[Docs] Redirect the former PD guide URL

Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>
[Docs] Document current PD design
2026-08-13 12:48:43 +08:00 · 2026-08-13 12:44:42 +08:00 · 2026-08-13 12:23:56 +08:00 · 2026-08-13 11:23:47 +08:00 · 2026-08-13 09:36:17 +08:00 · 2026-08-13 08:42:37 +08:00
446 changed files with 40904 additions and 4433 deletions
@@ -8,7 +8,7 @@
 # NPU naming: {queue}_{resource_class}
 #   a2b3_npu_1  — ascend-a2b3, resource_class npu-1 (A2/B3 CI image)
 #   a2b3_npu_4  — ascend-a2b3, resource_class npu-4
-#   a3_npu_2    — ascend-a3, resource_class npu-2 (A3 CI image tag …:$BUILDKITE_COMMIT-a3)
+#   a3_npu_2    — ascend-a3, resource_class npu-2

 x-npu-k8s-plugins: &npu_k8s_plugins
  - kubernetes:
@@ -139,6 +139,11 @@ 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

      - label: "Omni · MiniCPM-o 4.5 Duplex Test"
@@ -148,10 +153,10 @@ steps:
          - tests/e2e/online_serving/test_minicpmo_4_5_duplex.py
          - tests/e2e/online_serving/test_minicpmo_4_5_duplex_expansion.py
          - tests/e2e/online_serving/test_minicpmo_realtime_duplex_drivers.py
-          - tests/e2e/online_serving/minicpmo_realtime_duplex_scenarios.py
+          - tests/e2e/online_serving/helpers/minicpmo_realtime_duplex_scenarios.py
          - tests/e2e/online_serving/run_minicpmo_realtime_duplex_multi_session.py
          - tests/e2e/online_serving/run_minicpmo_realtime_duplex_soft_interrupt.py
-          - tests/helpers/minicpmo_4_5_duplex.py
+          - tests/e2e/online_serving/helpers/minicpmo_4_5_duplex.py
          - vllm_omni/deploy/minicpmo_4_5_duplex.yaml
          - vllm_omni/experimental/fullduplex/
          - vllm_omni/model_executor/models/minicpmo_4_5/
@@ -164,7 +169,7 @@ steps:
        timeout_in_minutes: 50
        commands:
          - pytest -s -v tests/e2e/online_serving/test_minicpmo_4_5_duplex.py -m 'advanced_model and cuda' --run-level 'advanced_model'
-        mirror_hardwares: h100_2
+        mirror_hardwares: h100_1

      - label: "TTS · VoxCPM2 Test"
        source_file_dependencies:
@@ -22,7 +22,7 @@ steps:
        timeout_in_minutes: 50
        commands:
          - pytest -s -v tests/e2e/online_serving/test_minicpmo_4_5_duplex_expansion.py -m "full_model and cuda and H100 and omni" --run-level "full_model"
-        mirror_hardwares: h100_2
+        mirror_hardwares: h100_1

      - label: ":full_moon: Omni · Doc Test with L4"
        timeout_in_minutes: 90
@@ -50,6 +50,19 @@ steps:
            exit $$EXIT
        mirror_hardwares: h100_2

+      - label: ":full_moon: Omni · MiniCPM-o 4.5 · Accuracy Test"
+        timeout_in_minutes: 180
+        commands:
+          - export SEED_TTS_WER_EVAL=1
+          - export SEED_TTS_EVAL_DEVICE=cuda:1
+          - |
+            set +e
+            pytest -s -v tests/e2e/accuracy/minicpmo_4_5/test_minicpmo_4_5.py -m "full_model and cuda" --run-level full_model
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/e2e/accuracy/minicpmo_4_5/results/*.json"
+            exit $$EXIT
+        mirror_hardwares: h100_2
+
      - label: ":full_moon: Omni · Perf Test · No Async Chunk"
        key: nightly-omni-performance-no-async-chunk
        timeout_in_minutes: 300
@@ -102,6 +115,32 @@ steps:
            exit $$EXIT
        mirror_hardwares: h100_3

