[Doc] Clarify private security contact guidance

Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>
[Doc] Move meeting details to dedicated page
2026-08-13 16:33:09 +08:00 · 2026-08-13 12:26:00 +08:00 · 2026-08-13 11:18:39 +08:00 · 2026-08-13 09:36:17 +08:00 · 2026-08-13 08:42:37 +08:00 · 2026-08-13 08:41:59 +08:00
216 changed files with 16564 additions and 1558 deletions
@@ -345,7 +345,7 @@ steps:
        commands:
          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
          - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
-          - export CACHE_DIT_VERSION=1.3.0
+          - export CACHE_DIT_VERSION=1.5.0
          - |
            set +e
            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_vllm_omni.json
@@ -361,7 +361,7 @@ steps:
        commands:
          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
          - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
-          - export CACHE_DIT_VERSION=1.3.0
+          - export CACHE_DIT_VERSION=1.5.0
          - |
            set +e
            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_edit_vllm_omni.json
@@ -377,7 +377,7 @@ steps:
        commands:
          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
          - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
-          - export CACHE_DIT_VERSION=1.3.0
+          - export CACHE_DIT_VERSION=1.5.0
          - |
            set +e
            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_edit_2511_vllm_omni.json
@@ -393,7 +393,7 @@ steps:
        commands:
          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
          - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
-          - export CACHE_DIT_VERSION=1.3.0
+          - export CACHE_DIT_VERSION=1.5.0
          - |
            set +e
            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_layered_vllm_omni.json
@@ -409,7 +409,7 @@ steps:
        commands:
          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
          - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
-          - export CACHE_DIT_VERSION=1.3.0
+          - export CACHE_DIT_VERSION=1.5.0
          - |
            set +e
            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_bagel_vllm_omni.json
@@ -227,6 +227,18 @@ steps:
            "
        mirror_hardwares: h100_1

+      - label: "Omni · MiniCPM-o 4.5 Perf Test"
+        timeout_in_minutes: 60
+        source_file_dependencies:
+          - tests/dfx/perf/tests/test_minicpmo_4_5_ready.json
+          - vllm_omni/deploy/minicpmo_4_5.yaml
+          - vllm_omni/model_executor/models/minicpmo_4_5/
+          - vllm_omni/model_executor/stage_input_processors/minicpmo_4_5_omni.py
+        commands:
+          - export BENCHMARK_DIR=tests/dfx/perf/results
+          - pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_minicpmo_4_5_ready.json -m 'core_model and cuda'
+        mirror_hardwares: h100_1
+
      - label: "TTS · VoxCPM2 Test"
        source_file_dependencies:
          - tests/e2e/online_serving/test_voxcpm2_tts.py
@@ -115,7 +115,7 @@ steps:
          DIFFUSION_ATTENTION_BACKEND: TORCH_SDPA
        commands:
          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
-          - export CACHE_DIT_VERSION=1.3.0
+          - export CACHE_DIT_VERSION=1.5.0
          - |
            set +e
            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py -m npu --test-config-file tests/dfx/perf/tests/test_qwen_image_edit_2511_vllm_omni.json
@@ -17,6 +17,15 @@ steps:
          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
        commands:
          - pytest -s -v tests/e2e/online_serving/test_minicpmo_4_5_duplex.py -m 'core_model and npu and A3 and omni' --run-level 'core_model'
+      - label: "Omni · MiniCPM-o 4.5 Perf Test"
+        timeout_in_minutes: 60
+        mirror_hardwares: a3_npu_2
+        env:
+          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
+        commands:
+          - export BENCHMARK_DIR=tests/dfx/perf/results
+          - pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_minicpmo_4_5_ready.json -m 'core_model and npu and A3 and omni'

  - group: ":card_index_dividers: TTS Model Test"
    key: ready-tts-group
@@ -40,9 +40,11 @@ steps:
    commands:
      - "bash .buildkite/release/scripts/publish-release-wheels-pypi.sh"

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

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

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

 set -euo pipefail

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

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

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

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

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

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

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

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

 echo ""
-echo "Successfully published release images for v${RELEASE_VERSION}"
+echo "Successfully published ${MODE} images"
@@ -1,6 +1,6 @@
 ---
 name: precheck-pr
-description: Self-check your branch before creating a PR — catch dead code, verify accuracy/perf claims, validate PR title format, and confirm merge readiness. Use when the user says "precheck", "self review", "pre-submit check", or "check my PR before I open it." Never posts to GitHub.
+description: Self-check your branch before creating a PR — catch dead code, prevent new model-specific Python examples, verify accuracy/perf claims, validate PR title format, and confirm merge readiness. Use when the user says "precheck", "self review", "pre-submit check", or "check my PR before I open it." Never posts to GitHub.
 ---

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

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

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

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

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

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

 Judgment calls that don't grep cleanly — apply them while reading the diff, not as a mechanical check:
@@ -0,0 +1,68 @@
+# Examples Python Policy
+
+Prevent new model-specific Python examples without forcing cleanup of the
+existing backlog. Run this check on every PR in quick and full modes.
+
+## Scope
+
+Set the merge base as in the main workflow, then list Python paths introduced
+by the PR, including additions, copies, and renames:
+
+```bash
+BASE="$(git merge-base HEAD origin/main)"
+git diff --name-status --diff-filter=ACR --find-renames --find-copies \
+  "${BASE}"...HEAD -- 'examples/**/*.py'
+```
+
+Inspect destination paths for copies and renames. Existing model-specific files
+that are only modified or deleted are grandfathered and must not be reported.
+
+## Blocking classification
+
+Mark ✗ when a newly introduced Python file does any of the following:
+
+- Names or scopes a path to one model, checkpoint, vendor, or model family.
+- Hard-codes a model identifier, model-specific prompt, request shape, output
+  conversion, or launch configuration behind an otherwise generic filename.
+- Duplicates an existing task/protocol runner to demonstrate one model.
+- Adds a per-model helper below a generic task hub such as `text_to_speech/`.
+
+Do not infer safety from a generic filename alone. For example, a file named
+`text_to_audio.py` is still model-specific if it only implements one model's
+contract.
+
+## Acceptable additions
+
+A new Python example may pass when it is a genuinely model-neutral,
+user-facing task or protocol entrypoint. It must accept model selection through
+configuration and keep model-specific behavior out of the script.
+
+Use ⚠ when the file is model-neutral but reusable logic appears misplaced under
+`examples/`. Route code by purpose:
+
+| Code purpose | Preferred location |
+|---|---|
+| Model prompt, defaults, request/output adaptation | `vllm_omni/model_extras/` or production model modules |
+| Runnable model command and validation evidence | Task documentation or `recipes/` |
+| UI or interactive application | `apps/` |
+| Benchmark or evaluator | `benchmarks/` |
+| Regression reproducer | `tests/` |
+| Download, conversion, or setup utility | `tools/` |
+
+If a model cannot use an existing shared runner, report the missing generic
+capability. Do not solve the gap by adding a model-specific Python example.
+
+## Report format
+
+Add one row to the pre-check report:
+
+```text
+Examples policy    ✓ no new Python example paths
+Examples policy    ⚠ generic helper belongs in tools/
+Examples policy    ✗ examples/offline_inference/new_model/end2end.py is model-specific
+Examples policy    ✗ examples/offline_inference/x_to_y_model_name.py is model-specific
+```
+
+For a blocker, name the path, the model-specific behavior, and the appropriate
+shared runner or destination. Do not propose deleting unrelated existing
+examples as part of the PR.
@@ -97,6 +97,7 @@ If docs and live code disagree, verify the code/tests and report the drift.
 | [test-quality-evaluation.md](references/checks/test-quality-evaluation.md) | Tests change, are absent for risky code, or may not exercise production behavior. |
 | [tests-docs-checklist.md](references/checks/tests-docs-checklist.md) | Coverage, CI markers, examples, user docs, or PR evidence need review. |
 | [verification.md](references/checks/verification.md) | Hardware, a server, or a runnable affected path is available for active verification. |
+| [examples-policy.md](../precheck-pr/references/examples-policy.md) | The PR adds, copies, or renames Python under `examples/`; apply the canonical policy shared with `precheck-pr`. |

 ### Delivery and reviewer coordination

@@ -152,6 +153,13 @@ contract they protect or use only the applicable evidence checks.
 Apply every category in [general-checks.md](references/process/general-checks.md) before
 lower-priority comments.

+If the diff census contains an added, copied, or renamed Python path under
+`examples/`, read and apply the canonical
+[examples policy](../precheck-pr/references/examples-policy.md). Treat a new
+model-specific Python example as blocking. Do not flag model-specific example
+debt that the PR only modifies or removes, and do not run the rest of the
+author-oriented `precheck-pr` workflow.
+
 For each changed value or behavior, trace:

 ```text
@@ -11,10 +11,15 @@ build:
    post_checkout:
      - git fetch --unshallow || true
    pre_install:
+      # `pre_install` runs before RTD upgrades pip, and the seeded pip 23.1
+      # rejects the `typing_extensions` wheel torch needs (fixed in pip 23.2).
+      - python -m pip install --upgrade pip
      # Pre-install CPU torch so the subsequent `pip install .[docs]` does not
      # pull ~2 GB of CUDA 13 libs (cuBLAS/cuDNN/NCCL/...) that exceed RTD's
-      # 1 GB memory cap and time out the mkdocs build.
-      - python -m pip install --index-url https://download.pytorch.org/whl/cpu torch
+      # 1 GB memory cap and time out the mkdocs build. Wheel-only: `--index-url`
+      # replaces PyPI, so an sdist fallback cannot reach its build deps. The
+      # quotes keep YAML from reading `:all:` as a mapping key.
+      - "python -m pip install --only-binary=:all: --index-url https://download.pytorch.org/whl/cpu torch"

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

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

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

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

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

                images = getattr(request_output, "images", [])
@@ -167,9 +167,11 @@ def main() -> None:
    ]
    native_cmd = [
        sys.executable,
-        "examples/offline_inference/text_to_video/text_to_video_lingbot.py",
+        "examples/offline_inference/text_to_video/text_to_video.py",
        "--model",
        args.model,
+        "--model-class-name",
+        "LingBotVideoPipeline",
        "--prompt",
        args.prompt,
        "--output",
@@ -163,7 +163,8 @@ nav:
 - CLI Reference: cli
 - Community:
  - Governance: community/governance.md
-  - community/*
+  - Contact Us: community/contact_us.md
+  - Community Meetings: community/meetings.md
  - Slack: https://slack.vllm.ai
  - Blog: https://blog.vllm.ai
  - Forum: https://discuss.vllm.ai
@@ -1,5 +1,9 @@
 # Contact Us

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

-## Diffusion Workload Division
+## Reviewer Routing

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

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

 ## Committer Nomination Process

@@ -0,0 +1,8 @@
+# Community Meetings
+
+vLLM-Omni hosts developer-facing meetings for Chinese- and English-language audiences. Schedules and access details can change, so use these maintained documents as the source of truth:
+
+- [Chinese-language meeting document](https://docs.google.com/document/d/1pdUBiS_7mdOUNDtdwy-9OUf7jsMWN324BIbps5olDME/edit?tab=t.0)
+- [English-language meeting document](https://docs.google.com/document/d/19AlD08T8uJ7wqfs8VtYCi0iGMTwlTYVVlAxFZ3S7OIg/edit?tab=t.0)
+
+Each document contains the current schedule, access details, agendas, and past meeting notes. For broader vLLM community events, see the [vLLM events calendar](https://vllm.ai/events/).
@@ -1 +0,0 @@
-# Meetups
@@ -1,12 +0,0 @@
-# Volunteers for Bugfix and CI
-
-We encourage you to check current docs and [issues](https://github.com/vllm-project/vllm-omni/issues) to find possible solutions for your questions. If non of these can solve it, please propose an issue to describe your questions about bug or CI problems for developing.
-
-If you have urgent need for locating and solving bugfix or CI problems, please find community volunteers below.
-
-| Dec 4-Dec 12 | Dec 15-Dec 19 | Dec 22-Dec 26 | Dec 29- Jan 2, 2026| Jan 5-Jan 9 | Jan 12-Jan 16 |
-|----------|----------|----------|----------|----------|----------|
-| <a href="https://github.com/congw729">Conw729</a> | <a href="https://github.com/yinpeiqi">yinpeiqi</a> | <a href="https://github.com/tzhouam">tzhouam</a> | <a href="https://github.com/SamitHuang">SamitHuang</a> | <a href="https://github.com/gcanlin">gcanlin</a> | <a href="https://github.com/natureofnature">natureofnature</a> |
-| <a href="https://github.com/david6666666">david6666666</a> | <a href="https://github.com/R2-Y">R2-Y</a> | <a href="https://github.com/hsliuustc0106">hsliuustc0106</a> | <a href="https://github.com/Gaohan123">Gaohan123</a> | <a href="https://github.com/ZJY0516">ZJY0516</a> | <a href="https://github.com/qibaoyuan">qibaoyuan</a> |
-
-We kindly welcome more contributors to fix bugs and contribute new features!
@@ -3,7 +3,7 @@
 Thank you for your interest in contributing to vLLM-Omni! This document provides guidelines and instructions for contributing.

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

 ## Getting Started

@@ -122,7 +122,7 @@ Repository skills guide the agent, but they do not replace contributor judgment,
 If you encounter a bug or have a feature request, please search existing issues first to see if it has already been reported. If not, please file a new issue, providing as much relevant information as possible.

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

 ## Pull Requests & Code Reviews

@@ -10,12 +10,12 @@ A: Refer to [GPU memory calculation and configuration](../configuration/gpu_memo

 > Q: I encounter some bugs or CI problems, which is urgent. How can I solve it?

-A: At first, you can check current [issues](https://github.com/vllm-project/vllm-omni/issues) to find possible solutions. If none of these satisfy your demand and it is urgent, please find these [volunteers](https://docs.vllm.ai/projects/vllm-omni/en/latest/community/volunteers/) for help.
+A: Search the current [issues](https://github.com/vllm-project/vllm-omni/issues) first. If no existing issue matches a bug or CI failure, open a [bug report](https://github.com/vllm-project/vllm-omni/issues/new?template=400-bug-report.yml) with a minimal reproducer, environment details, and relevant logs. For technical or usage questions, use the `sig-omni` channel in [vLLM Slack](https://slack.vllm.ai/) or the [vLLM Forum](https://discuss.vllm.ai/).

 > Q: Does vLLM-Omni support AWQ or any other quantization?

-A: We plan to introduce GGUF FP8 prequantized models and online FP8 quantization in version 0.16.0. Support for other quantization types will follow in future releases. For details, please see our [Q1 quantization roadmap](https://github.com/vllm-project/vllm-omni/issues/1057).
+A: AWQ is available through the inherited vLLM quantization registry, and vLLM-Omni supports several additional methods. Actual support and validation vary by model, component, hardware, and checkpoint. See the [quantization overview](../user_guide/quantization/overview.md) for validated combinations and method-specific guides.

 > Q: Does vLLM-Omni support multimodal streaming input and output?

-A: Not yet. We already put it on the [Roadmap](https://github.com/vllm-project/vllm-omni/issues/165). Please stay tuned!
+A: Yes, for specific models and serving paths. Current capabilities include [streaming video input](../serving/video_stream_api.md), [streaming diffusion output](../user_guide/diffusion/execution_modes.md#streaming-output), and experimental full-duplex real-time audio described in the [project overview](../README.md). Check the model and API documentation because input and output support is model-specific.
@@ -12,6 +12,7 @@ For backend selection and SageAttention usage, see the [Diffusion Attention Back
 | Model | Default Resolution | Default Frames | Default Steps | Guidance | VRAM (BF16) |
 |---|---|---|---|---|---|
 | `Wan-AI/Wan2.2-T2V-A14B-Diffusers` | 720x1280 | 81 | 40 | 4.0 | ~60 GiB |
+| `robbyant/lingbot-video-dense-1.3b` / `robbyant/lingbot-video-moe-30b-a3b` | 192x320 | 9 | 2 | 3.0 | ~68 GiB (MoE smoke) |
 | `Lightricks/LTX-2` | 512x768 | 121 | 40 | video 3.0 / audio 7.0 | Model-dependent |
 | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v` | 480x832 | 121 | 50 | 6.0 | 1×A100 80GB |
 | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v` | 720x1280 | 121 | 50 | 6.0 | FP8 + VAE tiling required |
@@ -35,6 +36,25 @@ python text_to_video.py \
  --output t2v_out.mp4
 ```

