[CI/Build] Clarify model-specific example filename

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

.buildkite/cuda/test-merge.yml

@@ -139,12 +139,7 @@ steps:
         timeout_in_minutes: 50
         commands:
           - pytest -s -v tests/e2e/offline_inference/test_minicpmo_4_5.py tests/e2e/online_serving/test_minicpmo_4_5.py -m 'advanced_model and cuda' --run-level 'advanced_model'
-        # h100_2, not h100_1: advanced_model asserts transcribe every audio
-        # response with Whisper, and tests/helpers/media.py only uses an
-        # accelerator for Whisper when a spare GPU exists. On a single card it
-        # grades on CPU: ~8-10 min per clip vs ~30 s, which alone overruns the
-        # 50-minute job timeout (build 2956).
-        mirror_hardwares: h100_2
+        mirror_hardwares: h100_1
 
       - label: "Omni · MiniCPM-o 4.5 Duplex Test"
         source_file_dependencies:

.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

@@ -128,21 +128,6 @@ steps:
               image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
               always-pull: true
               propagate-environment: true
-              # vLLM 0.27 added a pre-flight capacity check in
-              # distributed/device_communicators/shm_broadcast.py. Without this
-              # the container gets Docker's 64 MiB default and every test in
-              # this step dies at engine start with
-              #   RuntimeError: Insufficient space in /dev/shm:
-              #                 240 MiB required, 64 MiB available
-              # (9 failed in 8.7s on build #2949). vLLM 0.26 had no such check
-              # and allocated regardless, so this step passed on main while
-              # being under-provisioned the whole time — 0.27 turned a silent
-              # risk into an explicit error.
-              #
-              # This is the only inline docker plugin block in .buildkite/cuda/;
-              # every other step inherits common/ci_mirror_hardwares.yml, which
-              # already sets 8gb. Matching that rather than inventing a value.
-              shm-size: "8gb"
               environment:
                 - "HF_HOME=/fsx/hf_cache"
               volumes:
@@ -227,18 +212,6 @@ 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/intel/pipeline-intel.yml

@@ -10,7 +10,7 @@ steps:
           DOCKER_BUILDKIT: "1"
           # Buildkite will automatically replace this with the actual commit hash
           VLLM_IMAGE_TAG: "${BUILDKITE_COMMIT}"
-          VLLM_VERSION: "v0.27.0"
+          VLLM_VERSION: "v0.26.0"
         priority: 100
         timeout_in_minutes: 60
         soft_fail: false

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

@@ -34,23 +34,8 @@ docker_build() {
     docker build "${out[@]}" "$@" -f docker/Dockerfile.xpu .
 }
 
-build_vllm_base() (
-    local vllm_source_dir
-    vllm_source_dir=$(mktemp -d)
-    trap 'rm -rf "${vllm_source_dir}"' EXIT
-
-    git clone --depth 1 --branch "${VLLM_VERSION}" \
-        https://github.com/vllm-project/vllm "${vllm_source_dir}"
-
-    local out=("${export_args[@]/'{{IMAGE}}'/${base_image_name}}")
-    docker build "${out[@]}" \
-        --target vllm-openai \
-        -f "${vllm_source_dir}/docker/Dockerfile.xpu" \
-        "${vllm_source_dir}"
-)
-
 if [ -z "$(docker images -q "${base_image_name}")" ]; then
-    build_vllm_base
+    docker_build "${base_image_name}" --target vllm-base --build-arg "VLLM_VERSION=${VLLM_VERSION}"
 fi
 
 # Try building the docker image

.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.
-#
-# Usage:
-#   publish-release-images.sh            # release mode (default)
-#   publish-release-images.sh release
-#   publish-release-images.sh nightly
-#
-# Release tags: latest, v<version>
-# Nightly tags: nightly, nightly-<commit>
+# Publish release Docker images from ECR to DockerHub.
+# Pulls per-arch images, tags with `latest` and versioned tags, pushes them,
+# then creates and pushes a multi-arch manifest.
 
 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"
-SRC_ARM="${ECR_REPO}:${COMMIT}-aarch64"
+docker pull "${ECR_REPO}:${COMMIT}-x86_64"
+docker pull "${ECR_REPO}:${COMMIT}-aarch64"
 
-docker pull "${SRC_X86}"
-docker pull "${SRC_ARM}"
+docker tag "${ECR_REPO}:${COMMIT}-x86_64" "${DOCKERHUB_REPO}:latest-x86_64"
+docker tag "${ECR_REPO}:${COMMIT}-x86_64" "${DOCKERHUB_REPO}:v${RELEASE_VERSION}-x86_64"
+docker push "${DOCKERHUB_REPO}:latest-x86_64"
+docker push "${DOCKERHUB_REPO}:v${RELEASE_VERSION}-x86_64"
 
-publish_manifest() {
-  local tag="$1"
-  local arch_tag_base="$2"
-  docker manifest rm "${DOCKERHUB_REPO}:${tag}" || true
-  docker manifest create \
-    "${DOCKERHUB_REPO}:${tag}" \
-    "${DOCKERHUB_REPO}:${arch_tag_base}-x86_64" \
-    "${DOCKERHUB_REPO}:${arch_tag_base}-aarch64"
-  docker manifest push "${DOCKERHUB_REPO}:${tag}"
-}
+docker tag "${ECR_REPO}:${COMMIT}-aarch64" "${DOCKERHUB_REPO}:latest-aarch64"
+docker tag "${ECR_REPO}:${COMMIT}-aarch64" "${DOCKERHUB_REPO}:v${RELEASE_VERSION}-aarch64"
+docker push "${DOCKERHUB_REPO}:latest-aarch64"
+docker push "${DOCKERHUB_REPO}:v${RELEASE_VERSION}-aarch64"
 
-for tag in "${PRIMARY_TAGS[@]}"; do
-  docker tag "${SRC_X86}" "${DOCKERHUB_REPO}:${tag}-x86_64"
-  docker tag "${SRC_ARM}" "${DOCKERHUB_REPO}:${tag}-aarch64"
-  docker push "${DOCKERHUB_REPO}:${tag}-x86_64"
-  docker push "${DOCKERHUB_REPO}:${tag}-aarch64"
-  publish_manifest "${tag}" "${tag}"
-done
-
-ARCH_BASE="${PRIMARY_TAGS[0]}"
-for tag in "${EXTRA_MANIFEST_TAGS[@]}"; do
-  publish_manifest "${tag}" "${ARCH_BASE}"
-done
+docker manifest rm "${DOCKERHUB_REPO}:latest" || true
+docker manifest rm "${DOCKERHUB_REPO}:v${RELEASE_VERSION}" || true
+docker manifest create "${DOCKERHUB_REPO}:latest" "${DOCKERHUB_REPO}:latest-x86_64" "${DOCKERHUB_REPO}:latest-aarch64"
+docker manifest create "${DOCKERHUB_REPO}:v${RELEASE_VERSION}" "${DOCKERHUB_REPO}:v${RELEASE_VERSION}-x86_64" "${DOCKERHUB_REPO}:v${RELEASE_VERSION}-aarch64"
+docker manifest push "${DOCKERHUB_REPO}:latest"
+docker manifest push "${DOCKERHUB_REPO}:v${RELEASE_VERSION}"
 
 echo ""
-echo "Successfully published ${MODE} images"
+echo "Successfully published release images for v${RELEASE_VERSION}"

.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`
-      # replaces PyPI, so an sdist fallback cannot reach its build deps. The
-      # quotes keep YAML from reading `:all:` as a mapping key.
-      - "python -m pip install --only-binary=:all: --index-url https://download.pytorch.org/whl/cpu torch"
+      # 1 GB memory cap and time out the mkdocs build.
+      - python -m pip install --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]
-    elif isinstance(outputs, OmniRequestOutput):
-        first = outputs
-    else:
-        raise ValueError("Could not extract video frames from output.")
-
-    if hasattr(first, "images"):
-        frames = first.images[0] if first.images else None
+    first = outputs[0]
+    if hasattr(first, "request_output") and isinstance(first.request_output, list):
+        inner = first.request_output[0]
+        if isinstance(inner, OmniRequestOutput) and hasattr(inner, "images"):
+            frames = inner.images[0] if inner.images else None
+        else:
+            frames = inner
+    elif hasattr(first, "images") and first.images:
+        frames = first.images[0]
     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",

benchmarks/tts/README.md

@@ -139,34 +139,6 @@ python benchmarks/tts/bench_tts.py \
     --output-dir ./results
 ```
 
-#### Fixed IndexTTS 2.5 performance sweep
-
-IndexTTS 2.5 is registered in `model_configs.yaml` like the other TTS models.
-Use `--served-model-name` when the server was launched from a local native
-bundle instead of the registry key. The command below reproduces the acceptance
-workload: Seed-TTS Eval EN, `n=500`, concurrency `4/8/16/32`, five warmups, and
-dataset seed 0.
-
-```bash
-python benchmarks/tts/bench_tts.py \
-    --model IndexTeam/IndexTTS-2.5 \
-    --served-model-name /path/to/indextts-2.5 \
-    --task voice_clone --locale en \
-    --dataset-path /path/to/seedtts_testset \
-    --host 127.0.0.1 --port 8092 \
-    --concurrency 4 8 16 32 \
-    --num-prompts 500 --num-warmups 5 \
-    --output-dir ./results/indextts2_5 \
-    -- \
-    --tokenizer /path/to/indextts-2.5/qwen0.6bemo4-merge \
-    --seed 0 --metric-percentiles 50,95,99 --disable-tqdm --save-detailed
-```
-
-The wrapper benchmarks an already-running server. For reproducible quality
-comparisons, add `--request-seed 42`; production-performance runs should omit
-it. The trailing `--seed 0` controls Seed-TTS row selection rather than model
-sampling.
-
 ### 4. Plot a sweep
 
 ```bash

benchmarks/tts/bench_tts.py

@@ -84,17 +84,12 @@ def build_bench_args(
     result_filename: str | None,
     extra_cli_args: list[str],
     output_len: int | None = None,
-    served_model_name: str | None = None,
-    num_warmups: int = 2,
-    request_seed: int | None = None,
 ) -> list[str]:
     """Build the ``vllm bench serve --omni`` command for one (task, concurrency) run."""
     dataset_name = _TASK_TO_DATASET[task]
     backend: str = model_cfg["backend"]
     endpoint: str = model_cfg["endpoint"]
-    task_extra_body: dict[str, Any] = dict((model_cfg.get("task_extra_body") or {}).get(task) or {})
-    if request_seed is not None:
-        task_extra_body["seed"] = request_seed
+    task_extra_body: dict[str, Any] = (model_cfg.get("task_extra_body") or {}).get(task) or {}
 
     # Resolve dataset path
     if dataset_path:
@@ -114,7 +109,7 @@ def build_bench_args(
         "--port",
         str(port),
         "--model",
-        served_model_name or model,
+        model,
         "--backend",
         backend,
         "--endpoint",
@@ -124,7 +119,7 @@ def build_bench_args(
         "--num-prompts",
         str(num_prompts),
         "--num-warmups",
-        str(num_warmups),
+        "2",
         "--percentile-metrics",
         "ttft,e2el,audio_rtf,audio_ttfp,audio_duration,audio_underrun",
     ]
@@ -157,8 +152,7 @@ def build_bench_args(
         if result_filename:
             cmd += ["--result-filename", result_filename]
 
-    passthrough = extra_cli_args[1:] if extra_cli_args[:1] == ["--"] else extra_cli_args
-    cmd += passthrough
+    cmd += extra_cli_args
     return cmd
 
 
@@ -231,12 +225,6 @@ def main() -> None:
     parser.add_argument(
         "--model", required=True, help="HuggingFace model ID (e.g. Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice)"
     )
-    parser.add_argument(
-        "--served-model-name",
-        default=None,
-        help="Model name/path accepted by the running server. Use this when --model selects a registry entry "
-        "but the server was launched from a local bundle path.",
-    )
     parser.add_argument("--task", default="all", help="Task type: voice_clone | default_voice | voice_design | all")
     parser.add_argument("--locale", default="en", choices=["en", "zh"])
     parser.add_argument("--concurrency", type=int, nargs="+", default=[1, 4], metavar="N")
@@ -248,13 +236,6 @@ def main() -> None:
         metavar="N",
         help="Number of prompts per run. If one value, applied to all concurrency levels.",
     )
-    parser.add_argument("--num-warmups", type=int, default=2, help="Warmup requests before each measured run")
-    parser.add_argument(
-        "--request-seed",
-        type=int,
-        default=None,
-        help="Optional model sampling seed added to every request body for reproducible quality runs",
-    )
     parser.add_argument(
         "--dataset-path", default=None, help="Root of seed-tts-eval dataset (required for voice_clone/default_voice)"
     )
@@ -275,9 +256,6 @@ def main() -> None:
     parser.add_argument("extra", nargs=argparse.REMAINDER, help="Extra args passed directly to vllm bench serve")
     args = parser.parse_args()
 
-    if args.num_warmups < 0:
-        parser.error("--num-warmups cannot be negative")
-
     model_configs = load_model_configs(Path(args.model_configs))
     if args.model not in model_configs:
         known = "\n  ".join(model_configs.keys())
@@ -330,9 +308,6 @@ def main() -> None:
                 result_filename=result_filename,
                 extra_cli_args=args.extra or [],
                 output_len=args.output_len,
-                served_model_name=args.served_model_name,
-                num_warmups=args.num_warmups,
-                request_seed=args.request_seed,
             )
             result = run_one_benchmark(cmd)
             if result is not None:

benchmarks/tts/model_configs.yaml

@@ -7,17 +7,6 @@
 # is tracked here.
 
 models:
-  IndexTeam/IndexTTS-2.5:
-    supported_tasks: [voice_clone]
-    backend: openai-audio-speech
-    endpoint: /v1/audio/speech
-    trust_remote_code: true
-    task_extra_body:
-      voice_clone:
-        extra_params:
-          lang: en
-          text_normalization: true
-
   # -CustomVoice checkpoints lack speaker_encoder weights, so voice_clone is
   # NOT supported (an attempt raises ValueError from _extract_speaker_embedding
   # at model runtime).  Use -Base for voice_clone.

docker/Dockerfile.ci

@@ -1,24 +1,7 @@
-# Target vllm version: v0.27.0 (releases/v0.27.0 branch).
+# Target vllm version: v0.26.0 (releases/v0.26.0 branch).
 # Keep VLLM_BASE_TAG aligned with the target vLLM release on every rebase.
-#
-# Release-boundary form: there is no precompiled-wheel pin here, because the
-# base image already ships exactly the target vLLM. Everything the runtime
-# needs — torch, flashinfer, nvJitLink, numpy — comes from that image as one
-# self-consistent set.
-#
-# This block reappears only when the rebase targets a vLLM commit that is NOT a
-# released tag. During the 0.27 rebase, targeting v0.27.0rc1 on top of the
-# v0.26.0 base image required reinstalling vLLM from wheels.vllm.ai and then
-# hand-repairing the fallout: pinning torch ahead of flashinfer (installing it
-# after bound flashinfer's TVM FFI runtime to the old torch and aborted engine
-# core init), registering libnvJitLink.so.13 with ldconfig, and re-pinning numpy
-# under numba's ceiling. All of it was compensation for a base image and a vLLM
-# that disagreed. At a real release boundary they agree, so none of it is
-# needed. If you pin an untagged commit again, restore the reinstall block —
-# agent/tasks/20_update_wheel_url.sh fails the run rather than letting the
-# Dockerfile silently ship the wrong vLLM.
 ARG VLLM_BASE_IMAGE=vllm/vllm-openai
-ARG VLLM_BASE_TAG=v0.27.0
+ARG VLLM_BASE_TAG=v0.26.0
 
 # We use registry-based layer caching for the dependencies,
 # where the tag is determined based on the hash of the contents

docker/Dockerfile.xpu

@@ -88,7 +88,7 @@ ENV UV_EXTRA_INDEX_URL=${PIP_EXTRA_INDEX_URL}
 ENV UV_INDEX_STRATEGY="unsafe-best-match"
 ENV UV_LINK_MODE="copy"
 
-ARG VLLM_VERSION=v0.27.0
+ARG VLLM_VERSION=v0.26.0
 RUN git clone -b ${VLLM_VERSION} https://github.com/vllm-project/vllm
 WORKDIR /workspace/vllm
 
@@ -108,7 +108,7 @@ ENV VLLM_WORKER_MULTIPROC_METHOD=spawn
 
 RUN --mount=type=cache,target=/root/.cache/uv \
     uv pip uninstall triton triton-xpu && \
-    uv pip install triton-xpu==3.7.2 && \
+    uv pip install triton-xpu==3.7.1 && \
     uv pip install --no-build-isolation --no-deps .
 
 CMD ["/bin/bash"]
@@ -144,13 +144,12 @@ WORKDIR /workspace/vllm-omni
 COPY . .
 
 ENV VLLM_OMNI_TARGET_DEVICE=xpu
-# Keep the upstream oneCCL packages paired with the XPU torch runtime.
 RUN --mount=type=cache,target=/root/.cache/uv \
     uv pip install --no-build-isolation \
     --extra-index-url https://mirrors.ustc.edu.cn/pypi/simple/ \
     ".[dev]" && \
-    uv pip uninstall triton && \
-    uv pip install triton-xpu==3.7.2 --reinstall
+    uv pip uninstall triton oneccl oneccl-devel && \
+    uv pip install triton-xpu==3.7.1 --reinstall
 
