:
+        raise ValueError("chunk_index must be non-negative.")
+    if pattern == "forward":
+        return [["w"] for _ in range(FRAMES_PER_BLOCK)]
+    if pattern == _HOLD:
+        return [[] for _ in range(FRAMES_PER_BLOCK)]
+    if pattern == "orbit":
+        key = _ACTION_KEYS[chunk_index % len(_ACTION_KEYS)]
+        return [[key] for _ in range(FRAMES_PER_BLOCK)]
+    raise ValueError(f"Unknown camera pattern {pattern!r}; expected one of {sorted(CAMERA_PATTERNS)}.")
+
+
+CAMERA_PATTERNS = ("forward", "orbit", _HOLD)
+
+
+def build_camera_script(num_chunks: int, pattern: str) -> list[list[list[str]]]:
+    """Full ``camera_action_script``: one action list per generated chunk."""
+    if num_chunks < 1:
+        raise ValueError("num_chunks must be at least 1.")
+    return [camera_actions_for_chunk(pattern, index) for index in range(num_chunks)]
+
+
+def _validate_camera_script(script: Sequence[Any]) -> list[list[list[str]]]:
+    validated: list[list[list[str]]] = []
+    for chunk_index, actions in enumerate(script):
+        if not isinstance(actions, Sequence) or isinstance(actions, (str, bytes)):
+            raise ValueError(f"camera_action_script[{chunk_index}] must be a list of three action lists.")
+        if len(actions) != FRAMES_PER_BLOCK:
+            raise ValueError(
+                f"camera_action_script[{chunk_index}] must hold exactly {FRAMES_PER_BLOCK} action lists "
+                f"(got {len(actions)}); one AR block is {FRAMES_PER_BLOCK} latent frames."
+            )
+        frames: list[list[str]] = []
+        for frame_index, frame in enumerate(actions):
+            if not isinstance(frame, Sequence) or isinstance(frame, (str, bytes)):
+                raise ValueError(f"camera_action_script[{chunk_index}][{frame_index}] must be a list of strings.")
+            if any(not isinstance(action, str) for action in frame):
+                raise ValueError(f"camera_action_script[{chunk_index}][{frame_index}] must contain only strings.")
+            frames.append([str(action) for action in frame])
+        validated.append(frames)
+    if not validated:
+        raise ValueError("camera_action_script must not be empty.")
+    return validated
+
+
+@dataclass(frozen=True)
+class PromptUpdate:
+    """A mid-rollout ``session.interaction`` scheduled on a chunk boundary."""
+
+    after_chunk: int
+    prompt: str
+    transition_chunks: int = 3
+
+    def __post_init__(self) -> None:
+        if self.after_chunk < 0:
+            raise ValueError("PromptUpdate.after_chunk must be non-negative.")
+        if not self.prompt.strip():
+            raise ValueError("PromptUpdate.prompt must contain non-whitespace text.")
+        if self.transition_chunks < 0:
+            raise ValueError("PromptUpdate.transition_chunks must be non-negative.")
+
+    def to_payload(self) -> dict[str, Any]:
+        return {
+            "type": "session.interaction",
+            "interaction": {
+                "event_id": f"chunk-{self.after_chunk}",
+                "event": {"prompt": self.prompt},
+                "transition_chunks": self.transition_chunks,
+            },
+        }
+
+
+@dataclass(frozen=True)
+class Workload:
+    """One rollout: what the benchmark asks the server to generate."""
+
+    prompt: str
+    image_reference: str
+    camera_script: list[list[list[str]]]
+    width: int = DEFAULT_WIDTH
+    height: int = DEFAULT_HEIGHT
+    fps: int = DEFAULT_FPS
+    seed: int = DEFAULT_SEED
+    flow_shift: float = DEFAULT_FLOW_SHIFT
+    negative_prompt: str | None = None
+    prompt_updates: tuple[PromptUpdate, ...] = ()
+    extra_params: Mapping[str, Any] = field(default_factory=dict)
+
+    def __post_init__(self) -> None:
+        if not self.prompt.strip():
+            raise ValueError("Workload.prompt must contain non-whitespace text.")
+        if not self.image_reference:
+            raise ValueError("Workload.image_reference is required; LingBot-World is image-conditioned.")
+        if self.width <= 0 or self.height <= 0 or self.width % 16 or self.height % 16:
+            raise ValueError("Workload width/height must be positive multiples of 16.")
+        if self.fps <= 0:
+            raise ValueError("Workload.fps must be positive.")
+        object.__setattr__(self, "camera_script", _validate_camera_script(self.camera_script))
+        seen_update_chunks: set[int] = set()
+        for update in self.prompt_updates:
+            if update.after_chunk in seen_update_chunks:
+                raise ValueError(f"Duplicate prompt update after chunk {update.after_chunk}; event IDs must be unique.")
+            seen_update_chunks.add(update.after_chunk)
+            if update.after_chunk >= self.num_chunks:
+                raise ValueError(
+                    f"Prompt update after chunk {update.after_chunk} never fires in a {self.num_chunks}-chunk rollout."
+                )
+
+    @property
+    def num_chunks(self) -> int:
+        return len(self.camera_script)
+
+    @property
+    def num_frames(self) -> int:
+        return num_frames_for_chunks(self.num_chunks)
+
+    @property
+    def video_seconds(self) -> float:
+        return self.num_frames / self.fps
+
+    def session_start_payload(self, model: str) -> dict[str, Any]:
+        """The ``session.start`` event for ``WS /v1/realtime/video``."""
+        extra_params: dict[str, Any] = {
+            "flow_shift": self.flow_shift,
+            "camera_action_script": self.camera_script,
+        }
+        extra_params.update(self.extra_params)
+        payload: dict[str, Any] = {
+            "type": "session.start",
+            "model": model,
+            "prompt": self.prompt,
+            "image_reference": {"image_url": self.image_reference},
+            "width": self.width,
+            "height": self.height,
+            "num_frames": self.num_frames,
+            "fps": self.fps,
+            "seed": self.seed,
+            "extra_params": extra_params,
+        }
+        if self.negative_prompt is not None:
+            payload["negative_prompt"] = self.negative_prompt
+        return payload
+
+    def describe(self) -> dict[str, Any]:
+        return {
+            "num_chunks": self.num_chunks,
+            "num_frames": self.num_frames,
+            "width": self.width,
+            "height": self.height,
+            "fps": self.fps,
+            "seed": self.seed,
+            "flow_shift": self.flow_shift,
+            "video_seconds": self.video_seconds,
+            "prompt_updates": [
+                {
+                    "after_chunk": update.after_chunk,
+                    "transition_chunks": update.transition_chunks,
+                }
+                for update in self.prompt_updates
+            ],
+        }
+
+
+def default_workload(
+    *,
+    image_reference: str,
+    num_chunks: int,
+    camera_pattern: str = "forward",
+    prompt: str = DEFAULT_PROMPT,
+    width: int = DEFAULT_WIDTH,
+    height: int = DEFAULT_HEIGHT,
+    fps: int = DEFAULT_FPS,
+    seed: int = DEFAULT_SEED,
+    flow_shift: float = DEFAULT_FLOW_SHIFT,
+    negative_prompt: str | None = None,
+) -> Workload:
+    """Built-in rollout: a fixed prompt and a generated camera script."""
+    return Workload(
+        prompt=prompt,
+        image_reference=image_reference,
+        camera_script=build_camera_script(num_chunks, camera_pattern),
+        width=width,
+        height=height,
+        fps=fps,
+        seed=seed,
+        flow_shift=flow_shift,
+        negative_prompt=negative_prompt,
+    )
+
+
+def load_workload(
+    path: str | Path,
+    *,
+    image_resolver: Any = None,
+    overrides: Mapping[str, Any] | None = None,
+) -> Workload:
+    """Read a rollout spec from JSON.
+
+    ``image_resolver`` turns the spec's ``image`` field into an ``http(s)`` or
+    ``data:`` URL; the caller owns that because inlining a local file is I/O.
+    Keys in ``overrides`` that are not ``None`` win over the file, so CLI flags
+    stay meaningful next to ``--workload``.
+    """
+    document = json.loads(Path(path).read_text())
+    if not isinstance(document, dict):
+        raise ValueError("A workload file must contain a JSON object.")
+
+    merged = dict(document)
+    for key, value in (overrides or {}).items():
+        if value is not None:
+            merged[key] = value
+
+    camera_script = merged.get("camera_action_script")
+    if camera_script is None:
+        num_chunks = merged.get("num_chunks")
+        if num_chunks is None:
+            raise ValueError("A workload file needs either camera_action_script or num_chunks.")
+        camera_script = build_camera_script(int(num_chunks), str(merged.get("camera_pattern", "forward")))
+
+    image = merged.get("image") or merged.get("image_reference")
+    if image is None:
+        raise ValueError("A workload file needs an image or image_reference field.")
+    image_reference = image_resolver(str(image)) if image_resolver is not None else str(image)
+
+    updates_field = merged.get("prompt_updates") or ()
+    prompt_updates = tuple(
+        PromptUpdate(
+            after_chunk=int(entry["after_chunk"]),
+            prompt=str(entry["prompt"]),
+            transition_chunks=int(entry.get("transition_chunks", 3)),
+        )
+        for entry in updates_field
+    )
+
+    extra_params = merged.get("extra_params") or {}
+    if not isinstance(extra_params, Mapping):
+        raise ValueError("Workload extra_params must be a JSON object.")
+
+    return Workload(
+        prompt=str(merged.get("prompt", DEFAULT_PROMPT)),
+        image_reference=image_reference,
+        camera_script=camera_script,
+        width=int(merged.get("width", DEFAULT_WIDTH)),
+        height=int(merged.get("height", DEFAULT_HEIGHT)),
+        fps=int(merged.get("fps", DEFAULT_FPS)),
+        seed=int(merged.get("seed", DEFAULT_SEED)),
+        flow_shift=float(merged.get("flow_shift", DEFAULT_FLOW_SHIFT)),
+        negative_prompt=merged.get("negative_prompt"),
+        prompt_updates=prompt_updates,
+        extra_params=dict(extra_params),
+    )
+
+
+# --------------------------------------------------------------------------- #
+# Metrics
+# --------------------------------------------------------------------------- #
+
+
+@dataclass
+class ChunkRecord:
+    """One media chunk as the client observed it."""
+
+    index: int
+    arrival_s: float
+    """Seconds from ``session.start`` being sent to this chunk's bytes arriving."""
+    inter_arrival_s: float
+    """Seconds since the previous chunk; for chunk zero this equals ``arrival_s``."""
+    byte_length: int
+    num_frames: int
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "index": self.index,
+            "arrival_s": self.arrival_s,
+            "inter_arrival_ms": self.inter_arrival_s * 1000.0,
+            "byte_length": self.byte_length,
+            "num_frames": self.num_frames,
+        }
+
+
+def percentile(values: Sequence[float], fraction: float) -> float:
+    """Linearly interpolated percentile, so a 10-sample run still reports a p95.
+
+    Implemented here rather than pulled from numpy to keep this module importable
+    in a bare environment; the benchmark's own numbers must not depend on which
+    optional package happens to be installed.
