docs: align quantization overview

Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>

.buildkite/cuda/test-nightly.yml

@@ -345,7 +345,7 @@ steps:
         commands:
           - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
           - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
-          - export CACHE_DIT_VERSION=1.5.0
+          - export CACHE_DIT_VERSION=1.3.0
           - |
             set +e
             pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_vllm_omni.json
@@ -361,7 +361,7 @@ steps:
         commands:
           - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
           - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
-          - export CACHE_DIT_VERSION=1.5.0
+          - export CACHE_DIT_VERSION=1.3.0
           - |
             set +e
             pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_edit_vllm_omni.json
@@ -377,7 +377,7 @@ steps:
         commands:
           - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
           - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
-          - export CACHE_DIT_VERSION=1.5.0
+          - export CACHE_DIT_VERSION=1.3.0
           - |
             set +e
             pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_edit_2511_vllm_omni.json
@@ -393,7 +393,7 @@ steps:
         commands:
           - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
           - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
-          - export CACHE_DIT_VERSION=1.5.0
+          - export CACHE_DIT_VERSION=1.3.0
           - |
             set +e
             pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_layered_vllm_omni.json
@@ -409,7 +409,7 @@ steps:
         commands:
           - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
           - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
-          - export CACHE_DIT_VERSION=1.5.0
+          - export CACHE_DIT_VERSION=1.3.0
           - |
             set +e
             pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_bagel_vllm_omni.json

.buildkite/cuda/test-ready.yml

@@ -227,18 +227,6 @@ 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

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

@@ -115,7 +115,7 @@ steps:
           DIFFUSION_ATTENTION_BACKEND: TORCH_SDPA
         commands:
           - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
-          - export CACHE_DIT_VERSION=1.5.0
+          - export CACHE_DIT_VERSION=1.3.0
           - |
             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

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

@@ -17,15 +17,6 @@ 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

.buildkite/release/release-pipeline.yml

@@ -40,11 +40,9 @@ steps:
     commands:
       - "bash .buildkite/release/scripts/publish-release-wheels-pypi.sh"
 
-  # Auto-skip when NIGHTLY=1 (scheduled); otherwise require manual unblock.
   - block: "Unblock to build release Docker images"
     depends_on: ~
     key: block-build-release-images
-    if: build.env("NIGHTLY") != "1"
 
   - group: "Build release Docker images"
     key: "build-release-images"
@@ -65,7 +63,7 @@ steps:
               --progress plain \
               -f docker/Dockerfile.cuda .
           - "docker push public.ecr.aws/q9t5s3a7/vllm-omni-release-repo:$BUILDKITE_COMMIT-$(uname -m)"
-          # Fallback default tag (no arch suffix) if arm64 build fails.
+          # re-tag to default image tag and push, just in case arm64 build fails
           - "docker tag public.ecr.aws/q9t5s3a7/vllm-omni-release-repo:$BUILDKITE_COMMIT-$(uname -m) public.ecr.aws/q9t5s3a7/vllm-omni-release-repo:$BUILDKITE_COMMIT"
           - "docker push public.ecr.aws/q9t5s3a7/vllm-omni-release-repo:$BUILDKITE_COMMIT"
         env:
@@ -88,26 +86,6 @@ steps:
         env:
           DOCKER_BUILDKIT: "1"
 
-  # NIGHTLY=1 only: promote ECR images to DockerHub nightly tags, then prune.
-  - label: "Publish nightly multi-arch image to DockerHub"
-    depends_on:
-      - build-release-image-x86
-      - build-release-image-arm64
-    if: build.env("NIGHTLY") == "1"
-    key: publish-nightly-images-dockerhub
-    agents:
-      queue: small_cpu_queue_release
-    commands:
-      - "bash .buildkite/release/scripts/publish-release-images.sh nightly"
-      - "bash .buildkite/release/scripts/cleanup-nightly-builds.sh"
-    plugins:
-      - docker-login#v3.0.0:
-          username: vllmbot
-          password-env: DOCKERHUB_TOKEN
-    env:
-      DOCKER_BUILDKIT: "1"
-      DOCKERHUB_USERNAME: "vllmbot"
-
   - block: "Publish release images to DockerHub"
     key: block-publish-release-images
     depends_on:

.buildkite/release/scripts/cleanup-nightly-builds.sh

@@ -1,147 +0,0 @@
-#!/bin/bash
-# SPDX-License-Identifier: Apache-2.0
-#
-# Clean up old nightly builds from DockerHub (vllm/vllm-omni), keeping the
-# newest 14 tags with the nightly- prefix.
-
-set -ex
-
-TAG_PREFIX="nightly-"
-REPO="vllm/vllm-omni"
-REPO_API_URL="https://hub.docker.com/v2/repositories/${REPO}/tags"
-
-echo "Cleaning up tags with prefix: $TAG_PREFIX in repository: $REPO"
-
-if [ -z "$DOCKERHUB_TOKEN" ]; then
-  echo "Error: DOCKERHUB_TOKEN environment variable is not set"
-  exit 1
-fi
-
-if [ -z "$DOCKERHUB_USERNAME" ]; then
-  echo "Error: DOCKERHUB_USERNAME environment variable is not set"
-  exit 1
-fi
-
-echo "Getting DockerHub bearer token..."
-set +x
-BEARER_TOKEN=$(curl -s -X POST \
-  -H "Content-Type: application/json" \
-  -d "{\"username\": \"$DOCKERHUB_USERNAME\", \"password\": \"$DOCKERHUB_TOKEN\"}" \
-  "https://hub.docker.com/v2/users/login" | jq -r '.token')
-set -x
-
-if [ -z "$BEARER_TOKEN" ] || [ "$BEARER_TOKEN" = "null" ]; then
-  echo "Error: Failed to get DockerHub bearer token"
-  exit 1
-fi
-
-get_all_tags() {
-  local page=1
-  local all_tags=""
-  # Hub `name` is a partial match (not a strict prefix). Use it to narrow the
-  # list, then enforce startswith() client-side. Exhaust API pages; do not
-  # stop merely because a page has no matching tags after filtering.
-  local name_query
-  name_query=$(printf '%s' "$TAG_PREFIX" | jq -sRr @uri)
-
-  while true; do
-    set +x
-    local response
-    response=$(curl -s -H "Authorization: Bearer $BEARER_TOKEN" \
-      "$REPO_API_URL?page=$page&page_size=100&name=${name_query}")
-    set -x
-
-    local page_count
-    page_count=$(echo "$response" | jq -r '.results | length')
-    if [ -z "$page_count" ] || [ "$page_count" = "0" ] || [ "$page_count" = "null" ]; then
-      break
-    fi
-
-    local tags
-    tags=$(echo "$response" | jq -r --arg prefix "$TAG_PREFIX" \
-      '.results[] | select(.name | startswith($prefix)) | "\(.last_updated)|\(.name)"')
-
-    if [ -n "$tags" ]; then
-      all_tags="$all_tags$tags"$'\n'
-    fi
-    page=$((page + 1))
-  done
-
-  echo "$all_tags" | sort -r | cut -d'|' -f2
-}
-
-delete_tag() {
-  local tag_name="$1"
-  echo "Deleting tag: $tag_name"
-
-  local delete_url="https://hub.docker.com/v2/repositories/${REPO}/tags/$tag_name"
-  set +x
-  local response http_code body
-  response=$(curl -s -w "\n%{http_code}" -X DELETE \
-    -H "Authorization: Bearer $BEARER_TOKEN" "$delete_url")
-  set -x
-  http_code=$(printf '%s\n' "$response" | tail -n1)
-  body=$(printf '%s\n' "$response" | sed '$d')
-
-  if [ "$http_code" -ge 200 ] && [ "$http_code" -lt 300 ]; then
-    echo "Successfully deleted tag: $tag_name (HTTP ${http_code})"
-    return 0
-  fi
-
-  local detail=""
-  if echo "$body" | jq -e '.detail' > /dev/null 2>&1; then
-    detail=$(echo "$body" | jq -r '.detail')
-  elif [ -n "$body" ]; then
-    detail="$body"
-  else
-    detail="(empty response body)"
-  fi
-  echo "Error: Failed to delete tag $tag_name (HTTP ${http_code}): ${detail}"
-  return 1
-}
-
-echo "Fetching all tags from DockerHub..."
-all_tags=$(get_all_tags)
-
-if [ -z "$all_tags" ]; then
-  echo "No tags found to clean up"
-  exit 0
-fi
-
-total_tags=$(echo "$all_tags" | wc -l)
-echo "Found $total_tags tags"
-
-tags_to_keep=14
-tags_to_delete=$((total_tags - tags_to_keep))
-
-if [ $tags_to_delete -le 0 ]; then
-  echo "No tags need to be deleted (only $total_tags tags found, keeping $tags_to_keep)"
-  exit 0
-fi
-
-echo "Will delete $tags_to_delete old tags, keeping the newest $tags_to_keep"
-
-tags_to_delete_list=$(echo "$all_tags" | tail -n +$((tags_to_keep + 1)))
-
-if [ -z "$tags_to_delete_list" ]; then
-  echo "No tags to delete"
-  exit 0
-fi
-
-echo "Deleting old tags..."
-delete_failures=0
-while IFS= read -r tag; do
-  if [ -n "$tag" ]; then
-    if ! delete_tag "$tag"; then
-      delete_failures=$((delete_failures + 1))
-    fi
-    sleep 1
-  fi
-done <<< "$tags_to_delete_list"
-
-if [ "$delete_failures" -gt 0 ]; then
-  echo "Cleanup finished with ${delete_failures} deletion failure(s)"
-  exit 1
-fi
-
-echo "Cleanup completed successfully"