+      - label: ":full_moon: Omni · MiniCPM-o 4.5 · Perf Test"
+        key: nightly-omni-performance-minicpmo-4-5
+        timeout_in_minutes: 180
+        commands:
+          - export BENCHMARK_DIR=tests/dfx/perf/results
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_minicpmo_4_5.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
+            exit $$EXIT
+        mirror_hardwares: h100_1
+
+      - label: ":full_moon: Omni · MiniCPM-o 4.5 · Duplex Seed-TTS Perf Test"
+        key: nightly-omni-performance-minicpmo-4-5-duplex-seed-tts
+        timeout_in_minutes: 180
+        commands:
+          - export BENCHMARK_DIR=tests/dfx/perf/results
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_minicpmo_4_5_duplex_seed_tts.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
+            exit $$EXIT
+        mirror_hardwares: h100_1
+
      - label: ":full_moon: Omni · Multi-Replica Startup Test with 4x H100"
        timeout_in_minutes: 45
        commands:
@@ -306,7 +345,7 @@ steps:
        commands:
          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
          - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
-          - export CACHE_DIT_VERSION=1.3.0
+          - export CACHE_DIT_VERSION=1.5.0
          - |
            set +e
            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_vllm_omni.json
@@ -322,7 +361,7 @@ steps:
        commands:
          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
          - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
-          - export CACHE_DIT_VERSION=1.3.0
+          - export CACHE_DIT_VERSION=1.5.0
          - |
            set +e
            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_edit_vllm_omni.json
@@ -338,7 +377,7 @@ steps:
        commands:
          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
          - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
-          - export CACHE_DIT_VERSION=1.3.0
+          - export CACHE_DIT_VERSION=1.5.0
          - |
            set +e
            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_edit_2511_vllm_omni.json
@@ -354,7 +393,7 @@ steps:
        commands:
          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
          - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
-          - export CACHE_DIT_VERSION=1.3.0
+          - export CACHE_DIT_VERSION=1.5.0
          - |
            set +e
            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_layered_vllm_omni.json
@@ -370,7 +409,7 @@ steps:
        commands:
          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
          - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
-          - export CACHE_DIT_VERSION=1.3.0
+          - export CACHE_DIT_VERSION=1.5.0
          - |
            set +e
            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_bagel_vllm_omni.json
@@ -427,10 +466,10 @@ steps:
          - pytest -s -v tests/e2e/accuracy/test_diffusers_backend_similarity.py -k '2v_matches_diffusers' -m full_model --run-level full_model
        mirror_hardwares: h100_2

-      - label: ":full_moon: Diffusion X2V · MiniMax H3 T2VA Accuracy Test"
+      - label: ":full_moon: Diffusion X2V · MiniMax H3 I2VA/Ref2VA Accuracy Test"
        timeout_in_minutes: 180
        commands:
-          - pytest -s -v tests/e2e/accuracy/minimax_h3/test_minimax_h3_t2va_similarity.py -m full_model --run-level full_model
+          - pytest -s -v tests/e2e/accuracy/minimax_h3/test_minimax_h3_i2va_ref2va_similarity.py -m full_model --run-level full_model
        mirror_hardwares: h100_4

      - label: ":full_moon: Diffusion X2V · Other Accuracy Test"
@@ -128,6 +128,21 @@ 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:
@@ -172,7 +187,7 @@ steps:
            timeout 20m bash -c "
              pytest -s -v tests/e2e/offline_inference/test_minicpmo_4_5.py -m 'core_model' --run-level 'core_model'
            "
-        mirror_hardwares: h100_2
+        mirror_hardwares: h100_1

      - label: "Omni · MiniCPM-o 4.5 Online Test"
        source_file_dependencies:
@@ -183,7 +198,7 @@ steps:
            timeout 20m bash -c "
              pytest -s -v tests/e2e/online_serving/test_minicpmo_4_5.py -m 'core_model' --run-level 'core_model'
            "
-        mirror_hardwares: h100_2
+        mirror_hardwares: h100_1