 FROM vllm-omni AS vllm-omni-openai
 

docs/.nav.yml

@@ -40,30 +40,16 @@ nav:
   - Models:
     - models/supported_models.md
   - Features:
-    - Overview: features/README.md
-    - Runtime and Stage Execution:
-      - Execution Modes and Streaming: user_guide/diffusion/execution_modes.md
-      - Sleep Mode: features/sleep_mode.md
-    - Quantization:
-      - Overview: user_guide/quantization/overview.md
-      - Online Quantization: user_guide/quantization/online.md
-      - Quantized KV Cache: user_guide/quantization/quantized_kvcache.md
-      - FP8 W8A8: user_guide/quantization/fp8.md
-      - Int8 W8A8: user_guide/quantization/int8.md
-      - BitsAndBytes W4: user_guide/quantization/bitsandbytes.md
-      - MXFP8 W8A8: user_guide/quantization/mxfp8.md
-      - MXFP4 W4A4: user_guide/quantization/mxfp4.md
-      - ModelOpt: user_guide/quantization/modelopt.md
-      - AutoRound: user_guide/quantization/autoround.md
-      - msModelSlim: user_guide/quantization/msmodelslim.md
-    - Diffusion Acceleration:
-      - Overview and Compatibility:
-        - Overview: user_guide/diffusion_features.md
-        - Feature Compatibility: user_guide/feature_compatibility.md
-      - CPU Offloading: user_guide/diffusion/cpu_offload.md
+    - Sleep Mode: features/sleep_mode.md
+    - Diffusion Features:
+      - Overview: user_guide/diffusion_features.md
+      - Feature Compatibility: user_guide/feature_compatibility.md
+      - Regional Compilation: user_guide/diffusion/regional_compilation.md
       - Cache Acceleration:
         - TeaCache: user_guide/diffusion/cache_acceleration/teacache.md
         - Cache-DiT: user_guide/diffusion/cache_acceleration/cache_dit.md
+      - Attention Backends: user_guide/diffusion/attention_backends.md
+      - Frame Interpolation: user_guide/diffusion/frame_interpolation.md
       - Parallelism:
         - Overview: user_guide/diffusion/parallelism/overview.md
         - CFG Parallel: user_guide/diffusion/parallelism/cfg_parallel.md
@@ -72,15 +58,23 @@ nav:
         - Sequence Parallel: user_guide/diffusion/parallelism/sequence_parallel.md
         - Tensor Parallel: user_guide/diffusion/parallelism/tensor_parallel.md
         - VAE Parallelism: user_guide/diffusion/parallelism/vae_parallelism.md
-      - Attention Backends: user_guide/diffusion/attention_backends.md
-      - Regional Compilation: user_guide/diffusion/regional_compilation.md
-      - Frame Interpolation: user_guide/diffusion/frame_interpolation.md
-      - Startup and Loading: user_guide/diffusion/startup_and_loading.md
+      - CPU Offloading: user_guide/diffusion/cpu_offload.md
       - LoRA: user_guide/diffusion/lora.md
-    - Experimental:
-      - Session State Manager: features/session_state_manager.md
-  - Integrations:
-    - Custom Diffusion Pipeline: features/custom_pipeline.md
+      - Custom Pipeline: features/custom_pipeline.md
+      - Execution Modes: user_guide/diffusion/execution_modes.md
+    - Quantization:
+      - Quantization: user_guide/quantization/overview.md
+      - Online Quantization: user_guide/quantization/online.md
+      - Quantized KV Cache: user_guide/quantization/quantized_kvcache.md
+      - FP8 W8A8: user_guide/quantization/fp8.md
+      - Int8 W8A8: user_guide/quantization/int8.md
+      - BitsAndBytes W4: user_guide/quantization/bitsandbytes.md
+      - MXFP8 W8A8: user_guide/quantization/mxfp8.md
+      - MXFP4 W4A4: user_guide/quantization/mxfp4.md
+      - ModelOpt: user_guide/quantization/modelopt.md
+      - GGUF: user_guide/quantization/gguf.md
+      - AutoRound: user_guide/quantization/autoround.md
+      - msModelSlim: user_guide/quantization/msmodelslim.md
     - ComfyUI: features/comfyui.md
   - Fault Tolerance:
     - Failure Modes: user_guide/fault_injection_reliability_matrix.md
@@ -114,7 +108,6 @@ nav:
           - Shared Memory Connector: design/feature/omni_connectors/shared_memory_connector.md
           - Yuanrong Store Connector: design/feature/omni_connectors/yuanrong_connector.md
           - Yuanrong Transfer Engine Connector: design/feature/omni_connectors/yuanrong_transfer_engine_connector.md
-      - Quantization: design/feature/quantization.md
       - Diffusion Acceleration:
         - Parallelism:
           - CFG-Parallel: design/feature/cfg_parallel.md
@@ -124,8 +117,9 @@ nav:
           - Sequence Parallel: design/feature/sequence_parallel.md
           - Tensor Parallel: design/feature/tensor_parallel.md
           - VAE Patch Parallelism: design/feature/vae_parallel.md
-        - Attention Optimization:
+        - Attention Backends:
           - Skip-Softmax: design/feature/skip_softmax.md
+        - Quantization: design/feature/quantization.md
         - Cache-DiT: design/feature/cache_dit.md
         - TeaCache: design/feature/teacache.md
         - Diffusion Continuous Batching: design/feature/diffusion_continuous_batching.md
@@ -163,8 +157,7 @@ nav:
 - CLI Reference: cli
 - Community:
   - Governance: community/governance.md
-  - Contact Us: community/contact_us.md
-  - Community Meetings: community/meetings.md
+  - community/*
   - 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 and usage questions, join the `sig-omni` channel in [vLLM Slack](https://slack.vllm.ai/) or use the [vLLM Forum](https://discuss.vllm.ai/).
-- For coordinating contributions and development, use `sig-omni`, the forum, or join a [community meeting](meetings.md).
-
-## Security Disclosures
-
-Do not share vulnerability details in public GitHub issues, pull requests, discussions, or Slack channels. To arrange a private report, send a direct message to one of the [lead maintainers](governance.md#lead-maintainers) in [vLLM Slack](https://slack.vllm.ai/). If you cannot send a direct message, you may post a detail-free request in `sig-omni` asking a lead maintainer to contact you privately; do not include any vulnerability details in that request.
+- For technical questions and feature requests, please use GitHub [Issues](https://github.com/vllm-project/vllm-omni/issues)
+- For coordinating contributions and development and discussing with other users and developers, please join `sig-omni` channel in our [Slack](https://slack.vllm.ai/) or use the [vLLM Forum](https://discuss.vllm.ai/)
+- For security disclosures, please use GitHub's [Security Advisories](https://github.com/vllm-project/vllm-omni/security/advisories) feature

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/design/index.md

@@ -11,10 +11,6 @@ in the active navigation.
 
 ## Feature Design Documents
 
-For user-facing configuration and current compatibility, see the
-[Features overview](../features/README.md). A design document defines an
-implementation contract; it is not, by itself, a general support claim.
-
 ### Runtime and stage execution
 
 - [Disaggregated Inference](feature/disaggregated_inference.md)
@@ -34,10 +30,6 @@ implementation contract; it is not, by itself, a general support claim.
 - [Yuanrong Store Connector](feature/omni_connectors/yuanrong_connector.md)
 - [Yuanrong Transfer Engine Connector](feature/omni_connectors/yuanrong_transfer_engine_connector.md)
 
-### Quantization
-
-- [Quantization](feature/quantization.md)
-
 ### Diffusion acceleration
 
 #### Parallelism
@@ -50,14 +42,14 @@ implementation contract; it is not, by itself, a general support claim.
 - [Tensor Parallel](feature/tensor_parallel.md)
 - [VAE Patch Parallelism](feature/vae_parallel.md)
 
-#### Attention optimization
-
-The [Diffusion Attention Backends](../user_guide/diffusion/attention_backends.md)
-guide lists all selectable backends and their platform defaults. Skip-Softmax
-is the backend-related optimization with a standalone feature-design contract:
+#### Attention Backends
 
 - [Skip-Softmax](feature/skip_softmax.md)
 
+#### Quantization
+
+- [Quantization](feature/quantization.md)
+
 - [Cache-DiT](feature/cache_dit.md)
 - [TeaCache](feature/teacache.md)
 - [Diffusion Continuous Batching](feature/diffusion_continuous_batching.md)

docs/features/README.md

@@ -1,67 +0,0 @@
-# Features
-
-Use this section to choose and configure vLLM-Omni runtime and optimization
-features. The navigation follows the active
-[Feature Design](../design/index.md#feature-design-documents) taxonomy whenever
-an implementation contract has a user-facing workflow.
-
-!!! note "User guides, design documents, and recipes"
-
-    User guides explain how to enable a feature and where it is supported.
-    Design documents define internal contracts and do not, by themselves,
-    imply general or production support. For model-specific launch flags and
-    hardware requirements, use the linked examples and validated recipes from
-    [Supported Models](../models/supported_models.md).
-
-## Runtime and Stage Execution
-
-| Goal | User guide | Related design contract |
-| --- | --- | --- |
-| Choose serial, batched, step-wise, or streaming diffusion execution | [Execution Modes and Streaming](../user_guide/diffusion/execution_modes.md) | [Diffusion Continuous Batching](../design/feature/diffusion_continuous_batching.md), [Async Diffusion Output](../design/feature/async_diffusion_output.md) |
-| Reclaim stage memory without restarting the server | [Sleep Mode](sleep_mode.md) | Runtime lifecycle behavior is documented in the user guide |
-
-Some runtime designs are deliberately not promoted as standalone User Guide
-features yet:
-
-- [Disaggregated Inference](../design/feature/disaggregated_inference.md) and
-  [OmniConnector implementations](../design/index.md#communication) describe
-  topology and transport contracts. Practical deployment configuration remains
-  under [Pipeline and deploy configurations](../configuration/stage_configs.md).
-- [Async Chunk](../design/feature/async_chunk.md) and
-  [Async Omni Output Materialization](../design/feature/omni_async_output_materialization.md)
-  are model- and pipeline-dependent. Use the selected model's deploy
-  configuration and recipe for the supported settings.
-- [Automatic Prefix Caching](../design/feature/prefix_caching.md) remains a
-  design-level contract until its user-facing configuration and compatibility
-  surface is consolidated.
-
-## Quantization
-
-Quantization is a cross-model feature rather than a diffusion-only
-optimization. The unified [`quantization_config` guide](../user_guide/quantization/overview.md)
-covers diffusion-only models, multi-stage omni/TTS models, and multi-stage
-diffusion models. Its [design contract](../design/feature/quantization.md)
-defines the shared configuration and backend extension points.
-
-## Diffusion Acceleration
-
-| Goal | User guide | Related design contract |
-| --- | --- | --- |
-| Compare acceleration methods and supported combinations | [Overview](../user_guide/diffusion_features.md), [Feature Compatibility](../user_guide/feature_compatibility.md) | [Diffusion acceleration designs](../design/index.md#diffusion-acceleration) |
-| Move weights between host and device memory | [CPU Offloading](../user_guide/diffusion/cpu_offload.md) | [Distributed Layerwise Offload](../design/feature/distributed_layerwise_offload.md) |
-| Reuse denoising computation | [Cache Acceleration](../user_guide/diffusion/cache_acceleration/cache_dit.md) | [Cache-DiT](../design/feature/cache_dit.md), [TeaCache](../design/feature/teacache.md) |
-| Distribute diffusion work across devices | [Parallelism](../user_guide/diffusion/parallelism/overview.md) | [Parallelism designs](../design/index.md#parallelism) |
-| Select dense, sparse, or quantized attention paths | [Attention Backends](../user_guide/diffusion/attention_backends.md) | [Skip-Softmax](../design/feature/skip_softmax.md) |
-| Compile repeated diffusion regions | [Regional Compilation](../user_guide/diffusion/regional_compilation.md) | User-facing optimization guide |
-| Add generated video frames | [Frame Interpolation](../user_guide/diffusion/frame_interpolation.md) | User-facing extension guide |
-| Reduce diffusion model startup time | [Startup and Loading](../user_guide/diffusion/startup_and_loading.md) | User-facing loading guide |
-| Apply diffusion adapters | [LoRA](../user_guide/diffusion/lora.md) | User-facing extension guide |
-
-The [Pipeline Parallelism guide](../user_guide/diffusion/parallelism/pipeline_parallel.md)
-remains available by direct link, but it is not promoted in the primary User
-Guide navigation until its user-facing support and placement are settled.
-
-## Experimental
-
-[Session State Manager](session_state_manager.md) is opt-in and experimental.
-Its APIs and compatibility may change without notice.

docs/models/supported_models.md

@@ -80,7 +80,6 @@ th {
 | `HiggsAudioV2ForConditionalGeneration` | Higgs-Audio v2 | `bosonai/higgs-audio-v2-generation-3B-base` | ✅︎ | | | | — |
 | `HiggsMultimodalQwen3ForConditionalGeneration` | Higgs-Audio v3 (TTS) | `bosonai/higgs-audio-v3-tts-4b` | ✅︎ | | | | [Published](https://recipes.vllm.ai/bosonai/higgs-audio-v3-tts-4b) |
 | `IndexTTS2TalkerForConditionalGeneration` | IndexTTS-2 | `IndexTeam/IndexTTS-2` | ✅︎ | | | | — |
-| `IndexTTS25TalkerForConditionalGeneration` | IndexTTS-2.5 | native `checkpoints/` bundle | ✅︎ | | | | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/IndexTeam/IndexTTS-2_5.md) |
 | `NextStep11Pipeline` | NextStep-1.1 | `stepfun-ai/NextStep-1.1` | ✅︎ | ✅︎ | | ✅︎ | — |
 | `MiMoAudioModel` | MiMo-Audio-7B-Instruct | `XiaomiMiMo/MiMo-Audio-7B-Instruct` | ✅︎ | ✅︎ | | | — |
 | `MiMoV2ASRForCausalLM` | MiMo-V2.5-ASR | `XiaomiMiMo/MiMo-V2.5-ASR` | ✅︎ | ✅︎ | | | — |

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/diffusion/lora.md

@@ -1,18 +1,14 @@
 # LoRA (Low-Rank Adaptation) Guide
 
-LoRA (Low-Rank Adaptation) enables fine-tuning diffusion models by adding trainable low-rank matrices to existing model weights. vLLM-Omni supports two LoRA backends: **PEFT** for PEFT-style adapters, and **Distill** usually for few-steps inference. **PEFT** backend allows you to customize model behavior without modifying the base model weights. **Distill** backend fuses LoRA weights into the base model at initialization.
+LoRA (Low-Rank Adaptation) enables fine-tuning diffusion models by adding trainable low-rank matrices to existing model weights. vLLM-Omni currently supports PEFT-style LoRA adapters, allowing you to customize model behavior without modifying the base model weights.
 
 ## Overview
 
-LoRA adapters are lightweight, model-specific fine-tuning weights that can be applied to diffusion models in two ways:
-
-- **PEFT backend** (`--lora-backend peft`, default): Loads a PEFT-format adapter folder via `DiffusionLoRAManager`. Adapters are cached (LRU) and activated per request via `LoRARequest`. It uses a unified LoRA handling mechanisms similar to vLLM with LRU cache management.
-- **Distill backend** (`--lora-backend distill`): Calls `pipeline.load_lora_weights` once at initialization to fuse one or more concrete checkpoint files directly into the base weights. Typically used for distilled few-step LoRAs (e.g. Lightning, LightX2V).
+LoRA adapters are lightweight, model-specific fine-tuning weights that can be dynamically loaded and applied to diffusion models. vLLM-Omni uses a unified LoRA handling mechanism similar to vLLM with LRU cache management.
 
 ## LoRA Adapter Format
 
-### PEFT (Parameter-Efficient Fine-Tuning) format (default)
-A typical PEFT-format LoRA adapter directory structure:
+LoRA adapters must be in **PEFT (Parameter-Efficient Fine-Tuning)** format. A typical LoRA adapter directory structure:
 
 ```
 lora_adapter/
@@ -29,20 +25,19 @@ The `adapter_config.json` file contains metadata about the LoRA adapter, includi
 
 ### Offline Inference
 
-#### PEFT backend: pre-loaded LoRA
+#### Pre-loaded LoRA
 
-Load a PEFT-format LoRA adapter at initialization. The adapter is pre-loaded into the cache and can be activated per request:
+Load a LoRA adapter at initialization. This adapter is pre-loaded into the cache and can be activated by requests:
 
 ```python
 from vllm_omni import Omni
 from vllm_omni.lora.request import LoRARequest
 
-lora_path = "/path/to/lora_adapter"
+lora_path="/path/to/lora_adapter"
 
 omni = Omni(
     model="stabilityai/stable-diffusion-3.5-medium",
-    lora_path=lora_path,
-    lora_backend="peft",  # default, can be omitted
+    lora_path=lora_path
 )
 