+### LingBot-Video
+
+The shared runner recognizes both official LingBot checkpoint IDs and selects
+the native `LingBotVideoPipeline` automatically:
+
+```bash
+python text_to_video.py \
+  --model robbyant/lingbot-video-dense-1.3b \
+  --prompt "a robotic arm picks up a red block" \
+  --height 192 --width 320 --num-frames 9 --num-inference-steps 2 \
+  --guidance-scale 3.0 --flow-shift 3.0 --fps 24 \
+  --output lingbot_t2v.mp4
+```
+
+Use `robbyant/lingbot-video-moe-30b-a3b` for the MoE checkpoint. For local
+paths without `lingbot` in the directory name, add
+`--model-class-name LingBotVideoPipeline`. Model-only options use
+`--extra-body`, for example `'{"batch_cfg": true, "output_type": "np"}'`.
+
 ### LTX-2

 ```bash
@@ -12,20 +12,20 @@ For the internal architecture and backend extension points, see the

 | Mode | Guide | Description | Methods |
 |------|-------|-------------|---------|
-| Online quantization | [Online Quantization](online.md) | vLLM-Omni computes quantized weights and scales while loading the model. | FP8 W8A8, Int8 W8A8, MXFP8 W8A8, MXFP4 W4A4 |
+| Online quantization | [Online Quantization](online.md) | vLLM-Omni computes quantized weights and scales while loading the model. | FP8 W8A8, Int8 W8A8, BitsAndBytes W4, MXFP8 W8A8, MXFP4 W4A4 |
 | Runtime attention quantization | [Quantized KV Cache](quantized_kvcache.md) | vLLM-Omni dynamically quantizes eligible diffusion Flash Attention tensors during inference. | FP8 FA |
-| Pre-quantized checkpoints | Method-specific guides | The checkpoint or an offline quantizer provides quantized weights and scales before serving. | ModelOpt, GGUF, AutoRound, msModelSlim, serialized Int8, offline MXFP8, offline MXFP4 DualScale |
+| Pre-quantized checkpoints | Method-specific guides | The checkpoint or an offline quantizer provides quantized weights and scales before serving. | ModelOpt, AutoRound, msModelSlim, serialized Int8, offline MXFP8, offline MXFP4 DualScale |

 ## Hardware Support

-| Device | FP8 W8A8 | Int8 W8A8 | ModelOpt | MXFP8 W8A8 | MXFP4 W4A4 | GGUF | AutoRound | msModelSlim |
-|--------|----------|-----------|----------|------------|------------|------|-----------|-------------|
-| NVIDIA Blackwell GPU (SM 100+) | ✅ | ✅ | ✅ | ⭕ | ⭕ | ✅ | ✅ | ❌ |
-| NVIDIA Ada/Hopper GPU (SM 89+) | ✅ | ✅ | ✅ | ⭕ | ⭕ | ✅ | ✅ | ❌ |
-| NVIDIA Ampere GPU (SM 80+) | ✅ | ✅ | ⭕ | ⭕ | ⭕ | ✅ | ✅ | ❌ |
-| AMD ROCm | ⭕ | ⭕ | ⭕ | ⭕ | ✅ | ⭕ | ⭕ | ❌ |
-| Intel XPU | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ | ✅ | ❌ |
-| Ascend NPU | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ |
+| Device | FP8 W8A8 | Int8 W8A8 | BitsAndBytes W4 | ModelOpt | MXFP8 W8A8 | MXFP4 W4A4 | AutoRound | msModelSlim |
+|--------|----------|-----------|------------------|----------|------------|------------|-----------|-------------|
+| NVIDIA Blackwell GPU (SM 100+) | ✅ | ✅ | ✅ | ✅ | ⭕ | ⭕ | ✅ | ❌ |
+| NVIDIA Ada/Hopper GPU (SM 89+) | ✅ | ✅ | ✅ | ✅ | ⭕ | ⭕ | ✅ | ❌ |
+| NVIDIA Ampere GPU (SM 80+) | ✅ | ✅ | ✅ | ⭕ | ⭕ | ⭕ | ✅ | ❌ |
+| AMD ROCm | ⭕ | ⭕ | ❌ | ⭕ | ⭕ | ✅ | ⭕ | ❌ |
+| Intel XPU | ⭕ | ⭕ | ❌ | ⭕ | ⭕ | ⭕ | ✅ | ❌ |
+| Ascend NPU | ❌ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ |

 Legend: `✅` supported, `❌` unsupported, `⭕` not verified in this
 guide. FP8 on Ampere may use a weight-only path where available.
@@ -43,10 +43,10 @@ otherwise.
 |--------|-------|------|----------------|--------|
 | FP8 W8A8 | [FP8](fp8.md) | Online W8A8 or checkpoint FP8 | Qwen-Image; Wan2.2 is not validated | Validated for Qwen-Image family and other DiT models |
 | Int8 W8A8 | [Int8](int8.md) | Online or serialized W8A8 | Qwen-Image; Wan2.2 is not validated | Validated for Qwen-Image and Z-Image |
+| BitsAndBytes W4 | [BitsAndBytes](bitsandbytes.md) | Online W4 weight-only | Z-Image-Turbo; Qwen-Image/Wan2.2 not validated | Validated for Z-Image-Turbo |
 | ModelOpt | [ModelOpt](modelopt.md) | Pre-quantized FP8 checkpoints | Qwen-Image, Z-Image, FLUX.2, HunyuanImage-3.0 | Validated for ModelOpt FP8 diffusion checkpoints |
 | MXFP8 W8A8 | [MXFP8](mxfp8.md) | Online W8A8 or offline pre-quantized | Wan2.2-T2V-A14B, I2V-A14B, TI2V-5B | Ascend NPU only; validated for Wan2.2 |
 | MXFP4 W4A4 | [MXFP4](mxfp4.md) | `mxfp4`: online single-scale only; `mxfp4_dualscale`: online or offline dual-scale (offline recommended) | Wan2.2-T2V-A14B, I2V-A14B | Ascend NPU only; validated for Wan2.2 A14B cascade models; TI2V-5B not supported; offline `mxfp4_dualscale` uses calibrated `mul_scale` for best accuracy |
-| GGUF | [GGUF](gguf.md) | Pre-quantized transformer weights | Qwen-Image | Validated where a model-specific GGUF adapter exists |
 | AutoRound | [AutoRound](autoround.md) | Pre-quantized W4A16 checkpoints | FLUX.1-dev; Qwen-Image/Wan2.2 not validated | Checkpoint-driven |
 | msModelSlim | [msModelSlim](msmodelslim.md) | Pre-quantized Ascend checkpoints | Wan2.2 recipe; HunyuanImage-3.0 inference target | Ascend/NPU path |

@@ -61,9 +61,9 @@ in BF16 unless the model guide explicitly adds support.
 |--------|-------|-------|----------------|--------|
 | ModelOpt | [ModelOpt](modelopt.md) | Thinker or language-model checkpoint config | Qwen3-Omni thinker | ModelOpt checkpoint path |
 | Int8 | [Int8](int8.md) | Not currently validated for omni/TTS stages | Qwen3-Omni, Qwen3-TTS | Not validated |
+| BitsAndBytes | [BitsAndBytes](bitsandbytes.md) | Not currently validated for omni/TTS stages | Qwen3-Omni, Qwen3-TTS | Not validated |
 | MXFP8 | [MXFP8](mxfp8.md) | Not currently validated for omni/TTS stages | Qwen3-Omni, Qwen3-TTS | Not validated |
 | MXFP4 | [MXFP4](mxfp4.md) | Not currently validated for omni/TTS stages | Qwen3-Omni, Qwen3-TTS | Not validated |
-| GGUF | [GGUF](gguf.md) | Not currently validated for omni/TTS stages | Qwen3-Omni, Qwen3-TTS | Not validated |
 | AutoRound | [AutoRound](autoround.md) | Thinker or language-model checkpoint config | Qwen2.5-Omni, Qwen3-Omni | Supported through AutoRound checkpoints |
 | msModelSlim | [msModelSlim](msmodelslim.md) | Not currently validated for omni/TTS stages | Qwen3-Omni, Qwen3-TTS | Not validated |

@@ -76,10 +76,10 @@ attached to the intended stage rather than applied globally.
 |--------|-------|-------|----------------|--------|
 | FP8 | [FP8](fp8.md) | Stage-specific DiT or transformer module | BAGEL, GLM-Image | Requires model-specific validation |
 | Int8 | [Int8](int8.md) | Stage-specific DiT or transformer module | BAGEL, GLM-Image | Requires model-specific validation |
+| BitsAndBytes | [BitsAndBytes](bitsandbytes.md) | Stage-specific transformer or DiT module | BAGEL, GLM-Image | Not validated |
 | ModelOpt | [ModelOpt](modelopt.md) | Checkpoint-defined diffusion stage | BAGEL, GLM-Image | Requires model-specific validation |
 | MXFP8 | [MXFP8](mxfp8.md) | Stage-specific DiT or transformer module | BAGEL, GLM-Image | Not validated |
 | MXFP4 | [MXFP4](mxfp4.md) | Stage-specific DiT or transformer module | BAGEL, GLM-Image | Not validated |
-| GGUF | [GGUF](gguf.md) | Stage-specific transformer weights | BAGEL, GLM-Image | No validated adapter listed |
 | AutoRound | [AutoRound](autoround.md) | Checkpoint-defined stage | BAGEL, GLM-Image | No validated checkpoint listed |
 | msModelSlim | [msModelSlim](msmodelslim.md) | Ascend-generated stage weights | GLM-Image | Requires model-specific adaptation |

@@ -106,7 +106,7 @@ config = build_quant_config({

 | Component | Default quantized? | Notes |
 |-----------|--------------------|-------|
-| Diffusion transformer | Yes | Primary target for FP8, Int8, ModelOpt, MXFP8, MXFP4, GGUF, AutoRound, and msModelSlim |
+| Diffusion transformer | Yes | Primary target for FP8, Int8, BitsAndBytes, ModelOpt, MXFP8, MXFP4, AutoRound, and msModelSlim |
 | Text encoder | No | Keep BF16 unless a method-specific guide documents support |
 | VAE | No | Keep BF16; storage-only paths are method-specific |
 | Scheduler/tokenizer | No | Loaded from the base model repository |
@@ -139,8 +139,8 @@ from vllm_omni.quantization import build_quant_config

 build_quant_config("fp8")
 build_quant_config({"method": "fp8", "activation_scheme": "static"})
+build_quant_config("bitsandbytes")
 build_quant_config("auto-round", bits=4, group_size=128)
-build_quant_config({"method": "gguf", "gguf_model": "/path/to/model.gguf"})
 build_quant_config({"transformer": {"method": "fp8"}, "vae": None})
 build_quant_config(None)
 ```
@@ -131,7 +131,7 @@ def build_sampling_params() -> list[SamplingParams]:


 def save_stage_output(stage_outputs, output_dir: str) -> None:
-    output = stage_outputs.request_output
+    output = stage_outputs
    request_id = output.request_id
    if stage_outputs.final_output_type == "text":
        text_output = output.outputs[0].text
@@ -104,7 +104,7 @@ def main(args):
    os.makedirs(output_dir, exist_ok=True)

    for stage_outputs in omni_outputs:
-        output = stage_outputs.request_output
+        output = stage_outputs
        if stage_outputs.final_output_type == "text":
            request_id = output.request_id
            text_output = output.outputs[0].text
@@ -228,7 +228,7 @@ async def main():
    if not outputs:
        raise ValueError("No output produced from omni.generate()")

-    first_out = outputs[0].request_output
+    first_out = outputs[0]
    req_out: OmniRequestOutput = first_out

    # Verify trajectory data (from custom pipeline)
@@ -741,7 +741,7 @@ def make_mmu_prompt(

 def iter_mm_outputs(outputs: list[Any]):
    for omni_out in outputs:
-        req_out = getattr(omni_out, "request_output", None)
+        req_out = omni_out
        req_list = req_out if isinstance(req_out, list) else [req_out]
        for item in req_list:
            if item is None:
@@ -303,7 +303,7 @@ def main():
    )
    img_idx = 0
    for req_output in omni_outputs:
-        ro = getattr(req_output, "request_output", None)
+        ro = req_output
        stage_id = getattr(req_output, "stage_id", None)

        # AR stage text — each CompletionOutput.text is already a delta when
@@ -621,7 +621,7 @@ def main():
        images = getattr(output, "images", None)
        if images:
            break
-        req_out = getattr(output, "request_output", None)
+        req_out = output
        images = getattr(req_out, "images", None) if req_out is not None else None
        if images:
            break
@@ -646,8 +646,8 @@ def main():
        if frames.multimodal_output and "audio" in frames.multimodal_output:
            audio = frames.multimodal_output["audio"]
            audio_sample_rate = frames.multimodal_output.get("audio_sample_rate", audio_sample_rate)
-        if frames.is_pipeline_output and frames.request_output is not None:
-            inner_output = frames.request_output
+        if frames.is_pipeline_output and frames is not None:
+            inner_output = frames
            if isinstance(inner_output, OmniRequestOutput):
                if inner_output.multimodal_output and "audio" in inner_output.multimodal_output:
                    audio = inner_output.multimodal_output["audio"]
@@ -0,0 +1,117 @@
+"""
+Offline inference example: MammothModa2 image summarization (single AR stage).
+
+Example:
+  uv run python examples/offline_inference/mammothmodal2_preview/run_mammothmoda2_image_summarize.py \
+    --model path/to/MammothModa2-Preview \
+    --deploy-config vllm_omni/deploy/mammoth_moda2_ar.yaml \
+    --image /path/to/input.jpg \
+    --question "Please summarize the content of this image."
+"""
+
+from __future__ import annotations
+
+import argparse
+import os
+
+from PIL import Image
+from vllm import SamplingParams
+from vllm.multimodal.image import convert_image_mode
+
+from vllm_omni import Omni
+
+DEFAULT_SYSTEM = "You are a helpful assistant."
+DEFAULT_QUESTION = "Please summarize the content of this image."
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="MammothModa2 image summarization (offline, AR only).")
+    parser.add_argument("--model", type=str, required=True, help="Path to model directory or model id.")
+    parser.add_argument("--deploy-config", type=str, required=True, help="Path to deploy config yaml.")
+    parser.add_argument("--image", type=str, required=True, help="Path to input image.")
+    parser.add_argument("--question", type=str, default=DEFAULT_QUESTION, help="Question/instruction for the model.")
+    parser.add_argument("--system", type=str, default=DEFAULT_SYSTEM, help="System prompt.")
+    parser.add_argument(
+        "--max-tokens",
+        type=int,
+        default=512,
+        help="Max new tokens to generate.",
+    )
+    parser.add_argument("--temperature", type=float, default=0.2)
+    parser.add_argument("--top-p", type=float, default=0.9)
+    parser.add_argument("--seed", type=int, default=42)
+    parser.add_argument("--trust-remote-code", action="store_true")
+    parser.add_argument(
+        "--enable-diffusion-pipeline-profiler",
+        action="store_true",
+        help="Enable diffusion pipeline profiler to display stage durations.",
+    )
+    return parser.parse_args()
+
+
+def build_prompt(system: str, question: str) -> str:
+    return (
+        f"<|im_start|>system\n{system}<|im_end|>\n"
+        "<|im_start|>user\n"
+        "<|vision_start|><|image_pad|><|vision_end|>"
+        f"{question}<|im_end|>\n"
+        "<|im_start|>assistant\n"
+    )
+
+
+def main() -> None:
+    args = parse_args()
+
+    if not os.path.exists(args.image):
+        raise FileNotFoundError(f"Image file not found: {args.image}")
+
+    pil_image = Image.open(args.image)
+    image_data = convert_image_mode(pil_image, "RGB")
+    prompt = build_prompt(args.system, args.question)
+
+    omni = Omni(
+        model=args.model,
+        deploy_config=args.deploy_config,
+        trust_remote_code=args.trust_remote_code,
+        enable_diffusion_pipeline_profiler=args.enable_diffusion_pipeline_profiler,
+    )
+    try:
+        sp = SamplingParams(
+            temperature=float(args.temperature),
+            top_p=float(args.top_p),
+            top_k=-1,
+            max_tokens=int(args.max_tokens),
+            seed=int(args.seed),
+            detokenize=True,
+        )
+        # NOTE: omni.generate() returns a Generator[OmniRequestOutput, None, None].
+        # Consume it inside the try block so the worker isn't closed early.
+        outputs = list(
+            omni.generate(
+                [
+                    {
+                        "prompt": prompt,
+                        "multi_modal_data": {"image": image_data},
+                        "additional_information": {"omni_task": ["chat"]},
+                    }
+                ],
+                [sp],
+            )
+        )
+    finally:
+        omni.close()
+
+    lines: list[str] = []
+    for stage_outputs in outputs:
+        ro = stage_outputs
+        text = ro.outputs[0].text if getattr(ro, "outputs", None) else str(ro)
+        lines.append(f"request_id: {getattr(ro, 'request_id', 'unknown')}\n")
+        lines.append("answer:\n")
+        lines.append(text.strip() + "\n")
+        lines.append("\n")
+
+    print("\n".join(lines))
+
+
+if __name__ == "__main__":
+    main()
@@ -297,7 +297,7 @@ def main(args):
    os.makedirs(output_dir, exist_ok=True)

    for stage_outputs in omni_outputs:
-        output = stage_outputs.request_output
+        output = stage_outputs
        if stage_outputs.final_output_type == "text":
            request_id = output.request_id
            text_output = output.outputs[0].text
@@ -347,7 +347,7 @@ def main(args):
        omni.start_profile(stages=args.profiler_stages)