      - label: "Omni · MiniCPM-o 4.5 Duplex Test"
        source_file_dependencies:
@@ -192,10 +207,10 @@ steps:
          - tests/e2e/online_serving/test_minicpmo_4_5_duplex.py
          - tests/e2e/online_serving/test_minicpmo_4_5_duplex_expansion.py
          - tests/e2e/online_serving/test_minicpmo_realtime_duplex_drivers.py
-          - tests/e2e/online_serving/minicpmo_realtime_duplex_scenarios.py
+          - tests/e2e/online_serving/helpers/minicpmo_realtime_duplex_scenarios.py
          - tests/e2e/online_serving/run_minicpmo_realtime_duplex_multi_session.py
          - tests/e2e/online_serving/run_minicpmo_realtime_duplex_soft_interrupt.py
-          - tests/helpers/minicpmo_4_5_duplex.py
+          - tests/e2e/online_serving/helpers/minicpmo_4_5_duplex.py
          - vllm_omni/deploy/minicpmo_4_5_duplex.yaml
          - vllm_omni/experimental/fullduplex/
          - vllm_omni/model_executor/models/minicpmo_4_5/
@@ -210,7 +225,19 @@ steps:
            timeout 20m bash -c "
              pytest -s -v tests/e2e/online_serving/test_minicpmo_4_5_duplex.py -m 'core_model and cuda' --run-level 'core_model'
            "
-        mirror_hardwares: h100_2
+        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:
@@ -10,7 +10,7 @@ steps:
          DOCKER_BUILDKIT: "1"
          # Buildkite will automatically replace this with the actual commit hash
          VLLM_IMAGE_TAG: "${BUILDKITE_COMMIT}"
-          VLLM_VERSION: "v0.26.0"
+          VLLM_VERSION: "v0.27.0"
        priority: 100
        timeout_in_minutes: 60
        soft_fail: false
@@ -34,8 +34,23 @@ docker_build() {
    docker build "${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 "${base_image_name}")" ]; then
-    docker_build "${base_image_name}" --target vllm-base --build-arg "VLLM_VERSION=${VLLM_VERSION}"
+    build_vllm_base
 fi

 # Try building the docker image
@@ -39,7 +39,7 @@ steps:
            EXIT=$$?
            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
            exit $$EXIT
-      - label: ":robot: Omni · MiniCPM-o 4.5 Test"
+      - label: ":full_moon: Omni · MiniCPM-o 4.5 Test"
        mirror_hardwares: a3_npu_2
        env:
          VLLM_WORKER_MULTIPROC_METHOD: spawn
@@ -47,6 +47,34 @@ steps:
          HF_TOKEN: "${HF_TOKEN}"
        commands:
          - pytest -s -v tests/e2e/offline_inference/test_minicpmo_4_5.py tests/e2e/online_serving/test_minicpmo_4_5.py tests/e2e/online_serving/test_minicpmo_4_5_expansion.py -m 'full_model' --run-level 'full_model'
+      - label: ":full_moon: Omni · MiniCPM-o 4.5 · Duplex Test"
+        timeout_in_minutes: 180
+        mirror_hardwares: a3_npu_2
+        env:
+          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
+          HF_TOKEN: "${HF_TOKEN}"
+        commands:
+          - >-
+            pytest -s -v
+            tests/e2e/online_serving/test_minicpmo_4_5_duplex.py
+            tests/e2e/online_serving/test_minicpmo_4_5_duplex_expansion.py
+            -m "npu and A3 and omni"
+            --run-level full_model
+      - label: ":full_moon: Omni · MiniCPM-o 4.5 · Accuracy Test"
+        timeout_in_minutes: 180
+        mirror_hardwares: a3_npu_2
+        env:
+          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
+          HF_TOKEN: "${HF_TOKEN}"
+        commands:
+          - |
+            set +e
+            pytest -s -v tests/e2e/accuracy/minicpmo_4_5/test_minicpmo_4_5.py -m "full_model and npu and A3" --run-level full_model
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/e2e/accuracy/minicpmo_4_5/results/*.json"
+            exit $$EXIT
  - group: ":card_index_dividers: TTS Model Test"
    key: nightly-tts-test-group
    depends_on: upload-nightly-pipeline
@@ -87,7 +115,7 @@ steps:
          DIFFUSION_ATTENTION_BACKEND: TORCH_SDPA
        commands:
          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
-          - export CACHE_DIT_VERSION=1.3.0
+          - export CACHE_DIT_VERSION=1.5.0
          - |
            set +e
            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py -m npu --test-config-file tests/dfx/perf/tests/test_qwen_image_edit_2511_vllm_omni.json
@@ -3,22 +3,29 @@ steps:
    key: ready-omni-group
    depends_on: upload-ready-pipeline
    steps:
-      - label: ":robot: Omni · MiniCPM-o 4.5 Test"
-        agents:
-          queue: "ascend-a3"
-          resource_class: "npu-2"
-        image: "swr.cn-southwest-2.myhuaweicloud.com/modelfoundry/vllm-omni-ci-npu:${BUILDKITE_COMMIT}-a3"
-        plugins:
-          - kubernetes:
-              podSpecPatch:
-                imagePullSecrets:
-                  - name: swr-secret
+      - label: "Omni · MiniCPM-o 4.5 Test"
+        mirror_hardwares: a3_npu_2
        env:
          VLLM_WORKER_MULTIPROC_METHOD: spawn
          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
-          HF_TOKEN: "${HF_TOKEN}"
        commands:
-          - pytest -s -v tests/e2e/offline_inference/test_minicpmo_4_5.py tests/e2e/online_serving/test_minicpmo_4_5.py -m 'core_model' --run-level 'core_model'
+          - pytest -s -v tests/e2e/offline_inference/test_minicpmo_4_5.py tests/e2e/online_serving/test_minicpmo_4_5.py -m 'core_model and npu and A3 and omni' --run-level 'core_model'
+      - label: "Omni · MiniCPM-o 4.5 Duplex Test"
+        mirror_hardwares: a3_npu_2
+        env:
+          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
+        commands:
+          - pytest -s -v tests/e2e/online_serving/test_minicpmo_4_5_duplex.py -m 'core_model and npu and A3 and omni' --run-level 'core_model'
+      - label: "Omni · MiniCPM-o 4.5 Perf Test"
+        timeout_in_minutes: 60
+        mirror_hardwares: a3_npu_2
+        env:
+          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
+        commands:
+          - export BENCHMARK_DIR=tests/dfx/perf/results
+          - pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_minicpmo_4_5_ready.json -m 'core_model and npu and A3 and omni'