+    """
+    if not values:
+        raise ValueError("percentile() needs at least one value.")
+    if not 0.0 <= fraction <= 1.0:
+        raise ValueError("percentile fraction must be within [0, 1].")
+    ordered = sorted(values)
+    if len(ordered) == 1:
+        return ordered[0]
+    position = fraction * (len(ordered) - 1)
+    lower = int(position)
+    upper = min(lower + 1, len(ordered) - 1)
+    weight = position - lower
+    return ordered[lower] * (1.0 - weight) + ordered[upper] * weight
+
+
+def _stddev(values: Sequence[float]) -> float:
+    if len(values) < 2:
+        return 0.0
+    mean = sum(values) / len(values)
+    return (sum((value - mean) ** 2 for value in values) / (len(values) - 1)) ** 0.5
+
+
+def _latency_summary(values: Sequence[float]) -> dict[str, float]:
+    """Millisecond summary of a set of inter-arrival intervals."""
+    if not values:
+        return {}
+    milliseconds = [value * 1000.0 for value in values]
+    return {
+        "count": float(len(milliseconds)),
+        "mean_ms": sum(milliseconds) / len(milliseconds),
+        "median_ms": percentile(milliseconds, 0.5),
+        "std_ms": _stddev(milliseconds),
+        "min_ms": min(milliseconds),
+        "p90_ms": percentile(milliseconds, 0.90),
+        "p95_ms": percentile(milliseconds, 0.95),
+        "p99_ms": percentile(milliseconds, 0.99),
+        "max_ms": max(milliseconds),
+    }
+
+
+@dataclass(frozen=True)
+class PlaybackResult:
+    """What a viewer would have seen, given a start buffer and a playback rate."""
+
+    underrun_count: int
+    total_stall_s: float
+    playback_span_s: float
+    first_underrun_chunk: int | None
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "underrun_count": self.underrun_count,
+            "total_stall_ms": self.total_stall_s * 1000.0,
+            "playback_span_s": self.playback_span_s,
+            "stall_ratio": (self.total_stall_s / self.playback_span_s) if self.playback_span_s > 0 else 0.0,
+            "first_underrun_chunk": self.first_underrun_chunk,
+        }
+
+
+def simulate_playback(
+    records: Sequence[ChunkRecord],
+    *,
+    fps: float,
+    buffer_chunks: int = 1,
+) -> PlaybackResult:
+    """Replay arrivals against a wall clock and count stalls.
+
+    Playback starts when ``buffer_chunks`` chunks have arrived and then consumes
+    video at ``fps``. A chunk that has not arrived by the moment the player needs
+    it is an underrun, and the player resumes only when the bytes land -- which
+    is what a viewer experiences, and what a single p99 number cannot tell you:
+    the same p99 is harmless behind a three-chunk buffer and fatal behind none.
+    """
+    if fps <= 0:
+        raise ValueError("fps must be positive.")
+    if buffer_chunks < 1:
+        raise ValueError("buffer_chunks must be at least 1.")
+    if not records:
+        return PlaybackResult(0, 0.0, 0.0, None)
+    if buffer_chunks > len(records):
+        raise ValueError(f"buffer_chunks={buffer_chunks} exceeds the {len(records)} chunks received.")
+
+    ordered = sorted(records, key=lambda record: record.index)
+    start = ordered[buffer_chunks - 1].arrival_s
+    cursor = start
+    underruns = 0
+    total_stall = 0.0
+    first_underrun: int | None = None
+
+    for record in ordered:
+        if record.arrival_s > cursor:
+            stall = record.arrival_s - cursor
+            underruns += 1
+            total_stall += stall
+            if first_underrun is None:
+                first_underrun = record.index
+            cursor = record.arrival_s
+        cursor += record.num_frames / fps
+
+    return PlaybackResult(
+        underrun_count=underruns,
+        total_stall_s=total_stall,
+        playback_span_s=cursor - start,
+        first_underrun_chunk=first_underrun,
+    )
+
+
+def steady_chunk_deadline_ms(fps: float) -> float:
+    """Wall-clock budget a steady chunk must meet to hold real time.
+
+    ``fps`` here is the rate the frames are *intended to be played at*, which the
+    checkpoint does not declare: nothing in ``model_index.json``, the scheduler
+    config, or the transformer config carries a frame rate, and the pipeline
+    never reads one. It is a property of how the output is consumed, so both
+    this deadline and real-time factor scale linearly with the choice and the
+    caller has to state it rather than inherit it from a muxing default.
+    """
+    if fps <= 0:
+        raise ValueError("fps must be positive.")
+    return pixel_frames_in_chunk(1) / fps * 1000.0
+
+
+def compute_metrics(
+    records: Sequence[ChunkRecord],
+    *,
+    fps: float,
+    warmup_chunks: int = DEFAULT_WARMUP_CHUNKS,
+    slo_ms: float | None = None,
+    playback_buffer_chunks: int = 1,
+    ttfc_s: float | None = None,
+    session_wall_s: float | None = None,
+) -> dict[str, Any]:
+    """Summarize one session.
+
+    ``warmup_chunks`` are excluded from the steady-state block because the
+    attention window has not saturated before then (see the module docstring).
+    The terminal chunk also stays out of steady metrics because it skips
+    next-chunk preparation. All chunks stay in the playback simulation, which is
+    what a session really costs end to end.
+    """
+    if not records:
+        raise ValueError("compute_metrics() needs at least one chunk record.")
+    if warmup_chunks < 0:
+        raise ValueError("warmup_chunks must be non-negative.")
+
+    ordered = sorted(records, key=lambda record: record.index)
+    deadline_ms = slo_ms if slo_ms is not None else steady_chunk_deadline_ms(fps)
+
+    all_intervals = [record.inter_arrival_s for record in ordered[1:]]
+    # The terminal chunk skips next-chunk preparation and is not representative
+    # of sustained generation. Keep it in end-to-end and playback metrics.
+    steady = [record for record in ordered[:-1] if record.index >= warmup_chunks]
+    steady_intervals = [record.inter_arrival_s for record in steady if record.index > 0]
+
+    total_frames = sum(record.num_frames for record in ordered)
+    video_seconds = total_frames / fps
+    wall_seconds = session_wall_s if session_wall_s is not None else ordered[-1].arrival_s
+
+    metrics: dict[str, Any] = {
+        "chunks_received": len(ordered),
+        "warmup_chunks": warmup_chunks,
+        "steady_chunks": len(steady_intervals),
+        "steady_state_reached": bool(steady_intervals),
+        "total_frames": total_frames,
+        "total_bytes": sum(record.byte_length for record in ordered),
+        "video_seconds": video_seconds,
+        "wall_seconds": wall_seconds,
+        # RTF is processing time over content duration, as vLLM-Omni computes it
+        # (`patch.py`: generation_s / video_duration; `compute_audio_rtf`: latency
+        # / audio_duration, "SLO red line < 1"). LOWER IS BETTER and below 1.0 is
+        # real time. RTFX is the reciprocal the repo reports under its own name,
+        # kept here because "fraction of real time achieved" reads better on a
+        # dashboard -- but the two must never be confused for one another.
+        "video_rtf": (wall_seconds / video_seconds) if video_seconds > 0 else 0.0,
+        "video_rtfx": (video_seconds / wall_seconds) if wall_seconds > 0 else 0.0,
+        "frames_per_second": (total_frames / wall_seconds) if wall_seconds > 0 else 0.0,
+        "chunk_deadline_ms": deadline_ms,
+        "interval_all": _latency_summary(all_intervals),
+        "interval_steady": _latency_summary(steady_intervals),
+    }
+    if ttfc_s is not None:
+        metrics["ttfc_ms"] = ttfc_s * 1000.0
+
+    if steady_intervals:
+        steady_frames = sum(record.num_frames for record in steady if record.index > 0)
+        steady_wall = sum(steady_intervals)
+        steady_video = steady_frames / fps
+        metrics["steady_video_rtf"] = (steady_wall / steady_video) if steady_video > 0 else 0.0
+        metrics["steady_video_rtfx"] = (steady_video / steady_wall) if steady_wall > 0 else 0.0
+        within = sum(1 for value in steady_intervals if value * 1000.0 <= deadline_ms)
+        metrics["slo_attainment"] = within / len(steady_intervals)
+        metrics["slo_violations"] = len(steady_intervals) - within
+
+    metrics["playback"] = simulate_playback(
+        ordered,
+        fps=fps,
+        buffer_chunks=playback_buffer_chunks,
+    ).to_dict()
+    metrics["playback"]["buffer_chunks"] = playback_buffer_chunks
+    return metrics
+
+
+def aggregate_metrics(sessions: Iterable[Mapping[str, Any]], *, fps: float) -> dict[str, Any]:
+    """Combine per-session metrics across sequential repeats.
+
+    Steady intervals are pooled rather than averaging per-session means, so a
+    session that happened to produce fewer steady chunks does not carry the same
+    weight as a full one.
+    """
+    entries = list(sessions)
+    if not entries:
+        raise ValueError("aggregate_metrics() needs at least one session.")
+    if len(entries) == 1:
+        single = dict(entries[0])
+        single["sessions"] = 1
+        return single
+
+    deadline_ms = entries[0]["chunk_deadline_ms"]
+    if any(entry["chunk_deadline_ms"] != deadline_ms for entry in entries):
+        raise ValueError("Cannot aggregate sessions with different chunk deadlines.")
+    ttfcs = [float(entry["ttfc_ms"]) for entry in entries if entry.get("ttfc_ms") is not None]
+    # Lower is better, so "max" is the worst session, not the best.
+    rtfs = [float(entry["video_rtf"]) for entry in entries]
+    steady_means = [
+        float(entry["interval_steady"]["mean_ms"]) for entry in entries if entry.get("interval_steady", {}).get("count")
+    ]
+    steady_count = sum(entry.get("interval_steady", {}).get("count", 0) for entry in entries)
+    steady_total_ms = sum(
+        entry["interval_steady"]["mean_ms"] * entry["interval_steady"]["count"]
+        for entry in entries
+        if entry.get("interval_steady", {}).get("count")
+    )
+    return {
+        "sessions": len(entries),
+        "chunks_received": sum(int(entry["chunks_received"]) for entry in entries),
+        "total_frames": sum(int(entry["total_frames"]) for entry in entries),
+        "video_seconds": sum(float(entry["video_seconds"]) for entry in entries),
+        "wall_seconds": sum(float(entry["wall_seconds"]) for entry in entries),
+        "video_rtf_mean": sum(rtfs) / len(rtfs),
+        "video_rtf_best": min(rtfs),
+        "video_rtf_worst": max(rtfs),
+        "ttfc_ms_mean": (sum(ttfcs) / len(ttfcs)) if ttfcs else None,
+        "ttfc_ms_max": max(ttfcs) if ttfcs else None,
+        "steady_interval_ms_mean": (steady_total_ms / steady_count) if steady_count else None,
+        "steady_interval_ms_spread": (max(steady_means) - min(steady_means)) if steady_means else None,
+        "underrun_count": sum(int(entry["playback"]["underrun_count"]) for entry in entries),
+        "chunk_deadline_ms": deadline_ms,
+    }

benchmarks/qwen2_5_omni/README.md

@@ -0,0 +1,66 @@
+# Qwen2.5-Omni embedding microbenchmark
+
+Measure the host-observed latency of `embed_input_ids` before and after the
+redundant text embedding removal in PR #7477. Both variants use the same real
+checkpoint embedding table, dependencies, inputs and batch shapes. The baseline
+wrapper is extracted from the specified Git commit; the fixed wrapper comes from
+the current checkout. No full model is initialized.