.buildkite/release/scripts/publish-release-images.sh

@@ -1,87 +1,50 @@
 #!/bin/bash
 # SPDX-License-Identifier: Apache-2.0
 #
-# Publish Docker images from ECR staging to DockerHub.
+# Publish release Docker images from ECR to DockerHub.
-#
+# Pulls per-arch images, tags with `latest` and versioned tags, pushes them,
-# Usage:
+# then creates and pushes a multi-arch manifest.
-#   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
 
-MODE="${1:-release}"
+RELEASE_VERSION=$(buildkite-agent meta-data get release-version --default "" | sed 's/^v//')
+if [ -z "${RELEASE_VERSION}" ]; then
+  echo "ERROR: release-version metadata not set"
+  exit 1
+fi
 
+COMMIT="$BUILDKITE_COMMIT"
 DOCKERHUB_REPO="vllm/vllm-omni"
 ECR_REPO="public.ecr.aws/q9t5s3a7/vllm-omni-release-repo"
-COMMIT="$BUILDKITE_COMMIT"
-PRIMARY_TAGS=()
-EXTRA_MANIFEST_TAGS=()
-
-case "${MODE}" in
-  release)
-    RELEASE_VERSION=$(buildkite-agent meta-data get release-version --default "" | sed 's/^v//')
-    if [ -z "${RELEASE_VERSION}" ]; then
-      echo "ERROR: release-version metadata not set"
-      exit 1
-    fi
-    PRIMARY_TAGS=("latest" "v${RELEASE_VERSION}")
-    ;;
-  nightly)
-    # Commit-pinned manifest reuses nightly-{x86_64,aarch64} arch images.
-    PRIMARY_TAGS=("nightly")
-    EXTRA_MANIFEST_TAGS=("nightly-${COMMIT}")
-    ;;
-  *)
-    echo "Usage: $0 {release|nightly}"
-    exit 2
-    ;;
-esac
 
 echo "========================================"
-echo "Publishing ${MODE} images to ${DOCKERHUB_REPO}"
+echo "Publishing release images v${RELEASE_VERSION}"
 echo "  Commit: ${COMMIT}"
-echo "  Primary tags: ${PRIMARY_TAGS[*]}"
-if [ ${#EXTRA_MANIFEST_TAGS[@]} -gt 0 ]; then
-  echo "  Extra manifests: ${EXTRA_MANIFEST_TAGS[*]}"
-fi
 echo "========================================"
 
+# Login to ECR to pull staging images
 aws ecr-public get-login-password --region us-east-1 | \
   docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7
 
-SRC_X86="${ECR_REPO}:${COMMIT}-x86_64"
+docker pull "${ECR_REPO}:${COMMIT}-x86_64"
-SRC_ARM="${ECR_REPO}:${COMMIT}-aarch64"
+docker pull "${ECR_REPO}:${COMMIT}-aarch64"
 
-docker pull "${SRC_X86}"
+docker tag "${ECR_REPO}:${COMMIT}-x86_64" "${DOCKERHUB_REPO}:latest-x86_64"
-docker pull "${SRC_ARM}"
+docker tag "${ECR_REPO}:${COMMIT}-x86_64" "${DOCKERHUB_REPO}:v${RELEASE_VERSION}-x86_64"
+docker push "${DOCKERHUB_REPO}:latest-x86_64"
+docker push "${DOCKERHUB_REPO}:v${RELEASE_VERSION}-x86_64"
 
-publish_manifest() {
+docker tag "${ECR_REPO}:${COMMIT}-aarch64" "${DOCKERHUB_REPO}:latest-aarch64"
-  local tag="$1"
+docker tag "${ECR_REPO}:${COMMIT}-aarch64" "${DOCKERHUB_REPO}:v${RELEASE_VERSION}-aarch64"
-  local arch_tag_base="$2"
+docker push "${DOCKERHUB_REPO}:latest-aarch64"
-  docker manifest rm "${DOCKERHUB_REPO}:${tag}" || true
+docker push "${DOCKERHUB_REPO}:v${RELEASE_VERSION}-aarch64"
-  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}"
-}
 
-for tag in "${PRIMARY_TAGS[@]}"; do
+docker manifest rm "${DOCKERHUB_REPO}:latest" || true
-  docker tag "${SRC_X86}" "${DOCKERHUB_REPO}:${tag}-x86_64"
+docker manifest rm "${DOCKERHUB_REPO}:v${RELEASE_VERSION}" || true
-  docker tag "${SRC_ARM}" "${DOCKERHUB_REPO}:${tag}-aarch64"
+docker manifest create "${DOCKERHUB_REPO}:latest" "${DOCKERHUB_REPO}:latest-x86_64" "${DOCKERHUB_REPO}:latest-aarch64"
-  docker push "${DOCKERHUB_REPO}:${tag}-x86_64"
+docker manifest create "${DOCKERHUB_REPO}:v${RELEASE_VERSION}" "${DOCKERHUB_REPO}:v${RELEASE_VERSION}-x86_64" "${DOCKERHUB_REPO}:v${RELEASE_VERSION}-aarch64"
-  docker push "${DOCKERHUB_REPO}:${tag}-aarch64"
+docker manifest push "${DOCKERHUB_REPO}:latest"
-  publish_manifest "${tag}" "${tag}"
+docker manifest push "${DOCKERHUB_REPO}:v${RELEASE_VERSION}"
-done
-
-ARCH_BASE="${PRIMARY_TAGS[0]}"
-for tag in "${EXTRA_MANIFEST_TAGS[@]}"; do
-  publish_manifest "${tag}" "${ARCH_BASE}"
-done
 
 echo ""
-echo "Successfully published ${MODE} images"
+echo "Successfully published release images for v${RELEASE_VERSION}"