    for stage_outputs in omni.generate(prompts, sampling_params_list):
-        output = stage_outputs.request_output
+        output = stage_outputs
        if stage_outputs.final_output_type == "text":
            request_id = output.request_id
            text_output = output.outputs[0].text
@@ -0,0 +1,229 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Offline speech-to-speech with NVIDIA-NemotronLabs-VoiceChat-11B.
+
+Feeds a 16 kHz user utterance (plus a spoken-style system prompt) through the
+3-stage nemotron_voicechat pipeline and writes the agent's reply as text and a
+22.05 kHz WAV.
+
+Usage:
+    python end2end.py \
+        --checkpoint /path/to/NVIDIA-NemotronLabs-VoiceChat-11B \
+        --wav /path/to/user_question_16k.wav \
+        --output-dir results/nemotron_voicechat
+
+The system prompt defaults to the NeMo reference default. The text tokenizer
+resolves from the ``nvidia/NVIDIA-Nemotron-Nano-9B-v2`` HF id (or the
+``NEMOTRON_VOICECHAT_LLM_PATH`` env var for air-gapped runs).
+
+Sizing the input WAV: the timeline is frame-locked, so the reply budget IS the
+input duration — the acoustic channel trails the text channel, and a reply
+that has not finished when the frames run out is truncated SILENTLY (no error;
+ASR of the WAV will just disagree with the emitted text). Leave generous
+trailing silence after the question (a question ending at ~4.5 s truncated in
+an 8 s WAV but completed cleanly in a 16 s one). The script warns when the
+text channel is still emitting non-PAD tokens near the last frame.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+from pathlib import Path
+
+import numpy as np
+import soundfile as sf
+
+# Warn if the text channel is still speaking within this many final frames
+# (12 frames = ~1 s) — the frame-locked reply likely got cut off.
+TRUNCATION_GUARD_FRAMES = 12
+
+# The NeMo reference offline script's default system prompt.
+DEFAULT_SYSTEM_PROMPT = (
+    "You are an AI voice assistant developed by NVIDIA. "
+    "Your name is NVIDIA Voice Chat. "
+    "Answer in a spoken, conversational style rather than a written one. "
+    "Do not repeat the same sentence over and over again. "
+    "Start the conversation by greeting the user."
+)
+
+
+def load_wav_16k_mono(path: str) -> np.ndarray:
+    wav, sr = sf.read(path, dtype="float32")
+    if wav.ndim > 1:
+        wav = wav.mean(axis=1)
+    if sr != 16000:
+        from vllm.multimodal.audio import resample_audio_scipy
+
+        wav = resample_audio_scipy(wav, orig_sr=sr, target_sr=16000)
+    return wav.astype(np.float32)
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--checkpoint", required=True, help="NVIDIA-NemotronLabs-VoiceChat-11B directory")
+    parser.add_argument("--wav", required=True, help="user speech (any sr; resampled to 16 kHz mono)")
+    parser.add_argument("--output-dir", default="results/nemotron_voicechat")
+    parser.add_argument("--system-prompt", default=DEFAULT_SYSTEM_PROMPT)
+    parser.add_argument(
+        "--stage-configs-path",
+        default=None,
+        help=(
+            "Deploy yaml override. Pass vllm_omni/deploy/nemotron_labs_voicechat_streaming.yaml "
+            "for the async-chunk streaming pipeline (stages overlap; audio starts decoding "
+            "before the thinker finishes). Default: the bundled non-streaming yaml (bit-exact "
+            "parity path)."
+        ),
+    )
+    args = parser.parse_args()
+
+    if not Path(args.wav).is_file():
+        raise FileNotFoundError(f"Input WAV not found: {args.wav}")
+    ckpt = Path(args.checkpoint)
+    raw_cfg = json.loads((ckpt / "config.json").read_text())
+    stt_cfg = raw_cfg["model"]["stt"]["model"]
+
+    from transformers import AutoTokenizer
+    from vllm.sampling_params import SamplingParams
+
+    from vllm_omni.entrypoints.omni import Omni
+    from vllm_omni.model_executor.models.nemotron_voicechat.nemotron_voicechat_thinker import (
+        compute_acoustic_frame_count,
+    )
+
+    tok_ref = os.environ.get("NEMOTRON_VOICECHAT_LLM_PATH") or stt_cfg.get(
+        "pretrained_llm", "nvidia/NVIDIA-Nemotron-Nano-9B-v2"
+    )
+    tokenizer = AutoTokenizer.from_pretrained(tok_ref, trust_remote_code=False)
+    bos_id = tokenizer.convert_tokens_to_ids(stt_cfg.get("bos_token", "<s>"))
+    eos_id = tokenizer.convert_tokens_to_ids(stt_cfg.get("eos_token", "</s>"))
+    pad_id = tokenizer.convert_tokens_to_ids(stt_cfg.get("pad_token", "<SPECIAL_12>"))
+    # NeMo encode_system_prompt: [bos] + tokens + [eos].
+    prompt_ids = [bos_id] + tokenizer.encode(args.system_prompt, add_special_tokens=False) + [eos_id]
+
+    wav = load_wav_16k_mono(args.wav)
+    n_frames = compute_acoustic_frame_count(stt_cfg, int(wav.shape[0]))
+    print(f"input: {wav.shape[0]} samples @16kHz -> {n_frames} acoustic frames; prompt {len(prompt_ids)} tokens")
+
+    inputs = {
+        # vLLM prompt = system prompt + one placeholder position (acoustic
+        # frame 0); the thinker replaces every position with fused embeddings.
+        "prompt_token_ids": prompt_ids + [pad_id],
+        "additional_information": {
+            # Plain float list: raw ndarrays do not survive the engine-core
+            # message serialization.
+            "nvc_audio": wav.astype(np.float32).tolist(),
+            "nvc_sr": 16000,
+            "nvc_prompt_token_ids": prompt_ids,
+            "nvc_expected_frames": n_frames,
+        },
+    }
+    sampling_params_list = [
+        # thinker: greedy, frame-locked generation length. NO min_tokens: the
+        # tokenizer's eos_token is <SPECIAL_12> — the frame-locked PAD/silence
+        # token — and vLLM's min_tokens masks "EOS" to -inf, which would forbid
+        # silence and force the agent to babble through the whole utterance.
+        SamplingParams(temperature=0.0, max_tokens=n_frames, ignore_eos=True, seed=0),
+        # talker: placeholder loop, stops on the stage's stop token. 16383:
+        # the 1-token placeholder prompt + max_tokens must fit the stage's
+        # max_model_len (16384).
+        SamplingParams(temperature=0.0, max_tokens=16383, stop_token_ids=[1], detokenize=False, seed=0),
+        # code2wav: single decode step.
+        SamplingParams(temperature=0.0, max_tokens=1, detokenize=False, seed=0),
+    ]
+
+    omni_kwargs: dict = {}
+    if args.stage_configs_path:
+        omni_kwargs["stage_configs_path"] = args.stage_configs_path
+    omni = Omni(model=str(ckpt), trust_remote_code=True, **omni_kwargs)
+    outputs = omni.generate(inputs, sampling_params_list)
+
+    out_dir = Path(args.output_dir)
+    out_dir.mkdir(parents=True, exist_ok=True)
+    stem = Path(args.wav).stem
+
+    agent_text = None
+    audio = None
+    sr = 22050
+    for stage_outputs in outputs:
+        # One entry per final stage (text from the thinker, audio from code2wav);
+        # mirrors the qwen2_5_omni offline example's collection loop.
+        output = getattr(stage_outputs, "request_output", stage_outputs)
+        final_type = getattr(stage_outputs, "final_output_type", None)
+        completions = getattr(output, "outputs", None) or []
+        if not completions:
+            continue
+        completion = completions[0]
+        if final_type in (None, "text"):
+            text = getattr(completion, "text", None)
+            if text:
+                agent_text = text
+            token_ids = getattr(completion, "token_ids", None)
+            if token_ids:
+                (out_dir / f"{stem}_text_tokens.json").write_text(json.dumps(list(map(int, token_ids))))
+                # Frame lock: the reply budget IS the input length. If the text
+                # channel is still mid-sentence when the frames run out, the
+                # spoken answer is silently truncated — warn instead.
+                tail = list(map(int, token_ids))[-TRUNCATION_GUARD_FRAMES:]
+                if any(t != pad_id for t in tail):
+                    print(
+                        f"WARNING: the text channel emits non-PAD tokens within the last "
+                        f"{TRUNCATION_GUARD_FRAMES} frames ({TRUNCATION_GUARD_FRAMES * 80} ms) — the reply "
+                        "likely ran out of acoustic frames and is cut off mid-sentence. "
+                        "Pad the input WAV with more trailing silence and rerun."
+                    )
+            # Best-effort: dump the function-channel debug timeline if the
+            # engine surfaced the request info on this output.
+            info = getattr(output, "additional_information", None)
+            func_timeline = info.get("nvc_function_tokens") if isinstance(info, dict) else None
+            if func_timeline is not None:
+                values = func_timeline.tolist() if hasattr(func_timeline, "tolist") else list(func_timeline)
+                (out_dir / f"{stem}_function_tokens.json").write_text(json.dumps([int(v) for v in values]))
+                print(f"function-channel timeline: {len(values)} entries")
+        if final_type in (None, "audio"):
+            # multimodal_output is a MultimodalPayload (Mapping) — duck-type it.
+            mm = getattr(completion, "multimodal_output", None)
+            get = getattr(mm, "get", None)
+            if not callable(get):
+                continue
+            candidate = None
+            for key in ("audio", "model_outputs", "wav", "waveform"):
+                value = get(key)
+                if value is not None:
+                    candidate = value
+                    break
+            if isinstance(candidate, list):
+                candidate = candidate[0] if candidate else None
+            if candidate is not None:
+                arr = np.asarray(
+                    candidate.float().cpu() if hasattr(candidate, "cpu") else candidate, dtype=np.float32
+                ).reshape(-1)
+                if arr.size > 0:
+                    audio = arr
+                sr_val = get("sr")
+                if sr_val is not None:
+                    sr_item = sr_val[0] if isinstance(sr_val, list) else sr_val
+                    sr = int(sr_item.item() if hasattr(sr_item, "item") else sr_item)
+
+    if agent_text is not None:
+        # The frame-locked text channel emits PAD tokens on silent frames;
+        # strip them (NeMo's post-inference detokenizer does the same).
+        pad_token = stt_cfg.get("pad_token", "<SPECIAL_12>")
+        agent_text = " ".join(agent_text.replace(pad_token, " ").split())
+        text_path = out_dir / f"{stem}_output.txt"
+        text_path.write_text(agent_text + "\n")
+        print(f"agent text -> {text_path}\n{agent_text}")
+    else:
+        print("WARNING: no agent text in outputs")
+
+    if audio is not None and audio.size > 0:
+        wav_path = out_dir / f"{stem}_output.wav"
+        sf.write(wav_path, audio, sr)
+        print(f"agent audio -> {wav_path} ({audio.size / sr:.2f}s @ {sr} Hz)")
+    else:
+        raise RuntimeError("No audio produced by the code2wav stage.")
+
+
+if __name__ == "__main__":
+    main()
@@ -392,7 +392,7 @@ def main(args):
    total_requests = len(prompts)
    processed_count = 0
    for stage_outputs in omni_generator:
-        output = stage_outputs.request_output
+        output = stage_outputs
        if stage_outputs.final_output_type == "text":
            request_id = output.request_id
            text_output = output.outputs[0].text
@@ -367,7 +367,7 @@ def main(args):
    print(f"query type: {args.query_type}")

    for stage_outputs in omni_generator:
-        output = stage_outputs.request_output
+        output = stage_outputs
        if stage_outputs.final_output_type == "text":
            request_id = output.request_id
            text_output = output.outputs[0].text
@@ -237,7 +237,7 @@ async def run_single_request(
            sampling_params_list=sampling_params_list,
            output_modalities=output_modalities,
        ):
-            output = omni_output.request_output
+            output = omni_output
            if omni_output.final_output_type == "text":
                if stage_0_first_output_ts is None:
                    stage_0_first_output_ts = time.perf_counter()
@@ -26,7 +26,6 @@ import torch
 from vllm_omni.diffusion.data import DiffusionParallelConfig
 from vllm_omni.entrypoints.omni import Omni
 from vllm_omni.inputs.data import OmniDiffusionSamplingParams
-from vllm_omni.outputs import OmniRequestOutput
 from vllm_omni.platforms import current_omni_platform


@@ -352,8 +351,8 @@ def main():
        else:
            print("[Profiler] No valid profiling data returned.")

-    # Extract output from result
-    output = OmniRequestOutput.unwrap_result(result)
+    # omni.generate() returns a list for sync calls; unwrap single-result list.
+    output = result[0] if isinstance(result, list) else result

    if not output.images:
        raise ValueError("No video frames found in OmniRequestOutput.")
@@ -345,7 +345,7 @@ def main(args):
    # Process outputs from each stage
    for stage_idx, stage_outputs in enumerate(omni_outputs):
        # request_output may be a single object or a list
-        raw_output = stage_outputs.request_output
+        raw_output = stage_outputs
        outputs_list = raw_output if isinstance(raw_output, list) else [raw_output]

        if stage_outputs.final_output_type == "text":
@@ -43,6 +43,7 @@ from vllm_omni.diffusion.utils.param_utils import apply_declared_extra_args
 from vllm_omni.entrypoints.omni import Omni
 from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 from vllm_omni.model_extras import get_extra_body_params, get_model_class_name
+from vllm_omni.outputs import OmniRequestOutput
 from vllm_omni.platforms import current_omni_platform

 AUDIOX_TASKS = ("t2a", "t2m", "v2a", "v2m", "tv2a", "tv2m")
@@ -388,9 +389,9 @@ def main():
        raise ValueError("No output generated from omni.generate()")

    output = outputs[0]
-    if not hasattr(output, "request_output") or not output.request_output:
+    if not isinstance(output, OmniRequestOutput) or not output:
        raise ValueError("No request_output found in OmniRequestOutput")
-    request_output = output.request_output
+    request_output = output
    if not hasattr(request_output, "multimodal_output"):
        raise ValueError("No multimodal_output found in request_output")

@@ -115,7 +115,7 @@ def build_demo(args: argparse.Namespace) -> gr.Blocks:
        )
        images_outputs = []
        for output in outputs:
-            req_out = output.request_output
+            req_out = output
            if not isinstance(req_out, OmniRequestOutput) or not hasattr(req_out, "images"):
                raise ValueError("Invalid request_output structure or missing 'images' key")
            images = req_out.images
@@ -656,7 +656,7 @@ def main():
        images = getattr(output, "images", None)
        if images:
            break
-        req_out = getattr(output, "request_output", None)
+        req_out = output
        images = getattr(req_out, "images", None) if req_out is not None else None
        if images:
            break
@@ -372,18 +372,10 @@ python examples/offline_inference/text_to_speech/qwen3_tts/end2end.py \
 Streaming requires `async_chunk: true` in the stage config.

 ### Word Timestamps
-Generate a WAV offline and a JSON sidecar with word-level timestamps from
-`Qwen/Qwen3-ForcedAligner-0.6B`:
-```bash
-python examples/offline_inference/text_to_speech/qwen3_tts/word_timestamps.py \
-    --model Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice \
-    --forced-aligner Qwen/Qwen3-ForcedAligner-0.6B \
-    --text "Hello world." \
-    --output-dir /tmp/qwen3_tts_timestamps
-```
-The script writes `qwen3_tts_word_timestamps.wav` and
-`qwen3_tts_word_timestamps.json`. On machines without a local CUDA toolkit,
-set `VLLM_USE_FLASHINFER_SAMPLER=0` to avoid FlashInfer sampler JIT.
+Word-level timestamps are currently a serving-path feature: launch
+`vllm-omni serve` with `--forced-aligner` and request `word_timestamps`
+(see `examples/online_serving/text_to_speech/README.md`). An offline
+example is not provided in this release.

 ### Batched decoding
 The Code2Wav stage supports batched decoding through the SpeechTokenizer. Pass multiple prompts via `--txt-prompts` and set `--batch-size` accordingly. To raise `max_num_seqs` on either stage, point `--stage-configs-path` at a stage configs YAML with the desired values (see `vllm_omni/model_executor/stage_configs/` for templates):
@@ -168,7 +168,7 @@ def run_e2e():
    print(f"Received {len(outputs)} outputs.")
    for i, output in enumerate(outputs):
        try:
-            ro = output.request_output
+            ro = output
            if ro is None:
                print("No request_output found.")
                continue
@@ -179,7 +179,7 @@ def main(args):

    t_start = time.perf_counter()
    for stage_outputs in omni.generate(inputs):
-        request_output = stage_outputs.request_output
+        request_output = stage_outputs
        if request_output is None or not request_output.outputs:
            continue
        _save_wav(
@@ -215,7 +215,7 @@ async def main_streaming(args):
    sample_rate = None

    async for stage_output in omni.generate(prompt, request_id=request_id):
-        mm = stage_output.request_output.outputs[0].multimodal_output
+        mm = stage_output.outputs[0].multimodal_output
        if not stage_output.finished:
            t_now = time.perf_counter()
            sample_rate, n = _collect_audio_chunk(mm, all_audio_chunks, sample_rate)
@@ -137,7 +137,7 @@ def main(args) -> None:
    params_list = sampling_params

    for stage_outputs in omni.generate(inputs, params_list):
-        for i, req_output in enumerate(stage_outputs.request_output):
+        for i, req_output in enumerate(stage_outputs):
            for j, out in enumerate(req_output.outputs):
                mm = out.multimodal_output
                if mm is None:
@@ -135,7 +135,7 @@ def run_e2e():
    print(f"Received {len(outputs)} outputs.")
    for i, output in enumerate(outputs):
        try:
-            ro = output.request_output
+            ro = output
            if ro is None:
                print("No request_output found.")
                continue
@@ -391,7 +391,7 @@ def main(args):
    for batch_start in range(0, len(inputs), batch_size):
        batch = inputs[batch_start : batch_start + batch_size]
        for stage_outputs in omni.generate(batch):
-            output = stage_outputs.request_output
+            output = stage_outputs
            _save_wav(output_dir, output.request_id, output.outputs[0].multimodal_output)


@@ -412,7 +412,7 @@ async def main_streaming(args):
        t_prev = t_start
        chunk_idx = 0
        async for stage_output in omni.generate(prompt, request_id=request_id):
-            mm = stage_output.request_output.outputs[0].multimodal_output
+            mm = stage_output.outputs[0].multimodal_output
            if not stage_output.finished:
                t_now = time.perf_counter()
                audio = mm.get("audio")
@@ -116,7 +116,7 @@ def main(args: Any) -> None:
    prompt = _build_custom_voice_input(args)
    final_output = None
    for stage_outputs in omni.generate([prompt]):
-        final_output = stage_outputs.request_output
+        final_output = stage_outputs
    if final_output is None:
        raise RuntimeError("Qwen3-TTS did not produce an output.")