+
+## Run
+
+Use Linux, a CUDA GPU, the project dependencies, and a local
+`Qwen/Qwen2.5-Omni-7B` checkpoint with its safetensors index and weights.
+From the repository root:
+
+```bash
+git fetch origin 01a2f93256975c7ff9565c5414b0fe0225bc4765
+PYTHONPATH="$PWD" OMP_NUM_THREADS=1 python benchmarks/qwen2_5_omni/benchmark_embeddings.py \
+  --model /path/to/Qwen2.5-Omni-7B \
+  --baseline-ref 01a2f93256975c7ff9565c5414b0fe0225bc4765 \
+  --cpu-core 2 --lengths 1172 --warmup 50 --blocks 20 --iterations 20 \
+  --output-dir /tmp/qwen25-embedding-results
+```
+
+Choose a CPU core in your allowed affinity set. For the original CUDA length
+sweep, use `--lengths 512 1172 4096 16384`.
+
+## Method
+
+- BF16 checkpoint embeddings, TP=1, CUDA int32 token IDs, CPU multimodal masks.
+- One pinned CPU core, one PyTorch thread, garbage collection disabled.
+- 50 warmups per variant, then 20 paired blocks of 20 calls with balanced,
+  randomized variant order. Summary: median of block medians.
+- Host latency includes synchronization inside the wrapper. Explicit GPU
+  synchronization before and after the timed call is excluded from host latency.
+  Calling-thread CPU time and synchronized completion time are recorded separately.
+- CPU frequency is sampled at block boundaries; clocks are not locked.
+- Validation checks one text embedding call for the fixed wrapper, two for the
+  multimodal baseline, and `torch.equal` for identical inputs and batch shapes.
+  This does not establish batch invariance or full-model output equality.
+
+Interleaved audio/video uses a valid synthetic multimodal mask. This benchmark
+measures wrapper latency, not E2E throughput.
+
+## Reference results
+
+Original environment: H100 80GB; Python 3.12.3; vLLM 0.29.0+cu129;
+PyTorch 2.13.0+cu129; Transformers 5.14.0; Qwen2.5-Omni-7B BF16.
+At 1172 tokens, the September 13 measurements were:
+
+| Input | Before (μs) | After (μs) | Host latency reduction |
+| --- | ---: | ---: | ---: |
+| Text | 18.81 | 18.66 | 0.80% |
+| Audio | 104.32 | 85.30 | 18.23% |
+| Image | 104.72 | 86.27 | 17.62% |
+| Video | 104.73 | 85.76 | 18.11% |
+| Audio + image + video | 567.92 | 549.54 | 3.24% |
+| Interleaved audio/video | 622.76 | 597.84 | 4.00% |
+
+Results vary between runs and environments. These local savings did not produce
+a consistent E2E throughput improvement in the separate repeated E2E experiment.
+
+## Output
+
+The output directory contains `metadata.json`, `micro-raw.json`,
+`validation.json`, and `summary.json`. Raw records include per-call timings,
+block timestamps and CPU frequency samples. Timer overhead is measured separately.

benchmarks/qwen2_5_omni/benchmark_embeddings.py

@@ -0,0 +1,306 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+"""Reproduce PR #7477 host-observed embedding-wrapper latency on one CUDA GPU.
+
+Run from the PR checkout with PYTHONPATH=$PWD. Baseline and fixed methods share
+weights and inputs. Only the historical wrapper is loaded from --baseline-ref.
+"""
+
+import argparse
+import ast
+import gc
+import json
+import os
+import random
+import statistics
+import subprocess
+import textwrap
+import time
+from pathlib import Path
+
+import torch
+from safetensors import safe_open
+from torch import nn
+from transformers import Qwen2_5OmniThinkerConfig
+from vllm.model_executor.layers.vocab_parallel_embedding import VocabParallelEmbedding
+from vllm.multimodal.utils import set_mm_embedding_modality
+
+from vllm_omni.model_executor.models.qwen2_5_omni.qwen2_5_omni_thinker import (
+    Qwen2_5OmniThinkerForConditionalGeneration as Thinker,
+)
+from vllm_omni.platforms import current_omni_platform
+
+
+def baseline_method(cls, ref):
+    path = "vllm_omni/model_executor/models/qwen2_5_omni/qwen2_5_omni_thinker.py"
+    source = subprocess.check_output(
+        ["git", "-C", str(Path(__file__).resolve().parents[2]), "show", f"{ref}:{path}"], text=True
+    )
+    tree = ast.parse(source)
+    owner = next(n for n in tree.body if isinstance(n, ast.ClassDef) and n.name == cls.__name__)
+    node = next(n for n in owner.body if isinstance(n, ast.FunctionDef) and n.name == "embed_input_ids")
+    method = textwrap.dedent(ast.get_source_segment(source, node))
+    # Preserve zero-argument super() against the real model class and MRO.
+    factory = "from __future__ import annotations\ndef make_method(cls):\n    __class__ = cls\n"
+    factory += textwrap.indent(method, "    ") + "\n    return embed_input_ids\n"
+    namespace = dict(cls.embed_input_ids.__globals__)
+    exec(compile(factory, f"{ref}:{path}", "exec"), namespace)
+    return namespace["make_method"](cls)
+
+
+parser = argparse.ArgumentParser(description=__doc__)
+parser.add_argument("--model", type=Path, required=True)
+parser.add_argument("--baseline-ref", default="01a2f93256975c7ff9565c5414b0fe0225bc4765")
+parser.add_argument("--cpu-core", type=int, default=sorted(os.sched_getaffinity(0))[0])
+parser.add_argument("--lengths", type=int, nargs="+", default=[1172])
+parser.add_argument("--warmup", type=int, default=50)
+parser.add_argument("--blocks", type=int, default=20)
+parser.add_argument("--iterations", type=int, default=20)
+parser.add_argument("--output-dir", type=Path, default=Path("micro-results"))
+args = parser.parse_args()
+if min(args.lengths) < 300 or min(args.blocks, args.iterations) < 1 or args.warmup < 0:
+    parser.error("Require lengths >= 300, positive blocks/iterations, and nonnegative warmup")
+if args.cpu_core not in os.sched_getaffinity(0):
+    parser.error("CPU core is outside the allowed affinity set")
+ROOT, MODEL, CORE = args.output_dir, args.model, args.cpu_core
+ROOT.mkdir(parents=True, exist_ok=True)
+CONFIG = json.loads((MODEL / "config.json").read_text())["thinker_config"]
+HIDDEN = CONFIG["text_config"]["hidden_size"]
+VOCAB = CONFIG["text_config"]["vocab_size"]
+IDS = {m: CONFIG[m + "_token_index"] for m in ("audio", "image", "video")}
+METHODS = {"baseline": baseline_method(Thinker, args.baseline_ref), "fixed": Thinker.embed_input_ids}
+RESULTS, VALIDATIONS = [], []
+os.sched_setaffinity(0, {CORE})
+torch.set_num_threads(1)
+torch.set_num_interop_threads(1)
+gc.disable()
+
+
+def cpu_frequency():
+    path = Path(f"/sys/devices/system/cpu/cpu{CORE}/cpufreq/scaling_cur_freq")
+    if path.exists():
+        return float(path.read_text()) / 1000
+    for section in Path("/proc/cpuinfo").read_text().split("\n\n"):
+        fields = dict(line.split(":", 1) for line in section.splitlines() if ":" in line)
+        fields = {k.strip(): v.strip() for k, v in fields.items()}
+        if fields.get("processor") == str(CORE):
+            return float(fields.get("cpu MHz", "nan"))
+
+
+def payload(case, length, device):
+    generator = torch.Generator().manual_seed(7451 + length)
+    ids = torch.randint(2, 10000, (length,), generator=generator)
+    specs = {
+        "text": [],
+        "audio": [("audio", 100)],
+        "image": [("image", 16)],
+        "video": [("video", 128)],
+        "mixed": [("audio", 100), ("image", 16), ("video", 128)],
+        "interleaved": [("video", 64), ("audio", 50), ("video", 64), ("audio", 50)],
+    }[case]
+    offset = 44
+    counts = {}
+    for modality, count in specs:
+        ids[offset : offset + count] = IDS[modality]
+        counts[modality] = counts.get(modality, 0) + count
+        offset += count + (0 if case == "interleaved" else 2)
+    mask = torch.zeros(length, dtype=torch.bool)
+    for token in IDS.values():
+        mask |= ids == token
+    mm = [
+        set_mm_embedding_modality(torch.full((count, HIDDEN), i + 1, device=device, dtype=torch.bfloat16), modality)
+        for i, (modality, count) in enumerate(counts.items())
+    ]
+    return ids.to(device=device, dtype=torch.int32), None if case == "text" else mm, None if case == "text" else mask
+
+
+def once(fn, cuda):
+    # Drain preceding work before the host timer; the production function's
+    # own D2H synchronizations remain inside the timed call.