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

@@ -1,6 +1,6 @@
 ---
 name: precheck-pr
-description: Self-check your branch before creating a PR — catch dead code, prevent new model-specific Python examples, verify accuracy/perf claims, validate PR title format, and confirm merge readiness. Use when the user says "precheck", "self review", "pre-submit check", or "check my PR before I open it." Never posts to GitHub.
+description: Self-check your branch before creating a PR — catch dead code, verify accuracy/perf claims, validate PR title format, and confirm merge readiness. Use when the user says "precheck", "self review", "pre-submit check", or "check my PR before I open it." Never posts to GitHub.
 ---
 
 # PR Pre-Check
@@ -65,8 +65,6 @@ Ask: "Quick mode or full mode?" Then walk the checklist for the detected PR type
 
 **Also run the Code-Quality sweep on every PR**, regardless of type or mode: the five diff-scoped checks in [references/code-quality.md](references/code-quality.md) — kwargs fragility, broad-except swallow, `Any`/wrong type hints, hot-path `.clone()`/`deepcopy`, and event-loop blocking — plus the advisory conventions (log level, structured logging, synchronization, cleanup, dependencies, naming) in [checklists.md](references/checklists.md). These count only lines the PR *adds* — the pre-existing backlog across the repo is out of scope.
 
-**Also run the Examples-Policy check on every PR** using [references/examples-policy.md](references/examples-policy.md). Inspect only Python paths introduced by the diff. Treat a new model-, checkpoint-, vendor-, or family-specific Python example as blocking; do not report pre-existing example debt.
-
 ### Step 5: Print Report
 
 ```
@@ -79,7 +77,6 @@ Pre-check report for <branch>
   ─────────────────  ──────
   PR title format    ✓
   Code quality       ⚠ 1 broad except, 2 Any hints
-  Examples policy    ✗ new model-specific Python example
   PR desc integrity  ✓
   Registry/config    ✓
   Dead code          ⚠ 2 warnings

.claude/skills/precheck-pr/references/checklists.md

@@ -47,19 +47,6 @@ Diff-scoped sweep for five fragility patterns that are pervasive in the existing
 
 Roll the ⚠/✗ counts into the report as a single **Code quality** dimension row.
 
----
-
-## All PRs: Examples Policy (run on every PR, quick & full)
-
-Inspect Python files introduced under `examples/` using
-[examples-policy.md](examples-policy.md). This is a diff-scoped ratchet: existing
-model-specific examples may be edited or removed without triggering the check.
-
-- [ ] **No new model-specific Python examples:** the diff adds, copies, or renames no Python file whose path or behavior is specific to one model, checkpoint, vendor, or model family. ✗ for any violation. Route inference through a shared task/protocol entrypoint, model request behavior through `vllm_omni/model_extras`, and commands through task docs or `recipes/`.
-- [ ] **New generic example code is justified:** any new task-neutral Python file under `examples/` is an actual user-facing entrypoint rather than reusable production logic, a benchmark, an app, a setup tool, or a reproducer. ⚠ when the placement is questionable.
-
-Roll the result into the report as a separate **Examples policy** dimension.
-
 ### Conventions (eyeball — no grep)
 
 Judgment calls that don't grep cleanly — apply them while reading the diff, not as a mechanical check:

.claude/skills/precheck-pr/references/examples-policy.md

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

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

@@ -97,7 +97,6 @@ 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
 
@@ -153,13 +152,6 @@ 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

.readthedocs.yml

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

benchmarks/diffusion/quantization_quality.py

@@ -158,6 +158,7 @@ def _build_omni_kwargs(args, quantization=None):
 def _generate_image(omni, args, prompt, seed):
     """Generate a single image and return (PIL.Image, time_seconds, memory_gib)."""
     from vllm_omni.inputs.data import OmniDiffusionSamplingParams
+    from vllm_omni.outputs import OmniRequestOutput
     from vllm_omni.platforms import current_omni_platform
 
     generator = torch.Generator(device=current_omni_platform.device_type).manual_seed(seed)
@@ -175,7 +176,7 @@ def _generate_image(omni, args, prompt, seed):
     elapsed = time.perf_counter() - start
     peak_mem = torch.accelerator.max_memory_allocated() / (1024**3)
 
-    req_out = outputs[0] if isinstance(outputs, list) else outputs
+    req_out = OmniRequestOutput.unwrap_result(outputs)
     if not req_out.images:
         raise ValueError("Could not extract image output from result.")
     img = req_out.images[0]
@@ -205,15 +206,15 @@ def _generate_video(omni, args, prompt, seed):
     elapsed = time.perf_counter() - start
     peak_mem = torch.accelerator.max_memory_allocated() / (1024**3)
 
-    if isinstance(outputs, list) and isinstance(outputs[0], OmniRequestOutput):
+    first = outputs[0]
-        first = outputs[0]
+    if hasattr(first, "request_output") and isinstance(first.request_output, list):
-    elif isinstance(outputs, OmniRequestOutput):
+        inner = first.request_output[0]
-        first = outputs
+        if isinstance(inner, OmniRequestOutput) and hasattr(inner, "images"):
-    else:
+            frames = inner.images[0] if inner.images else None
-        raise ValueError("Could not extract video frames from output.")
+        else:
-
+            frames = inner
-    if hasattr(first, "images"):
+    elif hasattr(first, "images") and first.images:
-        frames = first.images[0] if first.images else None
+        frames = first.images[0]
     else:
         raise ValueError("Could not extract video frames from output.")
 

benchmarks/glm_image/vllm-omni/inference.py

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

benchmarks/lingbot_video/dense_pipeline_parity.py

@@ -167,11 +167,9 @@ def main() -> None:
     ]
     native_cmd = [
         sys.executable,
-        "examples/offline_inference/text_to_video/text_to_video.py",
+        "examples/offline_inference/text_to_video/text_to_video_lingbot.py",
         "--model",
         args.model,
-        "--model-class-name",
-        "LingBotVideoPipeline",
         "--prompt",
         args.prompt,
         "--output",

docs/.nav.yml

@@ -163,8 +163,7 @@ nav:
 - CLI Reference: cli
 - Community:
   - Governance: community/governance.md
-  - Contact Us: community/contact_us.md
+  - community/*
-  - Community Meetings: community/meetings.md
   - Slack: https://slack.vllm.ai
   - Blog: https://blog.vllm.ai
   - Forum: https://discuss.vllm.ai

docs/community/contact_us.md

@@ -1,9 +1,5 @@
 # Contact Us
 
-- For bugs and feature requests, search or open a GitHub [issue](https://github.com/vllm-project/vllm-omni/issues).
+- For technical questions and feature requests, please use GitHub [Issues](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 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 coordinating contributions and development, use `sig-omni`, the forum, or join a [community meeting](meetings.md).
+- For security disclosures, please use GitHub's [Security Advisories](https://github.com/vllm-project/vllm-omni/security/advisories) feature
-
-## 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.

docs/community/governance.md

@@ -52,11 +52,31 @@ Committers have write access and merge rights. They typically have deep expertis
 - [@ZJY0516](https://github.com/ZJY0516): Diffusion attention backend, kernel fusion, and CustomOp
 - [@yuanheng-zhao](https://github.com/yuanheng-zhao): Diffusion cache, offload, and Omni Support
 
-## Reviewer Routing
+## Diffusion Workload Division
 
-For path-based reviewer routing, consult [`.github/CODEOWNERS`](https://github.com/vllm-project/vllm-omni/blob/main/.github/CODEOWNERS). Detailed subsystem boundaries and safe-change guidance live in the [module design documents](../design/index.md#module-design-documents).