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

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

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

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

 set -euo pipefail

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

 Judgment calls that don't grep cleanly — apply them while reading the diff, not as a mechanical check:
@@ -0,0 +1,68 @@
+# Examples Python Policy
+
+Prevent new model-specific Python examples without forcing cleanup of the
+existing backlog. Run this check on every PR in quick and full modes.
+
+## Scope
+
+Set the merge base as in the main workflow, then list Python paths introduced
+by the PR, including additions, copies, and renames:
+
+```bash
+BASE="$(git merge-base HEAD origin/main)"
+git diff --name-status --diff-filter=ACR --find-renames --find-copies \
+  "${BASE}"...HEAD -- 'examples/**/*.py'
+```
+
+Inspect destination paths for copies and renames. Existing model-specific files
+that are only modified or deleted are grandfathered and must not be reported.
+
+## Blocking classification
+
+Mark ✗ when a newly introduced Python file does any of the following:
+
+- Names or scopes a path to one model, checkpoint, vendor, or model family.
+- Hard-codes a model identifier, model-specific prompt, request shape, output
+  conversion, or launch configuration behind an otherwise generic filename.
+- Duplicates an existing task/protocol runner to demonstrate one model.
+- Adds a per-model helper below a generic task hub such as `text_to_speech/`.
+
+Do not infer safety from a generic filename alone. For example, a file named
+`text_to_audio.py` is still model-specific if it only implements one model's
+contract.
+
+## Acceptable additions
+
+A new Python example may pass when it is a genuinely model-neutral,
+user-facing task or protocol entrypoint. It must accept model selection through
+configuration and keep model-specific behavior out of the script.
+
+Use ⚠ when the file is model-neutral but reusable logic appears misplaced under
+`examples/`. Route code by purpose:
+
+| Code purpose | Preferred location |
+|---|---|
+| Model prompt, defaults, request/output adaptation | `vllm_omni/model_extras/` or production model modules |
+| Runnable model command and validation evidence | Task documentation or `recipes/` |
+| UI or interactive application | `apps/` |
+| Benchmark or evaluator | `benchmarks/` |
+| Regression reproducer | `tests/` |
+| Download, conversion, or setup utility | `tools/` |
+
+If a model cannot use an existing shared runner, report the missing generic
+capability. Do not solve the gap by adding a model-specific Python example.
+
+## Report format
+
+Add one row to the pre-check report:
+
+```text
+Examples policy    ✓ no new Python example paths
+Examples policy    ⚠ generic helper belongs in tools/
+Examples policy    ✗ examples/offline_inference/new_model/end2end.py is model-specific
+Examples policy    ✗ examples/offline_inference/x_to_y_model_name.py is model-specific
+```
+
+For a blocker, name the path, the model-specific behavior, and the appropriate
+shared runner or destination. Do not propose deleting unrelated existing
+examples as part of the PR.
@@ -1,9 +1,9 @@
-# Claude Skills for vLLM-Omni
+# Repository Skills for vLLM-Omni

-This directory contains Claude Code skills maintained for the `vllm-omni`
-repository. These skills capture repeatable workflows for common contributor
-tasks such as model integration, pull request review, and release note
-generation.
+This directory contains repository-scale skills maintained for `vllm-omni`.
+They capture repeatable workflows for common contributor and maintainer tasks
+such as model integration, CI-aligned testing, performance optimization, and
+pull request review.