+    if cuda:
+        current_omni_platform.synchronize()
+    w0 = time.perf_counter_ns()
+    c0 = time.thread_time_ns()
+    value = fn()
+    c1 = time.thread_time_ns()
+    w1 = time.perf_counter_ns()
+    if cuda:
+        current_omni_platform.synchronize()
+    w2 = time.perf_counter_ns() if cuda else w1
+    del value
+    return [(w1 - w0) / 1000, (c1 - c0) / 1000, (w2 - w0) / 1000]
+
+
+def measure(functions, device, case, length, scope, rounds=20, iterations=20, warmups=50):
+    cuda = device == "cuda"
+    for fn in functions.values():
+        for _ in range(warmups):
+            once(fn, cuda)
+    order_rng = random.Random(7451 + length + len(case) + len(scope))
+    directions = [i % 2 for i in range(rounds)]
+    order_rng.shuffle(directions)
+    names = list(functions)
+    for block, direction in enumerate(directions):
+        order = names if direction == 0 else names[::-1]
+        for name in order:
+            frequency_start = cpu_frequency()
+            wall_start = time.time()
+            samples = [once(functions[name], cuda) for _ in range(iterations)]
+            wall_end = time.time()
+            RESULTS.append(
+                dict(
+                    device=device,
+                    case=case,
+                    tokens=length,
+                    scope=scope,
+                    variant=name,
+                    block=block,
+                    iterations=iterations,
+                    samples_us=samples,
+                    wall_start=wall_start,
+                    wall_end=wall_end,
+                    cpu_mhz_start=frequency_start,
+                    cpu_mhz_end=cpu_frequency(),
+                )
+            )
+    (ROOT / "micro-raw.json").write_text(json.dumps(RESULTS))
+    current = [r for r in RESULTS if (r["device"], r["case"], r["tokens"], r["scope"]) == (device, case, length, scope)]
+    print(
+        "RESULT",
+        json.dumps(
+            {
+                "device": device,
+                "case": case,
+                "tokens": length,
+                "scope": scope,
+                "host_median_us": {
+                    v: statistics.median(t[0] for r in current if r["variant"] == v for t in r["samples_us"])
+                    for v in names
+                },
+            }
+        ),
+        flush=True,
+    )
+
+
+def build_model(weights, device):
+    model = Thinker.__new__(Thinker)
+    nn.Module.__init__(model)
+    model.config = Qwen2_5OmniThinkerConfig(**CONFIG)
+    model._has_oov_mm_tokens = False
+    table = VocabParallelEmbedding(VOCAB, HIDDEN, params_dtype=torch.bfloat16, disable_tp=True)
+    table.weight = nn.Parameter(weights.to(device), requires_grad=False)
+
+    class LM(nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.embed_tokens = table
+
+        def embed_input_ids(self, ids):
+            return self.embed_tokens(ids)
+
+    model.language_model = LM()
+    return model
+
+
+def validate(model, args, case, length, device):
+    ids, mm, mask = args
+    lm = model.get_language_model()
+    original = lm.embed_input_ids
+    outputs, counts = {}, {}
+    for variant, method in METHODS.items():
+        calls = []
+
+        def counted(tokens):
+            calls.append(tokens.numel())
+            return original(tokens)
+
+        lm.embed_input_ids = counted
+        try:
+            outputs[variant] = method(model, ids, mm, is_multimodal=mask)
+        finally:
+            lm.embed_input_ids = original
+        counts[variant] = len(calls)
+    assert torch.equal(outputs["baseline"], outputs["fixed"])
+    assert counts == {"baseline": 1 if case == "text" else 2, "fixed": 1}
+    VALIDATIONS.append(dict(case=case, tokens=length, device=device, calls=counts, same_input_wrapper_torch_equal=True))
+    (ROOT / "validation.json").write_text(json.dumps(VALIDATIONS, indent=2))
+
+
+@torch.inference_mode()
+def main():
+    import transformers
+    import vllm
+
+    metadata = dict(
+        baseline_ref=args.baseline_ref,
+        checkout=subprocess.check_output(["git", "rev-parse", "HEAD"], text=True).strip(),
+        torch=torch.__version__,
+        vllm=vllm.__version__,
+        transformers=transformers.__version__,
+        gpu=torch.cuda.get_device_name(),
+        cpu_core=CORE,
+        torch_cpu_threads=1,
+        warmup=args.warmup,
+        blocks=args.blocks,
+        iterations=args.iterations,
+        lengths=args.lengths,
+        embedding_shape=[VOCAB, HIDDEN],
+        dtype="bfloat16",
+        input_ids_dtype="int32",
+        mask_device="cpu",
+        start_time=time.time(),
+        metric_order=["host_observed_us", "calling_thread_cpu_us", "synchronized_completion_us"],
+    )
+    index = json.loads((MODEL / "model.safetensors.index.json").read_text())["weight_map"]
+    key = "thinker.model.embed_tokens.weight"
+    with safe_open(MODEL / index[key], framework="pt", device="cpu") as f:
+        weights = f.get_tensor(key)
+    assert weights.shape == (VOCAB, HIDDEN) and weights.dtype == torch.bfloat16
+    model = build_model(weights, "cuda")
+    measure({"timer_floor": lambda: None}, "cuda", "timer", 0, "overhead", rounds=10, iterations=100)
+    for length in args.lengths:
+        for case in ("text", "audio", "image", "video", "mixed", "interleaved"):
+            ids, mm, mask = payload(case, length, "cuda")
+            validate(model, (ids, mm, mask), case, length, "cuda")
+            functions = {
+                variant: (lambda method=method: method(model, ids, mm, is_multimodal=mask))
+                for variant, method in METHODS.items()
+            }
+            measure(
+                functions,
+                "cuda",
+                case,
+                length,
+                "whole_wrapper",
+                rounds=args.blocks,
+                iterations=args.iterations,
+                warmups=args.warmup,
+            )
+    summary = []
+    for length in args.lengths:
+        for case in ("text", "audio", "image", "video", "mixed", "interleaved"):
+            medians = {
+                variant: statistics.median(
+                    statistics.median(sample[0] for sample in record["samples_us"])
+                    for record in RESULTS
+                    if record["tokens"] == length and record["case"] == case and record["variant"] == variant
+                )
+                for variant in METHODS
+            }
+            summary.append(
+                dict(
+                    tokens=length,
+                    case=case,
+                    host_us=medians,
+                    reduction_pct=100 * (1 - medians["fixed"] / medians["baseline"]),
+                )
+            )
+    metadata["end_time"] = time.time()
+    (ROOT / "metadata.json").write_text(json.dumps(metadata, indent=2))
+    (ROOT / "summary.json").write_text(json.dumps(summary, indent=2))
+    print(json.dumps(summary, indent=2))
+
+
+if __name__ == "__main__":
+    main()

benchmarks/qwen3_omni/embedding_harness.py

@@ -0,0 +1,226 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+"""Small fixtures for the real Qwen3 thinker embedding method and its oracle."""
+
+from __future__ import annotations
+
+import hashlib
+import json
+from dataclasses import asdict, dataclass
+from types import SimpleNamespace
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+SCENARIOS = ("text", "empty", "audio", "image", "video", "mixed", "interleaved", "vision_no_deepstack")
+
+
+@dataclass(frozen=True)
+class ModelShape:
+    vocab_size: int = 152064
+    hidden_size: int = 2048
+    deepstack_levels: int = 3
+    image_token_id: int = 151655
+    video_token_id: int = 151656
+    audio_token_id: int = 151675
+
+    def __post_init__(self):
+        if min(self.vocab_size, self.hidden_size, self.deepstack_levels) <= 0:
+            raise ValueError("vocab_size, hidden_size and deepstack_levels must be positive")
+        ids = (self.image_token_id, self.video_token_id, self.audio_token_id)
+        if len(set(ids)) != 3 or not all(0 < token < self.vocab_size for token in ids):
+            raise ValueError("Provide three distinct, positive, in-vocabulary modality IDs; use oov=True to test OOV")
+
+    def token_ids(self, oov=False):
+        if oov:
+            return dict(zip(("image", "video", "audio"), range(self.vocab_size, self.vocab_size + 3)))
+        return {name: getattr(self, f"{name}_token_id") for name in ("image", "video", "audio")}
+
+
+@dataclass
+class Feature:
+    modality: str
+    positions: torch.Tensor
+    main: torch.Tensor
+    deepstack: torch.Tensor | None
+    combined: torch.Tensor
+
+
+@dataclass
+class EmbeddingCase:
+    name: str
+    input_ids: torch.Tensor
+    is_multimodal: torch.Tensor | None
+    features: tuple[Feature, ...]
+    fingerprint: str
+
+    def fresh_embeddings(self):
+        # The thinker replaces list entries with main-scale views.
+        return None if self.name == "text" else [feature.combined for feature in self.features]
+
+
+def _tensor_bytes(tensor):
+    return tensor.detach().contiguous().view(torch.uint8).cpu().numpy().tobytes()
+
+
+def build_case(
+    name,
+    num_tokens,
+    num_mm_tokens,
+    shape,
+    *,
+    device="cuda",
+    dtype=torch.bfloat16,
+    mask_device="cpu",
+    seed=42,
+    oov=False,
+):
+    """Build known feature placements; no model merge is used to define them."""
+    if name not in SCENARIOS:
+        raise ValueError(f"Unknown scenario: {name}")
+    if num_tokens <= 0 or not 0 <= num_mm_tokens < num_tokens:
+        raise ValueError("Require N > 0 and 0 <= M < N")
+    if (name in {"text", "empty"}) != (num_mm_tokens == 0):
+        raise ValueError("Only text/empty scenarios accept M=0")
+    if name in {"mixed", "interleaved"} and (num_mm_tokens < 4 or num_mm_tokens % 2):
+        raise ValueError("Mixed/interleaved scenarios require an even M >= 4")
+    token_ids = shape.token_ids(oov)
+    generator = torch.Generator().manual_seed(seed)
+    ids = torch.randint(0, min(shape.vocab_size, *token_ids.values()), (num_tokens,), generator=generator)
+    mask = torch.zeros(num_tokens, dtype=torch.bool)
+    positions = torch.arange((num_tokens - num_mm_tokens) // 2, (num_tokens - num_mm_tokens) // 2 + num_mm_tokens)
+    groups = []
+    if name == "mixed":
+        groups = [("audio", positions[: num_mm_tokens // 2]), ("image", positions[num_mm_tokens // 2 :])]
+    elif name == "interleaved":
+        groups = [("video", positions[::2]), ("audio", positions[1::2])]
+    elif num_mm_tokens:
+        groups = [("image" if name == "vision_no_deepstack" else name, positions)]
+    features = []
+    for modality, indexes in groups:
+        ids[indexes] = token_ids[modality]
+        mask[indexes] = True
+        rows = torch.arange(len(indexes), dtype=torch.float32)[:, None].remainder(31) / 32
+        cols = torch.arange(shape.hidden_size, dtype=torch.float32)[None, :].remainder(7) / 16
+        main_cpu = rows + cols + {"audio": 2, "image": 4, "video": 6}[modality]
+        main = main_cpu.to(device=device, dtype=dtype)
+        deepstack = None
+        parts = [main]
+        if modality != "audio" and name != "vision_no_deepstack":
+            deepstack = torch.stack(
+                [(main_cpu + 16 + 4 * level).to(device=device, dtype=dtype) for level in range(shape.deepstack_levels)]
+            )
+            parts.extend(deepstack.unbind(0))
+        combined = torch.cat(parts, dim=-1)
+        # set_mm_embedding_modality simply attaches this attribute. Tag only
+        # after cat/to: views and copies do not preserve arbitrary attributes.
+        combined.modality = modality
+        features.append(Feature(modality, indexes, main, deepstack, combined))
+    digest = hashlib.sha256(json.dumps({"name": name, "shape": asdict(shape), "seed": seed, "oov": oov}).encode())
+    digest.update(_tensor_bytes(ids))
+    digest.update(_tensor_bytes(mask))
+    for feature in features:
+        digest.update(feature.modality.encode())
+        digest.update(_tensor_bytes(feature.combined))
+    return EmbeddingCase(
+        name,
+        ids.to(device=device),
+        None if name == "text" else mask.to(device=mask_device),
+        tuple(features),
+        digest.hexdigest(),
+    )
+
+
+class LanguageModelAdapter(nn.Module):
+    def __init__(self, embedding):
+        super().__init__()
+        self.embedding = embedding
+
+    def embed_input_ids(self, input_ids):
+        return self.embedding(input_ids)
+
+    def forward(self, input_ids):
+        return self.embed_input_ids(input_ids)
+
+
+def make_thinker(
+    shape,
+    *,
+    device="cuda",
+    dtype=torch.bfloat16,
+    seed=42,
+    backend="torch",
+    deepstack=True,
+    buffer_capacity=32,
+    oov=False,
+):
+    """Skip checkpoint construction, preserving the real class and its MRO."""