@@ -38,7 +38,7 @@ def _stage_durations_from_output(outputs) -> dict[str, float]:
    durations = getattr(omni_out, "stage_durations", None) or {}
    if durations:
        return dict(durations)
-    ro = getattr(omni_out, "request_output", None)
+    ro = omni_out
    if ro is not None:
        return dict(getattr(ro, "stage_durations", None) or {})
    return {}
@@ -99,7 +99,7 @@ def extract_audio(outputs) -> tuple[np.ndarray, int]:
    out = outputs[0]
    mm = getattr(out, "multimodal_output", None)
    if mm is None:
-        ro = getattr(out, "request_output", None)
+        ro = out
        if ro is not None:
            mm = getattr(ro, "multimodal_output", None)
            if mm is None and ro.outputs:
@@ -8,6 +8,7 @@ A unified script for text-to-video generation. Supports multiple models with mod
 |---|---|---|---|---|---|
 | `Wan-AI/Wan2.1-VACE-1.3B-diffusers` | 480x832 | 81 | 30 | 5.0 | ~20 GiB (RTX 5090, VAE tiling) |
 | `Wan-AI/Wan2.2-T2V-A14B-Diffusers` | 720x1280 | 81 | 40 | 4.0 | ~60 GiB |
+| `robbyant/lingbot-video-dense-1.3b` / `robbyant/lingbot-video-moe-30b-a3b` | 192x320 | 9 | 2 | 3.0 | ~68 GiB (MoE smoke) |
 | `Lightricks/LTX-2` | 512x768 | 121 | 40 | video 3.0 / audio 7.0 | Model-dependent |
 | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v` | 480x832 | 121 | 50 | 6.0 | 1×A100 80GB |
 | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v` | 720x1280 | 121 | 50 | 6.0 | FP8 + VAE tiling required |
@@ -57,18 +58,31 @@ python text_to_video.py \

 ### LingBot-Video

-The dedicated LingBot example explicitly selects the video output modality:
+The shared runner recognizes the official dense and MoE checkpoint IDs, selects
+`LingBotVideoPipeline`, and builds the canonical video request envelope:

 ```bash
-python examples/offline_inference/text_to_video/text_to_video_lingbot.py \
+python text_to_video.py \
  --model robbyant/lingbot-video-dense-1.3b \
+  --prompt "a robotic arm picks up a red block" \
  --height 192 --width 320 --num-frames 9 --num-inference-steps 2 \
+  --guidance-scale 3.0 --flow-shift 3.0 --fps 24 \
  --output lingbot_t2v.mp4
 ```

 Use the same command with `robbyant/lingbot-video-moe-30b-a3b` for the MoE
 checkpoint. Requested frame counts are rounded upward to the causal
-VAE's `4n+1` grid.
+VAE's `4n+1` grid. For a local checkpoint path whose name does not contain
+`lingbot`, pass `--model-class-name LingBotVideoPipeline`.
+
+LingBot-only options such as `batch_cfg` and `output_type` use the shared
+model-extra channel, for example:
+
+```bash
+python text_to_video.py \
+  --model robbyant/lingbot-video-dense-1.3b \
+  --extra-body '{"batch_cfg": true, "output_type": "np"}'
+```

 ### LTX-2

@@ -16,7 +16,7 @@ from vllm_omni.entrypoints.omni import Omni
 from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 from vllm_omni.lora.request import LoRARequest
 from vllm_omni.lora.utils import stable_lora_int_id
-from vllm_omni.model_extras import get_extra_body_params, get_model_class_name
+from vllm_omni.model_extras import get_extra_body_params, get_model_class_name, get_output_tensor_range
 from vllm_omni.outputs import OmniRequestOutput
 from vllm_omni.platforms import current_omni_platform

@@ -80,6 +80,17 @@ _MODEL_PRESETS = {
        "fps": 16,
        "output": "helios_output.mp4",
    },
+    "lingbot": {
+        "model_class_name": "LingBotVideoPipeline",
+        "height": 192,
+        "width": 320,
+        "num_frames": 9,
+        "num_inference_steps": 2,
+        "guidance_scale": 3.0,
+        "fps": 24,
+        "flow_shift": 3.0,
+        "output": "lingbot_video_output.mp4",
+    },
    "ltx2": {
        "height": 512,
        "width": 768,
@@ -110,27 +121,51 @@ _MODEL_PRESETS = {
 def _detect_preset(model: str, model_class_name: str | None = None) -> dict:
    model_lower = model.lower()
    class_lower = (model_class_name or "").lower()
-    if "distilled" in class_lower or "distilled" in model_lower:
-        return _MODEL_PRESETS["ltx2_distilled"]
-    if "ltx23" in class_lower or "ltx-2.3" in model_lower or "ltx_2.3" in model_lower:
-        return _MODEL_PRESETS["ltx23"]
-    if "ltx2" in class_lower or "ltx-2" in model_lower or "ltx_2" in model_lower:
+    if "lingbot" in model_lower or "lingbotvideo" in class_lower:
+        return _MODEL_PRESETS["lingbot"]
+    if "ltx" in class_lower or "ltx" in model_lower:
+        if "distilled" in class_lower or "distilled" in model_lower:
+            return _MODEL_PRESETS["ltx2_distilled"]
+        if "ltx23" in class_lower or "ltx-2.3" in model_lower or "ltx_2.3" in model_lower:
+            return _MODEL_PRESETS["ltx23"]
        return _MODEL_PRESETS["ltx2"]
-    if "vace" in model_lower:
+    if "vace" in model_lower or "vace" in class_lower:
        return _MODEL_PRESETS["vace"]
    # Edge must be matched before the generic cosmos branch (its "cosmos" substring would
    # otherwise pick up the Nano/Super 720p / gs 6.0 / flow_shift 10.0 preset).
    if ("cosmos" in model_lower or "cosmos" in class_lower) and ("edge" in model_lower or "edge" in class_lower):
        return _MODEL_PRESETS["cosmos3_edge"]
-    if "cosmos" in model_lower:
+    if "cosmos" in model_lower or "cosmos" in class_lower:
        return _MODEL_PRESETS["cosmos"]
-    if "hunyuan" in model_lower:
+    if "hunyuan" in model_lower or "hunyuan" in class_lower:
        return _MODEL_PRESETS["hunyuan"]
-    if "helios" in model_lower:
+    if "helios" in model_lower or "helios" in class_lower:
        return _MODEL_PRESETS["helios"]
    return _MODEL_PRESETS["wan"]


+def build_text_to_video_prompt(prompt: str, negative_prompt: str | None) -> dict[str, Any]:
+    """Build the canonical request envelope for the shared T2V example."""
+    result: dict[str, Any] = {
+        "prompt": prompt,
+        "modalities": ["video"],
+    }
+    if negative_prompt is not None:
+        result["negative_prompt"] = negative_prompt
+    return result
+
+
+def _normalize_float_tensor(tensor: torch.Tensor, source_range: str) -> torch.Tensor:
+    """Normalize decoded frames according to the pipeline's output contract."""
+    if not tensor.is_floating_point():
+        return tensor
+    if source_range == "negative_one_to_one":
+        return tensor.clamp(-1, 1) * 0.5 + 0.5
+    if source_range == "zero_to_one":
+        return tensor.clamp(0, 1)
+    raise ValueError(f"Unsupported floating-point tensor range: {source_range!r}")
+
+
 def parse_profiler_config(value: str) -> dict[str, Any]:
    try:
        config = json.loads(value)
@@ -160,7 +195,7 @@ def parse_extra_body(value: str) -> dict[str, Any]:
 def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser(
        description="Generate a video from a text prompt. "
-        "Supports Wan2.2, HunyuanVideo-1.5, Helios, and other text-to-video models."
+        "Supports Wan2.2, HunyuanVideo-1.5, Helios, LingBot-Video, and other text-to-video models."
    )
    parser.add_argument(
        "--model",
@@ -390,7 +425,7 @@ def _extract_peak_memory_mb(result: Any) -> float:
        return 0.0
    val = getattr(result, "peak_memory_mb", 0.0)
    if not val:
-        inner = getattr(result, "request_output", None)
+        inner = result
        if isinstance(inner, list):
            inner = inner[0] if inner else None
        val = getattr(inner, "peak_memory_mb", 0.0)
@@ -408,6 +443,7 @@ def main():
    for key, default_val in preset.items():
        if getattr(args, key.replace("-", "_"), None) is None:
            setattr(args, key.replace("-", "_"), default_val)
+    model_class_name = args.model_class_name

    generator = torch.Generator(device=current_omni_platform.device_type).manual_seed(args.seed)
    # Cache-dit config (Wan2.2 only)
@@ -480,6 +516,7 @@ def main():
    omni = Omni(**omni_kwargs)
    model_class_name = get_model_class_name(omni) or model_class_name
    declared_extra_body_params = get_extra_body_params(model_class_name)
+    output_tensor_range = get_output_tensor_range(model_class_name)

    if profiler_enabled:
        print("[Profiler] Starting profiling...")
@@ -513,12 +550,11 @@ def main():
            lora_path=lora_path,
        )

-    prompt_dict = {"prompt": args.prompt}
-    if args.negative_prompt is not None:
-        prompt_dict["negative_prompt"] = args.negative_prompt
-    elif preset not in (_MODEL_PRESETS["ltx2"], _MODEL_PRESETS["ltx23"]):
+    negative_prompt = args.negative_prompt
+    if negative_prompt is None and all(preset is not _MODEL_PRESETS[name] for name in ("lingbot", "ltx2", "ltx23")):
        # Preserve the historical empty-prompt behavior for non-LTX examples.
-        prompt_dict["negative_prompt"] = ""
+        negative_prompt = ""
+    prompt_dict = build_text_to_video_prompt(args.prompt, negative_prompt)

    extra_args = {}
    if lora_request:
@@ -575,8 +611,8 @@ def main():
        if frames.multimodal_output and "audio" in frames.multimodal_output:
            audio = frames.multimodal_output["audio"]
            audio_sample_rate = frames.multimodal_output.get("audio_sample_rate", audio_sample_rate)
-        if frames.is_pipeline_output and frames.request_output is not None:
-            inner_output = frames.request_output
+        if frames.is_pipeline_output and frames is not None:
+            inner_output = frames
            if isinstance(inner_output, OmniRequestOutput):
                if inner_output.multimodal_output and "audio" in inner_output.multimodal_output:
                    audio = inner_output.multimodal_output["audio"]
@@ -630,8 +666,7 @@ def main():
                frame_tensor = frame_tensor[0]
            if frame_tensor.dim() == 3 and frame_tensor.shape[0] in (3, 4):
                frame_tensor = frame_tensor.permute(1, 2, 0)
-            if frame_tensor.is_floating_point():
-                frame_tensor = frame_tensor.clamp(-1, 1) * 0.5 + 0.5
+            frame_tensor = _normalize_float_tensor(frame_tensor, output_tensor_range)
            return frame_tensor.float().numpy()
        if isinstance(frame, np.ndarray):
            frame_array = frame
@@ -683,8 +718,7 @@ def main():
                video_tensor = video_tensor[0]
        elif video_tensor.dim() == 4 and video_tensor.shape[0] in (3, 4):
            video_tensor = video_tensor.permute(1, 2, 3, 0)
-        if video_tensor.is_floating_point():
-            video_tensor = video_tensor.clamp(-1, 1) * 0.5 + 0.5
+        video_tensor = _normalize_float_tensor(video_tensor, output_tensor_range)
        video_array = video_tensor.float().numpy()
    elif isinstance(frames, np.ndarray):
        video_array = frames
@@ -1,131 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-from __future__ import annotations
-
-import argparse
-import time
-from pathlib import Path
-from typing import Any
-
-import numpy as np
-import torch
-
-from vllm_omni.diffusion.data import DiffusionParallelConfig
-from vllm_omni.entrypoints.omni import Omni
-from vllm_omni.inputs.data import OmniDiffusionSamplingParams
-from vllm_omni.outputs import OmniRequestOutput
-from vllm_omni.platforms import current_omni_platform
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(description="Generate text-to-video with a LingBot-Video checkpoint.")
-    parser.add_argument("--model", default="robbyant/lingbot-video-dense-1.3b")
-    parser.add_argument("--prompt", default="a robotic arm picks up a red block")
-    parser.add_argument("--negative-prompt", default=None)
-    parser.add_argument("--output", default="lingbot_video_output.mp4")
-    parser.add_argument("--height", type=int, default=192)
-    parser.add_argument("--width", type=int, default=320)
-    parser.add_argument("--num-frames", type=int, default=9)
-    parser.add_argument("--num-inference-steps", type=int, default=2)
-    parser.add_argument("--guidance-scale", type=float, default=3.0)
-    parser.add_argument("--flow-shift", type=float, default=3.0)
-    parser.add_argument("--seed", type=int, default=42)
-    parser.add_argument("--fps", type=int, default=24)
-    parser.add_argument("--output-type", choices=["pt", "np"], default="pt")
-    parser.add_argument("--batch-cfg", action="store_true")
-    return parser.parse_args()
-
-
-def _peak_memory_mb(output: Any) -> float:
-    result = output[0] if isinstance(output, list) and output else output
-    return float(getattr(result, "peak_memory_mb", 0.0) or 0.0)
-
-
-def _extract_video(output: Any) -> Any:
-    if isinstance(output, list):
-        output = output[0] if output else None
-    if isinstance(output, OmniRequestOutput):
-        if output.final_output_type != "image":
-            raise ValueError(f"Unexpected output type {output.final_output_type!r}.")
-        if not output.images:
-            raise ValueError("No video frames found in OmniRequestOutput.")
-        return output.images[0] if len(output.images) == 1 else output.images
-    return output
-
-
-def _to_frame_array(video: Any) -> np.ndarray:
-    if isinstance(video, torch.Tensor):
-        tensor = video.detach().cpu()
-        if tensor.dim() == 5:
-            tensor = tensor[0]
-        if tensor.dim() == 4 and tensor.shape[-1] not in (3, 4):
-            if tensor.shape[0] in (3, 4):
-                tensor = tensor.permute(1, 2, 3, 0)
-            elif tensor.shape[1] in (3, 4):
-                tensor = tensor.permute(0, 2, 3, 1)
-        if tensor.is_floating_point():
-            if float(tensor.min()) < 0.0:
-                tensor = tensor.clamp(-1, 1) * 0.5 + 0.5
-            else:
-                tensor = tensor.clamp(0, 1)
-        return tensor.float().numpy()
-    if isinstance(video, np.ndarray):
-        array = video[0] if video.ndim == 5 else video
-        if np.issubdtype(array.dtype, np.integer):
-            return array.astype(np.float32) / 255.0
-        return array.astype(np.float32, copy=False)
-    if isinstance(video, list):
-        return np.stack([_to_frame_array(frame) for frame in video], axis=0)
-    return np.asarray(video, dtype=np.float32)
-
-
-def main() -> None:
-    args = parse_args()
-    generator = torch.Generator(device=current_omni_platform.device_type).manual_seed(args.seed)
-    omni = Omni(
-        model=args.model,
-        model_class_name="LingBotVideoPipeline",
-        flow_shift=args.flow_shift,
-        parallel_config=DiffusionParallelConfig(),
-    )
-
-    prompt: dict[str, Any] = {
-        "prompt": args.prompt,
-        "modalities": ["video"],
-        "num_frames": args.num_frames,
-    }
-    if args.negative_prompt is not None:
-        prompt["negative_prompt"] = args.negative_prompt
-
-    sampling_params = OmniDiffusionSamplingParams(
-        height=args.height,
-        width=args.width,
-        num_frames=args.num_frames,
-        num_inference_steps=args.num_inference_steps,
-        guidance_scale=args.guidance_scale,
-        generator=generator,
-        output_type=args.output_type,
-        extra_args={"batch_cfg": args.batch_cfg, "shift": args.flow_shift},
-    )
-
-    start = time.perf_counter()
-    output = omni.generate(prompt, sampling_params)
-    elapsed = time.perf_counter() - start
-    print(f"Total generation time: {elapsed:.4f} seconds ({elapsed * 1000:.2f} ms)")
-    peak_mb = _peak_memory_mb(output)
-    if peak_mb:
-        print(f"Worker peak GPU memory (reserved): {peak_mb:.2f} MiB ({peak_mb / 1024:.2f} GiB)")
-
-    video = _to_frame_array(_extract_video(output))
-    output_path = Path(args.output)
-    output_path.parent.mkdir(parents=True, exist_ok=True)
-    from diffusers.utils import export_to_video
-
-    export_to_video(list(video), str(output_path), fps=args.fps)
-    print(f"Saved generated video to {output_path}")
-    omni.close()
-
-
-if __name__ == "__main__":
-    main()
@@ -138,7 +138,7 @@ def _extract_peak_memory_mb(result: Any) -> float:
        return 0.0
    val = getattr(result, "peak_memory_mb", 0.0)
    if not val:
-        inner = getattr(result, "request_output", None)
+        inner = result
        if isinstance(inner, list):
            inner = inner[0] if inner else None
        val = getattr(inner, "peak_memory_mb", 0.0)
@@ -605,6 +605,13 @@ python qwen3_tts/streaming_speech_client.py \
 The client writes one PCM file per sentence and a matching
 `sentence_XXX_timestamps.json` sidecar.