 ## Directory Structure

@@ -14,31 +14,41 @@ include:
 - `references/`: focused reference material used by the skill
 - `scripts/`: small helper scripts used by the skill

+## Using the Skills
+
+Coding agents that discover repository skills can invoke the relevant skill by
+name. For other agents, point them to the skill's `SKILL.md` and ask them to
+read it before changing code. Combine a domain skill with `vllm-omni-test` when
+tests or CI coverage change, then use `precheck-pr` for the contributor's final
+self-check.
+
 ## Available Skills

- `add-diffusion-model`: guides integration of a new diffusion model into
-  `vllm-omni`
- `diffusion-perf-opt`: guides diffusion model performance optimization,
-  including profiling traces, parallel strategies, stage timing analysis, and
-  benchmark-driven tuning
- `quantization`: guides quantization method selection, model integration,
-  checkpoint loading, and quality/performance validation for vLLM-Omni
- `add-omni-model`: covers addition of new omni-modality model support
- `add-tts-model`: covers integration of new TTS models and related serving
-  workflows
- `generate-release-note`: helps prepare release notes for repository changes
- `precheck-pr`: self-check a branch before creating a PR — validates PR title
-  format, catches dead code, verifies accuracy/perf claims, and confirms merge
-  readiness
- `vllm-omni-test`: guides generation and execution of CI-aligned tests (L1–L4),
-  pytest marker selection (`core_model` / `advanced_model` / `full_model`,
-  `omni` / `tts` / `diffusion`), Buildkite wiring (`test-ready.yml`,
-  `test-merge.yml`, `test-nightly.yml`, `test-weekly.yml`), and copy-paste
-  local plus CI-like `pytest` commands; see `references/test-routing.md` for
-  level-to-command mapping
- `review-pr`: provides a structured workflow for reviewing pull requests
- `vllm-omni-npu-model-runner-upgrade`: upgrades NPU model runners to align with the
-  latest vllm-ascend NPUModelRunner
+- [`add-diffusion-model`](add-diffusion-model/SKILL.md): guides integration of
+  a new diffusion model into `vllm-omni`
+- [`add-tts-model`](add-tts-model/SKILL.md): covers integration of new TTS
+  models and related serving workflows
+- [`diffusion-perf-opt`](diffusion-perf-opt/SKILL.md): guides diffusion model
+  performance optimization, including profiling traces, parallel strategies,
+  stage timing analysis, and benchmark-driven tuning
+- [`precheck-pr`](precheck-pr/SKILL.md): self-checks a branch before creating a
+  PR by validating title format, dead code, accuracy and performance claims,
+  and merge readiness
+- [`quantization`](quantization/SKILL.md): guides quantization method selection,
+  model integration, checkpoint loading, and quality/performance validation
+  for vLLM-Omni
+- [`review-pr`](review-pr/SKILL.md): provides a frozen-snapshot, contract-aware
+  workflow for maintainers and reviewers; contributors should use
+  `precheck-pr` for self-review
+- [`vllm-omni-npu-model-runner-upgrade`](vllm-omni-npu-upgrade/SKILL.md):
+  upgrades NPU model runners to align with the latest vllm-ascend
+  `NPUModelRunner`
+- [`vllm-omni-test`](vllm-omni-test/SKILL.md): guides generation and execution
+  of CI-aligned tests (L1–L4), pytest marker selection (`core_model` /
+  `advanced_model` / `full_model`, `omni` / `tts` / `diffusion`), Buildkite
+  wiring (`test-ready.yml`, `test-merge.yml`, `test-nightly.yml`,
+  `test-weekly.yml`), and copy-paste local plus CI-like `pytest` commands; see
+  `references/test-routing.md` for level-to-command mapping

 ## Maintenance Guidelines

@@ -97,6 +97,7 @@ If docs and live code disagree, verify the code/tests and report the drift.
 | [test-quality-evaluation.md](references/checks/test-quality-evaluation.md) | Tests change, are absent for risky code, or may not exercise production behavior. |
 | [tests-docs-checklist.md](references/checks/tests-docs-checklist.md) | Coverage, CI markers, examples, user docs, or PR evidence need review. |
 | [verification.md](references/checks/verification.md) | Hardware, a server, or a runnable affected path is available for active verification. |
+| [examples-policy.md](../precheck-pr/references/examples-policy.md) | The PR adds, copies, or renames Python under `examples/`; apply the canonical policy shared with `precheck-pr`. |