+This section breaks down diffusion-related responsibilities. If you have PRs touching these areas, please ping the listed owners for review.
 
-`CODEOWNERS` is operational routing metadata, not a governance or approval policy. Being listed does not grant committer status or merge rights, and it does not mean every listed reviewer must approve a change. The maintainer roster above is authoritative for project roles and merge rights.
+### Runtime-related
+
+- Diffusion models: @RuixiangMa, @wtomin, @xuechendi
+
+### Optimization
+
+- Parallel: @RuixiangMa, @wtomin
+- Attention backend: @ZJY0516
+- Cache: @yuanheng-zhao, @RuixiangMa, @SamitHuang
+- Offload: @yuanheng-zhao
+- Intel GPU support: @xuechendi
+- Quantization: @lishunyang12, @david6666666
+- Kernel fusion / communication-computation: @ZJY0516
+
+### Docs & Test
+
+- Diffusion docs: @RuixiangMa, @wtomin
+
+## Meetings
+
+Committers hold **bi-weekly meetings** to discuss future directions and collaborations of the project.
 
 ## Committer Nomination Process
 

docs/community/meetings.md

@@ -1,8 +0,0 @@
-# Community Meetings
-
-vLLM-Omni hosts developer-facing meetings for Chinese- and English-language audiences. Schedules and access details can change, so use these maintained documents as the source of truth:
-
-- [Chinese-language meeting document](https://docs.google.com/document/d/1pdUBiS_7mdOUNDtdwy-9OUf7jsMWN324BIbps5olDME/edit?tab=t.0)
-- [English-language meeting document](https://docs.google.com/document/d/19AlD08T8uJ7wqfs8VtYCi0iGMTwlTYVVlAxFZ3S7OIg/edit?tab=t.0)
-
-Each document contains the current schedule, access details, agendas, and past meeting notes. For broader vLLM community events, see the [vLLM events calendar](https://vllm.ai/events/).

docs/community/meetups.md

@@ -0,0 +1 @@
+# Meetups

docs/community/volunteers.md

@@ -0,0 +1,12 @@
+# Volunteers for Bugfix and CI
+
+We encourage you to check current docs and [issues](https://github.com/vllm-project/vllm-omni/issues) to find possible solutions for your questions. If non of these can solve it, please propose an issue to describe your questions about bug or CI problems for developing.
+
+If you have urgent need for locating and solving bugfix or CI problems, please find community volunteers below.
+
+| Dec 4-Dec 12 | Dec 15-Dec 19 | Dec 22-Dec 26 | Dec 29- Jan 2, 2026| Jan 5-Jan 9 | Jan 12-Jan 16 |
+|----------|----------|----------|----------|----------|----------|
+| <a href="https://github.com/congw729">Conw729</a> | <a href="https://github.com/yinpeiqi">yinpeiqi</a> | <a href="https://github.com/tzhouam">tzhouam</a> | <a href="https://github.com/SamitHuang">SamitHuang</a> | <a href="https://github.com/gcanlin">gcanlin</a> | <a href="https://github.com/natureofnature">natureofnature</a> |
+| <a href="https://github.com/david6666666">david6666666</a> | <a href="https://github.com/R2-Y">R2-Y</a> | <a href="https://github.com/hsliuustc0106">hsliuustc0106</a> | <a href="https://github.com/Gaohan123">Gaohan123</a> | <a href="https://github.com/ZJY0516">ZJY0516</a> | <a href="https://github.com/qibaoyuan">qibaoyuan</a> |
+
+We kindly welcome more contributors to fix bugs and contribute new features!

docs/contributing/README.md

@@ -3,7 +3,7 @@
 Thank you for your interest in contributing to vLLM-Omni! This document provides guidelines and instructions for contributing.
 
 !!! note
-    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.
+    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).
 
 ## 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
-    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.
+    If you discover a security vulnerability, please report it by creating a GitHub issue with the `security` label.
 
 ## Pull Requests & Code Reviews
 

docs/usage/faq.md

@@ -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: 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/).
+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.
 
 > Q: Does vLLM-Omni support AWQ or any other quantization?
 
-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.
+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).
 
 > Q: Does vLLM-Omni support multimodal streaming input and output?
 
-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.
+A: Not yet. We already put it on the [Roadmap](https://github.com/vllm-project/vllm-omni/issues/165). Please stay tuned!

docs/user_guide/examples/offline_inference/text_to_video.md

@@ -12,7 +12,6 @@ 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 |
@@ -36,25 +35,6 @@ 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

examples/offline_inference/aura_omni/end2end.py

@@ -131,7 +131,7 @@ def build_sampling_params() -> list[SamplingParams]:
 
 
 def save_stage_output(stage_outputs, output_dir: str) -> None:
-    output = stage_outputs
+    output = stage_outputs.request_output
     request_id = output.request_id
     if stage_outputs.final_output_type == "text":
         text_output = output.outputs[0].text

examples/offline_inference/covo_audio/end2end.py

@@ -104,7 +104,7 @@ def main(args):
     os.makedirs(output_dir, exist_ok=True)
 
     for stage_outputs in omni_outputs:
-        output = stage_outputs
+        output = stage_outputs.request_output
         if stage_outputs.final_output_type == "text":
             request_id = output.request_id
             text_output = output.outputs[0].text

examples/offline_inference/custom_pipeline/image_to_image/image_edit.py

@@ -228,7 +228,7 @@ async def main():
     if not outputs:
         raise ValueError("No output produced from omni.generate()")
 
-    first_out = outputs[0]
+    first_out = outputs[0].request_output
     req_out: OmniRequestOutput = first_out
 
     # Verify trajectory data (from custom pipeline)

examples/offline_inference/dynin_omni/end2end.py

@@ -741,7 +741,7 @@ def make_mmu_prompt(
 
 def iter_mm_outputs(outputs: list[Any]):
     for omni_out in outputs:
-        req_out = omni_out
+        req_out = getattr(omni_out, "request_output", None)
         req_list = req_out if isinstance(req_out, list) else [req_out]
         for item in req_list:
             if item is None:

examples/offline_inference/hunyuan_image3/end2end.py

@@ -303,7 +303,7 @@ def main():
     )
     img_idx = 0
     for req_output in omni_outputs:
-        ro = req_output
+        ro = getattr(req_output, "request_output", None)
         stage_id = getattr(req_output, "stage_id", None)
 
         # AR stage text — each CompletionOutput.text is already a delta when

examples/offline_inference/image_to_image/image_edit.py

@@ -621,7 +621,7 @@ def main():
         images = getattr(output, "images", None)
         if images:
             break
-        req_out = output
+        req_out = getattr(output, "request_output", None)
         images = getattr(req_out, "images", None) if req_out is not None else None
         if images:
             break

examples/offline_inference/image_to_video/image_to_video.py

@@ -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 is not None:
+        if frames.is_pipeline_output and frames.request_output is not None:
-            inner_output = frames
+            inner_output = frames.request_output
             if isinstance(inner_output, OmniRequestOutput):
                 if inner_output.multimodal_output and "audio" in inner_output.multimodal_output:
                     audio = inner_output.multimodal_output["audio"]

examples/offline_inference/mammothmodal2_preview/run_mammothmoda2_image_summarize.py

@@ -1,117 +0,0 @@
-"""
-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()

examples/offline_inference/mimo_audio/end2end.py

@@ -297,7 +297,7 @@ def main(args):
     os.makedirs(output_dir, exist_ok=True)
 
     for stage_outputs in omni_outputs:
-        output = stage_outputs
+        output = stage_outputs.request_output
         if stage_outputs.final_output_type == "text":
             request_id = output.request_id
             text_output = output.outputs[0].text