+    from tests.model_executor.helpers import bootstrap_vllm_layer_custom_op_modules
+
+    bootstrap_vllm_layer_custom_op_modules()
+    from vllm_omni.model_executor.models.qwen3_omni.qwen3_omni_moe_thinker import (
+        Qwen3OmniMoeThinkerForConditionalGeneration,
+    )
+
+    if buffer_capacity <= 0:
+        raise ValueError("buffer_capacity must be positive")
+    device = torch.device(device)
+    if backend == "torch":
+        embedding = nn.Embedding(shape.vocab_size, shape.hidden_size, device=device, dtype=dtype)
+    elif backend == "vllm":
+        from vllm.model_executor.layers.vocab_parallel_embedding import VocabParallelEmbedding
+
+        with torch.device(device):
+            embedding = VocabParallelEmbedding(shape.vocab_size, shape.hidden_size, params_dtype=dtype, disable_tp=True)
+    else:
+        raise ValueError(f"Unknown embedding backend: {backend}")
+    with torch.no_grad():
+        embedding.weight.uniform_(-0.5, 0.5, generator=torch.Generator(device=device).manual_seed(seed))
+    thinker = object.__new__(Qwen3OmniMoeThinkerForConditionalGeneration)
+    nn.Module.__init__(thinker)
+    thinker.config = SimpleNamespace(text_config=SimpleNamespace(hidden_size=shape.hidden_size))
+    for modality, token_id in shape.token_ids(oov).items():
+        setattr(thinker.config, f"{modality}_token_id", token_id)
+    thinker.visual = SimpleNamespace(
+        deepstack_visual_indexes=list(range(shape.deepstack_levels)) if deepstack else None
+    )
+    thinker.language_model = LanguageModelAdapter(embedding)
+    thinker._has_oov_mm_tokens = oov
+    thinker.use_deepstack = deepstack
+    thinker.deepstack_num_level = shape.deepstack_levels if deepstack else 0
+    thinker.deepstack_input_embeds = [
+        torch.zeros(buffer_capacity, shape.hidden_size, device=device, dtype=dtype)
+        for _ in range(thinker.deepstack_num_level)
+    ]
+    thinker.deepstack_input_embeds_num_tokens = 0
+    return thinker.eval()
+
+
+@torch.inference_mode()
+def expected_outputs(model, case):
+    """Independent oracle: direct lookup and explicit positions, not merge helpers."""
+    ids = case.input_ids
+    if model._has_oov_mm_tokens and case.is_multimodal is not None:
+        ids = ids.masked_fill(case.is_multimodal.to(ids.device), 0)
+    main = F.embedding(ids, model.language_model.embedding.weight)
+    has_deepstack = any(feature.deepstack is not None for feature in case.features)
+    deepstack = main.new_zeros((model.deepstack_num_level, *main.shape)) if has_deepstack else None
+    for feature in case.features:
+        indexes = feature.positions.to(main.device)
+        main[indexes] = feature.main
+        if feature.deepstack is not None:
+            deepstack[:, indexes, :] = feature.deepstack
+    return main, deepstack
+
+
+@torch.inference_mode()
+def assert_case_output(model, case, result):
+    expected, deepstack = expected_outputs(model, case)
+    torch.testing.assert_close(result, expected, rtol=0, atol=0)
+    if deepstack is None:
+        assert model.deepstack_input_embeds_num_tokens == 0, "Unexpected stale DeepStack buffer"
+    else:
+        assert model.deepstack_input_embeds_num_tokens == case.input_ids.numel()
+        actual = torch.stack([buffer[: case.input_ids.numel()] for buffer in model.deepstack_input_embeds])
+        torch.testing.assert_close(actual, deepstack, rtol=0, atol=0)

benchmarks/tts/README.md

@@ -2,7 +2,7 @@
 
 A model-agnostic serving benchmark for TTS models in vllm-omni. One CLI
 (`bench_tts.py`) + one YAML registry (`model_configs.yaml`) drive perf and
-quality runs for every registered checkpoint: **Qwen3-TTS** (Base / CustomVoice)
+quality runs for every registered checkpoint: **Qwen3-TTS** (Base / CustomVoice / VoiceDesign)
 and **VoxCPM2** today, more to come.
 
 The same three task types — `voice_clone`, `default_voice`, `voice_design` —
@@ -21,6 +21,11 @@ The server auto-loads its Deploy YAML from `vllm_omni/deploy/qwen3_tts.yaml`
 (Pipeline + Deploy schema introduced in #2383). No explicit `--deploy-config`
 flag is needed for a registered model.
 
+Start the checkpoint that matches the benchmark task below: `-Base` for
+`voice_clone`, `-CustomVoice` for `default_voice`, or `-VoiceDesign` for
+`voice_design`. Restart the server with the matching checkpoint when switching
+tasks; changing only the benchmark's `--model` does not reload the server.
+
 ### 2. Run the benchmark (`vllm bench serve --omni`)
 
 The primary, directly-controllable path. Copy-paste one of these and tweak
@@ -62,12 +67,12 @@ vllm bench serve --omni \
     --save-result --result-dir ./results
 ```
 
-#### voice_design (Qwen3-TTS-CustomVoice, bundled seed_tts_design)
+#### voice_design (Qwen3-TTS-VoiceDesign, bundled seed_tts_design)
 
 ```bash
 vllm bench serve --omni \
     --host 127.0.0.1 --port 8000 \
-    --model Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice \
+    --model Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign \
     --backend openai-audio-speech \
     --endpoint /v1/audio/speech \
     --dataset-name seed-tts-design \
@@ -80,6 +85,25 @@ vllm bench serve --omni \
     --save-result --result-dir ./results
 ```
 
+#### Set the sample rate for models that are not 24 kHz
+
+Streamed PCM carries no header, so the harness derives `audio_duration` from the
+byte count and a **default 24000 Hz** assumption
+(`vllm_omni/metrics/definitions.py:DEFAULT_AUDIO_SAMPLE_RATE`). For a model that
+emits another rate, export the real one or every duration-derived metric is
+wrong by `real_sr / 24000`:
+
+```bash
+# Audio8 TTS, Fish Speech S2 Pro, Ming-omni-tts, ... all decode at 44.1 kHz
+export VLLM_OMNI_BENCH_AUDIO_SAMPLE_RATE=44100
+```
+
+Without it, a 44.1 kHz model reports `audio_duration` 1.84x too long, so
+`audio_rtf` looks 1.84x *better* than reality and `audio_underrun` /
+`Streaming continuity OK rate` are measured against inflated chunk budgets.
+A quick sanity check: compare `Mean AUDIO_DURATION (s)` against the actual
+length of a saved WAV for the same prompt.
+
 #### Streaming continuity (`audio_underrun`)
 
 `audio_underrun` (seconds) is the per-request worst-case buffer deficit
@@ -180,11 +204,11 @@ Outputs TTFP / RTF / throughput curves (and a markdown table) for every
 
 ## Task types
 
-| Task            | Dataset           | Request body                                        | Checkpoints that support it              |
-|-----------------|-------------------|-----------------------------------------------------|------------------------------------------|
-| `voice_clone`   | `seed-tts`        | `ref_audio` + `ref_text` + `task_type=Base`         | `Qwen3-TTS-*-Base`, `VoxCPM2`            |
-| `default_voice` | `seed-tts-text`   | `voice=Vivian` + `task_type=CustomVoice`            | `Qwen3-TTS-*-CustomVoice`                |
-| `voice_design`  | `seed-tts-design` | `instructions=<natural-language description>` + `task_type=VoiceDesign` | `Qwen3-TTS-*-CustomVoice` |
+| Task            | Dataset           | Request body                                                            | Checkpoints that support it   |
+| --------------- | ----------------- | ----------------------------------------------------------------------- | ----------------------------- |
+| `voice_clone`   | `seed-tts`        | `ref_audio` + `ref_text` + `task_type=Base`                             | `Qwen3-TTS-*-Base`, `VoxCPM2` |
+| `default_voice` | `seed-tts-text`   | `voice=Vivian` + `task_type=CustomVoice`                                | `Qwen3-TTS-*-CustomVoice`     |
+| `voice_design`  | `seed-tts-design` | `instructions=<natural-language description>` + `task_type=VoiceDesign` | `Qwen3-TTS-*-VoiceDesign`     |
 
 **`-CustomVoice` checkpoints do NOT ship `speaker_encoder` weights**, so
 voice_clone requests raise `ValueError` at model runtime. Use `-Base` for
@@ -210,12 +234,12 @@ Then add the model's Deploy YAML under `vllm_omni/deploy/<model>.yaml`
 
 ## Datasets
 
-| Dataset            | Bundled? | Format            | Source                                                         |
-|--------------------|----------|-------------------|----------------------------------------------------------------|
-| `seed-tts-design`  | ✅       | 5-field meta.lst  | `benchmarks/build_dataset/seed_tts_design/en/meta.lst` (20 prompts) |
-| `seed_tts_smoke`   | ✅       | 4-field meta.lst  | `benchmarks/build_dataset/seed_tts_smoke/en/meta.lst` (20 text-only) |
-| `seed-tts`         | ❌       | 4-field meta.lst + WAVs | Google-Drive: [BytedanceSpeech/seed-tts-eval][seedtts] (~1.2 GB) |
-| `seed-tts-text`    | ❌       | 4-field meta.lst  | Same archive as `seed-tts` (wav column unused)                 |
+| Dataset           | Bundled? | Format                  | Source                                                               |
+| ----------------- | -------- | ----------------------- | -------------------------------------------------------------------- |
+| `seed-tts-design` | ✅       | 5-field meta.lst        | `benchmarks/build_dataset/seed_tts_design/en/meta.lst` (20 prompts)  |
+| `seed_tts_smoke`  | ✅       | 4-field meta.lst        | `benchmarks/build_dataset/seed_tts_smoke/en/meta.lst` (20 text-only) |
+| `seed-tts`        | ❌       | 4-field meta.lst + WAVs | Google-Drive: [BytedanceSpeech/seed-tts-eval][seedtts] (~1.2 GB)     |
+| `seed-tts-text`   | ❌       | 4-field meta.lst        | Same archive as `seed-tts` (wav column unused)                       |
 
 [seedtts]: https://github.com/BytedanceSpeech/seed-tts-eval
 
@@ -227,11 +251,11 @@ For manual voice_clone / default_voice runs against the full corpus, follow
 
 `tests/dfx/perf/tests/test_tts.json` wires three perf regimes plus quality:
 
-| eval_phase    | concurrency | purpose                                                 | Baseline metrics                        |
-|---------------|-------------|---------------------------------------------------------|-----------------------------------------|
-| `latency`     | 1           | Single-request TTFP / RTF SLO                           | `median_audio_ttfp_ms`, `median_audio_rtf` |
-| `throughput`  | 8           | Codec-batching cliff sentinel (PDF #272 concurrency≥8)  | `median_audio_ttfp_ms`, `median_audio_rtf` |
-| `quality`     | 4           | WER / SIM / UTMOS regression (disabled in CI by default)| `mean_audio_rtf`                        |
+| eval_phase   | concurrency | purpose                                                  | Baseline metrics                           |
+| ------------ | ----------- | -------------------------------------------------------- | ------------------------------------------ |
+| `latency`    | 1           | Single-request TTFP / RTF SLO                            | `median_audio_ttfp_ms`, `median_audio_rtf` |
+| `throughput` | 8           | Codec-batching cliff sentinel (PDF #272 concurrency≥8)   | `median_audio_ttfp_ms`, `median_audio_rtf` |
+| `quality`    | 4           | WER / SIM / UTMOS regression (disabled in CI by default) | `mean_audio_rtf`                           |
 
 Why `median_*` for latency/throughput and `mean_*` for quality: latency
 distributions have cold-start tails that drag the mean; quality aggregates
@@ -245,10 +269,15 @@ PR #2558 — quality runs are manual / release-validation, not nightly).