 lora_request = LoRARequest(
@@ -58,98 +53,6 @@ outputs = omni.generate(
 )
 ```
 
-#### Distill backend: fuse distilled LoRA at init
-
-For distilled few-step LoRAs, pass `lora_backend="distill"` together with one or more concrete `.safetensors` files. The weights are fused into the base model once at init; subsequent `generate()` calls do not need a `LoRARequest`.
-
-##### Supported pipelines
-
-For Qwen-Image and Wan pipelines, the distill backend calls `pipeline.load_lora_weights(...)` during worker initialization.
-
-| Pipeline | Supported distilled LoRA repo | Notes |
-|----------|--------------------------------|-------|
-| `QwenImagePipeline` | `lightx2v/Qwen-Image-2512-Lightning` | Used with Qwen-Image-2512 Lightning-style few-step inference. |
-| `Wan22Pipeline` | `lightx2v/Wan2.1-Distill-Loras`, `lightx2v/Wan2.2-Distill-Loras` | Wan2.1 uses one LoRA file. For dual-transformer Wan2.2 MoE, pass high-noise then low-noise LoRA files. |
-| `Wan22I2VPipeline` | `lightx2v/Wan2.2-Distill-Loras` | For dual-transformer Wan2.2 MoE, pass high-noise then low-noise LoRA files. |
-
-Other diffusion pipelines are not currently listed as supporting distilled LoRA. Use the PEFT backend for request-time adapters, or bake converted weights into a local Diffusers directory before serving.
-
-Single-file example (Qwen-Image-Lightning):
-
-```python
-from vllm_omni import Omni
-
-omni = Omni(
-    model="Qwen/Qwen-Image-2512",
-    lora_path="/path/to/Qwen-Image-2512-Lightning.safetensors",
-    lora_backend="distill",
-)
-
-outputs = omni.generate(prompt="A piece of cheesecake")
-```
-
-Multi-file example (Wan2.2 MoE, high + low noise):
-
-```python
-from vllm_omni import Omni
-
-omni = Omni(
-    model="Wan-AI/Wan2.2-T2V-A14B-Diffusers",
-    lora_path=[
-        "/path/to/wan2.2_high_noise_lora.safetensors",   # -> transformer
-        "/path/to/wan2.2_low_noise_lora.safetensors",    # -> transformer_2
-    ],
-    lora_backend="distill",
-)
-```
-
-The CLI examples under `examples/offline_inference/` accept the same flags, e.g.:
-
-```bash
-python examples/offline_inference/text_to_video/text_to_video.py \
-  --model Wan-AI/Wan2.2-T2V-A14B-Diffusers \
-  --lora-backend distill \
-  --lora-path /path/to/high.safetensors /path/to/low.safetensors \
-  --prompt "A cat playing with yarn"
-```
-
-##### Online serving
-
-Distilled LoRAs can also be fused when an online diffusion server starts. The
-adapter remains active for the lifetime of that server, so requests do not pass
-a per-request `lora` field.
-
-```bash
-vllm serve Qwen/Qwen-Image-2512 \
-  --omni \
-  --port 8091 \
-  --lora-backend distill \
-  --lora-path /path/to/Qwen-Image-2512-Lightning-4steps.safetensors
-```
-
-Then send a normal generation request with the sampling settings expected by
-the distilled checkpoint:
-
-```bash
-curl -s http://localhost:8091/v1/chat/completions \
-  -H "Content-Type: application/json" \
-  -d '{
-    "messages": [
-      {"role": "user", "content": "A Chinese female college student, around 20 years old, with a very short haircut that conveys a gentle, artistic vibe. Her hair naturally falls to partially cover her cheeks, projecting a tomboyish yet charming demeanor. She has cool-toned fair skin and delicate features, with a slightly shy yet subtly confident expression—her mouth crooked in a playful, youthful smirk. She wears an off-shoulder top, revealing one shoulder, with a well-proportioned figure. The image is framed as a close-up selfie: she dominates the foreground, while the background clearly shows her dormitory—a neatly made bed with white linens on the top bunk, a tidy study desk with organized stationery, and wooden cabinets and drawers. The photo is captured on a smartphone under soft, even ambient lighting, with natural tones, high clarity, and a bright, lively atmosphere full of youthful, everyday energy."}
-    ],
-    "extra_body": {
-      "height": 1024,
-      "width": 1024,
-      "num_inference_steps": 4,
-      "true_cfg_scale": 1.0,
-      "seed": 42
-    }
-  }' | jq -r '.choices[0].message.content[0].image_url.url' | cut -d',' -f2 | base64 -d > qwen_image_distill_lora_output.png
-```
-
-For Wan2.2 MoE serving, pass the high-noise checkpoint first and the low-noise
-checkpoint second to `--lora-path`.
-
 !!! note "Server-side Path Requirement"
     The LoRA adapter path (`local_path`) must be readable on the **server** machine. If your client and server are on different machines, ensure the LoRA adapter is accessible via a shared mount or copied to the server.
 
@@ -238,7 +141,6 @@ Notes:
 
 - This route avoids runtime LoRA loading changes in vLLM-Omni when you choose to bake converted weights into a local Diffusers directory.
 - Output quality and speed depend on the replacement checkpoints and sampling params you choose.
-- If you only need to fuse distilled LoRAs into a Wan2.2 checkpoint at load time (without the full LightX2V convert + assemble pipeline), you can instead pass them directly via `--lora-backend distill --lora-path <high>.safetensors <low>.safetensors`. See the [Distill backend](#distill-backend-fuse-distilled-lora-at-init) section above.
 
 
 ## See Also

docs/user_guide/diffusion/startup_and_loading.md

@@ -1,60 +0,0 @@
-# Diffusion Startup and Loading
-
-Large diffusion models can take several minutes to load at startup. vLLM-Omni
-loads safetensors shards in parallel to reduce this initialization time.
-
-Multi-thread weight loading is enabled by default with four threads. No
-configuration is needed for the default behavior.
-
-## Configuration
-
-| Parameter | CLI flag | Default | Description |
-| --- | --- | --- | --- |
-| `enable_multithread_weight_load` | `--disable-multithread-weight-load` | `True` | Pass the flag to disable multi-thread loading |
-| `num_weight_load_threads` | `--num-weight-load-threads` | `4` | Number of parallel weight-loading threads |
-
-!!! tip
-
-    The default balances startup speed and disk I/O contention. Fast NVMe
-    storage may benefit from more threads, while network storage or hard disks
-    may not.
-
-## Online Serving
-
-```bash
-# Default: multi-thread loading with four threads
-vllm serve Qwen/Qwen-Image --omni --port 8091
-
-# Increase the thread count
-vllm serve Wan-AI/Wan2.2-I2V-A14B-Diffusers --omni \
-  --num-weight-load-threads 8
-
-# Disable multi-thread loading
-vllm serve Qwen/Qwen-Image --omni --disable-multithread-weight-load
-```
-
-## Offline Inference
-
-```python
-from vllm_omni import Omni
-
-# Default: multi-thread loading with four threads
-omni = Omni(model="Qwen/Qwen-Image")
-
-# Increase the thread count
-omni = Omni(
-    model="Wan-AI/Wan2.2-I2V-A14B-Diffusers",
-    num_weight_load_threads=8,
-)
-```
-
-## Reference Benchmarks
-
-The following measurements were collected on NVIDIA H800 hardware. Treat them
-as reference results rather than a guarantee for other storage or hardware
-configurations.
-
-| Model | Sequential loading | Multi-thread loading | Speedup |
-| --- | ---: | ---: | ---: |
-| **Qwen/Qwen-Image** (53.7 GiB) | 168 s | 27 s | **6.2x** |
-| **Wan-AI/Wan2.2-I2V-A14B-Diffusers** (64.5 GiB) | 283 s | 56 s | **5.1x** |

docs/user_guide/diffusion_features.md

@@ -49,7 +49,7 @@ Parallelism methods distribute computation across GPUs without quality loss (mat
 
 | Method | Description | Best For |
 |--------|-------------|----------|
-| **[Multi-Thread Weight Loading](diffusion/startup_and_loading.md)** | Loads safetensors shards in parallel using a thread pool | All diffusion models; reduces startup from minutes to seconds |
+| **[Multi-Thread Weight Loading](#multi-thread-weight-loading)** | Loads safetensors shards in parallel using a thread pool | All diffusion models; reduces startup from minutes to seconds |
 
 **Note:** Some acceleration methods can be combined together for optimized performance. See [Feature Compatibility Table](#feature-compatibility) and [Feature Compatibility Tutorial](feature_compatibility.md) for detailed configuration examples.
 
@@ -100,9 +100,7 @@ The following tables show which models support each feature:
 
 - **🔀SP (Ulysses & Ring)**: Includes both Ulysses-SP and Ring-Attention methods
 - ✅ = Fully supported
-- ✅* = Supported with the constraint listed below the table
 - ❌ = Not supported
-- ❓ = Not verified; not recommended
 
 > Notes:
 
@@ -121,7 +119,7 @@ The following tables show which models support each feature:
 | **FLUX.2-dev**           |     ✅     |     ✅      |           ✅           |       ✅        |         ✅         |          ❌          |   ✅    |             ✅             |          ✅           |       ❌        |        ❌         |
 | **GLM-Image**            |     ❌     |     ❌      |           ❌           |       ✅        |         ✅         |          ❌          |   ✅    |             ❌             |          ❌           |       ❌        |        ❌         |
 | **Hidream-I1-Full**        |     ❌     |     ❌      |           ❌           |       ❌        |         ✅         |          ❌          |   ❌    |             ❌             |          ❌           |       ❌        |        ❌         |
-| **HunyuanImage3**        |     ❌     |     ✅      |           ❌           |       ❌        |         ✅         |          ❌          |   ❌    |             ❌             |          ❌           |       ✅        |        ✅*        |
+| **HunyuanImage3**        |     ❌     |     ✅      |           ❌           |       ❌        |         ✅         |          ❌          |   ❌    |             ❌             |          ❌           |       ✅        |        ❌         |
 | **Krea 2**               |     ❌     |     ✅      |           ❌           |       ❌        |         ❌         |          ❌          |   ✅    |             ✅             |      ✅ (decode)      |       ❌        |        ❌         |
 | **LongCat-Image**        |     ✅     |     ✅      |           ✅           |       ✅        |         ✅         |          ❌          |   ❌    |             ✅             |          ❌           |       ❌        |        ❌         |
 | **LongCat-Image-Edit**   |     ✅     |     ✅      |           ✅           |       ✅        |         ✅         |          ❌          |   ❌    |             ✅             |          ❌           |       ❌        |        ❌         |
@@ -147,7 +145,6 @@ The following tables show which models support each feature:
 > 2. `Tongyi-MAI/Z-Image-Turbo` and `SII-GAIR/daVinci-MagiHuman-Base-1080p` are distilled models with minimal NFEs; CFG-Parallel is not necessary.
 > 3. Cosmos3 T2I uses `Cosmos3OmniDiffusersPipeline` with `modalities=["image"]`. Model-level CPU offload swaps the nested UND reasoner and GEN generator pathways; layerwise offload remains available for blockwise GEN/UND offload.
 > 4. Krea 2 currently supports single-GPU inference plus LoRA, Cache-DiT, HSDP, CPU/layerwise offload, and VAE-patch-parallel (decode). TP/SP/CFG-Parallel are not yet wired. The few-step distilled (Turbo) checkpoint uses `is_distilled=true` (fixed timestep shift `mu=1.15`); generate at 2048x2048 by default with `num_inference_steps≈8` and `guidance_scale=0`. The Raw checkpoint uses 1024x1024, `num_inference_steps=28`, and `guidance_scale=4.5`.
-> 5. HunyuanImage3 supports step execution. Multi-request step execution requires `TORCH_SDPA`; see [Diffusion Execution Modes](diffusion/execution_modes.md#step-execution).
 
 ### VideoGen
 
@@ -158,16 +155,13 @@ The following tables show which models support each feature:
 | **Wan2.1-VACE**              |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ✅    |             ✅             |      ✅ (decode)      |       ❌        |        ❌         |
 | **LTX-2**                    |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ✅    |             ✅             |      ✅ (decode)      |       ❌        |        ❌         |
 | **LTX-2.3**                  |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ✅    |             ✅             |      ✅ (decode)      |       ❌        |        ❌         |
-| **Helios**                   |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ✅    |             ✅             |          ❌           |       ❌        |        ✅*        |
+| **Helios**                   |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ✅    |             ✅             |          ❌           |       ❌        |        ❌         |
 | **HunyuanVideo-1.5 T2V I2V** |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ✅    |             ✅             |  ✅ (encode/decode)   |       ✅        |        ❌         |
 | **DreamID-Omni**             |     ❌     |     ❌      |           ❌           |       ✅        |         ❌         |         ❌         |   ✅    |             ✅             |          ❌           |       ❌        |        ❌         |
 | **Cosmos3**                  |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ✅    |             ✅             |  ✅ (encode/decode)   |       ✅        |        ❌         |
 | **LongCat-Video-Avatar-1.5** |     ❌     |     ❌      |           ❌           |       ❌        |         ❌         |         ❌         |   ❌    |             ❌             |          ❌           |       ❌        |        ❌         |
 | **MiniMax-H3**               | ✅ (FL2VA) |     ✅      |           ✅           |       ❌        |       ✅ (DiT/TE)  |         ❌         |   ✅    |             ✅             |       ✅ (tile)       |      ✅ (DiT)      |        ❌         |
 
-> **Step execution note:** Helios supports single-request step execution only;
-> use `max_num_seqs=1`.
-
 **Frame Interpolation Support**
 
 - **Supported**: Wan2.2 text-to-video, image-to-video, and TI2V pipelines
@@ -196,7 +190,7 @@ The following tables show which models support each feature:
 | **🔀Ring-Attn** | ✅ | ✅ | ✅ | | | | | | | | | | | |
 | **🔀CFG-Parallel** | ✅ | ✅ | ✅ | ✅ | | | | | | | | | | |
 | **🔀Tensor Parallel** | ✅ | ✅ | ✅ | ✅ | ✅ | | | | | | | | | |
-| **🔀HSDP** | ❓ | ❓ | ✅ | ❓ | ✅ | ❌ | | | | | | | | |
+| **🔀HSDP** | ❓ | ❓ | ❓ | ❓ | ❓ | ❌ | | | | | | | | |
 | **🔀Expert Parallel** | ❓ | ❓ | ❓ | ❓ | ❓ | ❓ | ❓ | | | | | | | |
 | **💾CPU Offloading (Layerwise)** | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | | | | | |
 | **💾CPU Offloading (Module-wise)** | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❓ | ❓ | ❌ | | | | | |
@@ -207,43 +201,97 @@ The following tables show which models support each feature:
 
 !!! info
 
-    1. HSDP can be combined with Ulysses-SP or CFG-Parallel. Tensor Parallel
-       and HSDP are not compatible; other HSDP combinations in the table
-       remain unverified.
+    1. Tensor Parallel and HSDP are not compatible.
     2. TeaCache and Cache-DiT are not compatible.
     3. CPU Offloading (Layerwise) and CPU Offloading (Module-wise) are not compatible.
-    4. The CPU Offloading (Layerwise) row describes local layerwise offload.
-       Multi-device Distributed Layerwise Offload has a separate topology and
-       compatibility matrix in the [CPU Offloading guide](diffusion/cpu_offload.md#distributed-layerwise-offloading).
-    5. The compatibility matrix uses FP8 as the representative quantization method.
+    4. CPU Offloading (Layerwise) supports single-card for now.
+    5. Using FP8-Quant as an example of qunatization methods.
     6. Step Execution is not compatible with any diffusion cache backend. LoRA is supported, but each scheduled batch must use a single adapter (requests with different `lora_request` or `lora_scale` are kept in separate batches).
 
 
 ## Multi-Thread Weight Loading
 
-The loading guide now lives at [Diffusion Startup and
-Loading](diffusion/startup_and_loading.md). This heading remains so existing
-links to this section continue to work.
+Large diffusion models can take several minutes to load weights at startup (e.g., ~3 min for Qwen-Image, ~5 min for Wan2.2 I2V 14B). Multi-thread weight loading speeds up this process by loading safetensors shards in parallel using a thread pool instead of sequentially.
+
+This optimization is **enabled by default** with 4 threads. No configuration is needed for the default behavior.
+
+### Configuration
+
+| Parameter | CLI Flag | Default | Description |
+|-----------|----------|---------|-------------|
+| `enable_multithread_weight_load` | `--disable-multithread-weight-load` | `True` (enabled) | Pass the flag to disable multi-thread loading |
+| `num_weight_load_threads` | `--num-weight-load-threads` | `4` | Number of threads for parallel weight loading |
+
+!!! tip
+    The default of 4 threads balances speed and disk I/O contention. On fast NVMe storage you may benefit from more threads (e.g., 8). On HDD or network storage, the default of 4 avoids saturating I/O bandwidth.
+
+### Online Serving
+
+```bash
+# Default (multi-thread enabled, 4 threads)
+vllm serve Qwen/Qwen-Image --omni --port 8091
+
+# Custom thread count
+vllm serve Wan-AI/Wan2.2-I2V-A14B-Diffusers --omni --num-weight-load-threads 8
+
+# Disable multi-thread loading
+vllm serve Qwen/Qwen-Image --omni --disable-multithread-weight-load
+```
+
+### Offline Inference
+
+```python
+from vllm_omni import Omni
+
+# Default (multi-thread enabled, 4 threads)
+omni = Omni(model="Qwen/Qwen-Image")
+
+# Custom thread count
+omni = Omni(
+    model="Wan-AI/Wan2.2-I2V-A14B-Diffusers",
+    num_weight_load_threads=8,
+)
+```
+
+### Benchmarks
+
+Measured on NVIDIA H800:
+
+| Model | Before | After | Speedup |
+|-------|--------|-------|---------|
+| **Qwen/Qwen-Image** (53.7 GiB) | 168s | 27s | **6.2x** |
+| **Wan-AI/Wan2.2-I2V-A14B-Diffusers** (64.5 GiB) | 283s | 56s | **5.1x** |
 
 ## Learn More
 
-The Diffusion Acceleration navigation groups the remaining guides as follows:
+**Cache Acceleration:**
 
-| Area | Guide |
-| --- | --- |
-| Compatibility | [Feature Compatibility](feature_compatibility.md) |
-| CPU offloading | [CPU Offloading](diffusion/cpu_offload.md) |
-| Cache acceleration | [TeaCache](diffusion/cache_acceleration/teacache.md), [Cache-DiT](diffusion/cache_acceleration/cache_dit.md) |
-| Parallelism | [Parallelism Overview](diffusion/parallelism/overview.md) |
-| Attention | [Attention Backends](diffusion/attention_backends.md) |
-| Compilation | [Regional Compilation](diffusion/regional_compilation.md) |
-| Video extension | [Frame Interpolation](diffusion/frame_interpolation.md) |
-| Startup | [Startup and Loading](diffusion/startup_and_loading.md) |
-| Adapters | [LoRA](diffusion/lora.md) |
+- **[TeaCache Configuration Guide](diffusion/cache_acceleration/teacache.md)** - Parameter tuning, performance tips, troubleshooting
+- **[Cache-DiT Advanced Guide](diffusion/cache_acceleration/cache_dit.md)** - DBCache, TaylorSeer, SCM techniques and optimization
 
-Related cross-model and runtime features are documented separately:
+**Parallelism Methods:**
 
-- [Quantization](quantization/overview.md) covers diffusion-only models,
-  multi-stage omni/TTS models, and multi-stage diffusion models.
-- [Execution Modes and Streaming](diffusion/execution_modes.md) covers the
-  diffusion runtime, including batching, step execution, and streaming output.
+- **[Parallelism Overview](diffusion/parallelism/overview.md)** - Tensor Parallelism, Sequence Parallelism, CFG Parallelism, Pipeline Parallelism, HSDP, and Expert Parallelism
+
+**Memory Optimization:**
+
+- **[CPU Offload Guide](diffusion/cpu_offload.md)** - Offload model components to CPU, reduce GPU memory usage
+- **[VAE Parallelism Guide](diffusion/parallelism/vae_parallelism.md)** - Distribute VAE decode work across GPUs for high-resolution images and videos
+- **[Quantization](quantization/overview.md)** - Quantization methods for diffusion, multi-stage omni/TTS, and multi-stage diffusion models
+
+**Extensions:**
+
+- **[LoRA Inference Guide](diffusion/lora.md)** - Low-Rank Adaptation for style customization and fine-tuning
+- **[Frame Interpolation Guide](diffusion/frame_interpolation.md)** - Worker-side post-generation video frame interpolation for smoother motion
+
+**Execution Modes:**
+
+- **[Diffusion Execution Modes](diffusion/execution_modes.md)** - Configure request batching, step execution, continuous batching, and streaming output
+
+**Startup Optimization:**
+
+- **[Multi-Thread Weight Loading](#multi-thread-weight-loading)** - Speed up model startup by loading safetensors shards in parallel
+
+**Advanced Topics:**
+
+- **[Feature Compatibility](feature_compatibility.md)** - How to combine multiple features for maximum performance

docs/user_guide/examples/offline_inference/text_to_image.md

@@ -88,9 +88,8 @@ python text_to_image.py \
 | `--vae-use-tiling` | flag | off | Enable VAE tiling for memory optimization |
 | `--cfg-parallel-size` | int | `1` | Set to `2` to enable CFG Parallel |
 | `--enable-cpu-offload` | flag | off | Enable CPU offloading for diffusion models |
-| `--lora-path` | str | — | Path to PEFT LoRA adapter folder or checkpoint file |
+| `--lora-path` | str | — | Path to PEFT LoRA adapter folder |
 | `--lora-scale` | float | `1.0` | Scale factor for LoRA weights |
-| `--lora-backend` | str |`"peft"`| LoRA backend for loading LoRA adapters. Default: peft. Available options: peft, distill |
 
 **NextStep-1.1 specific arguments:**
 

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
@@ -145,9 +125,6 @@ python text_to_video.py \
 - `--audio-sample-rate`: fallback audio sample rate when the pipeline returns audio.
 - `--quantization`: quantization method (`fp8` for FP8, `gguf` for GGUF).
 - `--flow-shift`: scheduler flow_shift parameter.
-- `--lora-path`: path to PEFT LoRA adapter folder or checkpoint file.
-- `--lora-scale`: scale factor for LoRA weights.
-- `--lora-backend`: backend for loading LoRA adapters. Default: peft. Available options: peft, distill.
 
 ### Wan2.2-specific
 

docs/user_guide/quantization/overview.md

@@ -12,20 +12,20 @@ For the internal architecture and backend extension points, see the
 
 | Mode | Guide | Description | Methods |
 |------|-------|-------------|---------|
-| Online quantization | [Online Quantization](online.md) | vLLM-Omni computes quantized weights and scales while loading the model. | FP8 W8A8, Int8 W8A8, BitsAndBytes W4, MXFP8 W8A8, MXFP4 W4A4 |
+| Online quantization | [Online Quantization](online.md) | vLLM-Omni computes quantized weights and scales while loading the model. | FP8 W8A8, Int8 W8A8, MXFP8 W8A8, MXFP4 W4A4 |
 | Runtime attention quantization | [Quantized KV Cache](quantized_kvcache.md) | vLLM-Omni dynamically quantizes eligible diffusion Flash Attention tensors during inference. | FP8 FA |
-| Pre-quantized checkpoints | Method-specific guides | The checkpoint or an offline quantizer provides quantized weights and scales before serving. | ModelOpt, AutoRound, msModelSlim, serialized Int8, offline MXFP8, offline MXFP4 DualScale |
+| Pre-quantized checkpoints | Method-specific guides | The checkpoint or an offline quantizer provides quantized weights and scales before serving. | ModelOpt, GGUF, AutoRound, msModelSlim, serialized Int8, offline MXFP8, offline MXFP4 DualScale |
 
 ## Hardware Support
 
-| Device | FP8 W8A8 | Int8 W8A8 | BitsAndBytes W4 | ModelOpt | MXFP8 W8A8 | MXFP4 W4A4 | AutoRound | msModelSlim |
-|--------|----------|-----------|------------------|----------|------------|------------|-----------|-------------|
-| NVIDIA Blackwell GPU (SM 100+) | ✅ | ✅ | ✅ | ✅ | ⭕ | ⭕ | ✅ | ❌ |
-| NVIDIA Ada/Hopper GPU (SM 89+) | ✅ | ✅ | ✅ | ✅ | ⭕ | ⭕ | ✅ | ❌ |
-| NVIDIA Ampere GPU (SM 80+) | ✅ | ✅ | ✅ | ⭕ | ⭕ | ⭕ | ✅ | ❌ |
-| AMD ROCm | ⭕ | ⭕ | ❌ | ⭕ | ⭕ | ✅ | ⭕ | ❌ |
-| Intel XPU | ⭕ | ⭕ | ❌ | ⭕ | ⭕ | ⭕ | ✅ | ❌ |
-| Ascend NPU | ❌ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ |
+| Device | FP8 W8A8 | Int8 W8A8 | ModelOpt | MXFP8 W8A8 | MXFP4 W4A4 | GGUF | AutoRound | msModelSlim |
+|--------|----------|-----------|----------|------------|------------|------|-----------|-------------|
+| NVIDIA Blackwell GPU (SM 100+) | ✅ | ✅ | ✅ | ⭕ | ⭕ | ✅ | ✅ | ❌ |
+| NVIDIA Ada/Hopper GPU (SM 89+) | ✅ | ✅ | ✅ | ⭕ | ⭕ | ✅ | ✅ | ❌ |
+| NVIDIA Ampere GPU (SM 80+) | ✅ | ✅ | ⭕ | ⭕ | ⭕ | ✅ | ✅ | ❌ |
+| AMD ROCm | ⭕ | ⭕ | ⭕ | ⭕ | ✅ | ⭕ | ⭕ | ❌ |
+| Intel XPU | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ | ✅ | ❌ |
+| Ascend NPU | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ |
 
 Legend: `✅` supported, `❌` unsupported, `⭕` not verified in this
 guide. FP8 on Ampere may use a weight-only path where available.
@@ -43,10 +43,10 @@ otherwise.
 |--------|-------|------|----------------|--------|
 | FP8 W8A8 | [FP8](fp8.md) | Online W8A8 or checkpoint FP8 | Qwen-Image; Wan2.2 is not validated | Validated for Qwen-Image family and other DiT models |
 | Int8 W8A8 | [Int8](int8.md) | Online or serialized W8A8 | Qwen-Image; Wan2.2 is not validated | Validated for Qwen-Image and Z-Image |
-| BitsAndBytes W4 | [BitsAndBytes](bitsandbytes.md) | Online W4 weight-only | Z-Image-Turbo; Qwen-Image/Wan2.2 not validated | Validated for Z-Image-Turbo |
 | ModelOpt | [ModelOpt](modelopt.md) | Pre-quantized FP8 checkpoints | Qwen-Image, Z-Image, FLUX.2, HunyuanImage-3.0 | Validated for ModelOpt FP8 diffusion checkpoints |
 | MXFP8 W8A8 | [MXFP8](mxfp8.md) | Online W8A8 or offline pre-quantized | Wan2.2-T2V-A14B, I2V-A14B, TI2V-5B | Ascend NPU only; validated for Wan2.2 |
 | MXFP4 W4A4 | [MXFP4](mxfp4.md) | `mxfp4`: online single-scale only; `mxfp4_dualscale`: online or offline dual-scale (offline recommended) | Wan2.2-T2V-A14B, I2V-A14B | Ascend NPU only; validated for Wan2.2 A14B cascade models; TI2V-5B not supported; offline `mxfp4_dualscale` uses calibrated `mul_scale` for best accuracy |
+| GGUF | [GGUF](gguf.md) | Pre-quantized transformer weights | Qwen-Image | Validated where a model-specific GGUF adapter exists |
 | AutoRound | [AutoRound](autoround.md) | Pre-quantized W4A16 checkpoints | FLUX.1-dev; Qwen-Image/Wan2.2 not validated | Checkpoint-driven |
 | msModelSlim | [msModelSlim](msmodelslim.md) | Pre-quantized Ascend checkpoints | Wan2.2 recipe; HunyuanImage-3.0 inference target | Ascend/NPU path |
 
@@ -61,9 +61,9 @@ in BF16 unless the model guide explicitly adds support.
 |--------|-------|-------|----------------|--------|
 | ModelOpt | [ModelOpt](modelopt.md) | Thinker or language-model checkpoint config | Qwen3-Omni thinker | ModelOpt checkpoint path |
 | Int8 | [Int8](int8.md) | Not currently validated for omni/TTS stages | Qwen3-Omni, Qwen3-TTS | Not validated |
-| BitsAndBytes | [BitsAndBytes](bitsandbytes.md) | Not currently validated for omni/TTS stages | Qwen3-Omni, Qwen3-TTS | Not validated |
 | MXFP8 | [MXFP8](mxfp8.md) | Not currently validated for omni/TTS stages | Qwen3-Omni, Qwen3-TTS | Not validated |
 | MXFP4 | [MXFP4](mxfp4.md) | Not currently validated for omni/TTS stages | Qwen3-Omni, Qwen3-TTS | Not validated |
+| GGUF | [GGUF](gguf.md) | Not currently validated for omni/TTS stages | Qwen3-Omni, Qwen3-TTS | Not validated |
 | AutoRound | [AutoRound](autoround.md) | Thinker or language-model checkpoint config | Qwen2.5-Omni, Qwen3-Omni | Supported through AutoRound checkpoints |
 | msModelSlim | [msModelSlim](msmodelslim.md) | Not currently validated for omni/TTS stages | Qwen3-Omni, Qwen3-TTS | Not validated |
 
@@ -76,10 +76,10 @@ attached to the intended stage rather than applied globally.
 |--------|-------|-------|----------------|--------|
 | FP8 | [FP8](fp8.md) | Stage-specific DiT or transformer module | BAGEL, GLM-Image | Requires model-specific validation |
 | Int8 | [Int8](int8.md) | Stage-specific DiT or transformer module | BAGEL, GLM-Image | Requires model-specific validation |
-| BitsAndBytes | [BitsAndBytes](bitsandbytes.md) | Stage-specific transformer or DiT module | BAGEL, GLM-Image | Not validated |
 | ModelOpt | [ModelOpt](modelopt.md) | Checkpoint-defined diffusion stage | BAGEL, GLM-Image | Requires model-specific validation |
 | MXFP8 | [MXFP8](mxfp8.md) | Stage-specific DiT or transformer module | BAGEL, GLM-Image | Not validated |
 | MXFP4 | [MXFP4](mxfp4.md) | Stage-specific DiT or transformer module | BAGEL, GLM-Image | Not validated |
+| GGUF | [GGUF](gguf.md) | Stage-specific transformer weights | BAGEL, GLM-Image | No validated adapter listed |
 | AutoRound | [AutoRound](autoround.md) | Checkpoint-defined stage | BAGEL, GLM-Image | No validated checkpoint listed |
 | msModelSlim | [msModelSlim](msmodelslim.md) | Ascend-generated stage weights | GLM-Image | Requires model-specific adaptation |
 
@@ -106,7 +106,7 @@ config = build_quant_config({
 
 | Component | Default quantized? | Notes |
 |-----------|--------------------|-------|
-| Diffusion transformer | Yes | Primary target for FP8, Int8, BitsAndBytes, ModelOpt, MXFP8, MXFP4, AutoRound, and msModelSlim |
+| Diffusion transformer | Yes | Primary target for FP8, Int8, ModelOpt, MXFP8, MXFP4, GGUF, AutoRound, and msModelSlim |
 | Text encoder | No | Keep BF16 unless a method-specific guide documents support |
 | VAE | No | Keep BF16; storage-only paths are method-specific |
 | Scheduler/tokenizer | No | Loaded from the base model repository |
@@ -139,8 +139,8 @@ from vllm_omni.quantization import build_quant_config
 
 build_quant_config("fp8")
 build_quant_config({"method": "fp8", "activation_scheme": "static"})
-build_quant_config("bitsandbytes")
 build_quant_config("auto-round", bits=4, group_size=128)
+build_quant_config({"method": "gguf", "gguf_model": "/path/to/model.gguf"})
 build_quant_config({"transformer": {"method": "fp8"}, "vae": None})
 build_quant_config(None)
 ```