examples/offline_inference/ming_flash_omni/end2end.py

@@ -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
+        output = stage_outputs.request_output
         if stage_outputs.final_output_type == "text":
             request_id = output.request_id
             text_output = output.outputs[0].text

examples/offline_inference/nemotron_voicechat/end2end.py

@@ -1,229 +0,0 @@
-# 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()

examples/offline_inference/qwen2_5_omni/end2end.py

@@ -392,7 +392,7 @@ def main(args):
     total_requests = len(prompts)
     processed_count = 0
     for stage_outputs in omni_generator:
-        output = stage_outputs
+        output = stage_outputs.request_output
         if stage_outputs.final_output_type == "text":
             request_id = output.request_id
             text_output = output.outputs[0].text

examples/offline_inference/qwen3_omni/end2end.py

@@ -367,7 +367,7 @@ def main(args):
     print(f"query type: {args.query_type}")
 
     for stage_outputs in omni_generator:
-        output = stage_outputs
+        output = stage_outputs.request_output
         if stage_outputs.final_output_type == "text":
             request_id = output.request_id
             text_output = output.outputs[0].text

examples/offline_inference/qwen3_omni/end2end_async_chunk.py

@@ -237,7 +237,7 @@ async def run_single_request(
             sampling_params_list=sampling_params_list,
             output_modalities=output_modalities,
         ):
-            output = omni_output
+            output = omni_output.request_output
             if omni_output.final_output_type == "text":
                 if stage_0_first_output_ts is None:
                     stage_0_first_output_ts = time.perf_counter()

examples/offline_inference/speech_to_video/speech_to_video.py

@@ -26,6 +26,7 @@ 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
 
 
@@ -351,8 +352,8 @@ def main():
         else:
             print("[Profiler] No valid profiling data returned.")
 
-    # omni.generate() returns a list for sync calls; unwrap single-result list.
+    # Extract output from result
-    output = result[0] if isinstance(result, list) else result
+    output = OmniRequestOutput.unwrap_result(result)
 
     if not output.images:
         raise ValueError("No video frames found in OmniRequestOutput.")

examples/offline_inference/step_audio2/end2end.py

@@ -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
+        raw_output = stage_outputs.request_output
         outputs_list = raw_output if isinstance(raw_output, list) else [raw_output]
 
         if stage_outputs.final_output_type == "text":

examples/offline_inference/text_to_audio/text_to_audio.py

@@ -43,7 +43,6 @@ 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")
@@ -389,9 +388,9 @@ def main():
         raise ValueError("No output generated from omni.generate()")
 
     output = outputs[0]
-    if not isinstance(output, OmniRequestOutput) or not output:
+    if not hasattr(output, "request_output") or not output.request_output:
         raise ValueError("No request_output found in OmniRequestOutput")
-    request_output = output
+    request_output = output.request_output
     if not hasattr(request_output, "multimodal_output"):
         raise ValueError("No multimodal_output found in request_output")
 

examples/offline_inference/text_to_image/gradio_demo.py

@@ -115,7 +115,7 @@ def build_demo(args: argparse.Namespace) -> gr.Blocks:
         )
         images_outputs = []
         for output in outputs:
-            req_out = output
+            req_out = output.request_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

examples/offline_inference/text_to_image/text_to_image.py

@@ -656,7 +656,7 @@ def main():
         images = getattr(output, "images", None)
         if images:
             break
-        req_out = output
+        req_out = getattr(output, "request_output", None)
         images = getattr(req_out, "images", None) if req_out is not None else None
         if images:
             break

examples/offline_inference/text_to_speech/README.md

@@ -372,10 +372,18 @@ python examples/offline_inference/text_to_speech/qwen3_tts/end2end.py \
 Streaming requires `async_chunk: true` in the stage config.
 
 ### Word Timestamps
-Word-level timestamps are currently a serving-path feature: launch
+Generate a WAV offline and a JSON sidecar with word-level timestamps from
-`vllm-omni serve` with `--forced-aligner` and request `word_timestamps`
+`Qwen/Qwen3-ForcedAligner-0.6B`:
-(see `examples/online_serving/text_to_speech/README.md`). An offline
+```bash
-example is not provided in this release.
+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.
 
 ### 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):

examples/offline_inference/text_to_speech/cosyvoice3/end2end.py

@@ -168,7 +168,7 @@ def run_e2e():
     print(f"Received {len(outputs)} outputs.")
     for i, output in enumerate(outputs):
         try:
-            ro = output
+            ro = output.request_output
             if ro is None:
                 print("No request_output found.")
                 continue

examples/offline_inference/text_to_speech/fish_speech/end2end.py

@@ -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 = stage_outputs.request_output
         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.outputs[0].multimodal_output
+        mm = stage_output.request_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)

examples/offline_inference/text_to_speech/moss_tts_nano/end2end.py

@@ -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):
+        for i, req_output in enumerate(stage_outputs.request_output):
             for j, out in enumerate(req_output.outputs):
                 mm = out.multimodal_output
                 if mm is None:

examples/offline_inference/text_to_speech/omnivoice/end2end.py

@@ -135,7 +135,7 @@ def run_e2e():
     print(f"Received {len(outputs)} outputs.")
     for i, output in enumerate(outputs):
         try:
-            ro = output
+            ro = output.request_output
             if ro is None:
                 print("No request_output found.")
                 continue

examples/offline_inference/text_to_speech/qwen3_tts/end2end.py

@@ -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
+            output = stage_outputs.request_output
             _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.outputs[0].multimodal_output
+            mm = stage_output.request_output.outputs[0].multimodal_output
             if not stage_output.finished:
                 t_now = time.perf_counter()
                 audio = mm.get("audio")

examples/offline_inference/text_to_speech/qwen3_tts/word_timestamps.py

@@ -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
+        final_output = stage_outputs.request_output
     if final_output is None:
         raise RuntimeError("Qwen3-TTS did not produce an output.")
 

examples/offline_inference/text_to_speech/soulxsinger/benchmark.py

@@ -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 = omni_out
+    ro = getattr(omni_out, "request_output", None)
     if ro is not None:
         return dict(getattr(ro, "stage_durations", None) or {})
     return {}

examples/offline_inference/text_to_speech/soulxsinger/end2end.py

@@ -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 = out
+        ro = getattr(out, "request_output", None)
         if ro is not None:
             mm = getattr(ro, "multimodal_output", None)
             if mm is None and ro.outputs:

examples/offline_inference/text_to_video/text_to_video.md

@@ -8,7 +8,6 @@ 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 |
@@ -58,31 +57,18 @@ python text_to_video.py \
 
 ### LingBot-Video
 
-The shared runner recognizes the official dense and MoE checkpoint IDs, selects
+The dedicated LingBot example explicitly selects the video output modality:
-`LingBotVideoPipeline`, and builds the canonical video request envelope:
 