 
 Observed on H20-3e, Qwen3-TTS-1.7B (measured pre-merge on this branch):
 
-| Task          | Model         | c=1    | c=4    | **c=8**    | c=16   | c=32   |
-|---------------|---------------|--------|--------|------------|--------|--------|
-| voice_clone   | 1.7B-Base     | RTF 0.15 / TTFP 165ms | 0.28 / 412ms | **0.49 / 1701ms** | 0.72 / 3355ms | 0.77 / 3772ms |
-| voice_design  | 1.7B-CustomVoice | RTF 0.08 / TTFP 53ms  | 0.11 / 154ms | **0.21 / 872ms**  | 0.33 / 1801ms | 0.38 / 1989ms |
+| Task         | Model            | c=1                   | c=4          | **c=8**           | c=16          | c=32          |
+| ------------ | ---------------- | --------------------- | ------------ | ----------------- | ------------- | ------------- |
+| voice_clone  | 1.7B-Base        | RTF 0.15 / TTFP 165ms | 0.28 / 412ms | **0.49 / 1701ms** | 0.72 / 3355ms | 0.77 / 3772ms |
+| voice_design | 1.7B-CustomVoice | RTF 0.08 / TTFP 53ms  | 0.11 / 154ms | **0.21 / 872ms**  | 0.33 / 1801ms | 0.38 / 1989ms |
+
+The historical `voice_design` row above lists a CustomVoice checkpoint, which
+current task validation rejects for VoiceDesign requests. It does not establish
+a VoiceDesign checkpoint baseline; rerun with `Qwen3-TTS-12Hz-1.7B-VoiceDesign`
+before comparing performance.
 
 Both models show a **4–6× TTFP jump from c=4 to c=8** while audio throughput
 saturates around c=4–8 — the codec-bs=1 bottleneck documented in
@@ -257,7 +286,7 @@ sentinel for regressions in this area.
 
 ## File layout
 
-```
+```text
 benchmarks/tts/
 ├── README.md                  (this file)
 ├── bench_tts.py               CLI — serve-mode benchmark driver

benchmarks/tts/bench_cosyvoice3_concurrency_matrix.py

@@ -0,0 +1,517 @@
+#!/usr/bin/env python3
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
+"""CosyVoice3 streaming e2e benchmark: concurrency x utterance-length matrix.
+
+Launches the server once per (concurrency, step-size) combo, streams TTS
+requests cycling through several utterance lengths, and reports
+TTFA/E2E/RTF/per-chunk latency (mean, stdev, range) per cell. "Step size" is
+``codec_chunk_frames``, the token-hop length the first emitted chunk waits
+for; later chunks wait for a doubling hop capped at ``4 * codec_chunk_frames``
+(see ``_token_hop_lengths``). Per-chunk latency is reconstructed from that
+same ramp rather than from raw transport read boundaries, which don't align
+to model chunks (see ``one_request``'s docstring).
+
+Running with no flags reproduces the default matrix. Per-request correctness
+is checked: non-silent, long enough, and (with a fixed ``--seed``)
+byte-identical PCM across requests for the same text.
+"""
+
+import argparse
+import json
+import os
+import signal
+import statistics
+import subprocess
+import sys
+import threading
+import time
+from pathlib import Path
+
+import httpx
+import numpy as np
+
+REPO = Path(__file__).resolve().parents[2]
+DEFAULT_MODEL = "FunAudioLLM/Fun-CosyVoice3-0.5B-2512"
+
+DEFAULT_REF_AUDIO = "https://raw.githubusercontent.com/FunAudioLLM/CosyVoice/main/asset/zero_shot_prompt.wav"
+DEFAULT_REF_TEXT = "希望你以后能够做的比我还好呦。"
+
+DEFAULT_TEXTS = [
+    "Thank you so much for the birthday gift you sent me.",
+    "The weather is lovely today, with warm sunshine and a gentle breeze. "
+    "We decided to take a walk in the park to enjoy the spring flowers and the fresh air.",
+    "The weather is lovely today, with warm sunshine and a gentle breeze. We decided to take a walk in the "
+    "park to enjoy the spring flowers and the fresh air. Many people were out walking, children were "
+    "running and playing on the grass, and older folks sat on benches chatting with one another. We "
+    "strolled slowly along the path and came across a cherry tree in full bloom, its pink petals drifting "
+    "on the wind. It was so beautiful that we stopped to take some photos to remember the moment. Then we "
+    "continued on and reached a small lake, its water so clear you could see straight to the bottom, with "
+    "the blue sky and white clouds reflected on its surface. A few ducks paddled back and forth across the "
+    "water, occasionally letting out a soft quack. We sat on a bench by the lake for a while, enjoying the "
+    "peaceful afternoon. Time passed quickly, and before we knew it evening had arrived, with the setting "
+    "sun painting the sky in shades of gold. Reluctant to leave, we finally headed home, already planning "
+    "our next visit. On the way back we talked about everything we had seen and heard that day, feeling "
+    "wonderfully happy. It was truly an unforgettable weekend.",
+]
+
+# PCM16 mono @ 24 kHz (CosyVoice3 output rate)
+SAMPLE_RATE = 24000
+
+# Well below any real speech's RMS; catches all-zero/near-silent PCM that byte
+# count and duration checks alone pass through.
+MIN_RMS = 50.0
+
+# Overlay: only codec_chunk_frames is overridden; must live next to cosyvoice3.yaml (relative base_config)
+DEPLOY_TEMPLATE = """# Auto-generated by bench_cosyvoice3_concurrency_matrix.py -- do not edit.
+base_config: cosyvoice3.yaml
+
+connectors:
+  connector_of_shared_memory:
+    name: SharedMemoryConnector
+    extra:
+      codec_streaming: true
+      connector_get_sleep_s: 0.01
+      connector_get_max_wait_first_chunk: 3000
+      connector_get_max_wait: 300
+      codec_chunk_frames: {chunk_frames}
+      codec_max_chunk_frames: {max_chunk_frames}
+      codec_stream_scale_factor: {stream_scale_factor}
+      codec_left_context_frames: 25
+      codec_vocab_size: 6561
+"""
+
+# CosyVoice3Config constants (transformers_utils/configs/cosyvoice3.py): tokens
+# are consumed at token_mel_ratio mel frames each, and HiFT emits
+# samples_per_mel PCM16 samples per mel frame. Pinned here (not left to the
+# server's defaults) since one_request's byte-boundary reconstruction below
+# depends on knowing them exactly.
+TOKEN_MEL_RATIO = 2
+SAMPLES_PER_MEL = 480
+STREAM_SCALE_FACTOR = 2
+
+
+def _stats(values: list[float]) -> dict[str, float]:
+    """Mean/median/stdev/p95/min/max/n for a list of per-request measurements."""
+    n = len(values)
+    if n == 0:
+        return {
+            "n": 0,
+            "mean": float("nan"),
+            "median": float("nan"),
+            "stdev": float("nan"),
+            "p95": float("nan"),
+            "min": float("nan"),
+            "max": float("nan"),
+        }
+    return {
+        "n": n,
+        "mean": statistics.mean(values),
+        "median": statistics.median(values),
+        "stdev": statistics.stdev(values) if n >= 2 else 0.0,
+        "p95": statistics.quantiles(values, n=20)[18] if n >= 2 else values[0],
+        "min": min(values),
+        "max": max(values),
+    }
+
+
+def vllm_bin() -> str:
+    for c in (REPO / ".venv/bin/vllm", Path(sys.executable).parent / "vllm"):
+        if c.is_file():
+            return str(c)
+    return "vllm"
+
+
+def wait_ready(url: str, proc: subprocess.Popen, log: Path, timeout: float) -> None:
+    deadline = time.monotonic() + timeout
+    while time.monotonic() < deadline:
+        if proc.poll() is not None:
+            tail = log.read_text(errors="replace").splitlines()[-40:] if log.exists() else []
+            raise RuntimeError("server exited during startup:\n  " + "\n  ".join(tail))
+        try:
+            if httpx.get(f"{url}/health", timeout=2.0).status_code == 200:
+                return
+        except Exception:
+            pass
+        time.sleep(2.0)
+    raise TimeoutError(f"server not ready within {timeout}s (log: {log})")
+
+
+def _token_hop_lengths(chunk_frames: int, max_chunk_frames: int):
+    """The token-hop-length ramp talker2code2wav_async_chunk uses to decide
+    when to fire the next flow-solve/emission: chunk_frames, then doubling
+    (STREAM_SCALE_FACTOR) each step, capped at max_chunk_frames. This -- not
+    codec_chunk_frames alone -- is what actually paces emitted audio chunks:
+    with the defaults here, hops are chunk_frames, 2x, 4x, 4x, 4x, ...
+
+    The very first emission is consistently one token short of chunk_frames
+    (confirmed against real speech.audio.delta event sizes) even though the
+    ramp's own progression is computed from the nominal chunk_frames, not the
+    actual first hop -- so only the first yield gets the -1 adjustment.
+    """
+    yield chunk_frames - 1
+    hop = chunk_frames
+    while True:
+        hop = min(max_chunk_frames, max(chunk_frames, hop * STREAM_SCALE_FACTOR))
+        yield hop
+
+
+def one_request(client, url, model, text, ref_audio, ref_text, chunk_frames, seed=None):
+    """Run one streaming request, returning TTFA, E2E, per-chunk gaps, and audio bytes.
+
+    ``response_format=pcm`` + ``stream_format=audio`` streams raw bytes with no
+    framing, so a client read boundary alone is a transport artifact, not a
+    model chunk boundary. Reconstructed instead by replicating the server's
+    own token-hop-length ramp (see ``_token_hop_lengths``) to compute each
+    real chunk's expected byte size ahead of time, and slicing the
+    accumulated byte stream at those offsets -- independent of how the
+    transport happens to fragment the underlying reads. The final chunk
+    (finalize, which releases extra held-back frames) is whatever remains
+    when the stream closes, not a predicted hop.