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

@@ -60,12 +60,8 @@ from vllm_omni.diffusion.data import DiffusionParallelConfig
 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.lora.request import LoRARequest
-from vllm_omni.lora.utils import stable_lora_int_id
-from vllm_omni.model_extras import (
-    build_image_to_video_prompt as build_model_image_to_video_prompt,
-)
 from vllm_omni.model_extras import (
+    build_image_to_video_prompt,
     get_extra_body_params,
     get_model_class_name,
 )
@@ -87,22 +83,6 @@ def parse_json_object(value: str, flag_name: str = "argument") -> dict[str, Any]
 parse_profiler_config = functools.partial(parse_json_object, flag_name="--profiler-config")
 
 
-def build_image_to_video_prompt(
-    prompt: str,
-    negative_prompt: str | None,
-    media_inputs: dict[str, Any],
-) -> dict[str, Any]:
-    """Build the canonical request envelope for the shared I2V example."""
-    result: dict[str, Any] = {
-        "prompt": prompt,
-        "modalities": ["video"],
-        "multi_modal_data": media_inputs,
-    }
-    if negative_prompt is not None:
-        result["negative_prompt"] = negative_prompt
-    return result
-
-
 def parse_args() -> argparse.Namespace:
     parser = argparse.ArgumentParser(
         description=(
@@ -314,30 +294,6 @@ def parse_args() -> argparse.Namespace:
         default=None,
         help='JSON profiler config for torch/cuda profiling, e.g. \'{"profiler":"torch","torch_profiler_dir":"./perf"}\'.',
     )
-    parser.add_argument(
-        "--lora-path",
-        type=str,
-        nargs="+",
-        default=None,
-        help="Path to a LoRA adapter folder (PEFT format) or concrete LoRA checkpoint files. "
-        "For Wan2.2 MoE models, pass the high-noise checkpoint first and the low-noise checkpoint second.",
-    )
-    parser.add_argument(
-        "--lora-scale",
-        type=float,
-        default=1.0,
-        help="Scale factor for PEFT LoRA weights (default: 1.0).",
-    )
-    parser.add_argument(
-        "--lora-backend",
-        type=str,
-        default="peft",
-        choices=["peft", "distill"],
-        help=(
-            "LoRA backend. 'peft' loads a PEFT-format adapter for request-time activation; "
-            "'distill' fuses one or more concrete LoRA checkpoint files at initialization."
-        ),
-    )
     parser.add_argument(
         "--extra-body",
         type=functools.partial(parse_json_object, flag_name="--extra-body"),
@@ -531,12 +487,6 @@ def main():
         omni_kwargs["flow_shift"] = flow_shift
     if args.quantization is not None:
         omni_kwargs["quantization"] = args.quantization
-    if args.lora_path is not None:
-        lora_path = args.lora_path
-        if len(lora_path) == 1:
-            lora_path = lora_path[0]
-        omni_kwargs["lora_path"] = lora_path
-        omni_kwargs["lora_backend"] = args.lora_backend
     # Cosmos3 loads its (gated) guardrail models at build time, so the guardrails
     # gate is an engine-level config (offline analog of the server's --no-guardrails).
     if args.extra_body and "guardrails" in args.extra_body:
@@ -567,39 +517,19 @@ def main():
     print(f"  Video size: {width}x{height}")
     print(f"{'=' * 60}\n")
 
-    lora_request = None
-    if args.lora_path and args.lora_backend == "peft":
-        if len(args.lora_path) != 1:
-            raise ValueError("Only one LoRA path is expected for PEFT backend.")
-
-        lora_path = args.lora_path[0]
-        lora_request = LoRARequest(
-            lora_name=Path(lora_path).stem,
-            lora_int_id=stable_lora_int_id(lora_path),
-            lora_path=lora_path,
-        )
-
     negative_prompt = args.negative_prompt
     if negative_prompt is None and not is_ltx2:
         # Preserve the historical empty-prompt behavior for non-LTX examples.
         negative_prompt = ""
     prompt_dict = build_image_to_video_prompt(
+        model_class_name=model_class_name,
         prompt=args.prompt,
         negative_prompt=negative_prompt,
         media_inputs=media_inputs,
-    )
-    prompt_dict = build_model_image_to_video_prompt(
-        model_class_name=model_class_name,
-        prompt=prompt_dict,
         height=height,
         width=width,
         num_frames=num_frames,
     )
-    sampling_extra_args = {"sample_solver": args.sample_solver}
-    if lora_request is not None:
-        sampling_extra_args["lora_request"] = lora_request
-        sampling_extra_args["lora_scale"] = args.lora_scale
-
     sampling_params = OmniDiffusionSamplingParams(
         height=height,
         width=width,
@@ -610,7 +540,7 @@ def main():
         num_inference_steps=num_inference_steps,
         num_frames=num_frames,
         frame_rate=frame_rate,
-        extra_args=sampling_extra_args,
+        extra_args={"sample_solver": args.sample_solver},
     )
     if flow_shift is not None:
         sampling_params.extra_args["flow_shift"] = flow_shift
@@ -646,8 +576,8 @@ 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:
-            inner_output = frames
+        if frames.is_pipeline_output and frames.request_output is not None:
+            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.
-    output = result[0] if isinstance(result, list) else result
+    # Extract output from 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/README.md