+Non-streaming requests can also ask for timestamps: pass
+`"word_timestamps": true` to `POST /v1/audio/speech` and read the
+`X-Word-Timestamps` response header (JSON list of `{word, start_ms, end_ms}`,
+ASCII-escaped). Past 4 KB the header is replaced by
+`X-Word-Timestamps-Omitted: oversize; bytes=<n>; limit=4096` and the audio
+still returns — use the WebSocket path for long transcripts.
+
 To *see* the alignment instead of reading a JSON sidecar, run the
 word-timestamp Gradio demo (server must be launched with `--forced-aligner`):
 ```bash
@@ -73,6 +73,8 @@ plugins:
  - awesome-nav
  - redirects:
      redirect_maps:
+        community/meetups.md: community/meetings.md
+        community/volunteers.md: community/contact_us.md
        user_guide/diffusion/request_batching.md: user_guide/diffusion/execution_modes.md
        user_guide/diffusion/step_execution.md: user_guide/diffusion/execution_modes.md
        user_guide/diffusion/cpu_offload_diffusion.md: user_guide/diffusion/cpu_offload.md
@@ -111,6 +113,7 @@ plugins:
        - "vllm_omni.entrypoints.openai"  # avoid importing vllm in mkdocs building
        - "vllm_omni.entrypoints.openai.protocol"  # avoid importing vllm in mkdocs building
        - "vllm_omni.experimental"  # avoid importing optional serving deps (openai, fastapi, uvicorn, httpx) in mkdocs building
+        - "vllm_omni.model_executor.models.nemotron_voicechat.nemo_vendored"  # vendored NeMo code; upstream docstrings trip griffe strict warnings
  - mkdocstrings:
      handlers:
        python:
@@ -206,6 +206,12 @@ ignore = [
 # These files keep upstream conventions (star imports, long lines, math-symbol
 # argument names) so they stay easy to diff against upstream releases.
 "vllm_omni/model_executor/models/minicpmo_4_5/minicpmo_4_5_omni_llm.py" = ["E402", "E501", "F403", "F405", "N803"]
+# NemotronVoiceChat inference modules vendored from NVIDIA-NeMo/Speech (branch
+# nemotron-labs-voicechat). Numerics-bearing code is kept byte-close to
+# upstream to stay diffable and parity-safe, so upstream style (long lines,
+# legacy typing, whitespace, unused-import shims) is exempted. TID251 and the
+# F-correctness rules still apply.
+"vllm_omni/model_executor/models/nemotron_voicechat/nemo_vendored/**" = ["E501", "E721", "E741", "F401", "F841", "I001", "N803", "N814", "UP006", "UP007", "UP008", "UP011", "UP032", "UP035", "W291", "W293"]
 "vllm_omni/experimental/fullduplex/joyvl/decision/prompts.py" = ["E501"]  # System/summarizer prompts are long prose lines

 [tool.ruff.lint.flake8-tidy-imports.banned-api]
@@ -314,6 +320,10 @@ extend-ignore-identifiers-re = [
    ".*[Nn][Dd]3.*",
    ".*updator.*",
    "byt5.*",
+    # NemotronVoiceChat vendored triton kernel: dout/DOUT abbreviate d_out
+    # (output feature dim), not "doubt".
+    ".*dout.*",
+    ".*DOUT.*",
 ]
 extend-ignore-re = [
    "\\.\\.\\.n,d->\\.\\.\\.nd",
@@ -334,6 +344,9 @@ byt5 = "byt5"
 # "seeked" is the standard HTML media element DOM event name (fires when a
 # seek completes), used by the word-timestamps Gradio demo. Not a typo.
 seeked = "seeked"
+# Ryan Leary: author credited in the NemotronVoiceChat vendored NeMo
+# features.py copyright header. A surname, not a typo of "leery".
+Leary = "Leary"
 # IndexTTS 2.5 language codes and speech-emotion-recognition event token.
 fo = "fo"
 ba = "ba"
@@ -0,0 +1,113 @@
+# NemotronLabs VoiceChat 11B
+
+> Offline speech-to-speech: a 16 kHz user utterance in, the agent's spoken reply
+> (text + 22.05 kHz WAV) out, on a 3-stage vLLM-Omni pipeline.
+
+## Summary
+
+- Vendor: NVIDIA
+- Model: [`nvidia/NVIDIA-NemotronLabs-VoiceChat-11B`](https://huggingface.co/nvidia/NVIDIA-NemotronLabs-VoiceChat-11B)
+- Task: Speech-to-speech voice chat. The model runs a frame-locked 12.5 Hz
+  timeline: a Conformer + NemotronH hybrid-Mamba thinker emits one text token
+  per acoustic frame, a Gemma3-1B EAR-TTS talker turns the text timeline into
+  31-quantizer RVQ code stacks, and an RVQ-VAE codec decodes them to audio.
+- Mode: Offline single-turn inference (batch=1). Online/duplex serving,
+  function calling, and batch>1 are documented follow-ups.
+- Maintainer: [`@yuekaizhang`](https://github.com/yuekaizhang)
+
+## When to use this recipe
+
+Use this recipe to run a single-turn voice-chat exchange with
+`NVIDIA-NemotronLabs-VoiceChat-11B` on one GPU: you provide a user utterance as
+a WAV file (any sample rate; it is resampled to 16 kHz mono) plus an optional
+spoken-style system prompt, and get back the agent's reply as text and a
+22.05 kHz WAV. The integration is NeMo-free at runtime — the perception
+Conformer, EAR-TTS talker, and RVQ-VAE codec are vendored, dependency-stripped
+NeMo modules (`nemo_vendored/`), so no `nemo_toolkit` install is needed.
+
+## References
+
+- Offline example:
+  [`examples/offline_inference/nemotron_voicechat/end2end.py`](../../examples/offline_inference/nemotron_voicechat/end2end.py)
+- Model modules (thinker / talker / code2wav / vendored NeMo):
+  [`vllm_omni/model_executor/models/nemotron_voicechat/`](../../vllm_omni/model_executor/models/nemotron_voicechat/)
+- Staged pipeline config:
+  [`vllm_omni/deploy/nemotron_labs_voicechat.yaml`](../../vllm_omni/deploy/nemotron_labs_voicechat.yaml)
+- Upstream model card:
+  [`nvidia/NVIDIA-NemotronLabs-VoiceChat-11B`](https://huggingface.co/nvidia/NVIDIA-NemotronLabs-VoiceChat-11B)
+- Reference implementation: NVIDIA-NeMo/Speech, branch `nemotron-labs-voicechat`
+
+## Pipeline
+
+| stage | arch | dtype | role |
+|---|---|---|---|
+| 0 thinker | `NemotronVoiceChatThinkerForConditionalGeneration` (LLM_AR) | fp32 | WAV + system prompt -> frame-locked text-token timeline (+ function channel) |
+| 1 talker | `NemotronVoiceChatTalker` (LLM_AR) | fp32 | text timeline -> 31-quantizer RVQ code stacks (one per 80 ms frame) |
+| 2 code2wav | `NemotronVoiceChatCode2Wav` (LLM_GENERATION) | fp32 | RVQ-VAE decode -> 22.05 kHz PCM |
+
+Stages 0/1 default to fp32 for exact parity with the NeMo reference
+implementation (greedy decoding matches it token for token on the acceptance
+fixture). The deploy yaml documents a ~2x-faster bf16 thinker option whose
+output stayed within one word of the reference in testing.
+
+## Hardware Support
+
+## GPU
+
+### 1x H100 80GB
+
+#### Environment
+
+- OS: Linux
+- Python: 3.12
+- vLLM version: 0.26.0
+- vLLM-Omni version or commit: this PR / current `main`
+
+#### Command
+
+```bash
+# Tokenizer: the checkpoint ships no HF tokenizer; it resolves from the
+# nvidia/NVIDIA-Nemotron-Nano-9B-v2 HF id automatically. For air-gapped runs,
+# point NEMOTRON_VOICECHAT_LLM_PATH at a local snapshot of that repo instead.
+python examples/offline_inference/nemotron_voicechat/end2end.py \
+    --checkpoint /path/to/NVIDIA-NemotronLabs-VoiceChat-11B \
+    --wav /path/to/user_question.wav \
+    --output-dir results/nemotron_voicechat
+```
+
+#### Verification
+
+```bash
+ls results/nemotron_voicechat
+# <stem>_output.txt          the agent reply as text
+# <stem>_output.wav          the agent reply as 22.05 kHz audio
+# <stem>_text_tokens.json    the frame-locked text-token timeline
+```
+
+The reply text should read as a coherent spoken-style answer to the question in
+the input WAV, and the WAV should transcribe to (approximately) the same text
+with any ASR model.
+
+#### Notes
+
+- Memory usage: the shipped yaml runs all three stages on one GPU
+  (`gpu_memory_utilization` 0.62 / 0.12 / 0.06); peak usage is dominated by the
+  fp32 thinker. The fp32 default has a hard floor of roughly 43 GB of thinker
+  weights alone (9B backbone + 587M `embed_tokens` + 587M `function_head` +
+  0.6B Conformer), so 48 GB cards cannot run it — use the bf16 thinker option
+  documented in the deploy yaml on anything smaller than an 80 GB part.
+- Input sizing: the timeline is frame-locked, so the reply budget IS the input
+  duration. The acoustic channel trails the text channel; if the WAV does not
+  carry enough trailing silence for the reply to finish, the spoken answer is
+  truncated silently. Leave generous trailing silence (a question ending at
+  ~4.5 s truncated in an 8 s WAV but completed cleanly in 16 s); the offline
+  example warns when the text channel is still speaking near the last frame.
+- Key flags: sampling is greedy end to end. The thinker is frame-locked —
+  `max_tokens` equals the acoustic frame count with `ignore_eos=True`. Do NOT
+  set `min_tokens` on the thinker: the tokenizer's EOS token is also the
+  frame-locked PAD/silence token, so masking it forces the model to babble
+  instead of pausing.
+- The talker's `max_tokens` is 16383 (its stage prompt is one placeholder
+  token, and the stage context is 16384).
+- Known limitations: batch=1 only; offline single turn (no online/duplex
+  serving yet); the function-call channel is decoded but not yet acted on.
@@ -66,6 +66,7 @@ recipes/
 | [`OpenBMB/VoxCPM2.md`](./OpenBMB/VoxCPM2.md) | Online + offline TTS with native AR pipeline (48 kHz, 30+ languages) | 1x RTX 4090 24GB |
 | [`OpenMOSS/MOSS-TTS.md`](./OpenMOSS/MOSS-TTS.md) | Online + offline multilingual TTS (MOSS-TTS family, 8B) | 1x H100 80GB |
 | [`NVIDIA/Nemotron-Labs-Audex.md`](./NVIDIA/Nemotron-Labs-Audex.md) | TTS / text-to-audio / audio understanding / cascaded S2S (Audex 2B + 30B-A3B) | 1x H100 80GB |
+| [`NVIDIA/NemotronLabs-VoiceChat.md`](./NVIDIA/NemotronLabs-VoiceChat.md) | Offline speech-to-speech voice chat (11B, frame-locked 12.5 Hz, 3-stage thinker/talker/code2wav) | 1x H100 80GB |
 | [`Qwen/Qwen-Image.md`](./Qwen/Qwen-Image.md) | Text-to-image serving with step-wise continuous batching replay and ModelOpt mixed FP8/NVFP4 | 1x A100 80GB / 2x B200 |
 | [`Qwen/Qwen-Image-2512.md`](./Qwen/Qwen-Image-2512.md) | Text-to-image serving with step-wise continuous batching replay and ModelOpt FP8 / mixed FP8/NVFP4 | 1x A800 80GB / 2x B200 |
 | [`Qwen/Qwen-Image-Edit.md`](./Qwen/Qwen-Image-Edit.md) | Text-guided single-image editing | 1x or 2x H200 141GB |
@@ -27,7 +27,7 @@ parallel, cache, quantized, or expert-kernel backends.
 - Dense checkpoint: <https://huggingface.co/robbyant/lingbot-video-dense-1.3b>
 - MoE checkpoint: <https://huggingface.co/robbyant/lingbot-video-moe-30b-a3b>
 - Upstream project: <https://github.com/Robbyant/lingbot-video>
- Related offline example: [`examples/offline_inference/text_to_video/text_to_video_lingbot.py`](../../examples/offline_inference/text_to_video/text_to_video_lingbot.py)
+- Related offline example: [`examples/offline_inference/text_to_video/text_to_video.py`](../../examples/offline_inference/text_to_video/text_to_video.py)
 - Related online video API docs: [`docs/serving/videos_api.md`](../../docs/serving/videos_api.md)

 ## Hardware Support
@@ -52,7 +52,7 @@ The MoE path is validated with BF16 expert weights.

 ```bash
 CUDA_VISIBLE_DEVICES=0 \
-python examples/offline_inference/text_to_video/text_to_video_lingbot.py \
+python examples/offline_inference/text_to_video/text_to_video.py \
  --model robbyant/lingbot-video-dense-1.3b \
  --prompt "a robotic arm picks up a red block" \
  --output lingbot_t2v.mp4 \
@@ -70,7 +70,7 @@ python examples/offline_inference/text_to_video/text_to_video_lingbot.py \

 ```bash
 CUDA_VISIBLE_DEVICES=0 \
-python examples/offline_inference/text_to_video/text_to_video_lingbot.py \
+python examples/offline_inference/text_to_video/text_to_video.py \
  --model robbyant/lingbot-video-moe-30b-a3b \
  --prompt "a robotic arm picks up a red block" \
  --output lingbot_moe_t2v.mp4 \
@@ -82,7 +82,7 @@ example and the DiT sections below.
 - Optional environment variables:

 ```bash
-export CACHE_DIT_VERSION=1.3.0
+export CACHE_DIT_VERSION=1.5.0
 ```

 HunyuanImage-3.0 sets the diffusion attention backend to `TORCH_SDPA`
@@ -16,7 +16,7 @@ diffusers==0.38.0
 safetensors>=0.8.0
 accelerate==1.12.0
 soundfile>=0.13.1
-cache-dit==1.3.0
+cache-dit==1.5.0
 tqdm>=4.66.0
 torchsde>=0.2.6
 openai-whisper>=20250625
@@ -34,3 +34,5 @@ pydantic-settings>=2.0.0
 # base image does not ship it, so declare it explicitly. The loader also guards
 # this import so a missing gguf no longer breaks all diffusion model loading.
 gguf>=0.10.0
+# Required for running the Cosmos3 model with guardrails enabled (default behavior).
+cosmos-guardrail>=0.3.1
@@ -0,0 +1,92 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Offline Daily-Omni sources: local mirrors must not fall back to the Hub id."""
+
+import json
+import os
+import tarfile
+from pathlib import Path
+
+import pytest
+
+from vllm_omni.benchmarks.data_modules.daily_omni_dataset import (
+    daily_omni_local_qa_json,
+    daily_omni_local_videos_dir,
+    resolve_daily_omni_local_root,
+)
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu, pytest.mark.benchmark]
+
+
+def _write_qa_json(root: Path) -> Path:
+    qa = root / "qa.json"
+    qa.write_text(json.dumps([{"Question": "q", "Answer": "A", "video_id": "vid0"}]), encoding="utf-8")
+    return qa
+
+
+def test_resolve_local_root_returns_none_for_hub_id() -> None:
+    assert resolve_daily_omni_local_root("liarliar/Daily-Omni") is None
+    assert resolve_daily_omni_local_root("") is None
+    assert resolve_daily_omni_local_root(None) is None
+
+
+def test_plain_local_mirror(tmp_path: Path) -> None:
+    _write_qa_json(tmp_path)
+    (tmp_path / "Videos" / "vid0").mkdir(parents=True)
+
+    root = resolve_daily_omni_local_root(str(tmp_path))
+    assert root == tmp_path.resolve()
+    assert daily_omni_local_qa_json(root) == (tmp_path / "qa.json").resolve()
+    assert daily_omni_local_videos_dir(root) == root / "Videos"
+
+
+def test_hf_cache_dir_resolves_to_snapshot(tmp_path: Path) -> None:
+    cache_dir = tmp_path / "datasets--liarliar--Daily-Omni"
+    snapshot = cache_dir / "snapshots" / "deadbeef"
+    snapshot.mkdir(parents=True)
+    _write_qa_json(snapshot)
+    (cache_dir / "refs").mkdir()
+    (cache_dir / "refs" / "main").write_text("deadbeef", encoding="utf-8")
+
+    root = resolve_daily_omni_local_root(str(cache_dir))
+    assert root == snapshot.resolve()
+    assert daily_omni_local_qa_json(root) == (snapshot / "qa.json").resolve()
+
+
+def test_hf_cache_dir_without_refs_picks_newest_snapshot(tmp_path: Path) -> None:
+    cache_dir = tmp_path / "datasets--liarliar--Daily-Omni"
+    older = cache_dir / "snapshots" / "aaa"
+    newer = cache_dir / "snapshots" / "bbb"
+    older.mkdir(parents=True)
+    newer.mkdir(parents=True)
+    os.utime(older, (1, 1))
+    os.utime(newer, (10, 10))
+
+    assert resolve_daily_omni_local_root(str(cache_dir)) == newer.resolve()
+
+
+def test_videos_tar_is_extracted(tmp_path: Path, monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.setenv("HF_HOME", str(tmp_path / "hf_home"))
+    root = tmp_path / "mirror"
+    (root / "Videos" / "vid0").mkdir(parents=True)
+    (root / "Videos" / "vid0" / "vid0_video.mp4").write_bytes(b"fake")
+    with tarfile.open(root / "Videos.tar", "w") as tf:
+        tf.add(root / "Videos", arcname="Videos")
+    # Only the tarball is left in the mirror.
+    for child in (root / "Videos").rglob("*"):
+        if child.is_file():
+            child.unlink()
+    (root / "Videos" / "vid0").rmdir()
+    (root / "Videos").rmdir()
+
+    videos_dir = daily_omni_local_videos_dir(root)
+    assert videos_dir is not None
+    assert (videos_dir / "vid0" / "vid0_video.mp4").is_file()
+
+    # Second call reuses the extraction instead of redoing it.
+    assert daily_omni_local_videos_dir(root) == videos_dir
+
+
+def test_missing_qa_json_reports_none(tmp_path: Path) -> None:
+    root = resolve_daily_omni_local_root(str(tmp_path))
+    assert daily_omni_local_qa_json(root) is None
+    assert daily_omni_local_videos_dir(root) is None
@@ -2,9 +2,8 @@

 The ``minicpm-interleave`` mode is an accuracy-critical protocol: MiniCPM-o only reaches
 OpenBMB's reported Daily-Omni score when frames and 1s audio segments arrive strictly
-interleaved, with ``max_slice_nums=1`` / ``use_image_id=False`` and an empty MiniCPM
-system message (not the Qwen system prompt). These tests pin that contract so a refactor
-cannot silently regress the score.
+interleaved, with ``max_slice_nums=1`` / ``use_image_id=False`` and no Qwen system prompt.
+These tests pin that contract so a refactor cannot silently regress the score.

 vllm stubs are installed by tests/benchmarks/conftest.py before collection.
 """
@@ -99,22 +98,13 @@ def _stub_extract(monkeypatch, dataset: DailyOmniDataset, counter: list[int]) ->
 # ---------------------------------------------------------------------------


-@pytest.mark.parametrize(("n", "k"), [(10, 4), (100, 64), (1000, 64), (7, 7), (3, 5), (1, 1)])
-def test_uniform_sample_indices_matches_openbmb_midpoint(n: int, k: int) -> None:
-    """Pin OpenBMB midpoint-bin sampling (not np.linspace endpoints)."""
+@pytest.mark.parametrize(("n", "k"), [(10, 4), (100, 64), (7, 7), (3, 5), (1, 1)])
+def test_uniform_sample_indices_matches_numpy_linspace(n: int, k: int) -> None:
    got = _uniform_sample_indices(n, k)
    if n <= k:
        assert got == list(range(n))
    else:
-        gap = n / k
-        expected = [int(i * gap + gap / 2) for i in range(k)]
-        assert got == expected
-        # Endpoints differ from linspace for typical Daily-Omni downsample sizes.
-        linspace = [int(i) for i in np.linspace(0, n - 1, k, dtype=int)]
-        if n == 1000 and k == 64:
-            assert got[0] == 7 and got[-1] == 992
-            assert linspace[0] == 0 and linspace[-1] == 999
-            assert got != linspace
+        assert got == [int(i) for i in np.linspace(0, n - 1, k, dtype=int)]


 def test_uniform_sample_indices_non_positive_k() -> None:
@@ -223,16 +213,14 @@ def test_minicpm_interleave_missing_local_video_returns_none(qa_json, tmp_path)
 # ---------------------------------------------------------------------------


-def test_minicpm_interleave_keeps_empty_system_message(monkeypatch, qa_json, video_dir) -> None:
-    """Official MiniCPM omni mode still sends role=system with empty content."""
+def test_minicpm_interleave_omits_qwen_system_message(monkeypatch, qa_json, video_dir) -> None:
    ds = _make_dataset(qa_json, video_dir, pack_mode="minicpm-interleave", input_mode="all")
    _stub_extract(monkeypatch, ds, [])
    parts, _extra, _position = ds._compose_daily_omni_multimodal(_VIDEO_ID, None)

    messages = ds._build_daily_omni_openai_messages(parts, "q?", {"A": "a"})

-    assert [m["role"] for m in messages] == ["system", "user"]
-    assert messages[0]["content"][0]["text"] == ""
+    assert [m["role"] for m in messages] == ["user"]


 def test_qwen_pack_mode_keeps_system_message(qa_json, video_dir) -> None:
@@ -0,0 +1,71 @@
+import pytest
+
+import vllm_omni.config.pipeline_registry  # noqa: F401  (populate registry)
+from vllm_omni.config.pipeline_registry import OMNI_PIPELINES as _PIPELINE_REGISTRY
+from vllm_omni.config.stage_config import (
+    DeployConfig,
+    StageExecutionType,
+)
+from vllm_omni.model_executor.stage_input_processors.forced_aligner import (
+    POOLING_OUTPUT_DECODER_PATH,
+    TIMESTAMPS_MODALITY,
+)
+from vllm_omni.utils.forced_aligner import _DEFAULT_CONFIG_PATH, inject_forced_aligner_stage
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
+
+
+def test_inject_appends_pooling_aligner_stage():
+    pipeline = _PIPELINE_REGISTRY["qwen3_tts"]
+    deploy = DeployConfig()
+    n0 = len(pipeline.stages)
+
+    ext_pipeline, ext_deploy = inject_forced_aligner_stage(
+        pipeline, deploy, {"forced_aligner": "/models/Qwen3-ForcedAligner-0.6B"}
+    )
+
+    # A new pooling stage is appended; the original pipeline is unchanged.
+    assert len(pipeline.stages) == n0
+    assert len(ext_pipeline.stages) == n0 + 1
+    # The original deploy config is not mutated either (double-injection safe).
+    assert len(deploy.stages) == 0
+    assert len(ext_deploy.stages) == 1
+    aligner = ext_pipeline.stages[-1]
+    # Pooling stage reuses LLM_AR (+ runner=pooling), no dedicated LLM_POOLING.
+    assert aligner.execution_type == StageExecutionType.LLM_AR
+    assert aligner.final_output is True
+    assert aligner.final_output_type == TIMESTAMPS_MODALITY
+    assert aligner.input_sources == (n0 - 1,)
+    assert aligner.requires_multimodal_data is True
+    assert aligner.owns_tokenizer is True
+
+    # The deploy stage carries the separate aligner checkpoint + decoder hook.
+    ds = ext_deploy.stages[-1]
+    assert ds.engine_extras["model"] == "/models/Qwen3-ForcedAligner-0.6B"
+    assert ds.engine_extras["runner"] == "pooling"
+    assert ds.engine_extras["pooling_output_decoder"] == POOLING_OUTPUT_DECODER_PATH
+
+
+def test_inject_from_config_only():
+    # --forced-aligner-config alone (model in the YAML) must also inject.
+    pipeline = _PIPELINE_REGISTRY["qwen3_tts"]
+    deploy = DeployConfig()
+    n0 = len(pipeline.stages)
+
+    ext_pipeline, ext_deploy = inject_forced_aligner_stage(
+        pipeline, deploy, {"forced_aligner": None, "forced_aligner_config": str(_DEFAULT_CONFIG_PATH)}
+    )
+
+    assert len(ext_pipeline.stages) == n0 + 1
+    assert ext_pipeline.stages[-1].final_output_type == TIMESTAMPS_MODALITY
+    assert ext_deploy.stages[-1].engine_extras["model"] == "Qwen/Qwen3-ForcedAligner-0.6B"
+
+
+def test_inject_noop_without_forced_aligner():
+    pipeline = _PIPELINE_REGISTRY["qwen3_tts"]
+    deploy = DeployConfig()
+
+    ext_pipeline, ext_deploy = inject_forced_aligner_stage(pipeline, deploy, {})
+
+    assert ext_pipeline is pipeline
+    assert len(ext_deploy.stages) == 0
@@ -513,6 +513,7 @@ def test_sub_config_fields_match_structured_scopes():
        "enable_prefix_caching",
        "disable_hybrid_kv_cache_manager",
        "mm_processor_cache_gb",
+        "mamba_ssm_cache_dtype",
    }
    assert {f.name for f in fields(OmniStageSchedulerConfig)} == {
        "max_num_seqs",
@@ -27,6 +27,7 @@ _MIXIN_UPDATE_HELPERS = (
    "_attach_finished_request_sets",
    "_attach_scheduler_stats",
    "_capture_omni_connector_output",
+    "_maybe_decode_pooling_output",
 )


@@ -138,6 +139,7 @@ def _make_scheduler_stub(requests: list[_Request]) -> SimpleNamespace:
        pending_stop_after_extraction=set(),
        waiting_for_transfer_free=set(),
        _new_prompt_len_snapshot={},
+        _pooling_output_decoder=None,
        finished_req_ids=set(),
        finished_req_ids_dict=defaultdict(set),
        kv_cache_manager=SimpleNamespace(take_events=lambda: None),
@@ -267,3 +269,72 @@ def test_invalid_logprobs_finish_only_the_affected_scheduler_request() -> None:
    assert output_by_id["bad"].new_token_ids == []
    assert output_by_id["good"].new_token_ids == [8]
    np.testing.assert_array_equal(output_by_id["good"].new_logprobs.logprob_token_ids[:, 0], [8])
+
+
+def _pooling_model_runner_output(pooler_tensor) -> SimpleNamespace:
+    return SimpleNamespace(
+        sampled_token_ids=[[]],
+        logprobs=None,
+        prompt_logprobs_dict={},
+        pooler_output=[pooler_tensor],
+        num_nans_in_logits=None,
+        kv_connector_output=None,
+        cudagraph_stats=None,
+        req_id_to_index={"req": 0},
+        routed_experts=None,
+    )
+
+
+def _make_pooling_stub(decoder) -> tuple[_Request, SimpleNamespace]:
+    request = _Request("req")
+    request.sampling_params = SimpleNamespace(num_logprobs=None)
+    request.pooling_params = SimpleNamespace()
+    scheduler = _make_scheduler_stub([request])
+    scheduler._pooling_output_decoder = decoder
+    scheduler.vllm_config = SimpleNamespace(model_config=SimpleNamespace(hf_config=None))
+    _bind_request_lifecycle(scheduler, update_request=lambda req, tokens: (tokens, False))
+    return request, scheduler
+
+
+def test_pooling_decode_success_emits_decoded_payload() -> None:
+    import torch
+
+    decoded = torch.tensor([[1, 2]], dtype=torch.int32)
+    request, scheduler = _make_pooling_stub(lambda logits, req, hf: decoded)
+
+    scheduler_output = SimpleNamespace(
+        num_scheduled_tokens={"req": 1},
+        scheduled_spec_decode_tokens={},
+        num_invalid_spec_tokens=0,
+    )
+    outputs = OmniARScheduler.update_from_output(
+        scheduler, scheduler_output, _pooling_model_runner_output(torch.zeros(2, 4))
+    )
+    (output,) = outputs[0].outputs
+
+    assert output.finish_reason is FinishReason.STOP
+    assert output.pooling_output is decoded
+
+
+def test_pooling_decode_failure_finishes_request_with_error() -> None:
+    import torch
+
+    def raising_decoder(logits, req, hf):
+        raise ValueError("boom")
+
+    request, scheduler = _make_pooling_stub(raising_decoder)
+
+    scheduler_output = SimpleNamespace(
+        num_scheduled_tokens={"req": 1},
+        scheduled_spec_decode_tokens={},
+        num_invalid_spec_tokens=0,
+    )
+    outputs = OmniARScheduler.update_from_output(
+        scheduler, scheduler_output, _pooling_model_runner_output(torch.zeros(2, 4))
+    )
+    (output,) = outputs[0].outputs
+
+    # Decoder failure is a request error (500), not an empty success.
+    assert output.finish_reason is FinishReason.ERROR
+    assert output.pooling_output is None
+    assert "pooling output decode failed" in str(request.stop_reason)
@@ -120,6 +120,9 @@ class TestFullPayloadCoordinatorSelection(unittest.TestCase):
            ("MiMoAudioModel", "code2wav"),
            ("Qwen3TTSCode2Wav", "code2wav"),
            ("MiniCPMO45Code2Wav", "code2wav"),
+            # nemotron_voicechat: sync-path RVQ-VAE decode waits for the
+            # talker's full [frames, 31] code-stack payload.
+            ("NemotronVoiceChatCode2Wav", "code2wav"),
            ("CosyVoice3Model", "cosyvoice3_code2wav"),
            # audex: sync-path TTS decoder (async_chunk: false deploys) and
            # the TTA XCodec1 stage (always sync full-payload).
@@ -0,0 +1,80 @@
+[
+  {
+    "test_name": "test_minicpmo_4_5_ready",
+    "mark": [
+      {
+        "hardware_marks": {
+          "res": {
+            "cuda": "H100",
+            "npu": "A3"
+          },
+          "num_cards": 1
+        }
+      },
+      "core_model",
+      "omni"
+    ],
+    "server_params": {
+      "model": "openbmb/MiniCPM-o-4_5",
+      "extra_cli_args": [
+        "--deploy-config",
+        "vllm_omni/deploy/minicpmo_4_5.yaml",
+        "--trust-remote-code"
+      ]
+    },
+    "benchmark_params": [
+      {
+        "dataset_name": "seed-tts",
+        "dataset_path": "zhaochenyang20/seed-tts-eval",
+        "backend": "openai-chat-omni",
+        "endpoint": "/v1/chat/completions",
+        "num_prompts": [
+          10,
+          40
+        ],
+        "max_concurrency": [
+          1,
+          4
+        ],
+        "no_oversample": true,
+        "trust_remote_code": true,
+        "seed_tts_locale": "en",
+        "extra_body": {
+          "modalities": [
+            "text",
+            "audio"
+          ],
+          "chat_template_kwargs": {
+            "enable_thinking": false,
+            "use_tts_template": true
+          }
+        },
+        "percentile-metrics": "ttft,e2el,audio_ttfp,audio_rtf,audio_duration",
+        "baseline": {
+          "H100": {
+            "request_throughput": [
+              0.7803,
+              1.1008
+            ],
+            "mean_ttft_ms": [
+              166.9764,
+              307.7133
+            ],
+            "mean_e2el_ms": [
+              1361.1682,
+              3800.6332
+            ],
+            "mean_audio_ttfp_ms": [
+              635.8099,
+              1615.6591
+            ],
+            "mean_audio_rtf": [
+              0.3151,
+              0.8568
+            ]
+          }
+        }
+      }
+    ]
+  }
+]
@@ -125,7 +125,7 @@ def _extract_images(output: OmniRequestOutput) -> list:
        return output.images
    # When the output comes from the orchestrator pipeline, images may be
    # nested inside request_output.
-    inner = getattr(output, "request_output", None)
+    inner = output
    if inner is not None and hasattr(inner, "images") and inner.images:
        return inner.images
    return []
@@ -56,10 +56,10 @@ def test_teacache(model_name: str):
        # Extract images from request_output['images']
        first_output = outputs[0]
        assert first_output.final_output_type == "image"
-        if not hasattr(first_output, "request_output") or not first_output.request_output:
+        if not isinstance(first_output, OmniRequestOutput) or not first_output:
            raise ValueError("No request_output found in OmniRequestOutput")

-        req_out = first_output.request_output
+        req_out = first_output
        if not isinstance(req_out, OmniRequestOutput) or not hasattr(req_out, "images"):