+    """
+    payload = {
+        "model": model,
+        "input": text,
+        "response_format": "pcm",
+        "stream": True,
+        "stream_format": "audio",
+        "ref_audio": ref_audio,
+        "ref_text": ref_text,
+    }
+    if seed is not None:
+        payload["seed"] = seed
+    t0 = time.perf_counter()
+    ttfa = None
+    nbytes = 0
+    audio_bytes = bytearray()
+    chunk_gaps_ms = []  # inter-chunk arrival gaps (per model chunk), by step index
+    prev_t = None
+    step_idx = 0
+
+    bytes_per_token = TOKEN_MEL_RATIO * SAMPLES_PER_MEL * 2
+    hop_gen = _token_hop_lengths(chunk_frames, 4 * chunk_frames)
+    next_boundary = next(hop_gen) * bytes_per_token
+    last_crossed_bytes = 0
+    received = 0
+    last_now = t0
+
+    with client.stream("POST", f"{url}/v1/audio/speech", json=payload, headers={"Authorization": "Bearer EMPTY"}) as r:
+        if r.status_code != 200:
+            raise RuntimeError(f"{r.status_code}: {r.read()[:300]!r}")
+        for chunk in r.iter_bytes():
+            if not chunk:
+                continue
+            now = time.perf_counter()
+            if ttfa is None:
+                ttfa = now - t0
+            last_now = now
+            received += len(chunk)
+            nbytes += len(chunk)
+            audio_bytes += chunk
+            while received >= next_boundary:
+                if prev_t is not None:
+                    chunk_gaps_ms.append((step_idx, (now - prev_t) * 1e3))
+                    step_idx += 1
+                prev_t = now
+                last_crossed_bytes = next_boundary
+                next_boundary += next(hop_gen) * bytes_per_token
+
+    if received > last_crossed_bytes:
+        # Finalize chunk: releases extra held-back frames, so it doesn't
+        # match a predicted hop size -- its arrival is the stream closing.
+        if prev_t is not None:
+            chunk_gaps_ms.append((step_idx, (last_now - prev_t) * 1e3))
+            step_idx += 1
+
+    e2e = time.perf_counter() - t0
+    audio_s = nbytes / 2 / SAMPLE_RATE
+    return {
+        "ttfa_s": ttfa,
+        "e2e_s": e2e,
+        "audio_s": audio_s,
+        "rtf": e2e / audio_s if audio_s > 0 else float("nan"),
+        "chunk_gaps_ms": chunk_gaps_ms,
+        "nbytes": nbytes,
+        "audio_bytes": audio_bytes,
+    }
+
+
+def check_correctness(r: dict, min_audio_s: float) -> list[str]:
+    """Return a list of correctness violations (empty = pass)."""
+    problems = []
+    if r["nbytes"] <= 0:
+        problems.append("empty audio (0 bytes)")
+    if r["audio_s"] < min_audio_s:
+        problems.append(f"audio too short ({r['audio_s']:.2f}s < {min_audio_s:.2f}s)")
+    if r["nbytes"] > 0:
+        samples = np.frombuffer(bytes(r["audio_bytes"]), dtype=np.int16).astype(np.float64)
+        rms = float(np.sqrt(np.mean(samples**2)))
+        if rms < MIN_RMS:
+            problems.append(f"near-silent audio (rms={rms:.1f} < {MIN_RMS})")
+    return problems
+
+
+def drive(url, args, n, conc, label, results, lock, idx, chunk_frames):
+    """Run ``n`` requests at concurrency ``conc``, cycling through ``args.texts``."""
+    texts = args.texts
+
+    def worker():
+        with httpx.Client(timeout=600.0) as c:
+            while True:
+                with lock:
+                    i = idx[0]
+                    idx[0] += 1
+                if i >= n:
+                    return
+                text = texts[i % len(texts)]
+                try:
+                    r = one_request(
+                        c, url, args.model, text, args.ref_audio, args.ref_text, chunk_frames, seed=args.seed
+                    )
+                    r["text"] = text
+                    with lock:
+                        results.append(r)
+                except Exception as e:
+                    print(f"    [{label}] request {i} failed: {e}", flush=True)
+
+    ts = [threading.Thread(target=worker) for _ in range(conc)]
+    t0 = time.perf_counter()
+    for t in ts:
+        t.start()
+    for t in ts:
+        t.join()
+    return time.perf_counter() - t0
+
+
+def run_combo(args, conc: int, chunk_frames: int, out: Path) -> dict:
+    tag = f"c{conc}_chunk{chunk_frames}"
+    run_dir = out / tag
+    run_dir.mkdir(parents=True, exist_ok=True)
+    log = run_dir / "server.log"
+
+    deploy = REPO / "vllm_omni/deploy" / f"_autogen_steps_{tag}.yaml"
+    deploy.write_text(
+        DEPLOY_TEMPLATE.format(
+            chunk_frames=chunk_frames,
+            max_chunk_frames=4 * chunk_frames,
+            stream_scale_factor=STREAM_SCALE_FACTOR,
+        )
+    )
+
+    cmd = [
+        vllm_bin(),
+        "serve",
+        args.model,
+        "--omni",
+        "--deploy-config",
+        str(deploy),
+        "--host",
+        "127.0.0.1",
+        "--port",
+        str(args.port),
+        "--allowed-local-media-path",
+        str(REPO),
+    ]
+    env = dict(os.environ)
+    if args.cuda_visible_devices:
+        env["CUDA_VISIBLE_DEVICES"] = args.cuda_visible_devices
+    if args.trt is not None:
+        env["COSYVOICE3_TRT"] = args.trt
+
+    print(f"\n=== concurrency={conc} step_size={chunk_frames} ===", flush=True)
+    print(f"  launching server (log: {log})", flush=True)
+    with open(log, "w") as lf:
+        proc = subprocess.Popen(cmd, env=env, stdout=lf, stderr=subprocess.STDOUT, start_new_session=True)
+    url = f"http://127.0.0.1:{args.port}"
+    try:
+        wait_ready(url, proc, log, args.server_timeout)
+        print("  server ready", flush=True)
+
+        results: list[dict] = []
+        lock = threading.Lock()
+        idx = [0]
+        if args.warmup:
+            print(f"  warmup ({args.warmup} req)", flush=True)
+            drive(url, args, args.warmup, min(conc, args.warmup), "warmup", results, lock, idx, chunk_frames)
+            results.clear()
+            idx[0] = 0
+
+        print(f"  running {args.num_prompts} req @ concurrency {conc}", flush=True)
+        wall = drive(url, args, args.num_prompts, conc, "bench", results, lock, idx, chunk_frames)
+
+        ok = [r for r in results if r.get("ttfa_s") is not None]
+        problems = []
+        for r in ok:
+            problems += check_correctness(r, args.min_audio_s)
+        # With a fixed seed, same-text requests must produce byte-identical PCM (per text group)
+        if args.seed is not None:
+            by_text: dict[str, bytes] = {}
+            for r in ok:
+                t = r.get("text")
+                ref = by_text.get(t)
+                if ref is None:
+                    by_text[t] = r.get("audio_bytes")
+                elif r.get("audio_bytes") != ref:
+                    problems.append(f"byte-identity violation for text {t!r}: seeded requests produced different PCM")
+        n_ok = len(ok)
+        total_audio_s = sum(r["audio_s"] for r in ok)
+        all_gaps = [g for r in ok for g in r["chunk_gaps_ms"]]  # (step_index, gap_ms) per request
+        gap_vals = [g for _, g in all_gaps]
+
+        # bucket gaps by chunk position within a request, to see any O(n) slope
+        by_idx: dict[int, list[float]] = {}
+        for step_index, g in all_gaps:
+            by_idx.setdefault(step_index, []).append(g)
+        step_latency_by_idx = {i: statistics.median(vals) for i, vals in sorted(by_idx.items())}
+
+        agg = {
+            "concurrency": conc,
+            "step_size": chunk_frames,
+            "ok": n_ok,
+            "errors": args.num_prompts - n_ok,
+            "correctness_problems": len(problems),
+            "correctness_sample": problems[:5],
+            "total_audio_s": total_audio_s,
+            "throughput_audio_s_per_s": total_audio_s / wall if wall > 0 else float("nan"),
+            "ttfa_s": _stats([r["ttfa_s"] for r in ok]),
+            "e2e_s": _stats([r["e2e_s"] for r in ok]),
+            "rtf": _stats([r["rtf"] for r in ok]),
+            "step_latency_ms": _stats(gap_vals),
+            "step_latency_ms_by_idx": step_latency_by_idx,
+            "num_steps": len(all_gaps),
+            # per-utterance-length breakdown; n per text = num_prompts / len(texts)
+            "by_text": {
+                t: {
+                    "audio_s": _stats([r["audio_s"] for r in ok if r.get("text") == t]),
+                    "e2e_s": _stats([r["e2e_s"] for r in ok if r.get("text") == t]),
+                    "rtf": _stats([r["rtf"] for r in ok if r.get("text") == t]),
+                }
+                for t in sorted({r.get("text") for r in ok})
+            },
+        }
+        print(json.dumps(agg, indent=2, ensure_ascii=False), flush=True)
+        return agg
+    finally:
+        print("  shutting down server", flush=True)
+        try:
+            os.killpg(os.getpgid(proc.pid), signal.SIGTERM)
+        except Exception:
+            proc.terminate()
+        try:
+            proc.wait(timeout=60)
+        except subprocess.TimeoutExpired:
+            try:
+                os.killpg(os.getpgid(proc.pid), signal.SIGKILL)
+            except Exception:
+                proc.kill()
+            proc.wait(timeout=20)
+        deploy.unlink(missing_ok=True)
+
+
+def parse_args() -> argparse.Namespace:
+    p = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    p.add_argument("--model", default=DEFAULT_MODEL)
+    p.add_argument(
+        "--text",
+        dest="texts",
+        nargs="+",
+        default=DEFAULT_TEXTS,
+        help="One or more utterance texts. With several, requests cycle through "
+        "them so a single server launch covers all utterance lengths; results "
+        "are bucketed per text.",
+    )
+    p.add_argument("--ref-audio", default=DEFAULT_REF_AUDIO)
+    p.add_argument("--ref-text", default=DEFAULT_REF_TEXT)
+    p.add_argument("--concurrency", type=int, nargs="+", default=[1, 8])
+    p.add_argument(
+        "--step-size",
+        type=int,
+        nargs="+",
+        default=[15],
+        help="codec_chunk_frames values to sweep (the streaming step size).",
+    )
+    p.add_argument("--num-prompts", type=int, default=30)
+    p.add_argument("--warmup", type=int, default=2)
+    p.add_argument("--port", type=int, default=8091)
+    p.add_argument(
+        "--server-timeout",
+        type=float,
+        default=1800.0,
+        help="Startup budget; first run also downloads weights and builds the TRT engine.",
+    )
+    p.add_argument("--output-dir", default="./perf_steps")
+    p.add_argument("--cuda-visible-devices", default="0")
+    p.add_argument(
+        "--trt",
+        choices=["0", "1"],
+        default=None,
+        help="Pin COSYVOICE3_TRT. 0=force torch estimator, 1=allow TensorRT. Default: unset.",
+    )
+    p.add_argument(
+        "--min-audio-s",
+        type=float,
+        default=1.0,
+        help="Minimum expected audio seconds per request for the correctness check.",
+    )
+    p.add_argument(
+        "--seed",
+        type=int,
+        default=42,
+        help="Fixed seed for reproducible (byte-identical) audio. Pass --seed '' "
+        "equivalent (any non-int) to disable -- there is no unset sentinel here "
+        "by design, since the default sweep is meant to be exactly reproducible.",
+    )
+    return p.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+    out = Path(args.output_dir).resolve()
+    out.mkdir(parents=True, exist_ok=True)
+
+    all_results = []
+    failed = False
+    for conc in args.concurrency:
+        for chunk_frames in args.step_size:
+            try:
+                result = run_combo(args, conc, chunk_frames, out)
+                all_results.append(result)
+                if result["errors"] > 0 or result["correctness_problems"] > 0:
+                    failed = True
+            except Exception as e:
+                print(f"  combo c={conc} chunk={chunk_frames} FAILED: {e}", flush=True)
+                failed = True
+
+    out_path = out / "steps_results.json"
+    with open(out_path, "w", encoding="utf-8") as f:
+        json.dump({"model": args.model, "texts": args.texts, "runs": all_results}, f, indent=2, ensure_ascii=False)
+    print(f"\nResults written to {out_path}", flush=True)
+
+    print("\n=== SUMMARY ===")
+    header = (
+        f"{'conc':>4} {'step':>4} {'ok':>4} {'corr':>4} "
+        f"{'TTFA med(ms)':>12} {'step med(ms)':>12} {'step p95(ms)':>12} {'RTF mean+-sd':>14}"
+    )
+    print(header)
+    print("-" * len(header))
+    for r in all_results:
+        print(
+            f"{r['concurrency']:>4} {r['step_size']:>4} {r['ok']:>4} {r['correctness_problems']:>4} "
+            f"{r['ttfa_s']['median'] * 1e3:>12.1f} "
+            f"{r['step_latency_ms']['median']:>12.1f} {r['step_latency_ms']['p95']:>12.1f} "
+            f"{r['rtf']['mean']:>6.3f}+-{r['rtf']['stdev']:>5.3f}"
+        )
+        for t, stats in r.get("by_text", {}).items():
+            print(
+                f"      text {t[:24]!r}: e2e {stats['e2e_s']['mean'] * 1e3:8.1f}"
+                f"+-{stats['e2e_s']['stdev'] * 1e3:6.1f} ms  (n={stats['e2e_s']['n']}, "
+                f"audio {stats['audio_s']['mean']:.1f}s)"
+            )
+
+    if failed:
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()

benchmarks/tts/model_configs.yaml

@@ -17,12 +17,23 @@ models:
         extra_params:
           lang: en
           text_normalization: true
+  # Audio8 TTS Preview: DualAR, 44.1 kHz. trust_remote_code stays false.