@@ -57,9 +57,8 @@ python examples/offline_inference/text_to_image/text_to_image.py \
   --output lingbot_t2i.png
 ```
 
-The shared T2I entry point selects the `image` output modality. The LingBot
-pipeline validates that T2I requests produce one frame and do not include an
-input image.
+The LingBot prompt builder selects the `image` output modality and requests one
+frame. Passing an input image is not supported in LingBot T2I mode.
 
 ## Quick Start
 
@@ -110,9 +109,8 @@ python text_to_image.py \
 | `--ring-degree` | int | `1` | Ring sequence parallel degree for hybrid Ulysses + Ring inference |
 | `--ulysses-mode` | str | `"strict"` | Ulysses SP mode: `"strict"` or `"advanced_uaa"` |
 | `--enable-cpu-offload` | flag | off | Enable CPU offloading for diffusion models |
-| `--lora-path` | str | — | Path to PEFT LoRA adapter folder or checkpoint file |
+| `--lora-path` | str | — | Path to PEFT LoRA adapter folder |
 | `--lora-scale` | float | `1.0` | Scale factor for LoRA weights |
-| `--lora-backend` | str |`"peft"`| LoRA backend for loading LoRA adapters. Default: peft. Available options: peft, distill |
 | `--use-system-prompt` | str | `None` | System prompt preset: `en_unified`, `en_vanilla`, `en_recaption`, `en_think_recaption`, `dynamic`, `None`, or custom text. Recommended: `en_unified`. Only for HunyuanImage-3.0.|
 | `--system-prompt` | str | `None` | Custom system prompt text. Only used when `--use-system-prompt` is set to `custom`. Only for HunyuanImage-3.0.|
 | `--auxiliary-text-encoder` | str | `None` | Supplementary auxiliary text encoder parameters model name or path (especially for Hidream-l1-full). |

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

@@ -21,9 +21,7 @@ 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 (
-    build_text_to_image_prompt as build_model_text_to_image_prompt,
-)
-from vllm_omni.model_extras import (
+    build_text_to_image_prompt,
     get_extra_body_params,
     get_model_class_name,
     should_init_extra_args_for_non_diffusion_stages,
@@ -58,17 +56,6 @@ def parse_json_object(value: str, flag_name: str = "argument") -> dict[str, Any]
 parse_profiler_config = functools.partial(parse_json_object, flag_name="--profiler-config")
 
 
-def build_text_to_image_prompt(prompt: str, negative_prompt: str | None) -> dict[str, Any]:
-    """Build the canonical request envelope for the shared T2I example."""
-    result: dict[str, Any] = {
-        "prompt": prompt,
-        "modalities": ["image"],
-    }
-    if negative_prompt is not None:
-        result["negative_prompt"] = negative_prompt
-    return result
-
-
 def _normalize_images_for_save(images: list[Any]) -> list[Any]:
     """Convert NumPy diffusion outputs to PIL images before saving."""
     normalized = []
@@ -273,7 +260,6 @@ def parse_args() -> argparse.Namespace:
     parser.add_argument(
         "--lora-path",
         type=str,
-        nargs="+",
         default=None,
         help="Path to LoRA adapter folder (PEFT format). Loaded at initialization and used for generation.",
     )
@@ -283,15 +269,6 @@ def parse_args() -> argparse.Namespace:
         default=1.0,
         help="Scale factor for LoRA weights (default: 1.0).",
     )
-    parser.add_argument(
-        "--lora-backend",
-        type=str,
-        default="peft",
-        choices=["peft", "distill"],
-        help="LoRA backend for loading LoRA adapters. Default: peft"
-        "'peft' loads a PEFT-format adapter folder, used e.g. for RL"
-        "'distill' fuses one or more concrete LoRA checkpoint files, used e.g. for distilled few-step LoRAs",
-    )
     parser.add_argument(
         "--vae-patch-parallel-size",
         type=int,
@@ -427,12 +404,8 @@ def main():
     # Prepare LoRA kwargs for Omni initialization
     lora_args: dict[str, Any] = {}
     if args.lora_path:
-        lora_path = args.lora_path
-        if len(lora_path) == 1:
-            lora_path = lora_path[0]
-        lora_args["lora_path"] = lora_path
-        lora_args["lora_backend"] = args.lora_backend
-        print(f"Using LoRA from: {lora_path} with backend: {args.lora_backend}")
+        lora_args["lora_path"] = args.lora_path
+        print(f"Using LoRA from: {args.lora_path}")
 
     # Build quantization kwargs: use quantization_config dict when
     # ignored_layers is specified so the list flows through OmniDiffusionConfig
@@ -521,27 +494,20 @@ def main():
 
     # Build LoRA request when --lora-path is set
     lora_request = None
-    if args.lora_path and args.lora_backend == "peft":
-        if len(args.lora_path) != 1:
-            raise ValueError("Only one LoRA path is expected for PEFT backend.")
-
-        lora_path = args.lora_path[0]
-        lora_request_id = stable_lora_int_id(lora_path)
+    if args.lora_path:
+        lora_request_id = stable_lora_int_id(args.lora_path)
         lora_request = LoRARequest(
-            lora_name=Path(lora_path).stem,
+            lora_name=Path(args.lora_path).stem,
             lora_int_id=lora_request_id,
-            lora_path=lora_path,
+            lora_path=args.lora_path,
         )
 
     generation_start = time.perf_counter()
 
     prompt_dict = build_text_to_image_prompt(
+        model_class_name=model_class_name,
         prompt=args.prompt,
         negative_prompt=args.negative_prompt,
-    )
-    prompt_dict = build_model_text_to_image_prompt(
-        model_class_name=model_class_name,
-        prompt=prompt_dict,
         height=args.height,
         width=args.width,
     )
@@ -656,7 +622,7 @@ 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

@@ -24,7 +24,6 @@ list of supported architectures across all modalities, see
 | Qwen3-TTS | `Qwen/Qwen3-TTS-12Hz-1.7B-{CustomVoice,VoiceDesign,Base}` | 2 (talker + code2wav) | ✓ (Base) | ✓ | 3 task variants | 24 kHz |
 | VoxCPM2 | `openbmb/VoxCPM2` | single (native AR) | ✓ | ✓ (online) | continuation | 48 kHz |
 | IndexTTS-2 | `IndexTeam/IndexTTS-2` | 2 (AR talker + S2Mel DiT + BigVGAN) | ✓ (required) | — | emotion control (`--emo-audio`, `--emo-text`, `--emo-vector`) | 22.05 kHz |
-| IndexTTS-2.5 | native `checkpoints/` bundle | 2 (AR talker + EnhancedCodec + S2Mel DiT + BigVGAN) | ✓ (required) | — | multilingual (`--lang`) + emotion control | 22.05 kHz |
 | Voxtral TTS | `mistralai/Voxtral-4B-TTS-2603` | varies | ✓ | ✓ | voice presets | 24 kHz |
 
 ## Common Quick Start
@@ -372,10 +371,18 @@ 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
-`vllm-omni serve` with `--forced-aligner` and request `word_timestamps`
-(see `examples/online_serving/text_to_speech/README.md`). An offline
-example is not provided in this release.
+Generate a WAV offline and a JSON sidecar with word-level timestamps from
+`Qwen/Qwen3-ForcedAligner-0.6B`:
+```bash
+python examples/offline_inference/text_to_speech/qwen3_tts/word_timestamps.py \
+    --model Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice \
+    --forced-aligner Qwen/Qwen3-ForcedAligner-0.6B \
+    --text "Hello world." \
+    --output-dir /tmp/qwen3_tts_timestamps
+```
+The script writes `qwen3_tts_word_timestamps.wav` and
+`qwen3_tts_word_timestamps.json`. On machines without a local CUDA toolkit,
+set `VLLM_USE_FLASHINFER_SAMPLER=0` to avoid FlashInfer sampler JIT.
 
 ### 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):
@@ -430,14 +437,11 @@ Streaming is exposed through the online OpenAI Speech API (`stream=true`). See [
 
 ---
 
-## IndexTTS-2 and IndexTTS-2.5
+## IndexTTS-2
 
-Both versions use a 2-stage TTS pipeline and produce 22.05 kHz mono audio. Stage 0 is a GPT AR talker. IndexTTS-2 Stage 1 uses RepCodec semantic embeddings, GPT latent, S2Mel CFM/DiT, and BigVGAN. IndexTTS-2.5 replaces RepCodec with EnhancedCodec and uses the official code-only path (`use_gpt_latent=false`). Every request requires reference audio for zero-shot voice cloning.
+2-stage TTS pipeline (GPT AR talker + S2Mel CFM DiT + BigVGAN vocoder) at 22.05 kHz. Every request requires reference audio for zero-shot voice cloning. Supports emotion conditioning via audio, text, or 8-dim vector.
 
 ### Quick start
-
-IndexTTS-2:
-
 ```bash
 python examples/offline_inference/text_to_speech/indextts2/end2end.py \
     --model IndexTeam/IndexTTS-2 \
@@ -445,24 +449,6 @@ python examples/offline_inference/text_to_speech/indextts2/end2end.py \
     --ref-audio /path/to/reference.wav
 ```
 
-IndexTTS-2 uses its existing frontend and does not require the IndexTTS-2.5 text dependencies. Before running IndexTTS-2.5, install its optional frontend dependencies:
-
-```bash
-pip install 'vllm-omni[indextts2]'
-```
-
-IndexTTS-2.5 with a native repository/lab bundle whose model root contains `checkpoints/`:
-
-```bash
-python examples/offline_inference/text_to_speech/indextts2/end2end.py \
-    --model /path/to/indextts-2.5 \
-    --model-version 2.5 \
-    --lang ja \
-    --text "こんにちは、音声合成のテストです。" \
-    --ref-audio /path/to/reference.wav
-
-```
-
 ### Emotion control
 ```bash
 # Emotion from reference audio
@@ -488,13 +474,10 @@ python examples/offline_inference/text_to_speech/indextts2/end2end.py \
 ```
 
 ### Notes
-- `--ref-audio` is **required** — neither version provides text-only synthesis.
-- IndexTTS-2.5 Stage 0 (AR Talker) generates mel codes from text + reference audio using plain vLLM sampling. It does not reproduce the official default `num_beams=3` beam search. For parity comparisons, run upstream with `num_beams=1`; output quality can differ from the official beam-search result.
-- IndexTTS-2.5 accepts language codes such as `zh`, `en`, `zhen` (mixed Chinese/English), `ja`, and `yue`. `Mandarin` is a vLLM-Omni convenience alias for `zh`. Japanese (`ja`) uses `fugashi` tokenization and produces audio, but it does not automatically expand numbers, dates, or percentages; write those inputs as readable Japanese text before calling the model. Use `--no-text-normalization` only when the input is already normalized.
-- A seed controls Stage 0 AR sampling and per-request CFM noise. Different concurrent batch compositions do not guarantee a bit-identical waveform.
-- Stage 1 (semantic codec + S2Mel + BigVGAN): CFM/DiT converts the complete semantic-code sequence to waveform.
-- Deploy configs: `vllm_omni/deploy/indextts2.yaml` and `indextts2_5.yaml`.
-- `async_chunk=false` is intentional for IndexTTS-2.5 correctness: S2Mel consumes the completed semantic sequence.
+- `--ref-audio` is **required** — IndexTTS-2 does not support text-only synthesis.
+- Stage 0 (AR Talker): GPT-2 generates mel codes from text + reference audio.
+- Stage 1 (S2Mel + BigVGAN): CFM DiT converts mel codes to waveform at 22.05 kHz.
+- Deploy config: `vllm_omni/deploy/indextts2.yaml`. Stage 1 runs with `enforce_eager: true` (DiT has dynamic shapes).
 
 ---
 

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/indextts2/end2end.py

@@ -1,16 +1,13 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Offline inference example for IndexTTS 2.0 and 2.5 via vLLM-Omni.
+"""Offline inference example for IndexTTS2 via vLLM-Omni.
 
-Two-stage pipeline: GPT AR (Stage 0) → semantic codec + S2Mel + BigVGAN
-(Stage 1).
+Two-stage pipeline: GPT AR (Stage 0) → S2Mel + BigVGAN (Stage 1).
 Output is 22050 Hz mono WAV.
 
 Usage:
   python end2end.py \
-    --model /path/to/indextts-2.5 \
-    --model-version 2.5 \
-    --lang zh \
+    --model /path/to/IndexTeam/IndexTTS-2 \
     --text "你好，这是一个语音合成测试。" \
     --ref-audio /path/to/ref.wav
 
@@ -50,10 +47,6 @@ def build_request(
     emo_alpha: float | None = None,
     use_emo_text: bool = False,
     use_random: bool = False,
-    model_type: str = "indextts2",
-    lang: str = "zh",
-    text_normalization: bool = True,
-    tokenizer_file: str | None = None,
 ) -> dict:
     additional: dict = {"text": [text]}
     if ref_audio_path:
@@ -70,44 +63,12 @@ def build_request(
         additional["use_emo_text"] = [True]
     if use_random:
         additional["use_random"] = [True]
-    if model_type == "indextts2_5":
-        additional["lang"] = [lang]
-        additional["text_normalization"] = [text_normalization]
-    prompt_kwargs = {
-        "model_type": model_type,
-        "lang": lang,
-        "text_normalization": text_normalization,
-    }
-    if tokenizer_file is not None:
-        prompt_kwargs["tokenizer_file"] = tokenizer_file
     return {
-        "prompt_token_ids": build_indextts2_prefill_prompt_ids(
-            model,
-            text,
-            **prompt_kwargs,
-        ),
+        "prompt_token_ids": build_indextts2_prefill_prompt_ids(model, text),
         "additional_information": additional,
     }
 
 
-def _get_stage0_tokenizer_file(omni: Omni) -> str | None:
-    """Read the effective tokenizer override from the resolved Stage 0 config."""
-    stage_configs = omni.stage_configs
-    if not stage_configs:
-        return None
-    engine_args = getattr(stage_configs[0], "engine_args", None)
-    if engine_args is None:
-        return None
-    hf_overrides = getattr(engine_args, "hf_overrides", None)
-    if hf_overrides is None and isinstance(engine_args, dict):
-        hf_overrides = engine_args.get("hf_overrides")
-    if hf_overrides is None:
-        return None
-    if hasattr(hf_overrides, "get"):
-        return hf_overrides.get("tokenizer_file")
-    return getattr(hf_overrides, "tokenizer_file", None)
-
-
 def save_audio(waveform: torch.Tensor, path: str, sample_rate: int = 22050) -> None:
     audio_np = waveform.float().numpy()
     sf.write(path, audio_np, sample_rate)
@@ -134,14 +95,9 @@ def extract_audio(mm: dict) -> tuple[torch.Tensor | None, int]:
 
 
 def main(args) -> None:
-    model_type = "indextts2_5" if args.model_version == "2.5" else "indextts2"
-    deploy_config = args.deploy_config
-    if deploy_config is None and args.model_version == "2.5":
-        repo_root = Path(__file__).resolve().parents[4]
-        deploy_config = str(repo_root / "vllm_omni" / "deploy" / "indextts2_5.yaml")
     omni = Omni(
         model=args.model,
-        deploy_config=deploy_config,
+        deploy_config=args.deploy_config,
         stage_init_timeout=args.stage_init_timeout,
     )
 
@@ -170,7 +126,7 @@ def main(args) -> None:
     print(f"Synthesizing: {args.text!r}")
     if args.ref_audio:
         print(f"  ref_audio: {args.ref_audio}")
-    request_kwargs = dict(
+    inputs = build_request(
         model=args.model,
         text=args.text,
         ref_audio_path=args.ref_audio,
@@ -180,17 +136,7 @@ def main(args) -> None:
         emo_alpha=args.emo_alpha,
         use_emo_text=args.use_emo_text,
         use_random=args.use_random,
-        model_type=model_type,
-        lang=args.lang,
-        text_normalization=args.text_normalization,
     )
-    if model_type == "indextts2_5":
-        tokenizer_file = _get_stage0_tokenizer_file(omni)
-        if tokenizer_file is not None:
-            request_kwargs["tokenizer_file"] = tokenizer_file
-    inputs = build_request(**request_kwargs)
-    if model_type == "indextts2_5":
-        inputs["additional_information"]["duration_factor"] = [1.0 / getattr(args, "speed", 1.0)]
 
     for i, omni_out in enumerate(omni.generate(inputs, sampling_params_list=sampling_params)):
         mm = omni_out.multimodal_output
@@ -208,37 +154,8 @@ def main(args) -> None:
 
 
 def parse_args():
-    parser = FlexibleArgumentParser(description="IndexTTS 2.0/2.5 offline inference")
-    parser.add_argument(
-        "--model",
-        required=True,
-        help="HF model path or native bundle containing checkpoints/.",
-    )
-    parser.add_argument(
-        "--model-version",
-        choices=("2.0", "2.5"),
-        default="2.0",
-    )
-    parser.add_argument(
-        "--lang",
-        default="zh",
-        help=(
-            "IndexTTS 2.5 language code, for example zh/en/zhen/ja/yue; "
-            "zhen is mixed Chinese/English and Mandarin is a vLLM-Omni alias for zh."
-        ),
-    )
-    parser.add_argument(
-        "--no-text-normalization",
-        action="store_false",
-        dest="text_normalization",
-        help="Disable IndexTTS 2.5 text normalization.",
-    )
-    parser.add_argument(
-        "--speed",
-        type=float,
-        default=1.0,
-        help="IndexTTS 2.5 native synthesis speed in [0.5, 2.0]; 2.0 is faster.",
-    )
+    parser = FlexibleArgumentParser(description="IndexTTS2 offline inference")
+    parser.add_argument("--model", required=True, help="HF model path for IndexTTS2.")
     parser.add_argument("--text", default="你好，这是IndexTTS2语音合成测试。")
     parser.add_argument("--ref-audio", required=True, help="Reference audio for voice cloning.")
     parser.add_argument("--emo-audio", default=None, help="Emotion reference audio.")
@@ -253,15 +170,7 @@ def parse_args():
     parser.add_argument("--emo-alpha", type=float, default=None, help="Emotion weight in [0, 1].")
     parser.add_argument("--use-emo-text", action="store_true", help="Infer emotion vector from emo-text or text.")
     parser.add_argument("--use-random", action="store_true", help="Use random emotion prototypes.")
-    parser.add_argument(
-        "--seed",
-        type=int,
-        default=None,
-        help=(
-            "Seed Stage 0 AR sampling and per-request CFM noise; changing the "
-            "concurrent batch composition may still change the final waveform."
-        ),
-    )
+    parser.add_argument("--seed", type=int, default=None)
     parser.add_argument(
         "--output-dir",
         default=os.path.join(
@@ -271,10 +180,7 @@ def parse_args():
     )
     parser.add_argument("--deploy-config", default=None)
     parser.add_argument("--stage-init-timeout", type=int, default=600)
-    args = parser.parse_args()
-    if args.model_version == "2.5" and not 0.5 <= args.speed <= 2.0:
-        parser.error("IndexTTS 2.5 --speed must be between 0.5 and 2.0")
-    return args
+    return parser.parse_args()
 
 
 if __name__ == "__main__":

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
-`LingBotVideoPipeline`, and builds the canonical video request envelope:
+The dedicated LingBot example explicitly selects the video output modality:
 
 ```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
-`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"}'
-```
+VAE's `4n+1` grid.
 
 ### LTX-2
 
@@ -232,9 +218,6 @@ python text_to_video.py \
 - `--audio-sample-rate`: fallback audio sample rate when the pipeline returns audio.
 - `--quantization`: quantization method (such as `fp8` for FP8).
 - `--flow-shift`: scheduler flow_shift parameter.
-- `--lora-path`: path to PEFT LoRA adapter folder or checkpoint file.
-- `--lora-scale`: scale factor for LoRA weights.
-- `--lora-backend`: backend for loading LoRA adapters. Default: peft. Available options: peft, distill.
 - `--extra-body`: JSON object of model-specific generation params, filtered against the model's declared `extra_body_params` (see [`vllm_omni/model_extras`](../../../vllm_omni/model_extras)). Used by Cosmos3 (see above).
 
 ### Wan2.2-specific

examples/offline_inference/text_to_video/text_to_video.py

@@ -14,9 +14,7 @@ from vllm_omni.diffusion.data import DiffusionParallelConfig
 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.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 +78,6 @@ _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 +108,27 @@ _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:
-        return _MODEL_PRESETS["lingbot"]
-    if "ltx" in class_lower or "ltx" in model_lower:
-        if "distilled" in class_lower or "distilled" in model_lower:
-            return _MODEL_PRESETS["ltx2_distilled"]
-        if "ltx23" in class_lower or "ltx-2.3" in model_lower or "ltx_2.3" in model_lower:
-            return _MODEL_PRESETS["ltx23"]
+    if "distilled" in class_lower or "distilled" in model_lower:
+        return _MODEL_PRESETS["ltx2_distilled"]
+    if "ltx23" in class_lower or "ltx-2.3" in model_lower or "ltx_2.3" in model_lower:
+        return _MODEL_PRESETS["ltx23"]
+    if "ltx2" in class_lower or "ltx-2" in model_lower or "ltx_2" in model_lower:
         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 +158,7 @@ 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",
@@ -371,29 +334,6 @@ def parse_args() -> argparse.Namespace:
         action="store_true",
         help="Enable expert parallelism for MoE layers.",
     )
-    parser.add_argument(
-        "--lora-path",
-        type=str,
-        nargs="+",
-        default=None,
-        help="Path to LoRA adapter folder (PEFT format) or concrete LoRA checkpoint files. Loaded at initialization and used for generation."
-        "Note: for Wan2.2 MoE models, two checkpoints positionally mapped to high-noise and low-noise modules.",
-    )
-    parser.add_argument(
-        "--lora-scale",
-        type=float,
-        default=1.0,
-        help="Scale factor for LoRA weights (default: 1.0).",
-    )
-    parser.add_argument(
-        "--lora-backend",
-        type=str,
-        default="peft",
-        choices=["peft", "distill"],
-        help="LoRA backend for loading LoRA adapters. Default: peft"
-        "'peft' loads a PEFT-format adapter folder, used e.g. for RL"
-        "'distill' fuses one or more concrete LoRA checkpoint files, used e.g. for distilled few-step LoRAs",
-    )
     parser.add_argument(
         "--use-hsdp",
         action="store_true",
@@ -425,7 +365,7 @@ 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 +383,6 @@ 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)
@@ -501,12 +440,6 @@ def main():
         omni_kwargs["cache_backend"] = args.cache_backend
         omni_kwargs["cache_config"] = cache_config
         omni_kwargs["enable_cache_dit_summary"] = args.enable_cache_dit_summary
-    if args.lora_path is not None:
-        lora_path = args.lora_path
-        if len(lora_path) == 1:
-            lora_path = lora_path[0]
-        omni_kwargs["lora_path"] = lora_path
-        omni_kwargs["lora_backend"] = args.lora_backend
 
     # Cosmos3 loads its (gated) guardrail models at build time, so the guardrails
     # gate is an engine-level config (offline analog of the server's --no-guardrails).
@@ -516,7 +449,6 @@ 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...")
@@ -537,29 +469,12 @@ def main():
     print(f"  Video size: {args.width}x{args.height}")
     print(f"{'=' * 60}\n")
 
-    lora_request = None
-    if args.lora_path and args.lora_backend == "peft":
-        if len(args.lora_path) != 1:
-            raise ValueError("Only one LoRA path is expected for PEFT backend.")
-
-        lora_path = args.lora_path[0]
-        lora_request_id = stable_lora_int_id(lora_path)
-        lora_request = LoRARequest(
-            lora_name=Path(lora_path).stem,
-            lora_int_id=lora_request_id,
-            lora_path=lora_path,
-        )
-
-    negative_prompt = args.negative_prompt
-    if negative_prompt is None and all(preset is not _MODEL_PRESETS[name] for name in ("lingbot", "ltx2", "ltx23")):
+    prompt_dict = {"prompt": args.prompt}
+    if args.negative_prompt is not None:
+        prompt_dict["negative_prompt"] = args.negative_prompt
+    elif preset not in (_MODEL_PRESETS["ltx2"], _MODEL_PRESETS["ltx23"]):
         # Preserve the historical empty-prompt behavior for non-LTX examples.
-        negative_prompt = ""
-    prompt_dict = build_text_to_video_prompt(args.prompt, negative_prompt)
-
-    extra_args = {}
-    if lora_request:
-        extra_args["lora_request"] = lora_request
-        extra_args["lora_scale"] = args.lora_scale
+        prompt_dict["negative_prompt"] = ""
 
     sampling_kwargs = dict(
         height=args.height,
@@ -568,7 +483,6 @@ def main():
         guidance_scale=args.guidance_scale,
         num_inference_steps=args.num_inference_steps,
         num_frames=args.num_frames,
-        extra_args=extra_args,
     )
     if args.guidance_scale_high is not None:
         sampling_kwargs["guidance_scale_2"] = args.guidance_scale_high
@@ -611,8 +525,8 @@ 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:
-            inner_output = frames
+        if frames.is_pipeline_output and frames.request_output is not None:
+            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 +580,8 @@ 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 +633,8 @@ 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/image_to_video/README.md

@@ -1,9 +1,6 @@
 # Image-To-Video
 
-This example demonstrates online image-to-video generation with vLLM-Omni.
-The startup script defaults to Wan2.2, but `MODEL` can select any supported
-image-to-video model. The existing curl helper remains a concrete Wan2.2
-request through the standard Videos API.
+This example demonstrates how to deploy the Wan2.2 image-to-video model for online video generation using vLLM-Omni.
 
 ## Start Server
 
@@ -23,7 +20,9 @@ bash run_server.sh
 
 The script allows overriding:
 - `MODEL` (default: `Wan-AI/Wan2.2-I2V-A14B-Diffusers`)
-- `PORT` (default: `8099`)
+- `PORT` (default: `8091`)
+- `BOUNDARY_RATIO` (default: `0.875`)
+- `FLOW_SHIFT` (default: `12.0`)
 - `CACHE_BACKEND` (default: `none`)
 - `ENABLE_CACHE_DIT_SUMMARY` (default: `0`)
 

examples/online_serving/image_to_video/run_server.sh

@@ -1,12 +1,12 @@
 #!/bin/bash
-# Shared image-to-video server start script
+# Wan2.2 image-to-video server start script
 
 MODEL="${MODEL:-Wan-AI/Wan2.2-I2V-A14B-Diffusers}"
 PORT="${PORT:-8099}"
 CACHE_BACKEND="${CACHE_BACKEND:-none}"
 ENABLE_CACHE_DIT_SUMMARY="${ENABLE_CACHE_DIT_SUMMARY:-0}"
 
-echo "Starting image-to-video server..."
+echo "Starting Wan2.2 I2V server..."
 echo "Model: $MODEL"
 echo "Port: $PORT"
 echo "Cache backend: $CACHE_BACKEND"

examples/online_serving/text_to_image/README.md

@@ -1,9 +1,6 @@
 # Text-To-Image
 
-This example demonstrates online text-to-image generation with vLLM-Omni. The
-startup script and Python clients default to Qwen-Image, but `MODEL` can select
-any supported text-to-image model. The existing curl helper remains a concrete
-Qwen-Image request through the standard Images API.
+This example demonstrates how to deploy Qwen-Image model for online image generation service using vLLM-Omni.
 