            raise ValueError("Invalid request_output structure or missing 'images' key")

@@ -131,7 +131,7 @@ def _run_inference(
            elapsed_ms = (time.time() - start) * 1000

            return InferenceResult(
-                images=outputs[0].request_output.images,
+                images=outputs[0].images,
                elapsed_ms=elapsed_ms,
            )
    finally:
@@ -49,10 +49,10 @@ def _diff_metrics(a: Image.Image, b: Image.Image) -> tuple[float, float]:
 def _extract_single_image(outputs) -> Image.Image:
    first_output = outputs[0]
    assert first_output.final_output_type == "image"
-    if not hasattr(first_output, "request_output") or not first_output.request_output:
+    if not isinstance(first_output, OmniRequestOutput) or not first_output:
        raise ValueError("No request_output found in OmniRequestOutput")

-    req_out = first_output.request_output
+    req_out = first_output
    if not isinstance(req_out, OmniRequestOutput) or not hasattr(req_out, "images"):
        raise ValueError("Invalid request_output structure or missing 'images' key")

@@ -231,6 +231,128 @@ def test_reshard_from_trimmed_width_pads_invalid_columns(monkeypatch: pytest.Mon
    assert torch.equal(out.flatten(), torch.tensor([4.0, 0.0]))


+@pytest.mark.core_model
+@pytest.mark.cpu
+@pytest.mark.parametrize("split_dim", ["height", "width"])
+@pytest.mark.parametrize(
+    ("full_extent", "world_size"),
+    [
+        pytest.param(5, 4, id="four-ranks-empty-tail"),
+        pytest.param(20, 8, id="production-eight-ranks-empty-tail"),
+        pytest.param(39, 2, id="two-ranks-partial-tail"),
+    ],
+)
+def test_reshard_from_trimmed_extent_clamps_and_reconstructs(
+    monkeypatch: pytest.MonkeyPatch,
+    split_dim: str,
+    full_extent: int,
+    world_size: int,
+):
+    local_extent = (full_extent + world_size - 1) // world_size
+    shape = (1, 1, 1, full_extent, 1) if split_dim == "height" else (1, 1, 1, 1, full_extent)
+    x = torch.arange(full_extent, dtype=torch.float32).reshape(shape)
+    outputs = []
+
+    for rank in range(world_size):
+        monkeypatch.setattr(wan_spatial_shard, "_rank_world", lambda group, rank=rank: (rank, world_size))
+        token = wan_spatial_shard._SPATIAL_SHARD_CONTEXT.set(
+            wan_spatial_shard.SpatialShardContext(
+                input_extent=full_extent,
+                local_input_extent=local_extent,
+                split_dim=split_dim,
+                rank=rank,
+                world_size=world_size,
+            )
+        )
+        try:
+            out = wan_spatial_shard.reshard_from_trimmed_extent(
+                x,
+                local_extent=local_extent,
+                split_dim=split_dim,
+                group=object(),
+            )
+        finally:
+            wan_spatial_shard._SPATIAL_SHARD_CONTEXT.reset(token)
+
+        start = rank * local_extent
+        valid_extent = min(local_extent, max(0, full_extent - start))
+        expected = torch.zeros(local_extent, dtype=torch.float32)
+        if valid_extent:
+            expected[:valid_extent] = torch.arange(start, start + valid_extent, dtype=torch.float32)
+        assert out.shape[_spatial_dim_for_test(split_dim)] == local_extent
+        assert torch.equal(out.flatten(), expected)
+        outputs.append(out)
+
+    reconstructed = torch.cat(outputs, dim=_spatial_dim_for_test(split_dim))
+    reconstructed = reconstructed.narrow(_spatial_dim_for_test(split_dim), 0, full_extent)
+    assert torch.equal(reconstructed, x)
+
+
+def _spatial_dim_for_test(split_dim: str) -> int:
+    return -2 if split_dim == "height" else -1
+
+
+@pytest.mark.core_model
+@pytest.mark.cpu
+def test_reshard_from_trimmed_extent_rejects_rank_mismatch(monkeypatch: pytest.MonkeyPatch):
+    monkeypatch.setattr(wan_spatial_shard, "_rank_world", lambda group: (1, 2))
+    x = torch.zeros((1, 1, 1, 1, 4), dtype=torch.float32)
+    token = wan_spatial_shard._SPATIAL_SHARD_CONTEXT.set(
+        wan_spatial_shard.SpatialShardContext(
+            input_extent=4,
+            local_input_extent=2,
+            split_dim="width",
+            rank=0,
+            world_size=2,
+        )
+    )
+    try:
+        with pytest.raises(RuntimeError, match="group_rank=1, context_rank=0"):
+            wan_spatial_shard.reshard_from_trimmed_extent(
+                x,
+                local_extent=2,
+                split_dim="width",
+                group=object(),
+            )
+    finally:
+        wan_spatial_shard._SPATIAL_SHARD_CONTEXT.reset(token)
+
+
+@pytest.mark.core_model
+@pytest.mark.cpu
+def test_attention_wrapper_rejects_spatial_extent_change(monkeypatch: pytest.MonkeyPatch):
+    class ShrinkingWanAttentionBlock(torch.nn.Module):
+        def forward(self, x):
+            return x[..., :-1]
+
+    monkeypatch.setattr(wan_spatial_shard, "_rank_world", lambda group: (0, 2))
+    monkeypatch.setattr(
+        wan_spatial_shard,
+        "all_gather_along_dim",
+        lambda x, *, group, dim, dst=None: torch.cat([x, x], dim=dim),
+    )
+    module = ShrinkingWanAttentionBlock()
+    wan_spatial_shard._patch_attention_block(module, group=object(), split_dim="width")
+    x = torch.zeros((1, 1, 1, 1, 2), dtype=torch.float32)
+    token = wan_spatial_shard._SPATIAL_SHARD_CONTEXT.set(
+        wan_spatial_shard.SpatialShardContext(
+            input_extent=4,
+            local_input_extent=2,
+            split_dim="width",
+            rank=0,
+            world_size=2,
+        )
+    )
+    try:
+        with pytest.raises(
+            RuntimeError,
+            match=r"local_extent=2, gathered_extent=4, trimmed_extent=4, output_extent=3",
+        ):
+            module(x)
+    finally:
+        wan_spatial_shard._SPATIAL_SHARD_CONTEXT.reset(token)
+
+
@pytest.mark.core_model
@pytest.mark.cpu
 def test_halo_exchange_single_rank_noop(monkeypatch: pytest.MonkeyPatch):
@@ -29,10 +29,10 @@ def test_diffusion_model(tmp_path: Path, omni_runner):
            raise ValueError("Empty outputs from Omni.generate()")
        first_output = outputs[0]
        assert first_output.final_output_type == "image"
-        if not hasattr(first_output, "request_output") or not first_output.request_output:
+        if not isinstance(first_output, OmniRequestOutput) or not first_output:
            raise ValueError("No request_output found in OmniRequestOutput")

-        req_out = first_output.request_output
+        req_out = first_output
        if not isinstance(req_out, OmniRequestOutput) or not hasattr(req_out, "images"):
            raise ValueError("Invalid request_output structure or missing 'images' key")
        return req_out.images
@@ -0,0 +1,26 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import torch
+
+from vllm_omni.diffusion.layers.norm import RMSNorm
+from vllm_omni.diffusion.models.minimax_h3.encoder import MiniMaxH3Qwen3VLRMSNorm
+
+
+def test_qwen3_vl_rmsnorm_uses_common_fused_rmsnorm_contract() -> None:
+    """Qwen keeps BF16 gamma while native fallback accumulates in FP32."""
+    eps = 1e-6
+    norm = MiniMaxH3Qwen3VLRMSNorm(hidden_size=4, eps=eps, dtype=torch.bfloat16)
+    norm.weight.data.copy_(torch.tensor([1.0, 0.5, 1.5, 2.0], dtype=torch.bfloat16))
+    x = torch.tensor(
+        [[1.0, -2.0, 3.0, -4.0], [-5.0, 6.0, -7.0, 8.0]],
+        dtype=torch.bfloat16,
+    )
+
+    x_fp32 = x.float()
+    expected = (x_fp32 * torch.rsqrt(x_fp32.square().mean(-1, keepdim=True) + eps) * norm.weight.float()).to(x.dtype)
+
+    assert isinstance(norm, RMSNorm)
+    assert norm.weight.dtype == torch.bfloat16
+    assert set(norm.state_dict()) == {"weight"}
+    torch.testing.assert_close(norm.forward_native(x), expected, atol=0, rtol=0)
@@ -122,8 +122,8 @@ def test_cpu_offload_diffusion_model(model_name: str):
    print(f"No offload peak memory: {no_offload_peak_memory} MB")

    if model_name == "stabilityai/stable-audio-open-1.0":
-        audio_offload = output_offload[0].request_output.multimodal_output.get("audio")
-        audio_no_offload = output_no_offload[0].request_output.multimodal_output.get("audio")
+        audio_offload = output_offload[0].multimodal_output.get("audio")
+        audio_no_offload = output_no_offload[0].multimodal_output.get("audio")
        check_audio_determinism(audio_offload, audio_no_offload, atol=1e-2)

    # Set platform-specific VRAM saving thresholds to account
@@ -20,18 +20,10 @@ IMAGE_VIDEO_MODELS = {

 MODELS = {**AUDIO_MODEL, **IMAGE_VIDEO_MODELS}

-LAYERWISE_MODEL_CASES = [
-    pytest.param(
-        "stabilityai/stable-audio-open-1.0",
-        marks=[pytest.mark.full_model, pytest.mark.diffusion],
-        id="stable_audio",
-    ),
-    pytest.param(
-        "riverclouds/qwen_image_random",
-        marks=[pytest.mark.core_model, pytest.mark.diffusion],
-        id="qwen_image_random",
-    ),
-]
+MODEL_MARKS = {
+    "riverclouds/qwen_image_random": pytest.mark.core_model,
+    "stabilityai/stable-audio-open-1.0": pytest.mark.full_model,
+}

 AUDIO_MODEL_PARAMS = {
    "runner_params": {},
@@ -101,8 +93,9 @@ def run_inference(
    return peak, output


+@pytest.mark.diffusion
@hardware_test(res={"cuda": "L4", "rocm": "MI325"})
-@pytest.mark.parametrize("model_name", LAYERWISE_MODEL_CASES)
+@pytest.mark.parametrize("model_name", list(MODELS.keys()))
 def test_layerwise_offload_diffusion_model(model_name: str):
    """Test that layerwise offloading reduces GPU memory usage.

@@ -134,8 +127,8 @@ def test_layerwise_offload_diffusion_model(model_name: str):
    print(f"No offload peak memory: {no_offload_peak_memory} MB")

    if model_name == "stabilityai/stable-audio-open-1.0":
-        audio_offload = output_offload[0].request_output.multimodal_output.get("audio")
-        audio_no_offload = output_no_offload[0].request_output.multimodal_output.get("audio")
+        audio_offload = output_offload[0].multimodal_output.get("audio")
+        audio_no_offload = output_no_offload[0].multimodal_output.get("audio")
        # Match the sibling cpu-offload test's tolerance: layerwise offload moves
        # blocks across the PCIe bus on a side stream, which can perturb cuBLAS
        # algorithm selection and produce ~ULP-level drift larger than 1e-3.
@@ -94,8 +94,8 @@ def _generate_single_stage_image(
    if hasattr(first_output, "images") and first_output.images:
        images = first_output.images
    else:
-        assert hasattr(first_output, "request_output") and first_output.request_output
-        request_output = first_output.request_output
+        assert isinstance(first_output, OmniRequestOutput) and first_output
+        request_output = first_output
        if isinstance(request_output, list):
            req_out = request_output[0]
        else:
@@ -88,8 +88,8 @@ def _generate_single_stage_image(
        if hasattr(first_output, "images") and first_output.images:
            images = first_output.images
        else:
-            assert hasattr(first_output, "request_output") and first_output.request_output
-            request_output = first_output.request_output
+            assert isinstance(first_output, OmniRequestOutput) and first_output
+            request_output = first_output
            if isinstance(request_output, list):
                req_out = request_output[0]
            else:
@@ -147,10 +147,10 @@ def _generate_single_stage_video(

        first = outputs[0]

-        # Unwrap pipeline-style outputs (multi-stage / OmniRequestOutput.request_output).
+        # Unwrap pipeline-style outputs (multi-stage / OmniRequestOutput).
        frames: Any = None
-        if hasattr(first, "request_output") and isinstance(first.request_output, list):
-            inner = first.request_output[0]
+        if isinstance(first, OmniRequestOutput) and isinstance(first, list):
+            inner = first[0]
            if isinstance(inner, OmniRequestOutput) and inner.images:
                frames = inner.images[0]
        if frames is None and hasattr(first, "images") and first.images:
@@ -234,8 +234,8 @@ def _generate_bagel_image(
            if images := getattr(req_output, "images", None):
                generated_image = images[0]
                break
-            if hasattr(req_output, "request_output") and req_output.request_output:
-                stage_outputs = req_output.request_output
+            if isinstance(req_output, OmniRequestOutput) and req_output:
+                stage_outputs = req_output
                if not isinstance(stage_outputs, list):
                    stage_outputs = [stage_outputs]
                for stage_out in stage_outputs:
@@ -250,8 +250,8 @@ def _generate_bagel_image(

        # Check LLM stage output — should have finish_reason=stop (not length)
        for req_output in omni_outputs:
-            if hasattr(req_output, "request_output") and req_output.request_output:
-                stage_outputs = req_output.request_output
+            if isinstance(req_output, OmniRequestOutput) and req_output:
+                stage_outputs = req_output
                if not isinstance(stage_outputs, list):
                    stage_outputs = [stage_outputs]
                for stage_out in stage_outputs:
@@ -310,7 +310,7 @@ def _generate_image(omni, config: QualityTestConfig):
    first = outputs[0]
    if hasattr(first, "images") and first.images:
        return first.images[0], peak_mem
-    inner = first.request_output
+    inner = first
    if inner is not None and hasattr(inner, "images") and inner.images:
        return inner.images[0], peak_mem
    raise ValueError("Could not extract image from output.")
@@ -341,15 +341,15 @@ def _generate_video(omni, config: QualityTestConfig):
    )

    peak_mem = torch.accelerator.max_memory_allocated() / (1024**3)
-    first = outputs[0]
-    if hasattr(first, "request_output") and isinstance(first.request_output, list):
-        inner = first.request_output[0]
-        if isinstance(inner, OmniRequestOutput) and hasattr(inner, "images"):
-            frames = inner.images[0] if inner.images else None
-        else:
-            frames = inner
-    elif hasattr(first, "images") and first.images:
-        frames = first.images[0]
+    if isinstance(outputs, list) and isinstance(outputs[0], OmniRequestOutput):
+        first = outputs[0]
+    elif isinstance(outputs, OmniRequestOutput):
+        first = outputs
+    else:
+        raise ValueError("Could not extract video frames from output.")
+
+    if hasattr(first, "images"):
+        frames = first.images[0] if first.images else None
    else:
        raise ValueError("Could not extract video frames from output.")

@@ -444,11 +444,11 @@ def test_benchmark_generate_image_unwraps_nested_omni_request_output(monkeypatch

    image = Image.new("RGB", (2, 2))
    inner = OmniRequestOutput.from_diffusion(request_id="req", images=[image])
-    outer = OmniRequestOutput(
+    outer = OmniRequestOutput.from_stage_output(
+        inner,
        request_id="req",
        stage_id=0,
        final_output_type="image",
-        request_output=inner,
        finished=True,
    )

@@ -73,11 +73,9 @@ def test_run_summary_reports_worker_peak_memory():
    assert summary["per_run_peak_memory_mb"] == [100.0, 150.0]


-def test_request_peak_memory_prefers_inner_diffusion_output():
-    inner = type("InnerOutput", (), {"peak_memory_mb": 321.0})()
-    outer = type("OuterOutput", (), {"request_output": inner, "peak_memory_mb": 123.0})()
-
-    assert _request_peak_memory_mb(outer) == 321.0
+def test_request_peak_memory_reads_peak_memory_mb_directly():
+    result = type("Output", (), {"peak_memory_mb": 123.0})()
+    assert _request_peak_memory_mb(result) == 123.0


 def test_metric_guidance_describes_thresholds():
@@ -273,7 +273,7 @@ class TestPipelineStreamingOutputToEntrypoint:
            await _wait_for(lambda: len(pipeline.requests) == 1)
            assert pipeline.requests[0].request_id.startswith("req-omni-")
            assert [output.multimodal_output["metadata"]["stream"]["chunk"] for output in outputs] == [0, 1]
-            assert [getattr(output.request_output, "finished", output.finished) for output in outputs] == [False, True]
+            assert [output.finished for output in outputs] == [False, True]
        finally:
            await self._shutdown_pipeline_omni_harness(omni, fixture, inline_client)

@@ -25,6 +25,7 @@ from tests.helpers.mark import hardware_test
 from tests.helpers.runtime import OmniRunner
 from tests.helpers.stage_config import get_deploy_config_path, modify_stage_config
 from vllm_omni.entrypoints.omni import Omni
+from vllm_omni.outputs import OmniRequestOutput

 pytestmark = [pytest.mark.usefixtures("clean_gpu_memory_between_tests")]

@@ -95,8 +96,8 @@ def _extract_generated_image(omni_outputs: list) -> Image.Image | None:
    for req_output in omni_outputs:
        if images := getattr(req_output, "images", None):
            return images[0]
-        if hasattr(req_output, "request_output") and req_output.request_output:
-            stage_out = req_output.request_output
+        if isinstance(req_output, OmniRequestOutput) and req_output:
+            stage_out = req_output
            if hasattr(stage_out, "images") and stage_out.images:
                return stage_out.images[0]
    return None
@@ -24,6 +24,7 @@ from tests.helpers.mark import hardware_test
 from tests.helpers.runtime import OmniRunner
 from tests.helpers.stage_config import get_deploy_config_path, modify_stage_config
 from vllm_omni.entrypoints.omni import Omni
+from vllm_omni.outputs import OmniRequestOutput

 pytestmark = [pytest.mark.usefixtures("clean_gpu_memory_between_tests")]

@@ -122,8 +123,8 @@ def _extract_generated_image(omni_outputs: list) -> Image.Image | None:
    for req_output in omni_outputs:
        if images := getattr(req_output, "images", None):
            return images[0]
-        if hasattr(req_output, "request_output") and req_output.request_output:
-            stage_out = req_output.request_output
+        if isinstance(req_output, OmniRequestOutput) and req_output:
+            stage_out = req_output
            if hasattr(stage_out, "images") and stage_out.images:
                return stage_out.images[0]
    return None
@@ -3,8 +3,8 @@

 Daily-Omni settings follow the MiniCPM interleaved AV recipe that reaches
 ~78% overall accuracy on Daily-Omni (``minicpm-interleave``, ``temperature=0``,
-text modalities with ``use_tts_template``, and server ``--interleave-mm-strings``
-+ 1fps / 128-frame media-io kwargs, also pinned in ``minicpmo_4_5.yaml``).
+text modalities, and server ``--interleave-mm-strings`` + 1fps / 128-frame
+media-io kwargs, also pinned in ``minicpmo_4_5.yaml``).
 """

 from __future__ import annotations
@@ -40,10 +40,7 @@ _MAX_SEED_TTS_MEAN_WER = 0.05
 # Match the validated Daily-Omni client body from daily_omni_bench.sh.
 _DAILY_EXTRA_BODY = {
    "modalities": ["text"],
-    "chat_template_kwargs": {
-        "enable_thinking": False,
-        "use_tts_template": True,
-    },
+    "chat_template_kwargs": {"enable_thinking": False},
 }
 _SEED_EXTRA_BODY = {
    "modalities": ["text", "audio"],
@@ -328,7 +328,7 @@ def _run_offline(deploy_config_path: str, output_path: Path) -> tuple[Image.Imag
    images = None
    cot_text = ""
    for out in outputs:
-        ro = getattr(out, "request_output", None)
+        ro = out
        if ro and getattr(ro, "outputs", None):
            cot_text = "".join(getattr(o, "text", "") or "" for o in ro.outputs)
        if not cot_text:
@@ -467,7 +467,7 @@ def _extract_image(outputs) -> Image.Image:
    assert outputs, "Pipeline produced no outputs"
    for output in outputs:
        images = getattr(output, "images", None)
-        request_output = getattr(output, "request_output", None)
+        request_output = output
        if not images and request_output is not None:
            images = getattr(request_output, "images", None)
        if images:
@@ -141,8 +141,8 @@ def _extract_frames(result) -> list[Image.Image]:

    if isinstance(result, list):
        result = result[0] if result else None
-    if isinstance(result, OmniRequestOutput) and result.is_pipeline_output and result.request_output is not None:
-        result = result.request_output
+    if isinstance(result, OmniRequestOutput) and result.is_pipeline_output and result is not None:
+        result = result
    images = getattr(result, "images", None)
    if not images:
        raise AssertionError("Pipeline output had no images")
@@ -171,11 +171,11 @@ def _build_text_output(text: str = "This is a test response.") -> OmniRequestOut
        metrics=None,
        lora_request=None,
    )
-    return OmniRequestOutput(
+    return OmniRequestOutput.from_stage_output(
+        request_output,
        request_id="test_req_text",
        finished=True,
        final_output_type="text",
-        request_output=request_output,
    )


@@ -200,11 +200,11 @@ def _build_audio_chat_output(num_samples: int = 24000) -> OmniRequestOutput:
        metrics=None,
        lora_request=None,
    )
-    return OmniRequestOutput(
+    return OmniRequestOutput.from_stage_output(
+        request_output,
        request_id="test_req_audio_chat",
        finished=True,
        final_output_type="audio",
-        request_output=request_output,
    )


@@ -240,11 +240,11 @@ def _build_diffusion_image_output_for_chat_endpoint() -> OmniRequestOutput:
        images=[_build_image_output(color="blue")],
        finished=True,
    )
-    return OmniRequestOutput(
+    return OmniRequestOutput.from_stage_output(
+        request_output,
        request_id="test_req_img_chat",
-        finished=True,
+        images=[_build_image_output(color="blue")],
        final_output_type="image",
-        request_output=request_output,
    )


@@ -515,8 +515,19 @@ def mock_async_omni(


@pytest.fixture
-def api_server(unused_tcp_port_factory, server_case: ServerCase, mock_async_omni):
+def api_server(unused_tcp_port_factory, server_case: ServerCase, mock_async_omni, tmp_path):
    """Set up a API server in background process from command line with parametrized model name and mocked AsyncOmni."""
+    # Override the STORAGE_MANAGER path to a writable temp directory before
+    # forking the server subprocess.  The default /tmp/storage may be owned
+    # by a different user (e.g. from a previous CI run) and cause
+    # PermissionError when the server tries to save generated video files.
+    from vllm_omni.entrypoints.openai.storage import STORAGE_MANAGER
+
+    old_storage_path = STORAGE_MANAGER.storage_path
+    new_storage_path = tmp_path / "omni_storage"
+    new_storage_path.mkdir(exist_ok=True)
+    STORAGE_MANAGER.storage_path = str(new_storage_path)
+
    parser = TrackingArgumentParser()
    subparsers = parser.add_subparsers(dest="command")
    cmd = OmniServeCommand()
@@ -555,6 +566,9 @@ def api_server(unused_tcp_port_factory, server_case: ServerCase, mock_async_omni

    yield f"http://127.0.0.1:{port}/v1"

+    # Restore the original storage path after the test.
+    STORAGE_MANAGER.storage_path = old_storage_path
+
    if server_process.is_alive():
        server_process.terminate()
    server_process.join(timeout=10)
@@ -294,7 +294,7 @@ class vLLMOmniHttpServerLocal:
            "global_steps": self.global_steps,
        }

-        req_output = final_res.request_output
+        req_output = final_res
        if req_output is not None and hasattr(req_output, "finish_reason"):
            finish_reason = req_output.finish_reason or "stop"
        else:
@@ -53,9 +53,9 @@ def test_audiox_model(omni_runner: OmniRunner) -> None:
    assert outputs is not None
    first_output = outputs[0]
    assert first_output.final_output_type == "audio"
-    assert hasattr(first_output, "request_output") and first_output.request_output
+    assert isinstance(first_output, OmniRequestOutput) and first_output

-    req_out = first_output.request_output
+    req_out = first_output
    assert isinstance(req_out, OmniRequestOutput)
    assert req_out.final_output_type == "audio"
    assert hasattr(req_out, "multimodal_output") and req_out.multimodal_output
@@ -55,7 +55,7 @@ pytestmark = [
 def _extract_text(omni_outputs: list) -> str:
    """Extract generated text from OmniRequestOutput list."""
    for req_output in omni_outputs:
-        ro = getattr(req_output, "request_output", None)
+        ro = req_output
        if ro and getattr(ro, "outputs", None):
            return "".join(getattr(o, "text", "") or "" for o in ro.outputs)
    return ""
@@ -28,7 +28,6 @@ from tests.helpers.media import get_asset_path
 from tests.helpers.runtime import OmniRunner
 from tests.helpers.stage_config import get_deploy_config_path
 from vllm_omni.model_executor.models.cosyvoice3.tokenizer import get_qwen_tokenizer
-from vllm_omni.outputs import OmniRequestOutput
 from vllm_omni.transformers_utils.configs.cosyvoice3 import CosyVoice3Config

 MODEL = "FunAudioLLM/Fun-CosyVoice3-0.5B-2512"
@@ -176,12 +175,12 @@ def test_cosyvoice3_offline_reference_zero_shot(omni_runner: OmniRunner) -> None
    assert 2.8 <= duration_s <= 8.8, f"Unexpected duration={duration_s:.3f}s (samples={audio.size}, sr={sr})"

    # Code2wav is ``final_output`` (stage 1); ``Omni.generate`` yields its ``request_output`` / completions.
-    pipeline_out = OmniRequestOutput.unwrap_result(outputs[0])
+    pipeline_out = outputs[0]
    assert pipeline_out.stage_id == 1, f"expected final stage 1, got {pipeline_out.stage_id}"
    assert pipeline_out.final_output_type == "audio", (
        f"expected audio final_output_type, got {pipeline_out.final_output_type!r}"
    )
-    ro = pipeline_out.request_output
+    ro = pipeline_out
    assert ro is not None, "stage 1 should include request_output"
    completions = getattr(ro, "outputs", None) or []
    assert completions, "stage 1 request_output should include at least one completion"
@@ -175,7 +175,7 @@ def _find_stage_output(outputs: list[Any], output_type: str) -> Any | None:

    # Prefer the latest finished chunk to avoid picking an intermediate stream output.
    for stage_output in reversed(matched):
-        if _is_finished_request_output(getattr(stage_output, "request_output", None)):
+        if _is_finished_request_output(stage_output):
            return stage_output
    return matched[-1]

@@ -205,7 +205,7 @@ def _to_token_list(value: Any) -> list[int]:


 def _extract_text(stage_output: Any, tokenizer: Any | None = None) -> str:
-    request_output = getattr(stage_output, "request_output", None)
+    request_output = stage_output
    if request_output is None:
        return ""
    req_list = request_output if isinstance(request_output, list) else [request_output]
@@ -240,7 +240,7 @@ def _extract_text(stage_output: Any, tokenizer: Any | None = None) -> str:


 def _extract_audio(stage_output: Any) -> Any | None:
-    request_output = getattr(stage_output, "request_output", None)
+    request_output = stage_output
    if request_output is None:
        return None
    req_list = request_output if isinstance(request_output, list) else [request_output]
@@ -256,7 +256,7 @@ def _extract_audio(stage_output: Any) -> Any | None:


 def _extract_image(stage_output: Any) -> Any | None:
-    request_output = getattr(stage_output, "request_output", None)
+    request_output = stage_output
    if request_output is None:
        return None
    req_list = request_output if isinstance(request_output, list) else [request_output]
@@ -54,7 +54,7 @@ def _sampling_params() -> OmniDiffusionSamplingParams:
 def _first_request_images(outputs) -> list:
    first_output = outputs[0]
    assert first_output.final_output_type == "image"
-    req_out = first_output.request_output
+    req_out = first_output
    assert isinstance(req_out, OmniRequestOutput) and hasattr(req_out, "images")
    return req_out.images

@@ -161,7 +161,7 @@ def test_glm_image_autoround_w4a16_generates_image(omni_runner_handler: OmniRunn

    first_output = outputs[0]
    assert first_output.final_output_type == "image"
-    req_out = first_output.request_output
+    req_out = first_output
    assert isinstance(req_out, OmniRequestOutput) and hasattr(req_out, "images")
    images = req_out.images

@@ -224,7 +224,7 @@ def test_glm_image_autoround_w4a16_image_to_image(omni_runner_handler: OmniRunne

    first_output = outputs[0]
    assert first_output.final_output_type == "image"
-    req_out = first_output.request_output
+    req_out = first_output
    assert isinstance(req_out, OmniRequestOutput) and hasattr(req_out, "images")
    images = req_out.images

@@ -288,7 +288,7 @@ def _extract_generated_image(outputs: list[object]) -> Image.Image:
    if images := getattr(first_output, "images", None):
        return images[0]

-    request_output = getattr(first_output, "request_output", None)
+    request_output = first_output
    if request_output is not None and (images := getattr(request_output, "images", None)):
        return images[0]
Author	SHA1	Message	Date
Hongsheng Liu	b031feef06	[Doc] Clarify private security contact guidance Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-13 16:33:09 +08:00
Hongsheng Liu	c8bc80cb96	[Doc] Move meeting details to dedicated page Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-13 12:26:00 +08:00
Hongsheng Liu	8ff87320c2	[Doc] Refresh community documentation Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-13 11:18:39 +08:00
liwenerandamy-why-3459	877beeaa49	[Perf][MiniCPM-O-4.5]Add CUDA Graph For CFM DiT estimator (#6082 ) Signed-off-by: liwener <liwener.c@gmail.com> Co-authored-by: amy-why-3459 <wuhaiyan17@huawei.com>	2026-08-13 09:36:17 +08:00
Hongsheng Liu	8b8d3f56db	[Misc] Consolidate LingBot text-to-video runner (#6076 ) Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-13 08:42:37 +08:00
Hongsheng Liu	c57178c0e1	[Dependency] Upgrade Cache-DiT to 1.5.0 (#6065 ) Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-13 08:41:59 +08:00
mjZhaoElaineandClaude Opus 5	9a59475015	[CI/Build] Fix ReadTheDocs build under RTD's seeded pip 23.1 (#6129 ) Signed-off-by: Mengjie Zhao <zmj0129@gmail.com> Co-authored-by: Claude Opus 5 <noreply@anthropic.com>	2026-08-13 06:52:01 +08:00
Nick Cao	12f8168179	[Tests] Enable tiny model testing for Qwen-Image Edit and EditPlus (#5656 ) Signed-off-by: Nick Cao <ncao@redhat.com>	2026-08-12 16:03:49 -04:00
Zhenghan Song	72a02b492a	[Bugfix] Fix diffusion TTS adapter lookup in speech serving (#6121 ) Signed-off-by: HaningZS <resossr0v0@gmail.com>	2026-08-12 15:20:12 -04:00
Juan Pablo ZuluagaandYueqian Lin	5358f5db01	[Perf][TTS] omnivoice hot path: D2H batching, mask caching, cached text embeddings (#5174 ) Signed-off-by: JuanPZuluaga <juanz9312@gmail.com> Co-authored-by: Yueqian Lin <70319226+linyueqian@users.noreply.github.com>	2026-08-12 17:15:35 +00:00
MaciejBalaNV	e677360168	Add cosmos-guardrail dependency and update error message (#6107 ) Signed-off-by: Maciej Bala <mbala@nvidia.com>	2026-08-12 17:04:07 +00:00
Juan Pablo ZuluagaandYueqian Lin	208c6776a8	[Perf][TTS] glm_tts: hoist loop-invariant text embed, RoPE, mask and CFG batch out of Euler loop (#5068 ) Signed-off-by: JuanPZuluaga <juanz9312@gmail.com> Co-authored-by: Yueqian Lin <70319226+linyueqian@users.noreply.github.com>	2026-08-12 17:02:02 +00:00
Shi Boao Bowie 汪志鹏 ruirui yang amy-why-3459 Gao Han Hongsheng Liu	fcf1085395	[Refactor][OutputProcessor 2/8]: OmniRequestOutput should inherit RequestOutput and no nested wrap-up (#5146 ) Signed-off-by: Boao Shi <aoibosh@connect.hku.hk> Signed-off-by: bowieshi <aoibosh@connect.hku.hk> Signed-off-by: princepride <wangzhipeng628@gmail.com> Signed-off-by: R2-Y <ruiruyang2@gmail.com> Signed-off-by: amy-why-3459 <wuhaiyan17@huawei.com> Signed-off-by: Shi Boao Bowie <aoibosh@connect.hku.hk> Co-authored-by: 汪志鹏 <wangzhipeng628@gmail.com> Co-authored-by: ruirui(rein) yang <73573651+R2-Y@users.noreply.github.com> Co-authored-by: amy-why-3459 <wuhaiyan17@huawei.com> Co-authored-by: Gao Han <hgaoaf@connect.ust.hk> Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-12 22:58:23 +08:00
Wenjia LiandHongsheng Liu	38a2500bdf	[Perf] MiniMax H3 Qwen3-VL support fused RMSNorm on NPU (#5915 ) Signed-off-by: Wenjia Li <wjialish@gmail.com> Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com> Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-12 19:12:54 +08:00
amy-why-3459	7cd826d28c	[Bugfix][MiniCPM-o] Align Daily-Omni offline loading and duplex soft-… (#6095 ) Signed-off-by: amy-why-3459 <wuhaiyan17@huawei.com>	2026-08-12 18:36:31 +08:00
wangyuandHongsheng Liu	31384cb39c	[CI/Build] Add nightly DockerHub publish and cleanup to release pipeline (#6048 ) Signed-off-by: wangyu <410167048@qq.com> Signed-off-by: [Your Name] [Your Email] Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-12 17:30:55 +08:00
Yuekai ZhangandClaude Fable 5	5be7541d01	[Model] Support NVIDIA-NemotronLabs-VoiceChat-11B offline speech-to-speech (#5842 ) Signed-off-by: Yuekai Zhang <zhangyuekai@foxmail.com> Co-authored-by: Claude Fable 5 <noreply@anthropic.com>	2026-08-12 16:25:48 +08:00
王金旭 xurui Claude Opus 4.8	9b0df3909a	[Feature] timestamps for TTS via a forced-aligner pooling (#4795 ) Signed-off-by: wjinxu <1299461899@qq.com> Co-authored-by: xurui <xurui1@modelbest.onaliyun.com> Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>	2026-08-12 05:33:34 +00:00
Hongsheng Liu	a15b5b4bcd	[CI/Build] Add examples policy to PR skills (#6046 ) Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-12 11:37:34 +08:00
Hongsheng Liu	3c3989ed04	docs: align quantization overview with navigation (#6074 ) Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-12 11:37:01 +08:00
Rahul SteigerandRahul Steiger	854bff7fe5	[Bugfix] Fix Wan spatial reshard boundary (#6062 ) Signed-off-by: Rahul Steiger <rsteiger@nvl72d106-T18.cm.cluster> Co-authored-by: Rahul Steiger <rsteiger@nvl72d106-T18.cm.cluster>	2026-08-12 11:36:24 +08:00
amy-why-3459	b792578d89	[CI][MiniCPM-o] Add MiniCPM-o 4.5 perf coverage to the ready gate (#6079 ) Signed-off-by: amy-why-3459 <wuhaiyan17@huawei.com>	2026-08-12 11:32:36 +08:00
zzh	bdb1d996be	[Bugfix] Fix HunyuanImage3 accuracy test (#5981 ) Signed-off-by: zzh <943967662@qq.com>	2026-08-12 02:40:05 +00:00