+  # Bench at 44.1 kHz: export VLLM_OMNI_BENCH_AUDIO_SAMPLE_RATE=44100, otherwise
+  # the streamed-PCM duration defaults to 24 kHz and RTF looks 1.84x too good.
+  Audio8/Audio8-TTS-Preview-0.6b:
+    supported_tasks: [default_voice, voice_clone]
+    backend: openai-audio-speech
+    endpoint: /v1/audio/speech
+    trust_remote_code: false
+    task_extra_body:
+      default_voice: {}
+      voice_clone: {}
 
   # -CustomVoice checkpoints lack speaker_encoder weights, so voice_clone is
   # NOT supported (an attempt raises ValueError from _extract_speaker_embedding
   # at model runtime).  Use -Base for voice_clone.
   Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice:
-    supported_tasks: [default_voice, voice_design]
+    supported_tasks: [default_voice]
     backend: openai-audio-speech
     endpoint: /v1/audio/speech
     trust_remote_code: true
@@ -31,6 +42,13 @@ models:
         voice: Vivian
         language: English
         task_type: CustomVoice
+
+  Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign:
+    supported_tasks: [voice_design]
+    backend: openai-audio-speech
+    endpoint: /v1/audio/speech
+    trust_remote_code: true
+    task_extra_body:
       voice_design:
         task_type: VoiceDesign
         language: English
@@ -132,3 +150,14 @@ models:
     endpoint: /v1/audio/speech
     task_extra_body:
       voice_design: {}
+
+  # OmniVoice serves plain text-to-speech over /v1/audio/speech; voice cloning
+  # and voice design are offline-only for this model, so default_voice is the
+  # only task the server can answer.
+  k2-fsa/OmniVoice:
+    supported_tasks: [default_voice]
+    backend: openai-audio-speech
+    endpoint: /v1/audio/speech
+    trust_remote_code: true
+    task_extra_body:
+      default_voice: {}

docker/Dockerfile.ci

@@ -1,24 +1,10 @@
-# Target vllm version: v0.27.0 (releases/v0.27.0 branch).
-# Keep VLLM_BASE_TAG aligned with the target vLLM release on every rebase.
-#
-# Release-boundary form: there is no precompiled-wheel pin here, because the
-# base image already ships exactly the target vLLM. Everything the runtime
-# needs — torch, flashinfer, nvJitLink, numpy — comes from that image as one
-# self-consistent set.
-#
-# This block reappears only when the rebase targets a vLLM commit that is NOT a
-# released tag. During the 0.27 rebase, targeting v0.27.0rc1 on top of the
-# v0.26.0 base image required reinstalling vLLM from wheels.vllm.ai and then
-# hand-repairing the fallout: pinning torch ahead of flashinfer (installing it
-# after bound flashinfer's TVM FFI runtime to the old torch and aborted engine
-# core init), registering libnvJitLink.so.13 with ldconfig, and re-pinning numpy
-# under numba's ceiling. All of it was compensation for a base image and a vLLM
-# that disagreed. At a real release boundary they agree, so none of it is
-# needed. If you pin an untagged commit again, restore the reinstall block —
-# agent/tasks/20_update_wheel_url.sh fails the run rather than letting the
-# Dockerfile silently ship the wrong vLLM.
+# Target vLLM version: v0.29.0 (tag @ 98dff2a81d74).
+# Use the matching release image and its coherent CUDA/PyTorch/FlashInfer
+# runtime. Do not reinstall a commit wheel or repair dependencies on top of
+# an older base image at a release boundary. For a future unreleased target,
+# restore an explicit wheel pin and validate its complete runtime stack.
 ARG VLLM_BASE_IMAGE=vllm/vllm-openai
-ARG VLLM_BASE_TAG=v0.27.0
+ARG VLLM_BASE_TAG=v0.29.0
 
 # We use registry-based layer caching for the dependencies,
 # where the tag is determined based on the hash of the contents

docker/Dockerfile.cuda

@@ -1,4 +1,4 @@
-ARG BASE_IMAGE=vllm/vllm-openai:v0.26.0
+ARG BASE_IMAGE=vllm/vllm-openai:v0.29.0
 FROM ${BASE_IMAGE}
 
 ARG COMMON_WORKDIR=/app

docker/Dockerfile.npu

@@ -1,20 +1,7 @@
 ARG VLLM_ASCEND_IMAGE=quay.io/atlas-ci/vllm-ascend
-ARG VLLM_ASCEND_TAG=v0.27.0
-FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}
-
-# WORKDIR /vllm-workspace/vllm
-# RUN git fetch origin --tags && git checkout v0.18.0
-
-# WORKDIR /vllm-workspace/vllm-ascend
-# RUN git fetch origin releases/v0.18.0 && git checkout d781902ce9dbda8ab1e11bb0f2f0c1bc508fee7a
-# # Install vllm-ascend
-# # Append `libascend_hal.so` path (devlib) to LD_LIBRARY_PATH
-# RUN export PIP_EXTRA_INDEX_URL=https://mirrors.huaweicloud.com/ascend/repos/pypi && \
-#     source /usr/local/Ascend/ascend-toolkit/set_env.sh && \
-#     source /usr/local/Ascend/nnal/atb/set_env.sh && \
-#     export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/Ascend/ascend-toolkit/latest/`uname -i`-linux/devlib && \
-#     python3 -m pip install -v -e /vllm-workspace/vllm-ascend/ --extra-index https://download.pytorch.org/whl/cpu/ && \
-#     python3 -m pip cache purge
+ARG VLLM_ASCEND_VERSION=v0.29.0
+ARG VLLM_ASCEND_DEVICE_SUFFIX=
+FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_VERSION}${VLLM_ASCEND_DEVICE_SUFFIX}
 
 ARG APP_DIR=/vllm-workspace/vllm-omni
 WORKDIR ${APP_DIR}
@@ -24,12 +11,14 @@ RUN export PIP_EXTRA_INDEX_URL=https://mirrors.huaweicloud.com/ascend/repos/pypi
     source /usr/local/Ascend/ascend-toolkit/set_env.sh && \
     source /usr/local/Ascend/nnal/atb/set_env.sh && \
     export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/Ascend/ascend-toolkit/latest/`uname -i`-linux/devlib && \
+    python3 -m pip install -v "mindiesd==3.1.0" && \
     python3 -m pip install -v -e /vllm-workspace/vllm-omni/ --no-build-isolation
 
 RUN python3 -m pip install -v --no-deps \
       "stepaudio2-minicpmo>=0.1.1" \
       "step-audio2>=1.0.0"
 
+ENV VLLM_TARGET_DEVICE=npu
 ENV VLLM_WORKER_MULTIPROC_METHOD=spawn
 
 ENTRYPOINT []

docker/Dockerfile.npu.a3

@@ -1,35 +0,0 @@
-ARG VLLM_ASCEND_IMAGE=quay.io/atlas-ci/vllm-ascend
-ARG VLLM_ASCEND_TAG=v0.27.0-a3
-FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}
-
-# WORKDIR /vllm-workspace/vllm
-# RUN git fetch origin --tags && git checkout v0.18.0
-
-# WORKDIR /vllm-workspace/vllm-ascend
-# RUN git fetch origin releases/v0.18.0 && git checkout d781902ce9dbda8ab1e11bb0f2f0c1bc508fee7a
-# # Install vllm-ascend
-# # Append `libascend_hal.so` path (devlib) to LD_LIBRARY_PATH
-# RUN export PIP_EXTRA_INDEX_URL=https://mirrors.huaweicloud.com/ascend/repos/pypi && \
-#     source /usr/local/Ascend/ascend-toolkit/set_env.sh && \
-#     source /usr/local/Ascend/nnal/atb/set_env.sh && \
-#     export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/Ascend/ascend-toolkit/latest/`uname -i`-linux/devlib && \
-#     python3 -m pip install -v -e /vllm-workspace/vllm-ascend/ --extra-index https://download.pytorch.org/whl/cpu/ && \
-#     python3 -m pip cache purge
-
-ARG APP_DIR=/vllm-workspace/vllm-omni
-WORKDIR ${APP_DIR}
-COPY . .
-
-RUN export PIP_EXTRA_INDEX_URL=https://mirrors.huaweicloud.com/ascend/repos/pypi && \
-    source /usr/local/Ascend/ascend-toolkit/set_env.sh && \
-    source /usr/local/Ascend/nnal/atb/set_env.sh && \
-    export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/Ascend/ascend-toolkit/latest/`uname -i`-linux/devlib && \
-    python3 -m pip install -v -e /vllm-workspace/vllm-omni/ --no-build-isolation
-
-RUN python3 -m pip install -v --no-deps \
-      "stepaudio2-minicpmo>=0.1.1" \
-      "step-audio2>=1.0.0"
-
-ENV VLLM_WORKER_MULTIPROC_METHOD=spawn
-
-ENTRYPOINT []