 ## Start Server
 
@@ -118,23 +115,7 @@ python gradio_demo.py
 
 ## LoRA
 
-This example supports both startup-time distilled LoRAs and request-time
-PEFT-compatible adapters. The LoRA path must be readable on the **server**
-machine (usually a local path or a mounted directory).
-
-### Start Server with a Distilled LoRA
-
-```bash
-vllm serve Qwen/Qwen-Image-2512 \
-  --omni \
-  --port 8091 \
-  --lora-backend distill \
-  --lora-path /path/to/Qwen-Image-2512-Lightning-4steps.safetensors
-```
-
-The distilled LoRA is fused once during server initialization and applies to
-every request. Use the checkpoint's few-step sampling settings, for example
-`num_inference_steps=4` and `true_cfg_scale=1.0`.
+This example supports Peft-compatible LoRA (Low-Rank Adaptation) adapters for diffusion models. The LoRA adapter path must be readable on the **server** machine (usually a local path or a mounted directory).
 
 ### Using Python Client with LoRA
 
@@ -245,8 +226,8 @@ count, use `size` and `n` rather than `height`, `width`, or
 | `height`                 | int   | None    | Image height in pixels         |
 | `width`                  | int   | None    | Image width in pixels          |
 | `size`                   | str   | None    | Image size (e.g., "1024x1024") |
-| `num_inference_steps`    | int   | model   | Number of denoising steps      |
-| `true_cfg_scale`         | float | model   | True CFG scale when supported  |
+| `num_inference_steps`    | int   | 50      | Number of denoising steps      |
+| `true_cfg_scale`         | float | 4.0     | Qwen-Image CFG scale           |
 | `seed`                   | int   | None    | Random seed (reproducible)     |
 | `negative_prompt`        | str   | None    | Negative prompt                |
 | `num_outputs_per_prompt` | int   | 1       | Number of images to generate   |

examples/online_serving/text_to_image/gradio_demo.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 """
-Shared text-to-image Gradio demo for online serving.
+Qwen-Image Gradio Demo for online serving.
 
 Usage:
     python gradio_demo.py [--server http://localhost:8091] [--port 7860]
@@ -77,9 +77,9 @@ def generate_image(
 def create_demo(server_url: str):
     """Create Gradio demo interface."""
 
-    with gr.Blocks(title="Text-to-Image Demo") as demo:
-        gr.Markdown("# Text-to-Image Online Generation")
-        gr.Markdown("Generate images using the model configured on the server")
+    with gr.Blocks(title="Qwen-Image Demo") as demo:
+        gr.Markdown("# Qwen-Image Online Generation")
+        gr.Markdown("Generate images using Qwen-Image model")
 
         with gr.Row():
             with gr.Column(scale=1):
@@ -115,6 +115,7 @@ def create_demo(server_url: str):
                         label="Inference Steps",
                         minimum=10,
                         maximum=100,
+                        # Default steps aligned with run_curl_text_to_image.sh to 100
                         value=100,
                         step=5,
                     )
@@ -172,7 +173,7 @@ def create_demo(server_url: str):
 
 
 def main():
-    parser = argparse.ArgumentParser(description="Shared text-to-image Gradio demo")
+    parser = argparse.ArgumentParser(description="Qwen-Image Gradio Demo")
     parser.add_argument("--server", default="http://localhost:8091", help="Server URL")
     parser.add_argument("--port", type=int, default=7860, help="Gradio port")
     parser.add_argument("--share", action="store_true", help="Create public link")

examples/online_serving/text_to_image/openai_chat_client.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 """
-Shared OpenAI-compatible text-to-image generation client.
+Qwen-Image OpenAI-compatible image generation client.
 
 Usage:
     python openai_chat_client.py --prompt "A beautiful landscape" --output output.png
@@ -39,7 +39,7 @@ def generate_image(
         height: Image height in pixels
         width: Image width in pixels
         steps: Number of diffusion steps
-        true_cfg_scale: True CFG scale for models that support it
+        true_cfg_scale: Qwen-Image CFG scale
         seed: Random seed
         negative_prompt: Negative prompt
         num_outputs_per_prompt: Number of images to generate
@@ -70,7 +70,7 @@ def generate_image(
         payload["num_inference_steps"] = steps
     if true_cfg_scale is not None:
         payload["true_cfg_scale"] = true_cfg_scale
-    if negative_prompt is not None:
+    if negative_prompt:
         payload["negative_prompt"] = negative_prompt
     if seed is not None:
         payload["seed"] = seed
@@ -115,15 +115,15 @@ def generate_image(
 
 
 def main():
-    parser = argparse.ArgumentParser(description="Shared text-to-image client")
+    parser = argparse.ArgumentParser(description="Qwen-Image chat client")
     parser.add_argument("--prompt", "-p", default="a cup of coffee on the table", help="Text prompt")
-    parser.add_argument("--output", "-o", default="text_to_image_output.png", help="Output file")
+    parser.add_argument("--output", "-o", default="qwen_image_output.png", help="Output file")
     parser.add_argument("--server", "-s", default="http://localhost:8091", help="Server URL")
-    parser.add_argument("--height", type=int, help="Image height; defaults to the model setting")
-    parser.add_argument("--width", type=int, help="Image width; defaults to the model setting")
-    parser.add_argument("--steps", type=int, help="Inference steps; defaults to the model setting")
-    parser.add_argument("--cfg-scale", type=float, help="True CFG scale when supported")
-    parser.add_argument("--seed", type=int, help="Random seed")
+    parser.add_argument("--height", type=int, default=1024, help="Image height")
+    parser.add_argument("--width", type=int, default=1024, help="Image width")
+    parser.add_argument("--steps", type=int, default=50, help="Inference steps")
+    parser.add_argument("--cfg-scale", type=float, default=4.0, help="True CFG scale")
+    parser.add_argument("--seed", type=int, default=0, help="Random seed")
     parser.add_argument("--negative", help="Negative prompt")
 
     parser.add_argument("--lora-path", default=None, help="Server-local LoRA adapter folder (PEFT format)")

examples/online_serving/text_to_image/run_server.sh

@@ -1,10 +1,10 @@
 #!/bin/bash
-# Shared text-to-image online serving startup script
+# Qwen-Image online serving startup script
 
 MODEL="${MODEL:-Qwen/Qwen-Image}"
 PORT="${PORT:-8091}"
 
-echo "Starting text-to-image server..."
+echo "Starting Qwen-Image server..."
 echo "Model: $MODEL"
 echo "Port: $PORT"
 

examples/online_serving/text_to_speech/README.md

@@ -16,8 +16,7 @@ For the full list of supported architectures across all modalities, see
 |---|---|---|---|---|---|
 | Fish Speech S2 Pro | `fishaudio/s2-pro` | ✓ (`ref_audio`+`ref_text`) | ✓ (PCM stream) | — | ✓ |
 | GLM-TTS | `zai-org/GLM-TTS` | ✓ (`ref_audio`+`ref_text`, required) | ✓ (PCM stream) | — | ✓ |
-| IndexTTS-2 | `IndexTeam/IndexTTS-2` | ✓ (`ref_audio` or uploaded `voice`) | `stream=true` response, non-chunk | uploaded audio voice only; no presets | — |
-| IndexTTS-2.5 | native `checkpoints/` bundle | ✓ (`ref_audio` or uploaded `voice`) | `stream=true` response, non-chunk | uploaded audio voice only; no presets | — |
+| IndexTTS-2 | `IndexTeam/IndexTTS-2` | ✓ (`ref_audio` or uploaded `voice`) | compat only, non-chunk | uploaded audio voice only; no presets | — |
 | Ming-omni-tts | `inclusionAI/Ming-omni-tts-0.5B` | ✓ (`ref_audio` / `speaker_embedding`) | ✓ (PCM stream) | IP labels + structured `instructions` | — |
 | Ming-flash-omni-TTS | `Jonathan1909/Ming-flash-omni-2.0` | — (caption-controlled) | — | caption fields (`instructions`) | — |
 | MOSS-TTS-Nano | `OpenMOSS-Team/MOSS-TTS-Nano` | ✓ (`ref_audio` required) | ✓ (PCM stream) | — | ✓ |
@@ -139,29 +138,15 @@ bash examples/online_serving/text_to_speech/glm_tts/run_gradio_demo.sh
 
 ---
 
-## IndexTTS-2 and IndexTTS-2.5
+## IndexTTS-2
 
-2-stage TTS at 22.05 kHz. Requests use `ref_audio` for voice cloning, or an uploaded audio `voice` from `/v1/audio/voices`. IndexTTS-2.5 uses a multilingual tokenizer, CAMPPlus speaker projection, EnhancedCodec, and code-only Stage 0→1 transfer by default. Both versions support emotion conditioning via `emo_audio`, `emo_text`, or `emo_vector` passed in `extra_params`.
-
-### Prerequisites
-
-IndexTTS-2 uses its existing frontend and does not require the IndexTTS-2.5 text dependencies. Before launching IndexTTS-2.5, install its optional frontend dependencies:
-
-```bash
-pip install 'vllm-omni[indextts2]'
-```
+2-stage TTS (GPT AR + S2Mel CFM DiT + BigVGAN) at 22.05 kHz. Requests use `ref_audio` for voice cloning, or an uploaded audio `voice` from `/v1/audio/voices`. Supports emotion conditioning via `emo_audio`, `emo_text`, or `emo_vector` passed in `extra_params`.
 
 ### Launch
 ```bash
 vllm serve IndexTeam/IndexTTS-2 --omni --trust-remote-code --port 8092
 # or, to pass the bundled deploy config explicitly:
 bash examples/online_serving/text_to_speech/indextts2/run_server.sh
-
-# IndexTTS-2.5 default: use_gpt_latent=false
-MODEL_VERSION=2.5 \
-MODEL=/path/to/indextts-2.5 \
-bash examples/online_serving/text_to_speech/indextts2/run_server.sh
-
 ```
 