 ### Delivery and reviewer coordination

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

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

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

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

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

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

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

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

                images = getattr(request_output, "images", [])
@@ -167,9 +167,11 @@ def main() -> None:
    ]
    native_cmd = [
        sys.executable,
-        "examples/offline_inference/text_to_video/text_to_video_lingbot.py",
+        "examples/offline_inference/text_to_video/text_to_video.py",
        "--model",
        args.model,
+        "--model-class-name",
+        "LingBotVideoPipeline",
        "--prompt",
        args.prompt,
        "--output",
@@ -139,6 +139,34 @@ 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
@@ -84,12 +84,17 @@ 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] = (model_cfg.get("task_extra_body") or {}).get(task) or {}
+    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

    # Resolve dataset path
    if dataset_path:
@@ -109,7 +114,7 @@ def build_bench_args(
        "--port",
        str(port),
        "--model",
-        model,
+        served_model_name or model,
        "--backend",
        backend,
        "--endpoint",
@@ -119,7 +124,7 @@ def build_bench_args(
        "--num-prompts",
        str(num_prompts),
        "--num-warmups",
-        "2",
+        str(num_warmups),
        "--percentile-metrics",
        "ttft,e2el,audio_rtf,audio_ttfp,audio_duration,audio_underrun",
    ]
@@ -152,7 +157,8 @@ def build_bench_args(
        if result_filename:
            cmd += ["--result-filename", result_filename]

-    cmd += extra_cli_args
+    passthrough = extra_cli_args[1:] if extra_cli_args[:1] == ["--"] else extra_cli_args
+    cmd += passthrough
    return cmd


@@ -225,6 +231,12 @@ 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")
@@ -236,6 +248,13 @@ 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)"
    )
@@ -256,6 +275,9 @@ 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())
@@ -308,6 +330,9 @@ 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:
@@ -7,6 +7,17 @@
 # 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.
@@ -1,7 +1,24 @@
-# Target vllm version: v0.26.0 (releases/v0.26.0 branch).
+# Target vllm version: v0.27.0 (releases/v0.27.0 branch).
 # Keep VLLM_BASE_TAG aligned with the target vLLM release on every rebase.
+#
+# Release-boundary form: there is no precompiled-wheel pin here, because the
+# base image already ships exactly the target vLLM. Everything the runtime
+# needs — torch, flashinfer, nvJitLink, numpy — comes from that image as one
+# self-consistent set.
+#
+# This block reappears only when the rebase targets a vLLM commit that is NOT a
+# released tag. During the 0.27 rebase, targeting v0.27.0rc1 on top of the
+# v0.26.0 base image required reinstalling vLLM from wheels.vllm.ai and then
+# hand-repairing the fallout: pinning torch ahead of flashinfer (installing it
+# after bound flashinfer's TVM FFI runtime to the old torch and aborted engine
+# core init), registering libnvJitLink.so.13 with ldconfig, and re-pinning numpy
+# under numba's ceiling. All of it was compensation for a base image and a vLLM
+# that disagreed. At a real release boundary they agree, so none of it is
+# needed. If you pin an untagged commit again, restore the reinstall block —
+# agent/tasks/20_update_wheel_url.sh fails the run rather than letting the
+# Dockerfile silently ship the wrong vLLM.
 ARG VLLM_BASE_IMAGE=vllm/vllm-openai
-ARG VLLM_BASE_TAG=v0.26.0
+ARG VLLM_BASE_TAG=v0.27.0

 # We use registry-based layer caching for the dependencies,
 # where the tag is determined based on the hash of the contents
@@ -45,6 +45,7 @@ RUN source /usr/local/Ascend/ascend-toolkit/set_env.sh && \
      "hyperpyyaml>=1.2.0" \
      "onnx>=1.16.0" \
      "librosa>=0.10.0" \
+      "datasets>=3.0.0" \
      --index-url http://cache-service.nginx-pypi-cache.svc.cluster.local/pypi/simple \
      --trusted-host cache-service.nginx-pypi-cache.svc.cluster.local \
      --default-timeout=60 && \
@@ -46,6 +46,7 @@ RUN source /usr/local/Ascend/ascend-toolkit/set_env.sh && \
      "onnx>=1.16.0" \
      "librosa>=0.10.0" \
      "torchmetrics>=1.9.0" \
+      "datasets>=3.0.0" \
      --index-url http://cache-service.nginx-pypi-cache.svc.cluster.local/pypi/simple \
      --trusted-host cache-service.nginx-pypi-cache.svc.cluster.local \
      --default-timeout=60 && \
@@ -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=v0.26.0
+ARG VLLM_VERSION=v0.27.0
 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==3.7.1 && \
+    uv pip install triton-xpu==3.7.2 && \
    uv pip install --no-build-isolation --no-deps .