 ```bash
-python text_to_video.py \
+python examples/offline_inference/text_to_video/text_to_video_lingbot.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. For a local checkpoint path whose name does not contain
+VAE's `4n+1` grid.
-`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
 

examples/offline_inference/text_to_video/text_to_video.py

@@ -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, get_output_tensor_range
+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
 
@@ -80,17 +80,6 @@ _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,
@@ -121,51 +110,27 @@ _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 "lingbot" in model_lower or "lingbotvideo" in class_lower:
+    if "distilled" in class_lower or "distilled" in model_lower:
-        return _MODEL_PRESETS["lingbot"]
+        return _MODEL_PRESETS["ltx2_distilled"]
-    if "ltx" in class_lower or "ltx" in model_lower:
+    if "ltx23" in class_lower or "ltx-2.3" in model_lower or "ltx_2.3" in model_lower:
-        if "distilled" in class_lower or "distilled" in model_lower:
+        return _MODEL_PRESETS["ltx23"]
-            return _MODEL_PRESETS["ltx2_distilled"]
+    if "ltx2" in class_lower or "ltx-2" in model_lower or "ltx_2" in model_lower:
-        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 or "vace" in class_lower:
+    if "vace" in model_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 or "cosmos" in class_lower:
+    if "cosmos" in model_lower:
         return _MODEL_PRESETS["cosmos"]
-    if "hunyuan" in model_lower or "hunyuan" in class_lower:
+    if "hunyuan" in model_lower:
         return _MODEL_PRESETS["hunyuan"]
-    if "helios" in model_lower or "helios" in class_lower:
+    if "helios" in model_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)
@@ -195,7 +160,7 @@ 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, LingBot-Video, and other text-to-video models."
+        "Supports Wan2.2, HunyuanVideo-1.5, Helios, and other text-to-video models."
     )
     parser.add_argument(
         "--model",
@@ -425,7 +390,7 @@ def _extract_peak_memory_mb(result: Any) -> float:
         return 0.0
     val = getattr(result, "peak_memory_mb", 0.0)
     if not val:
-        inner = result
+        inner = getattr(result, "request_output", None)
         if isinstance(inner, list):
             inner = inner[0] if inner else None
         val = getattr(inner, "peak_memory_mb", 0.0)
@@ -443,7 +408,6 @@ 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)
@@ -516,7 +480,6 @@ 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...")
@@ -550,11 +513,12 @@ def main():
             lora_path=lora_path,
         )
 
-    negative_prompt = args.negative_prompt
+    prompt_dict = {"prompt": args.prompt}
-    if negative_prompt is None and all(preset is not _MODEL_PRESETS[name] for name in ("lingbot", "ltx2", "ltx23")):
+    if args.negative_prompt is not None:
+        prompt_dict["negative_prompt"] = args.negative_prompt
+    elif preset not in (_MODEL_PRESETS["ltx2"], _MODEL_PRESETS["ltx23"]):
         # Preserve the historical empty-prompt behavior for non-LTX examples.
-        negative_prompt = ""
+        prompt_dict["negative_prompt"] = ""
-    prompt_dict = build_text_to_video_prompt(args.prompt, negative_prompt)
 
     extra_args = {}
     if lora_request:
@@ -611,8 +575,8 @@ 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 is not None:
+        if frames.is_pipeline_output and frames.request_output is not None:
-            inner_output = frames
+            inner_output = frames.request_output
             if isinstance(inner_output, OmniRequestOutput):
                 if inner_output.multimodal_output and "audio" in inner_output.multimodal_output:
                     audio = inner_output.multimodal_output["audio"]
@@ -666,7 +630,8 @@ 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)
-            frame_tensor = _normalize_float_tensor(frame_tensor, output_tensor_range)
+            if frame_tensor.is_floating_point():
+                frame_tensor = frame_tensor.clamp(-1, 1) * 0.5 + 0.5
             return frame_tensor.float().numpy()
         if isinstance(frame, np.ndarray):
             frame_array = frame
@@ -718,7 +683,8 @@ 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)
-        video_tensor = _normalize_float_tensor(video_tensor, output_tensor_range)
+        if video_tensor.is_floating_point():
+            video_tensor = video_tensor.clamp(-1, 1) * 0.5 + 0.5
         video_array = video_tensor.float().numpy()
     elif isinstance(frames, np.ndarray):
         video_array = frames

examples/offline_inference/text_to_video/text_to_video_lingbot.py

@@ -0,0 +1,131 @@
+# 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()

examples/offline_inference/x_to_video_audio/x_to_video_audio.py

@@ -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 = result
+        inner = getattr(result, "request_output", None)
         if isinstance(inner, list):
             inner = inner[0] if inner else None
         val = getattr(inner, "peak_memory_mb", 0.0)

examples/online_serving/text_to_speech/README.md

@@ -605,13 +605,6 @@ 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

mkdocs.yml

@@ -73,8 +73,6 @@ 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
@@ -113,7 +111,6 @@ 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:

pyproject.toml

@@ -206,12 +206,6 @@ 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]
@@ -320,10 +314,6 @@ 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",
@@ -344,9 +334,6 @@ 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"

recipes/NVIDIA/NemotronLabs-VoiceChat.md

@@ -1,113 +0,0 @@
-# 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.

recipes/README.md

@@ -66,7 +66,6 @@ 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 |

recipes/Robbyant/LingBot-Video.md

@@ -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.py`](../../examples/offline_inference/text_to_video/text_to_video.py)
+- 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 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.py \
+python examples/offline_inference/text_to_video/text_to_video_lingbot.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.py \
 
 ```bash
 CUDA_VISIBLE_DEVICES=0 \
-python examples/offline_inference/text_to_video/text_to_video.py \
+python examples/offline_inference/text_to_video/text_to_video_lingbot.py \
   --model robbyant/lingbot-video-moe-30b-a3b \
   --prompt "a robotic arm picks up a red block" \
   --output lingbot_moe_t2v.mp4 \

recipes/Tencent/HunyuanImage-3.0-Instruct.md

@@ -82,7 +82,7 @@ example and the DiT sections below.
 - Optional environment variables:
 