 ### Sending requests
@@ -176,35 +161,14 @@ python examples/online_serving/text_to_speech/indextts2/speech_client.py \
     --text "今天心情很好！" \
     --ref-audio /path/to/ref.wav \
     --emo-audio /path/to/happy.wav
-
-# IndexTTS-2.5 Japanese request
-python examples/online_serving/text_to_speech/indextts2/speech_client.py \
-    --model-version 2.5 \
-    --model /path/to/indextts-2.5 \
-    --lang ja \
-    --text "こんにちは、音声合成のテストです。" \
-    --ref-audio /path/to/ref.wav
-
-# IndexTTS-2.5 native speed control
-python examples/online_serving/text_to_speech/indextts2/speech_client.py \
-    --model-version 2.5 \
-    --model /path/to/indextts-2.5 \
-    --speed 2.0 \
-    --text "这是两倍速度的语音合成测试。" \
-    --ref-audio /path/to/ref.wav
 ```
 
 ### Notes
 - Output: 22.05 kHz mono WAV.
-- Provide `ref_audio` on the documented raw request path, or pass `voice` only when it names an uploaded audio voice; neither version provides a built-in text-only preset voice.
-- IndexTTS-2.5 request controls `lang` and `text_normalization` are carried in `extra_params`; the client does this for `--model-version 2.5`.
-- IndexTTS-2.5 accepts the public `speed` request field in `[0.5, 2.0]`: `2.0` generates shorter, faster speech and `0.5` generates longer, slower speech. This is native Stage 1 duration control, so serving does not apply the generic playback-speed adjustment a second time. IndexTTS-2 does not use this control.
-- IndexTTS-2.5 accepts language codes such as `zh`, `en`, `zhen` (mixed Chinese/English), `ja`, and `yue`. `Mandarin` is a vLLM-Omni convenience alias for `zh`. Japanese (`ja`) uses `fugashi` tokenization and produces audio, but it does not automatically expand numbers, dates, or percentages; callers should first write those inputs as readable Japanese text.
-- A request `seed` controls Stage 0 AR sampling and per-request CFM noise. Different concurrent batch compositions do not guarantee a bit-identical waveform.
-- IndexTTS-2.5 Stage 0 uses plain vLLM sampling and does not reproduce the official default `num_beams=3` beam search. For parity comparisons, run upstream with `num_beams=1`; output quality can differ from the official beam-search result.
+- Provide `ref_audio` on the documented raw request path, or pass `voice` only when it names an uploaded audio voice; IndexTTS-2 does not provide a built-in text-only preset voice.
 - Emotion params (`emo_audio`, `emo_text`, `emo_vector`, `emo_alpha`, `use_emo_text`, `use_random`) are passed via the `extra_params` field. Official precedence is `use_emo_text` > `emo_vector` > `emo_audio` > same emotion as the speaker reference.
-- IndexTTS-2.5 uses `vllm_omni/deploy/indextts2_5.yaml` with the official code-only Stage 0 to Stage 1 contract.
-- For IndexTTS-2.5, set `MODEL` to the local native bundle; asset discovery accepts its nested `checkpoints/` layout.
+- `stream=true` is accepted as an OpenAI-compatible response path, but IndexTTS-2 is not async-chunk streaming; audio is produced after S2Mel receives the full mel-code sequence.
+- Deploy config: `vllm_omni/deploy/indextts2.yaml` (auto-loaded).
 
 ---
 
@@ -605,13 +569,6 @@ 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

examples/online_serving/text_to_speech/indextts2/run_server.sh

@@ -1,37 +1,20 @@
 #!/bin/bash
-# Launch vLLM-Omni server for IndexTTS 2.0 or 2.5.
+# Launch vLLM-Omni server for IndexTTS2
 #
 # Usage from repository root:
 #   examples/online_serving/text_to_speech/indextts2/run_server.sh
-#   MODEL_VERSION=2.5 MODEL=/path/to/native/bundle \
-#     examples/online_serving/text_to_speech/indextts2/run_server.sh
+#   CUDA_VISIBLE_DEVICES=0 PORT=8092 MODEL=/path/to/IndexTeam/IndexTTS-2 examples/online_serving/text_to_speech/indextts2/run_server.sh
 
 set -e
 
 SCRIPT_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd)"
 ROOT_DIR="$(cd -- "$SCRIPT_DIR/../../../.." && pwd)"
 
-MODEL_VERSION="${MODEL_VERSION:-2.0}"
+MODEL="${MODEL:-IndexTeam/IndexTTS-2}"
 PORT="${PORT:-8092}"
+DEPLOY_CONFIG="${DEPLOY_CONFIG:-$ROOT_DIR/vllm_omni/deploy/indextts2.yaml}"
 
-if [[ "$MODEL_VERSION" == "2.5" ]]; then
-    if [[ -z "${MODEL:-}" ]]; then
-        echo "Usage: MODEL_VERSION=2.5 MODEL=/path/to/native/bundle bash $0" >&2
-        exit 2
-    fi
-    DEFAULT_DEPLOY_CONFIG="$ROOT_DIR/vllm_omni/deploy/indextts2_5.yaml"
-elif [[ "$MODEL_VERSION" == "2.0" ]]; then
-    MODEL="${MODEL:-IndexTeam/IndexTTS-2}"
-    DEFAULT_DEPLOY_CONFIG="$ROOT_DIR/vllm_omni/deploy/indextts2.yaml"
-else
-    echo "MODEL_VERSION must be 2.0 or 2.5" >&2
-    exit 2
-fi
-
-DEPLOY_CONFIG="${DEPLOY_CONFIG:-$DEFAULT_DEPLOY_CONFIG}"
-
-echo "Starting IndexTTS $MODEL_VERSION server with model: $MODEL"
-echo "Deploy config: $DEPLOY_CONFIG"
+echo "Starting IndexTTS2 server with model: $MODEL"
 
 FLASHINFER_DISABLE_VERSION_CHECK=1 \
 vllm serve "$MODEL" \

examples/online_serving/text_to_speech/indextts2/speech_client.py

@@ -1,6 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""OpenAI-compatible client for IndexTTS 2.0/2.5 via /v1/audio/speech.
+"""OpenAI-compatible client for IndexTTS2 TTS via /v1/audio/speech endpoint.
 
 Examples:
     # With reference audio for voice cloning
@@ -54,44 +54,13 @@ def main() -> None:
     parser.add_argument("--emo-alpha", type=float, default=None, help="Emotion weight in [0, 1]")
     parser.add_argument("--use-emo-text", action="store_true", help="Infer emotion vector from emo-text or text")
     parser.add_argument("--use-random", action="store_true", help="Use random emotion prototypes")
-    parser.add_argument(
-        "--model-version",
-        choices=("2.0", "2.5"),
-        default="2.0",
-    )
-    parser.add_argument("--model", type=str, default=None)
-    parser.add_argument(
-        "--lang",
-        default="zh",
-        help=(
-            "IndexTTS 2.5 language code, for example zh/en/zhen/ja/yue; "
-            "zhen is mixed Chinese/English and Mandarin is a vLLM-Omni alias for zh"
-        ),
-    )
-    parser.add_argument(
-        "--no-text-normalization",
-        action="store_false",
-        dest="text_normalization",
-        help="Disable IndexTTS 2.5 text normalization",
-    )
-    parser.add_argument(
-        "--speed",
-        type=float,
-        default=1.0,
-        help="IndexTTS 2.5 native synthesis speed in [0.5, 2.0]; 2.0 is faster",
-    )
+    parser.add_argument("--model", type=str, default="IndexTeam/IndexTTS-2")
     parser.add_argument("--voice", type=str, default=None, help="Uploaded voice name to use instead of --ref-audio")
     parser.add_argument("--output", type=str, default="output.wav")
     parser.add_argument("--api-base", type=str, default=DEFAULT_API_BASE)
     parser.add_argument("--api-key", type=str, default=DEFAULT_API_KEY)
     parser.add_argument("--response-format", type=str, default="wav")
     args = parser.parse_args()
-    if args.model is None:
-        if args.model_version == "2.5":
-            parser.error("--model is required for IndexTTS 2.5")
-        args.model = "IndexTeam/IndexTTS-2"
-    if args.model_version == "2.5" and not 0.5 <= args.speed <= 2.0:
-        parser.error("IndexTTS 2.5 --speed must be between 0.5 and 2.0")
 
     if not args.ref_audio and not args.voice:
         parser.error("IndexTTS2 requires --ref-audio or --voice for voice cloning")
@@ -128,10 +97,6 @@ def main() -> None:
         extra_params["use_emo_text"] = True
     if args.use_random:
         extra_params["use_random"] = True
-    if args.model_version == "2.5":
-        payload["speed"] = args.speed
-        extra_params["lang"] = args.lang
-        extra_params["text_normalization"] = args.text_normalization
     if extra_params:
         payload["extra_params"] = extra_params
 

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

@@ -84,13 +84,6 @@ forced-aligner = [
     "qwen-asr>=0.0.6",  # faithful Japanese/Korean word segmentation for the TTS forced aligner
 ]
 
-indextts2 = [
-    "fugashi>=1.2.0",
-    "unidic-lite>=1.0.0",
-    "wetext>=0.0.9; sys_platform != 'linux'",
-    "WeTextProcessing; sys_platform == 'linux'",
-]
-
 # Local-test-only extras
 local = [
     "FlagEmbedding",
@@ -206,12 +199,6 @@ 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 +307,6 @@ 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",
@@ -344,10 +327,3 @@ 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"
-SER = "SER"

recipes/IndexTeam/IndexTTS-2_5.md

@@ -1,308 +0,0 @@
-# IndexTTS-2.5 for multilingual voice-cloned TTS on 1x GPU
-
-## Summary
-
-- Vendor: IndexTeam
-- Model: `IndexTeam/IndexTTS-2.5`
-- Task: Multilingual text-to-speech with voice cloning, emotion control, and
-  native speed control
-- Mode: Online serving with the OpenAI-compatible `/v1/audio/speech` API, or
-  offline inference through `Omni`
-- Maintainer: Community
-
-## When to use this recipe
-
-Use this recipe to serve IndexTTS-2.5 as a two-stage TTS system. Stage 0 is an
-autoregressive talker; Stage 1 uses EnhancedCodec, S2Mel CFM/DiT, and BigVGAN
-to produce 22.05 kHz mono speech. Each request supplies synthesis text and
-reference audio, or an uploaded audio voice, for zero-shot voice cloning.
-
-IndexTTS-2.5 adds multilingual text processing and model-native speed control
-to the IndexTTS-2 serving contract.
-
-## References
-
-- Upstream model:
-  [IndexTeam/IndexTTS-2.5 on Hugging Face](https://huggingface.co/IndexTeam/IndexTTS-2.5)
-- Online serving example:
-  [`examples/online_serving/text_to_speech/README.md#indextts-2-and-indextts-25`](../../examples/online_serving/text_to_speech/README.md#indextts-2-and-indextts-25)
-- Offline inference example:
-  [`examples/offline_inference/text_to_speech/README.md#indextts-2-and-indextts-25`](../../examples/offline_inference/text_to_speech/README.md#indextts-2-and-indextts-25)
-- OpenAI-compatible client:
-  [`examples/online_serving/text_to_speech/indextts2/speech_client.py`](../../examples/online_serving/text_to_speech/indextts2/speech_client.py)
-- Standard deploy config:
-  [`vllm_omni/deploy/indextts2_5.yaml`](../../vllm_omni/deploy/indextts2_5.yaml)
-
-## Hardware Support
-
-### GPU
-
-### 1x NVIDIA H20 96GB
-
-#### Environment
-
-- OS: Linux
-- Python: 3.10+
-- Driver / runtime: NVIDIA CUDA environment
-- vLLM version: Match the repository requirements for your checkout
-- vLLM-Omni version or commit: Use the commit you are deploying from
-
-Install vLLM-Omni with the IndexTTS text-processing dependencies:
-
-```bash
-pip install 'vllm-omni[indextts2]'
-```
-
-Obtain the native IndexTTS-2.5 bundle and point `MODEL` at its root. The model
-loader accepts the upstream nested `checkpoints/` layout.
-
-#### Command
-
-Start the standard code-only server from the repository root:
-
-```bash
-MODEL_VERSION=2.5 \
-MODEL=/path/to/indextts-2.5 \
-bash examples/online_serving/text_to_speech/indextts2/run_server.sh
-```
-
-This selects `vllm_omni/deploy/indextts2_5.yaml`, which uses
-`use_gpt_latent=false`. To launch directly:
-
-```bash
-vllm serve /path/to/indextts-2.5 \
-  --omni \
-  --trust-remote-code \
-  --port 8092 \
-  --deploy-config vllm_omni/deploy/indextts2_5.yaml
-```
-
-#### Optional: NVIDIA MPS for higher single-GPU throughput
-
-Stage 0 and Stage 1 run in separate processes on the same GPU. Enabling
-NVIDIA Multi-Process Service (MPS) can improve steady-state throughput by
-allowing work from both stages to overlap more effectively. MPS is an opt-in
-deployment optimization: this recipe does not start it automatically, and the
-gain depends on the request mix and concurrency, so benchmark it with the
-intended workload before enabling it in production.
-
-Pay attention to CUDA device renumbering. For example, if the MPS control
-daemon is started with physical `CUDA_VISIBLE_DEVICES=1`, that GPU is exposed
-to MPS clients as logical device `0`; launch the server with
-`CUDA_VISIBLE_DEVICES=0`, not `1`. Use a dedicated MPS pipe/log directory when
-the host is shared with other services, and stop only the MPS daemon owned by
-this deployment.
-
-#### Verification
-
-Send a multilingual voice-cloning request with the bundled client. Local audio
-paths are converted to base64 data URLs before transmission:
-
-```bash
-python examples/online_serving/text_to_speech/indextts2/speech_client.py \
-  --api-base http://localhost:8092 \
-  --model-version 2.5 \
-  --model /path/to/indextts-2.5 \
-  --lang zh \
-  --text "你好，这是 IndexTTS-2.5 语音合成测试。" \
-  --ref-audio /path/to/reference.wav \
-  --output indextts2_5.wav
-```
-
-##### Native speed control
-
-The public `speed` field is handled natively. Its accepted range is
-`[0.5, 2.0]`; values above `1.0` produce shorter, faster speech. The serving
-adapter converts the API convention to the model-native control with
-`duration_factor = 1.0 / speed`, so `speed=1.25` uses
-`duration_factor=0.8`:
-
-```bash
-python examples/online_serving/text_to_speech/indextts2/speech_client.py \
-  --api-base http://localhost:8092 \
-  --model-version 2.5 \
-  --model /path/to/indextts-2.5 \
-  --speed 1.25 \
-  --text "这是使用模型原生语速控制的测试。" \
-  --ref-audio /path/to/reference.wav \
-  --output indextts2_5_speed.wav
-```
-
-##### Upload and reuse a named voice
-
-Upload a reference recording once when callers should reuse the same voice
-without sending `ref_audio` on every request. `consent` is required by the
-voice-storage API:
-
-```bash
-curl -X POST http://localhost:8092/v1/audio/voices \
-  -F "audio_sample=@/path/to/reference.wav" \
-  -F "consent=user-consent-id" \
-  -F "name=indextts_demo_voice" \
-  -F "speaker_description=IndexTTS-2.5 demonstration voice"
-```
-
-Confirm that the voice was stored, then synthesize with its name instead of a
-reference-audio payload:
-
-```bash
-curl http://localhost:8092/v1/audio/voices
-
-python examples/online_serving/text_to_speech/indextts2/speech_client.py \
-  --api-base http://localhost:8092 \
-  --model-version 2.5 \
-  --model /path/to/indextts-2.5 \
-  --voice indextts_demo_voice \
-  --lang zh \
-  --text "你好，这是复用已上传音色的语音合成测试。" \
-  --output indextts2_5_named_voice.wav
-```
-
-There are no built-in text-only preset voices. Each named IndexTTS voice must
-first be created from an uploaded reference recording.
-
-##### Multilingual synthesis and text normalization
-
-The client accepts `zh` (Mandarin), `en` (English), `zhen` (mixed Chinese and
-English), `ja` (Japanese), and `yue` (Cantonese). The following commands use
-the same reference voice across all supported language modes:
-
-```bash
-CLIENT=examples/online_serving/text_to_speech/indextts2/speech_client.py
-MODEL=/path/to/indextts-2.5
-REF_AUDIO=/path/to/reference.wav
-
-python "$CLIENT" --model-version 2.5 --model "$MODEL" \
-  --ref-audio "$REF_AUDIO" --lang zh \
-  --text "你好，这是中文语音合成测试。" --output indextts2_5_zh.wav
-
-python "$CLIENT" --model-version 2.5 --model "$MODEL" \
-  --ref-audio "$REF_AUDIO" --lang en \
-  --text "Hello, this is an English speech synthesis test." \
-  --output indextts2_5_en.wav
-
-python "$CLIENT" --model-version 2.5 --model "$MODEL" \
-  --ref-audio "$REF_AUDIO" --lang zhen \
-  --text "Hello，欢迎使用 IndexTTS 二点五。" --output indextts2_5_zhen.wav
-
-python "$CLIENT" --model-version 2.5 --model "$MODEL" \
-  --ref-audio "$REF_AUDIO" --lang ja \
-  --text "こんにちは、これは日本語の音声合成テストです。" \
-  --output indextts2_5_ja.wav
-
-python "$CLIENT" --model-version 2.5 --model "$MODEL" \
-  --ref-audio "$REF_AUDIO" --lang yue \
-  --text "你好，呢段係粵語語音合成測試。" --output indextts2_5_yue.wav
-```
-
-Text normalization is enabled by default. Disable it only when the input is
-already written exactly as it should be spoken:
-
-```bash
-python examples/online_serving/text_to_speech/indextts2/speech_client.py \
-  --model-version 2.5 --model /path/to/indextts-2.5 \
-  --ref-audio /path/to/reference.wav --lang en --no-text-normalization \
-  --text "The temperature is twenty five degrees." \
-  --output indextts2_5_pre_normalized.wav
-```
-
-For raw HTTP requests, put the same language and text-normalization controls
-under `extra_params`:
-
-```bash
-curl http://localhost:8092/v1/audio/speech \
-  -H "Content-Type: application/json" \
-  -d '{
-    "model": "/path/to/indextts-2.5",
-    "input": "This is an IndexTTS-2.5 test.",
-    "response_format": "wav",
-    "speed": 1.0,
-    "ref_audio": "data:audio/wav;base64,<BASE64_ENCODED_AUDIO>",
-    "extra_params": {
-      "lang": "en",
-      "text_normalization": true
-    }
-  }' \
-  --output indextts2_5_en.wav
-```
-
-##### Emotion control
-
-IndexTTS-2.5 supports an explicit eight-value emotion vector, an emotion text
-description, or a separate emotion-reference recording. Use one source at a
-time. Vector values are ordered as `happy`, `angry`, `sad`, `afraid`,
-`disgusted`, `melancholic`, `surprised`, and `calm`; each value must be in
-`[0, 1.2]`. `emo_alpha` controls the strength in `[0, 1]`.
-
-Use an explicit emotion vector for deterministic control:
-
-```bash
-python examples/online_serving/text_to_speech/indextts2/speech_client.py \
-  --model-version 2.5 --model /path/to/indextts-2.5 \
-  --ref-audio /path/to/reference.wav --lang zh \
-  --text "今天真是令人开心的一天！" \
-  --emo-vector 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \
-  --emo-alpha 0.8 --output indextts2_5_happy_vector.wav
-```
-
-Infer the emotion from a natural-language description with `use_emo_text`:
-
-```bash
-python examples/online_serving/text_to_speech/indextts2/speech_client.py \
-  --model-version 2.5 --model /path/to/indextts-2.5 \
-  --ref-audio /path/to/reference.wav --lang zh \
-  --text "我们终于完成了这项工作！" \
-  --use-emo-text --emo-text "开心、兴奋而且充满活力" \
-  --emo-alpha 0.8 --output indextts2_5_happy_text.wav
-```
-
-Transfer emotion from a recording while retaining the speaker identity from
-`ref_audio`:
-
-```bash
-python examples/online_serving/text_to_speech/indextts2/speech_client.py \
-  --model-version 2.5 --model /path/to/indextts-2.5 \
-  --ref-audio /path/to/reference.wav --lang zh \
-  --text "请用情绪参考音频中的表达方式朗读这句话。" \
-  --emo-audio /path/to/emotion_reference.wav \
-  --emo-alpha 0.8 --output indextts2_5_emotion_audio.wav
-```
-
-If multiple emotion sources are supplied, the official precedence is
-`use_emo_text` > `emo_vector` > `emo_audio` > the emotion in the speaker
-reference. `--use-random` is also available for exploratory random emotion
-prototypes, but should not be used when reproducible conditioning is required.
-
-For offline inference:
-
-```bash
-python examples/offline_inference/text_to_speech/indextts2/end2end.py \
-  --model /path/to/indextts-2.5 \
-  --model-version 2.5 \
-  --lang zh \
-  --speed 1.0 \
-  --text "你好，这是离线语音合成测试。" \
-  --ref-audio /path/to/reference.wav
-```
-
-#### Notes
-
-- Hardware scope: the standard recipe has been exercised on one NVIDIA H200.
-  It does not claim an unverified throughput or quality result.
-- Audio output: 22.05 kHz mono WAV.
-- Voice cloning: reference audio is required on the documented raw request
-  path. Alternatively, `voice` may name an uploaded audio voice; there is no
-  built-in text-only preset voice.
-- Native speed: serving maps `speed` to the model's duration factor and skips
-  generic waveform speed adjustment, so speed is applied exactly once.
-- Languages: common codes include `zh`, `en`, `zhen` (mixed Chinese/English),
-  `ja`, and `yue`; `Mandarin` is accepted as an alias for `zh`.
-- Japanese normalization: `ja` tokenization does not automatically expand
-  numbers, dates, or percentages. Write these inputs as readable Japanese text
-  before inference.
-- Emotion controls: `use_emo_text`, `emo_vector`, and `emo_audio` are
-  alternative conditioning modes. Their precedence is `use_emo_text` >
-  `emo_vector` > `emo_audio` > the speaker-reference emotion.
-- Sampling difference: Stage 0 uses plain vLLM sampling, not the upstream
-  default `num_beams=3` beam search. Use upstream `num_beams=1` for parity
-  comparisons; output may differ from the official beam-search result.

recipes/MiniMaxAI/MiniMax-H3-RTX-PRO-5000.md

@@ -1,198 +0,0 @@
-# MiniMax-H3 on RTX PRO 5000 Blackwell GPUs
-
-This recipe runs MiniMax-H3 in BF16 on 72 GiB RTX PRO 5000 Blackwell GPUs. It
-contains the validated two-GPU DLO configuration and the recommended resident
-configurations: TP1 x Ulysses2 with 20 resident layers on two GPUs, TP2 x
-Ulysses2 on four GPUs, and TP4 x Ulysses2 on eight GPUs. The four- and
-eight-GPU routes do not require offload.
-
-## Capacity requirements
-
-| Resource | Requirement |
-| --- | ---: |
-| GPUs | 2, 4, or 8 x RTX PRO 5000 Blackwell |
-| GPU HBM | 72 GiB per GPU |
-| Checkpoint storage | 135 GiB per partition |
-| Available system RAM | 200 GiB minimum |
-| Recommended system RAM | 384 GiB |
-
-`FL2VA` and `Ref2VA` are separate checkpoint partitions. Start one server at a
-time on a host sized for the minimum system-memory requirement.
-
-## PCIe topology and GPU order
-
-RTX PRO 5000 does not provide NVLink. Before starting the server, identify the
-closest PCIe pairs and their NUMA affinity:
-
-```bash
-nvidia-smi topo -m
-nvidia-smi nvlink -s
-```
-
-For two GPUs, select the closest PCIe pair on one NUMA node. For four GPUs,
-select all cards from one NUMA node. On the validated host,
-physical GPUs `(0,1)` and `(2,3)` are the two `PXB` pairs on NUMA node 0. The
-order `CUDA_VISIBLE_DEVICES=0,2,1,3` maps the Ulysses groups to those local
-pairs. For eight GPUs, the validated order is `0,4,1,5,2,6,3,7`, with host
-memory interleaved across NUMA nodes 0 and 1. Do not copy these IDs blindly:
-reproduce the same PCIe and NUMA relationships on the target host.
-
-## Recommended serving configurations
-
-### Two GPUs
-
-Two 72 GiB cards require distributed layerwise offload. The validated route
-uses TP1 x Ulysses2, keeps 20 leading DiT layers resident, and streams
-rank-local weights without AllGather. Eager execution avoids regional-compile
-instability on this offload path.
-
-```bash
-export MODEL_ROOT=/path/to/MiniMax-H3
-export MODEL="${MODEL_ROOT}/FL2VA"
-export PORT=8091
-
-CUDA_VISIBLE_DEVICES=0,1 \
-VLLM_WORKER_MULTIPROC_METHOD=spawn \
-VLLM_OMNI_VIDEO_SYNC_TIMEOUT=1800 \
-numactl --cpunodebind=0 --membind=0 \
-vllm serve "${MODEL}" \
-  --omni \
-  --host 0.0.0.0 \
-  --port "${PORT}" \
-  --trust-remote-code \
-  --num-gpus 2 \