 CMD ["/bin/bash"]
@@ -144,12 +144,13 @@ 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 oneccl oneccl-devel && \
-    uv pip install triton-xpu==3.7.1 --reinstall
+    uv pip uninstall triton && \
+    uv pip install triton-xpu==3.7.2 --reinstall

 FROM vllm-omni AS vllm-omni-openai

@@ -40,30 +40,13 @@ nav:
  - Models:
    - models/supported_models.md
  - Features:
-    - 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
-        - Expert Parallel: user_guide/diffusion/parallelism/expert_parallel.md
-        - Hybrid Sharded Data Parallel: user_guide/diffusion/parallelism/hsdp.md
-        - 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
-      - CPU Offloading: user_guide/diffusion/cpu_offload.md
-      - LoRA: user_guide/diffusion/lora.md
-      - Custom Pipeline: features/custom_pipeline.md
-      - Execution Modes: user_guide/diffusion/execution_modes.md
+    - Overview: features/README.md
+    - Runtime and Stage Execution:
+      - Execution Modes and Streaming: user_guide/diffusion/execution_modes.md
+      - Prefill-Decode Disaggregation (experimental): features/pd_disaggregation.md
+      - Sleep Mode: features/sleep_mode.md
    - Quantization:
-      - Quantization: user_guide/quantization/overview.md
+      - 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
@@ -72,9 +55,33 @@ nav:
      - 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
+    - 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
+      - Cache Acceleration:
+        - TeaCache: user_guide/diffusion/cache_acceleration/teacache.md
+        - Cache-DiT: user_guide/diffusion/cache_acceleration/cache_dit.md
+      - Parallelism:
+        - Overview: user_guide/diffusion/parallelism/overview.md
+        - CFG Parallel: user_guide/diffusion/parallelism/cfg_parallel.md
+        - Expert Parallel: user_guide/diffusion/parallelism/expert_parallel.md
+        - Hybrid Sharded Data Parallel: user_guide/diffusion/parallelism/hsdp.md
+        - 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
+      - LoRA: user_guide/diffusion/lora.md
+    - Experimental:
+      - Session State Manager: features/session_state_manager.md
+  - Integrations:
+    - Custom Diffusion Pipeline: features/custom_pipeline.md
    - ComfyUI: features/comfyui.md
  - Fault Tolerance:
    - Failure Modes: user_guide/fault_injection_reliability_matrix.md
@@ -108,6 +115,7 @@ 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
@@ -117,9 +125,8 @@ nav:
          - Sequence Parallel: design/feature/sequence_parallel.md
          - Tensor Parallel: design/feature/tensor_parallel.md
          - VAE Patch Parallelism: design/feature/vae_parallel.md
-        - Attention Backends:
+        - Attention Optimization:
          - 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
@@ -14,10 +14,6 @@ For introduction, please check [Introduction for stage config](./stage_configs.m

 - **[GPU Memory Calculation and Configuration](./gpu_memory_utilization.md)** - Guide on how to calculate memory requirements and set up `gpu_memory_utilization` for optimal performance

-## Multi-Stage Recipes
-
- **[Prefill-Decode Disaggregation](./pd_disaggregation.md)** - How to derive a PD-aware Qwen3-Omni stage config from the default config without introducing another bundled YAML
-
 ## Optimization Features