 ```bash
-export CACHE_DIT_VERSION=1.5.0
+export CACHE_DIT_VERSION=1.3.0
 ```
 
 HunyuanImage-3.0 sets the diffusion attention backend to `TORCH_SDPA`

requirements/common.txt

@@ -16,7 +16,7 @@ diffusers==0.38.0
 safetensors>=0.8.0
 accelerate==1.12.0
 soundfile>=0.13.1
-cache-dit==1.5.0
+cache-dit==1.3.0
 tqdm>=4.66.0
 torchsde>=0.2.6
 openai-whisper>=20250625
@@ -34,5 +34,3 @@ 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

tests/benchmarks/test_daily_omni_local_source.py

@@ -1,92 +0,0 @@
-# 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

tests/benchmarks/test_daily_omni_pack_modes.py

@@ -2,8 +2,9 @@
 
 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 no Qwen system prompt.
+interleaved, with ``max_slice_nums=1`` / ``use_image_id=False`` and an empty MiniCPM
-These tests pin that contract so a refactor cannot silently regress the score.
+system message (not the 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.
 """
@@ -98,13 +99,22 @@ def _stub_extract(monkeypatch, dataset: DailyOmniDataset, counter: list[int]) ->
 # ---------------------------------------------------------------------------
 
 
-@pytest.mark.parametrize(("n", "k"), [(10, 4), (100, 64), (7, 7), (3, 5), (1, 1)])
+@pytest.mark.parametrize(("n", "k"), [(10, 4), (100, 64), (1000, 64), (7, 7), (3, 5), (1, 1)])
-def test_uniform_sample_indices_matches_numpy_linspace(n: int, k: int) -> None:
+def test_uniform_sample_indices_matches_openbmb_midpoint(n: int, k: int) -> None:
+    """Pin OpenBMB midpoint-bin sampling (not np.linspace endpoints)."""
     got = _uniform_sample_indices(n, k)
     if n <= k:
         assert got == list(range(n))
     else:
-        assert got == [int(i) for i in np.linspace(0, n - 1, k, dtype=int)]
+        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
 
 
 def test_uniform_sample_indices_non_positive_k() -> None:
@@ -213,14 +223,16 @@ def test_minicpm_interleave_missing_local_video_returns_none(qa_json, tmp_path)
 # ---------------------------------------------------------------------------
 
 
-def test_minicpm_interleave_omits_qwen_system_message(monkeypatch, qa_json, video_dir) -> None:
+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."""
     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] == ["user"]
+    assert [m["role"] for m in messages] == ["system", "user"]
+    assert messages[0]["content"][0]["text"] == ""
 
 
 def test_qwen_pack_mode_keeps_system_message(qa_json, video_dir) -> None:

tests/config/test_forced_aligner_injection.py

@@ -1,71 +0,0 @@
-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

tests/config/test_omni_config.py

@@ -513,7 +513,6 @@ 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",

tests/core/sched/test_omni_ar_scheduler_logprobs.py

@@ -27,7 +27,6 @@ _MIXIN_UPDATE_HELPERS = (
     "_attach_finished_request_sets",
     "_attach_scheduler_stats",
     "_capture_omni_connector_output",
-    "_maybe_decode_pooling_output",
 )
 
 
@@ -139,7 +138,6 @@ 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),
@@ -269,72 +267,3 @@ 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)

tests/core/sched/test_omni_scheduling_coordinator.py

@@ -120,9 +120,6 @@ 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).

tests/dfx/perf/tests/test_minicpmo_4_5_ready.json

@@ -1,80 +0,0 @@
-[
-  {
-    "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
-            ]
-          }
-        }
-      }
-    ]
-  }
-]

tests/diffusion/batching/test_diffusion_batching.py

@@ -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 = output
+    inner = getattr(output, "request_output", None)
     if inner is not None and hasattr(inner, "images") and inner.images:
         return inner.images
     return []

tests/diffusion/cache/test_teacache.py

@@ -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 isinstance(first_output, OmniRequestOutput) or not first_output:
+        if not hasattr(first_output, "request_output") or not first_output.request_output:
             raise ValueError("No request_output found in OmniRequestOutput")
 
-        req_out = first_output
+        req_out = first_output.request_output
         if not isinstance(req_out, OmniRequestOutput) or not hasattr(req_out, "images"):
             raise ValueError("Invalid request_output structure or missing 'images' key")
 

tests/diffusion/distributed/test_sequence_parallel.py

@@ -131,7 +131,7 @@ def _run_inference(
             elapsed_ms = (time.time() - start) * 1000
 
             return InferenceResult(
-                images=outputs[0].images,
+                images=outputs[0].request_output.images,
                 elapsed_ms=elapsed_ms,
             )
     finally:

tests/diffusion/distributed/test_tensor_parallel.py

@@ -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 isinstance(first_output, OmniRequestOutput) or not first_output:
+    if not hasattr(first_output, "request_output") or not first_output.request_output:
         raise ValueError("No request_output found in OmniRequestOutput")
 
-    req_out = first_output
+    req_out = first_output.request_output
     if not isinstance(req_out, OmniRequestOutput) or not hasattr(req_out, "images"):
         raise ValueError("Invalid request_output structure or missing 'images' key")
 

tests/diffusion/distributed/test_wan_spatial_shard.py

@@ -231,128 +231,6 @@ 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):

tests/diffusion/lora/test_diffusion_lora.py

@@ -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 isinstance(first_output, OmniRequestOutput) or not first_output:
+        if not hasattr(first_output, "request_output") or not first_output.request_output:
             raise ValueError("No request_output found in OmniRequestOutput")
 
-        req_out = first_output
+        req_out = first_output.request_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

tests/diffusion/models/minimax_h3/test_minimax_h3_rmsnorm.py

@@ -1,26 +0,0 @@
-# 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)

tests/diffusion/offloader/test_diffusion_cpu_offload.py

@@ -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].multimodal_output.get("audio")
+        audio_offload = output_offload[0].request_output.multimodal_output.get("audio")
-        audio_no_offload = output_no_offload[0].multimodal_output.get("audio")
+        audio_no_offload = output_no_offload[0].request_output.multimodal_output.get("audio")
         check_audio_determinism(audio_offload, audio_no_offload, atol=1e-2)
 
     # Set platform-specific VRAM saving thresholds to account

tests/diffusion/offloader/test_diffusion_layerwise_offload.py

@@ -20,10 +20,18 @@ IMAGE_VIDEO_MODELS = {
 
 MODELS = {**AUDIO_MODEL, **IMAGE_VIDEO_MODELS}
 
-MODEL_MARKS = {
+LAYERWISE_MODEL_CASES = [
-    "riverclouds/qwen_image_random": pytest.mark.core_model,
+    pytest.param(
-    "stabilityai/stable-audio-open-1.0": pytest.mark.full_model,
+        "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",
+    ),
+]
 
 AUDIO_MODEL_PARAMS = {
     "runner_params": {},
@@ -93,9 +101,8 @@ def run_inference(
     return peak, output
 
 
-@pytest.mark.diffusion
 @hardware_test(res={"cuda": "L4", "rocm": "MI325"})
-@pytest.mark.parametrize("model_name", list(MODELS.keys()))
+@pytest.mark.parametrize("model_name", LAYERWISE_MODEL_CASES)
 def test_layerwise_offload_diffusion_model(model_name: str):
     """Test that layerwise offloading reduces GPU memory usage.
 
@@ -127,8 +134,8 @@ 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].multimodal_output.get("audio")
+        audio_offload = output_offload[0].request_output.multimodal_output.get("audio")
-        audio_no_offload = output_no_offload[0].multimodal_output.get("audio")
+        audio_no_offload = output_no_offload[0].request_output.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.

tests/diffusion/quantization/test_gguf_diffusion.py

@@ -94,8 +94,8 @@ def _generate_single_stage_image(
     if hasattr(first_output, "images") and first_output.images:
         images = first_output.images
     else:
-        assert isinstance(first_output, OmniRequestOutput) and first_output
+        assert hasattr(first_output, "request_output") and first_output.request_output
-        request_output = first_output
+        request_output = first_output.request_output
         if isinstance(request_output, list):
             req_out = request_output[0]
         else:

tests/diffusion/quantization/test_quantization_fp8.py

@@ -88,8 +88,8 @@ def _generate_single_stage_image(
         if hasattr(first_output, "images") and first_output.images:
             images = first_output.images
         else:
-            assert isinstance(first_output, OmniRequestOutput) and first_output
+            assert hasattr(first_output, "request_output") and first_output.request_output
-            request_output = first_output
+            request_output = first_output.request_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).
+        # Unwrap pipeline-style outputs (multi-stage / OmniRequestOutput.request_output).
         frames: Any = None
-        if isinstance(first, OmniRequestOutput) and isinstance(first, list):
+        if hasattr(first, "request_output") and isinstance(first.request_output, list):
-            inner = first[0]
+            inner = first.request_output[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 isinstance(req_output, OmniRequestOutput) and req_output:
+            if hasattr(req_output, "request_output") and req_output.request_output:
-                stage_outputs = req_output
+                stage_outputs = req_output.request_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 isinstance(req_output, OmniRequestOutput) and req_output:
+            if hasattr(req_output, "request_output") and req_output.request_output:
-                stage_outputs = req_output
+                stage_outputs = req_output.request_output
                 if not isinstance(stage_outputs, list):
                     stage_outputs = [stage_outputs]
                 for stage_out in stage_outputs:

tests/diffusion/quantization/test_quantization_quality.py

@@ -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
+    inner = first.request_output
     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)
-    if isinstance(outputs, list) and isinstance(outputs[0], OmniRequestOutput):
+    first = outputs[0]
-        first = outputs[0]
+    if hasattr(first, "request_output") and isinstance(first.request_output, list):
-    elif isinstance(outputs, OmniRequestOutput):
+        inner = first.request_output[0]
-        first = outputs
+        if isinstance(inner, OmniRequestOutput) and hasattr(inner, "images"):
-    else:
+            frames = inner.images[0] if inner.images else None
-        raise ValueError("Could not extract video frames from output.")
+        else:
-
+            frames = inner
-    if hasattr(first, "images"):
+    elif hasattr(first, "images") and first.images:
-        frames = first.images[0] if first.images else None
+        frames = first.images[0]
     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.from_stage_output(
+    outer = OmniRequestOutput(
-        inner,
         request_id="req",
         stage_id=0,
         final_output_type="image",
+        request_output=inner,
         finished=True,
     )
 

tests/diffusion/quantization/test_trajectory_similarity_tool.py

@@ -73,9 +73,11 @@ def test_run_summary_reports_worker_peak_memory():
     assert summary["per_run_peak_memory_mb"] == [100.0, 150.0]
 
 
-def test_request_peak_memory_reads_peak_memory_mb_directly():
+def test_request_peak_memory_prefers_inner_diffusion_output():
-    result = type("Output", (), {"peak_memory_mb": 123.0})()
+    inner = type("InnerOutput", (), {"peak_memory_mb": 321.0})()
-    assert _request_peak_memory_mb(result) == 123.0
+    outer = type("OuterOutput", (), {"request_output": inner, "peak_memory_mb": 123.0})()
+
+    assert _request_peak_memory_mb(outer) == 321.0
 
 
 def test_metric_guidance_describes_thresholds():

tests/diffusion/test_diffusion_streaming_output.py

@@ -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 [output.finished for output in outputs] == [False, True]
+            assert [getattr(output.request_output, "finished", output.finished) for output in outputs] == [False, True]
         finally:
             await self._shutdown_pipeline_omni_harness(omni, fixture, inline_client)
 

tests/distributed/omni_connectors/test_bagel_mooncake_connector.py

@@ -25,7 +25,6 @@ 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")]
 
@@ -96,8 +95,8 @@ 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 isinstance(req_output, OmniRequestOutput) and req_output:
+        if hasattr(req_output, "request_output") and req_output.request_output:
-            stage_out = req_output
+            stage_out = req_output.request_output
             if hasattr(stage_out, "images") and stage_out.images:
                 return stage_out.images[0]
     return None

tests/distributed/omni_connectors/test_bagel_shared_memory_connector.py

@@ -24,7 +24,6 @@ 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")]
 
@@ -123,8 +122,8 @@ 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 isinstance(req_output, OmniRequestOutput) and req_output:
+        if hasattr(req_output, "request_output") and req_output.request_output:
-            stage_out = req_output
+            stage_out = req_output.request_output
             if hasattr(stage_out, "images") and stage_out.images:
                 return stage_out.images[0]
     return None

tests/e2e/accuracy/minicpmo_4_5/test_minicpmo_4_5.py

@@ -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, and server ``--interleave-mm-strings`` + 1fps / 128-frame
+text modalities with ``use_tts_template``, and server ``--interleave-mm-strings``
-media-io kwargs, also pinned in ``minicpmo_4_5.yaml``).
++ 1fps / 128-frame media-io kwargs, also pinned in ``minicpmo_4_5.yaml``).
 """
 
 from __future__ import annotations
@@ -40,7 +40,10 @@ _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},
+    "chat_template_kwargs": {
+        "enable_thinking": False,
+        "use_tts_template": True,
+    },
 }
 _SEED_EXTRA_BODY = {
     "modalities": ["text", "audio"],

tests/e2e/accuracy/test_hunyuan_image3.py

@@ -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 = out
+        ro = getattr(out, "request_output", None)
         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 = output
+        request_output = getattr(output, "request_output", None)
         if not images and request_output is not None:
             images = getattr(request_output, "images", None)
         if images:

tests/e2e/accuracy/test_videogen_modelopt_quant.py

@@ -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 is not None:
+    if isinstance(result, OmniRequestOutput) and result.is_pipeline_output and result.request_output is not None:
-        result = result
+        result = result.request_output
     images = getattr(result, "images", None)
     if not images:
         raise AssertionError("Pipeline output had no images")

tests/e2e/features/comfyui/test_comfyui_integration.py

@@ -171,11 +171,11 @@ def _build_text_output(text: str = "This is a test response.") -> OmniRequestOut
         metrics=None,
         lora_request=None,
     )
-    return OmniRequestOutput.from_stage_output(
+    return OmniRequestOutput(
-        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.from_stage_output(
+    return OmniRequestOutput(
-        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.from_stage_output(
+    return OmniRequestOutput(
-        request_output,
         request_id="test_req_img_chat",
-        images=[_build_image_output(color="blue")],
+        finished=True,
         final_output_type="image",
+        request_output=request_output,
     )
 
 
@@ -515,19 +515,8 @@ def mock_async_omni(
 
 
 @pytest.fixture
-def api_server(unused_tcp_port_factory, server_case: ServerCase, mock_async_omni, tmp_path):
+def api_server(unused_tcp_port_factory, server_case: ServerCase, mock_async_omni):
     """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()
@@ -566,9 +555,6 @@ 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)

tests/e2e/features/helpers/verl_omni_server.py

@@ -294,7 +294,7 @@ class vLLMOmniHttpServerLocal:
             "global_steps": self.global_steps,
         }
 
-        req_output = final_res
+        req_output = final_res.request_output
         if req_output is not None and hasattr(req_output, "finish_reason"):
             finish_reason = req_output.finish_reason or "stop"
         else:

tests/e2e/offline_inference/test_audiox_model_expansion.py

@@ -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 isinstance(first_output, OmniRequestOutput) and first_output
+    assert hasattr(first_output, "request_output") and first_output.request_output
 
-    req_out = first_output
+    req_out = first_output.request_output
     assert isinstance(req_out, OmniRequestOutput)
     assert req_out.final_output_type == "audio"
     assert hasattr(req_out, "multimodal_output") and req_out.multimodal_output

tests/e2e/offline_inference/test_bagel.py

@@ -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 = req_output
+        ro = getattr(req_output, "request_output", None)
         if ro and getattr(ro, "outputs", None):
             return "".join(getattr(o, "text", "") or "" for o in ro.outputs)
     return ""

tests/e2e/offline_inference/test_cosyvoice3_expansion.py

@@ -28,6 +28,7 @@ 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"
@@ -175,12 +176,12 @@ 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 = outputs[0]
+    pipeline_out = OmniRequestOutput.unwrap_result(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
+    ro = pipeline_out.request_output
     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"

tests/e2e/offline_inference/test_dynin_omni_expansion.py

@@ -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(stage_output):
+        if _is_finished_request_output(getattr(stage_output, "request_output", None)):
             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 = stage_output
+    request_output = getattr(stage_output, "request_output", None)
     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 = stage_output
+    request_output = getattr(stage_output, "request_output", None)
     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 = stage_output
+    request_output = getattr(stage_output, "request_output", None)
     if request_output is None:
         return None
     req_list = request_output if isinstance(request_output, list) else [request_output]

tests/e2e/offline_inference/test_flux1_dev_autoround_w4a16_expansion.py

@@ -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
+    req_out = first_output.request_output
     assert isinstance(req_out, OmniRequestOutput) and hasattr(req_out, "images")
     return req_out.images
 

tests/e2e/offline_inference/test_glm_image_autoround_w4a16_expansion.py

@@ -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
+    req_out = first_output.request_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
+    req_out = first_output.request_output
     assert isinstance(req_out, OmniRequestOutput) and hasattr(req_out, "images")
     images = req_out.images
 

tests/e2e/offline_inference/test_hunyuanimage3.py

@@ -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 = first_output
+    request_output = getattr(first_output, "request_output", None)
     if request_output is not None and (images := getattr(request_output, "images", None)):
         return images[0]
 

tests/e2e/offline_inference/test_hunyuanimage3_i2t_expansion.py

@@ -74,7 +74,7 @@ def test_i2t_generates_text(omni: Omni) -> None:
     outputs = omni.generate(prompts=[prompt_dict])
     assert outputs, "No outputs returned from Omni.generate()"
 
-    request_output = outputs[0]
+    request_output = outputs[0].request_output
     assert request_output.outputs, "No completion outputs"
 
     completion = request_output.outputs[0]