-  --tensor-parallel-size 1 \
-  --usp 2 \
-  --ring 1 \
-  --text-encoder-tp-size 2 \
-  --vae-patch-parallel-size 2 \
-  --vae-parallel-mode tile \
-  --vae-use-tiling \
-  --diffusion-attention-backend CUDNN_ATTN \
-  --enable-distributed-layerwise-offload \
-  --dlo-no-use-allgather \
-  --dlo-resident-layers 20 \
-  --enforce-eager
-```
-
-### Four GPUs
-
-The validated baseline uses TP2 x Ulysses2, text-encoder TP4, VAE patch
-parallelism 4, and explicit cuDNN BF16 attention. Selecting the backend
-explicitly keeps the recipe independent of platform-default backend changes.
-
-```bash
-export MODEL_ROOT=/path/to/MiniMax-H3
-export MODEL="${MODEL_ROOT}/FL2VA"
-export PORT=8091
-
-CUDA_VISIBLE_DEVICES=0,2,1,3 \
-VLLM_WORKER_MULTIPROC_METHOD=spawn \
-VLLM_OMNI_VIDEO_SYNC_TIMEOUT=1800 \
-numactl --cpunodebind=0 --membind=0 \
-vllm serve "${MODEL}" \
-  --omni \
-  --host 0.0.0.0 \
-  --port "${PORT}" \
-  --trust-remote-code \
-  --num-gpus 4 \
-  --tensor-parallel-size 2 \
-  --usp 2 \
-  --ring 1 \
-  --text-encoder-tp-size 4 \
-  --vae-patch-parallel-size 4 \
-  --vae-parallel-mode tile \
-  --vae-use-tiling \
-  --diffusion-attention-backend CUDNN_ATTN
-```
-
-### Eight GPUs
-
-The recommended eight-GPU route uses TP4 x Ulysses2, text-encoder TP8, VAE
-patch parallelism 8, and host-memory interleaving across both NUMA nodes.
-
-```bash
-export MODEL_ROOT=/path/to/MiniMax-H3
-export MODEL="${MODEL_ROOT}/FL2VA"
-export PORT=8091
-
-CUDA_VISIBLE_DEVICES=0,4,1,5,2,6,3,7 \
-VLLM_WORKER_MULTIPROC_METHOD=spawn \
-VLLM_OMNI_VIDEO_SYNC_TIMEOUT=1800 \
-numactl --interleave=0,1 \
-vllm serve "${MODEL}" \
-  --omni \
-  --host 0.0.0.0 \
-  --port "${PORT}" \
-  --trust-remote-code \
-  --num-gpus 8 \
-  --tensor-parallel-size 4 \
-  --usp 2 \
-  --ring 1 \
-  --text-encoder-tp-size 8 \
-  --vae-patch-parallel-size 8 \
-  --vae-parallel-mode tile \
-  --vae-use-tiling \
-  --diffusion-attention-backend CUDNN_ATTN
-```
-
-For the four- and eight-GPU resident routes, do not add `--enforce-eager`.
-Warm the server once before measuring so regional compilation is outside the
-measured request. The two-GPU DLO route intentionally remains eager.
-
-For Ref2VA, stop the FL2VA server and restart the same command with
-`MODEL="${MODEL_ROOT}/Ref2VA"`.
-
-## Target-hardware validation
-
-All three configurations were exercised on a PCIe-only, dual-socket host with
-eight RTX PRO 5000 GPUs. The run used PyTorch 2.11.0+cu130, CUDA 13.0, driver
-580.95.05, 1344x768 output, 124 frames, and two warmups.
-
-The four-GPU route also received a five-step profiling validation for GPU
-balance:
-
-| Measurement | Result |
-| --- | ---: |
-| Maximum externally sampled peak | 69,219 MiB (67.60 GiB) per GPU |
-| T2VA worker-reported peak | 65,314 MiB |
-| First-frame FL2VA worker-reported peak | 66,780 MiB |
-| T2VA maximum GPU kernel-time deviation | 0.56% |
-| First-frame FL2VA maximum GPU kernel-time deviation | 0.09% |
-
-The recommended routes produced the following 50-step results. MiniMax-H3
-requested 50 denoise steps and executed 49 denoise updates, so per-step latency
-is `denoise / 49`.
-
-| GPUs | Workload | Parallelism | E2E (s) | Text encode (s) | Denoise (s) | VAE decode (s) | Per step (ms) | Peak memory (GiB) |
-| ---: | --- | --- | ---: | ---: | ---: | ---: | ---: | ---: |
-| 2 | T2VA | TP1 x Ulysses2, DLO resident 20 | 515.57 | 1.19 | 504.69 | 8.79 | 10,300 | 36.38 |
-| 2 | FL2VA first-frame I2VA | TP1 x Ulysses2, DLO resident 20 | 553.45 | 1.27 | 541.90 | 8.76 | 11,059 | 36.38 |
-| 4 | T2VA | TP2 x Ulysses2 | 284.01 | 0.03 | 278.73 | 4.36 | 5,688 | 67.58 |
-| 4 | FL2VA first-frame I2VA | TP2 x Ulysses2 | 305.72 | 0.28 | 299.85 | 4.36 | 6,119 | 67.58 |
-| 8 | T2VA | TP4 x Ulysses2 | 163.20 | 0.04 | 159.40 | 2.58 | 3,253 | 45.83 |
-| 8 | FL2VA first-frame I2VA | TP4 x Ulysses2 | 171.62 | 0.27 | 167.25 | 2.57 | 3,413 | 45.83 |
-
-Peak memory is the maximum per-GPU value sampled externally with
-`nvidia-smi`. The two-GPU route leaves about 34.7 GiB below the reported
-73,415 MiB device capacity, the four-GPU route leaves about 4.1 GiB, and the
-eight-GPU route leaves about 25.9 GiB. Re-measure memory for longer reference
-inputs, concurrency greater than one, or a different output shape.
-
-## T2VA request example
-
-```bash
-export API_URL="http://127.0.0.1:${PORT}/v1/videos/sync"
-
-curl -sS --max-time 1800 -X POST "${API_URL}" \
-  -F 'prompt=At night, three cats march into a bedroom playing tiny brass instruments, then abruptly file out, with synchronized room ambience.' \
-  -F 'width=1344' \
-  -F 'height=768' \
-  -F 'aspect_ratio=16:9' \
-  -F 'fps=24' \
-  -F 'num_inference_steps=50' \
-  -F 'flow_shift=12' \
-  -F 'seed=1101' \
-  -F 'extra_params={"task":"t2va","duration":5.0,"audio_flow_shift":3.0}' \
-  -o t2va.mp4
-```

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

@@ -50,7 +50,6 @@ recipes/
 | [`inclusionAI/Ming-flash-omni-2.0.md`](./inclusionAI/Ming-flash-omni-2.0.md) | Online serving for multimodal chat + standalone TTS | 4x H100 / 1x H100 80GB |
 | [`inclusionAI/Ming-omni-tts.md`](./inclusionAI/Ming-omni-tts.md) | Offline + online dense Ming TTS/audio generation | 1x H100 80GB / 1x AMD MI300X (ROCm 7.2) |
 | [`IndexTeam/IndexTTS-2.md`](./IndexTeam/IndexTTS-2.md) | Online serving for voice-cloned TTS with optional emotion control | 1x L4 24GB or larger CUDA GPU |
-| [`IndexTeam/IndexTTS-2_5.md`](./IndexTeam/IndexTTS-2_5.md) | Online + offline multilingual voice-cloned TTS with native speed and emotion control | 1x NVIDIA H20 96GB |
 | [`MiniMaxAI/MiniMax-H3.md`](./MiniMaxAI/MiniMax-H3.md) | T2VA, FL2VA, image+audio Ref2VA, and multi-video Ref2VA serving | 1x GPU with CPU offload / 4x B300 |
 | [`MiniMaxAI/MiniMax-H3-MUSA.md`](./MiniMaxAI/MiniMax-H3-MUSA.md) | MiniMax H3 T2VA, FL2VA, and Ref2VA on MUSA | 4x MTT S5000 (TP4 Ref2VA profile) |
 | [`krea/Krea-2.md`](./krea/Krea-2.md) | Text-to-image (Turbo + Raw), offline + online, with LoRA | 1x H100 80GB |
@@ -66,7 +65,6 @@ 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_bench_tts_cli.py

@@ -51,14 +51,6 @@ def test_load_model_configs(model_configs_path: Path) -> None:
     assert configs["test/ModelA"]["supported_tasks"] == ["voice_clone", "default_voice"]
 
 
-def test_indextts25_is_registered_in_shared_model_configs() -> None:
-    config_path = Path(bench_tts.__file__).with_name("model_configs.yaml")
-    config = bench_tts.load_model_configs(config_path)["IndexTeam/IndexTTS-2.5"]
-
-    assert config["supported_tasks"] == ["voice_clone"]
-    assert config["task_extra_body"]["voice_clone"]["extra_params"]["lang"] == "en"
-
-
 def test_build_bench_args_voice_clone(model_configs_path: Path) -> None:
     configs = bench_tts.load_model_configs(model_configs_path)
     cmd = bench_tts.build_bench_args(
@@ -128,34 +120,6 @@ def test_build_bench_args_wer_eval_adds_flag(model_configs_path: Path) -> None:
     assert "--seed-tts-wer-eval" in cmd
 
 
-def test_build_bench_args_supports_local_model_and_shared_sweep_options(model_configs_path: Path) -> None:
-    configs = bench_tts.load_model_configs(model_configs_path)
-    cmd = bench_tts.build_bench_args(
-        host="localhost",
-        port=8092,
-        model="test/ModelA",
-        served_model_name="/models/indextts25",
-        task="voice_clone",
-        model_cfg=configs["test/ModelA"],
-        locale="en",
-        num_prompts=500,
-        num_warmups=5,
-        request_seed=42,
-        concurrency=8,
-        dataset_path="/data/seed-tts",
-        wer_eval=False,
-        output_dir=None,
-        result_filename=None,
-        extra_cli_args=["--", "--tokenizer", "/models/indextts25/qwen0.6bemo4-merge"],
-    )
-
-    assert cmd[cmd.index("--model") + 1] == "/models/indextts25"
-    assert cmd[cmd.index("--num-warmups") + 1] == "5"
-    assert cmd[-2:] == ["--tokenizer", "/models/indextts25/qwen0.6bemo4-merge"]
-    extra_body = json.loads(cmd[cmd.index("--extra-body") + 1])
-    assert extra_body == {"task_type": "Base", "seed": 42}
-
-
 def test_unsupported_task_exits(model_configs_path: Path, capsys: pytest.CaptureFixture, mocker) -> None:
     # ModelB does not support voice_design
     mocker.patch.object(

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.
-These tests pin that contract so a refactor cannot silently regress the score.
+interleaved, with ``max_slice_nums=1`` / ``use_image_id=False`` and an empty MiniCPM
+system message (not the Qwen system prompt). These tests pin that contract so a refactor
+cannot silently regress the score.
 
 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)])
-def test_uniform_sample_indices_matches_numpy_linspace(n: int, k: int) -> None:
+@pytest.mark.parametrize(("n", "k"), [(10, 4), (100, 64), (1000, 64), (7, 7), (3, 5), (1, 1)])
+def test_uniform_sample_indices_matches_openbmb_midpoint(n: int, k: int) -> None:
+    """Pin OpenBMB midpoint-bin sampling (not np.linspace endpoints)."""
     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_config_factory.py

@@ -2206,24 +2206,6 @@ class TestBaseConfigInheritance:
 class TestPlatformOverrides:
     """Test platform-specific deploy config overrides."""
 
-    def test_qwen3_omni_musa_clears_parent_quantization_for_audio_stages(self):
-        pipeline = resolve_pipeline_config(
-            "qwen3_omni_moe",
-            Q3_OMNI_ALL_STAGES_HF_CONFIG,
-        )
-        assert isinstance(pipeline, PipelineConfig)
-        deploy_path = Path(get_deploy_config_path("qwen3_omni_moe.yaml"))
-
-        base_stages = merge_pipeline_deploy(pipeline, load_deploy_config(deploy_path))
-        assert "hf_overrides" not in base_stages[1].yaml_engine_args
-        assert "hf_overrides" not in base_stages[2].yaml_engine_args
-
-        musa = _apply_platform_overrides(load_deploy_config(deploy_path), platform="musa")
-        musa_stages = merge_pipeline_deploy(pipeline, musa)
-        expected = {"quantization_config": None}
-        assert musa_stages[1].yaml_engine_args["hf_overrides"] == expected
-        assert musa_stages[2].yaml_engine_args["hf_overrides"] == expected
-
     def test_qwen3_tts_rocm_disables_code2wav_outer_cudagraph(self):
         deploy_path = Path(get_deploy_config_path("qwen3_tts.yaml"))
 
@@ -2276,16 +2258,6 @@ class TestPlatformOverrides:
         # Stage 2 unaffected fields stay at base
         assert deploy.stages[2].enforce_eager is False
 
-    def test_qwen2_5_omni_xpu_uses_eager_ar_stages(self):
-        deploy_path = Path(get_deploy_config_path("qwen2_5_omni.yaml"))
-
-        deploy = load_deploy_config(deploy_path)
-        deploy = _apply_platform_overrides(deploy, platform="xpu")
-
-        assert deploy.stages[0].enforce_eager is True
-        assert deploy.stages[1].enforce_eager is True
-        assert deploy.stages[2].enforce_eager is True
-
     def test_xpu_overrides(self):
         deploy_path = Path(get_deploy_config_path("qwen3_omni_moe.yaml"))
         if not deploy_path.exists():

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",
 )
 
 
@@ -93,9 +92,6 @@ class _Request:
         self.num_computed_tokens = 0
         self.num_in_flight_tokens = 0
         self.num_output_placeholders = 0
-        # vLLM 0.27 (a0c092ee72): Request gained num_stale_output_tokens to
-        # track in-flight outputs discarded at preemption/streaming-stop.
-        self.num_stale_output_tokens = 0
         self.has_encoder_inputs = False
         self.pooling_params = None
         self.resumable = True
@@ -139,7 +135,6 @@ 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 +264,3 @@ 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_ar_scheduler_stale_drain.py

@@ -1,223 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-"""Investigation tests for the num_stale_output_tokens drain (59b9e719).
-
-The seed-TTS WER regression appeared with the vLLM 0.27 rebase (nightly
-2946 -> 2949: mean WER 0.2695 -> 0.3936, median 0.0 in both), whose prime
-suspect is the reimplemented async-token discard: vLLM 0.26 drained
-``async_tokens_to_discard`` one arriving frame at a time and suppressed
-only the token append; the 0.27 adaptation seeds
-``num_stale_output_tokens`` with the discarded *placeholder count* and
-drains it by each arriving frame's ``num_tokens_scheduled``, dropping the
-whole per-request update. Whenever seed and arrivals disagree, the counter
-outlives the old segment and the drain consumes *valid* frames of the new
-segment — a hole in the talker's codec stream (garbled audio, text/audio
-mismatch) — or underflows its own assert on the new segment's prefill
-frame (engine-core death mid-step).
-
-The fix seeds the counter from ``num_in_flight_tokens`` — the same
-scheduled-token units the drain subtracts — so every pre-discard frame
-drains exactly its own contribution and new-segment frames can never be
-swallowed. These tests pin that contract one frame at a time; the
-delivered / swallowed observable is whether ``_update_request_with_output``
-runs for the frame.
-"""
-
-from __future__ import annotations
-
-from types import SimpleNamespace
-from unittest.mock import MagicMock
-
-import pytest
-
-# Imports must run in this order: vllm_omni applies patches to vllm.v1.request
-# before Request / StreamingUpdate are bound in this module.
-# isort: off
-import vllm_omni  # noqa: F401 - import for side effects (patch vLLM)
-from vllm.sampling_params import SamplingParams
-from vllm.v1.core.sched.output import SchedulerOutput
-from vllm.v1.outputs import ModelRunnerOutput
-from vllm.v1.request import Request, RequestStatus, StreamingUpdate
-from vllm_omni.core.sched.omni_ar_scheduler import OmniARScheduler
-
-# isort: on
-
-pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
-
-
-def _make_session() -> Request:
-    session = Request(
-        request_id="req-stale-drain-test",
-        prompt_token_ids=[1, 2, 3],
-        sampling_params=SamplingParams(max_tokens=32),
-        pooling_params=None,
-        arrival_time=100.0,
-        block_hasher=None,
-    )
-    session.status = RequestStatus.RUNNING
-    if not hasattr(session, "num_stale_output_tokens"):
-        # vLLM 0.27 defines this Request field; 0.26 (local dev) does not.
-        session.num_stale_output_tokens = 0
-    if not hasattr(session, "num_in_flight_tokens"):
-        session.num_in_flight_tokens = 0
-    return session
-
-
-def _replace_streaming_session(session: Request) -> None:
-    """Run the real stage-0 streaming replacement (the discard/seed site)."""
-    sched = OmniARScheduler.__new__(OmniARScheduler)
-    sched._new_prompt_len_snapshot = {}
-    sched.vllm_config = SimpleNamespace(model_config=SimpleNamespace(stage_id=0))
-    sched.num_waiting_for_streaming_input = 0
-    sched.log_stats = False
-    sched.chunk_transfer_adapter = None
-    sched.skipped_waiting = set()
-    session.status = RequestStatus.WAITING_FOR_STREAMING_REQ
-    update = StreamingUpdate(
-        mm_features=None,
-        prompt_token_ids=[10, 20],
-        max_tokens=32,
-        arrival_time=200.0,
-        sampling_params=SamplingParams(max_tokens=16),
-    )
-    sched._update_request_as_session(session, update)
-    session.status = RequestStatus.RUNNING
-
-
-def _make_drain_sched(session: Request) -> MagicMock:
-    sched = MagicMock()
-    sched.requests = {session.request_id: session}
-    sched.perf_metrics = None
-    sched.structured_output_manager.should_advance.return_value = False
-    sched._update_request_with_output.return_value = ([42], False)
-    sched._process_kv_transfer_trigger.return_value = False
-    sched.chunk_transfer_adapter = MagicMock()
-    sched.running = [session]
-    sched.waiting_for_transfer_free = set()
-    sched.transfer_triggered_requests = set()
-    sched.active_kv_transfers = set()
-    sched.pending_stop_after_extraction = set()
-    sched.connector = None
-    sched.kv_cache_manager.take_events.return_value = None
-    sched.finished_req_ids_dict = {}
-    sched.make_stats.return_value = None
-    return sched
-
-
-def _run_step(sched: MagicMock, session: Request, *, num_scheduled: int, token: int) -> bool:
-    """Feed one model-runner frame through update_from_output.
-
-    Returns True when the frame's tokens were delivered (the append ran),
-    False when the frame was swallowed by the stale drain.
-    """
-    scheduler_output = MagicMock(spec=SchedulerOutput)
-    scheduler_output.num_scheduled_tokens = {session.request_id: num_scheduled}
-    scheduler_output.scheduled_spec_decode_tokens = {}
-    scheduler_output.num_invalid_spec_tokens = 0
-
-    model_runner_output = MagicMock(spec=ModelRunnerOutput)
-    model_runner_output.sampled_token_ids = [[token]]
-    model_runner_output.logprobs = None
-    model_runner_output.prompt_logprobs_dict = {}
-    model_runner_output.pooler_output = None
-    model_runner_output.num_nans_in_logits = None
-    model_runner_output.kv_connector_output = None
-    model_runner_output.cudagraph_stats = None
-    model_runner_output.req_id_to_index = {session.request_id: 0}
-    model_runner_output.routed_experts = None
-    model_runner_output.inter_stage_outputs = None
-
-    sched._update_request_with_output.reset_mock()
-    OmniARScheduler.update_from_output(sched, scheduler_output, model_runner_output)
-    return sched._update_request_with_output.called
-
-
-def test_exact_drain_delivers_new_segment_frame() -> None:
-    """One in-flight decode frame at replacement: its late output drains the
-    counter exactly and the new segment's first frame is delivered."""
-    session = _make_session()
-    session.append_output_token_ids([7, 8, 9])
-    session.num_computed_tokens = 6
-    session.num_output_placeholders = 1
-    session.num_in_flight_tokens = 1
-
-    _replace_streaming_session(session)
-    assert session.num_stale_output_tokens == 1
-
-    sched = _make_drain_sched(session)
-    assert _run_step(sched, session, num_scheduled=1, token=42) is False  # late frame dropped
-    assert session.num_stale_output_tokens == 0
-    assert _run_step(sched, session, num_scheduled=1, token=43) is True  # new segment survives
-
-
-def test_two_in_flight_frames_drain_without_swallowing() -> None:
-    """Two in-flight decode frames: both late frames drop, the new segment's
-    first valid frame is delivered. A placeholder-based seed swallowed it
-    whenever fewer frames arrived than placeholders counted (the driver of
-    the seed-TTS WER regression, nightly 2946 -> 2949: 0.2695 -> 0.3936)."""
-    session = _make_session()
-    session.append_output_token_ids([7, 8, 9])
-    session.num_computed_tokens = 7
-    session.num_output_placeholders = 2
-    session.num_in_flight_tokens = 2
-
-    _replace_streaming_session(session)
-    assert session.num_stale_output_tokens == 2
-
-    sched = _make_drain_sched(session)
-    assert _run_step(sched, session, num_scheduled=1, token=42) is False  # late frame 1
-    assert _run_step(sched, session, num_scheduled=1, token=44) is False  # late frame 2
-    assert session.num_stale_output_tokens == 0
-
-    delivered = _run_step(sched, session, num_scheduled=1, token=43)  # new segment, frame 1
-    assert delivered is True, (
-        "valid new-segment frame was swallowed by leftover stale count "
-        f"(num_stale_output_tokens left at {session.num_stale_output_tokens})"
-    )
-
-
-def test_spec_placeholder_asymmetry_does_not_swallow_valid_frame() -> None:
-    """Placeholder counts can exceed scheduled counts (e.g. a draft
-    invalidated before execution): 1 in-flight MTP frame scheduled 2 tokens
-    but contributed 3 placeholders. Seeding from num_in_flight_tokens keeps
-    seed == drain, so the new segment's valid frame is delivered."""
-    session = _make_session()
-    session.append_output_token_ids([7])
-    session.num_computed_tokens = 7
-    session.num_output_placeholders = 3  # 1 sampled + 2 spec placeholders
-    session.num_in_flight_tokens = 2  # the frame actually scheduled 1 + 1 tokens
-
-    _replace_streaming_session(session)
-    assert session.num_stale_output_tokens == 2
-
-    sched = _make_drain_sched(session)
-    assert _run_step(sched, session, num_scheduled=2, token=42) is False  # late MTP frame
-    assert session.num_stale_output_tokens == 0
-    delivered = _run_step(sched, session, num_scheduled=1, token=43)  # new segment, frame 1
-    assert delivered is True, (
-        "valid new-segment frame swallowed after spec placeholder asymmetry "
-        f"(num_stale_output_tokens left at {session.num_stale_output_tokens})"
-    )
-
-
-def test_in_flight_prefill_chunk_drains_exactly_without_underflow() -> None:
-    """An in-flight prefill chunk carries no placeholders but 5 scheduled
-    tokens. The placeholder-based seed missed it entirely (its late output
-    was appended to the rolled-back session) and any leftover counter
-    underflowed 'assert num_stale_output_tokens >= 0' on the chunk's arrival,
-    killing the engine-core step. Seeding from num_in_flight_tokens drops the
-    late chunk and drains to exactly zero."""
-    session = _make_session()
-    session.append_output_token_ids([7, 8, 9])
-    session.num_computed_tokens = 7
-    session.num_output_placeholders = 0  # prefill chunk: no placeholders
-    session.num_in_flight_tokens = 5  # the chunk's scheduled tokens
-
-    _replace_streaming_session(session)
-    assert session.num_stale_output_tokens == 5
-
-    sched = _make_drain_sched(session)
-    assert _run_step(sched, session, num_scheduled=5, token=42) is False  # late prefill chunk dropped
-    assert session.num_stale_output_tokens == 0
-    assert _run_step(sched, session, num_scheduled=1, token=43) is True  # new segment survives

tests/core/sched/test_omni_scheduling_coordinator.py

@@ -120,16 +120,12 @@ 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).
             ("AudexCode2Wav", "audex_code2wav"),
             ("AudexXCodec1", "audex_xcodec"),
             ("IndexTTS2S2MelDecoder", "indextts2_s2mel_decoder"),
-            ("IndexTTS25S2MelDecoder", "indextts2_5_s2mel_decoder"),
             ("DyninOmniForConditionalGeneration", "token2image"),
             ("DyninOmniForConditionalGeneration", "token2audio"),
         }

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")