 - **[Diffusion Features Overview](../user_guide/diffusion_features.md)** - Complete overview of all diffusion model features and supported models
@@ -1,171 +0,0 @@
-# Prefill-Decode (PD) Disaggregation
-
-PD disaggregation splits the Qwen3-Omni thinker into separate prefill and decode
-stages so prompt processing and token generation can run on different workers.
-
-This is documented as a stage-config recipe instead of a bundled YAML because the
-deployment-specific values usually change per environment:
-
- GPU placement
- `tensor_parallel_size`
- connector backend and connector ports
- connector IPs or bootstrap addresses
-
-Start from the [default Qwen3-Omni stage config](gh-file:vllm_omni/deploy/qwen3_omni_moe.yaml)
-and copy it to your own file, for example `qwen3_omni_pd.yaml`. Then apply the
-changes below.
-
-## Requirements
-
- 3+ GPUs for a basic layout: prefill, decode, and talker+code2wav
- A KV connector supported by vLLM, such as `MooncakeConnector`
- Matching `tensor_parallel_size` on the prefill and decode thinker stages
-
-## 1. Split the thinker into prefill and decode stages
-
-Replace the original thinker stage with two stages:
-
-```yaml
-stage_args:
-  - stage_id: 0
-    stage_type: llm
-    is_prefill_only: true
-    runtime:
-      devices: "0"
-    engine_args:
-      max_num_seqs: 16
-      model_stage: thinker
-      model_arch: Qwen3OmniMoeForConditionalGeneration
-      worker_type: ar
-      scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
-      gpu_memory_utilization: 0.9
-      enforce_eager: true
-      trust_remote_code: true
-      engine_output_type: latent
-      distributed_executor_backend: "mp"
-      enable_prefix_caching: false
-      max_num_batched_tokens: 32768
-      hf_config_name: thinker_config
-      tensor_parallel_size: 1
-      kv_transfer_config:
-        kv_connector: "MooncakeConnector"
-        kv_role: "kv_producer"
-        kv_rank: 0
-        kv_parallel_size: 2
-        kv_connector_extra_config:
-          mooncake_bootstrap_port: 25201
-    final_output: false
-    is_comprehension: true
-    default_sampling_params:
-      temperature: 0.4
-      top_p: 0.9
-      top_k: 1
-      max_tokens: 2048
-      seed: 42
-      detokenize: True
-      repetition_penalty: 1.05
-
-  - stage_id: 1
-    stage_type: llm
-    is_decode_only: true
-    runtime:
-      devices: "1"
-    engine_args:
-      max_num_seqs: 64
-      model_stage: thinker
-      model_arch: Qwen3OmniMoeForConditionalGeneration
-      worker_type: ar
-      scheduler_cls: vllm_omni.core.sched.omni_ar_scheduler.OmniARScheduler
-      gpu_memory_utilization: 0.9
-      enforce_eager: true
-      trust_remote_code: true
-      engine_output_type: latent
-      distributed_executor_backend: "mp"
-      enable_prefix_caching: false
-      max_num_batched_tokens: 32768
-      hf_config_name: thinker_config
-      tensor_parallel_size: 1
-      kv_transfer_config:
-        kv_connector: "MooncakeConnector"
-        kv_role: "kv_consumer"
-        kv_rank: 1
-        kv_parallel_size: 2
-        kv_connector_extra_config:
-          mooncake_bootstrap_port: 25202
-    engine_input_source: [0]
-    final_output: true
-    final_output_type: text
-    is_comprehension: true
-    default_sampling_params:
-      temperature: 0.4
-      top_p: 0.9
-      top_k: 1
-      max_tokens: 2048
-      seed: 42
-      detokenize: True
-      repetition_penalty: 1.05
-```
-
-Notes:
-
- `is_prefill_only: true` marks the thinker stage that only saves KV.
- `is_decode_only: true` marks the thinker stage that resumes from remote KV.
- `kv_transfer_config` is required on both stages.
- The orchestrator forces the prefill stage to run with `max_tokens=1`, so the
-  prefill side only processes the prompt and exports KV.
-
-## 2. Shift the downstream stages by one index
-
-After inserting the extra thinker stage, renumber the remaining stages:
-
-```yaml
-  - stage_id: 2
-    runtime:
-      devices: "2"
-    engine_input_source: [1]
-    sync_process_input_func: vllm_omni.model_executor.stage_input_processors.qwen3_omni.thinker2talker_token_only
-
-  - stage_id: 3
-    runtime:
-      devices: "2"
-    engine_args:
-      max_num_seqs: 1
-    engine_input_source: [2]
-    sync_process_input_func: vllm_omni.model_executor.stage_input_processors.qwen3_omni.talker2code2wav_token_only
-```
-
-Compared with the default Qwen3-Omni config:
-
- the talker becomes stage `2` instead of stage `1`
- the code2wav stage becomes stage `3` instead of stage `2`
- the talker now reads from decode stage `1`
-
-## 3. Add runtime edges for the four-stage pipeline
-
-```yaml
-runtime:
-  enabled: true
-  edges:
-    - from: 0
-      to: 1
-    - from: 1
-      to: 2
-    - from: 2
-      to: 3
-```
-
-## 4. Launch with your custom config
-
-```bash
-vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091 \
-    --deploy-config /path/to/qwen3_omni_pd.yaml
-```
-
-## Operational Notes
-
- `MooncakeConnector` does not support heterogeneous TP sizes across the PD
-  pair. Keep prefill and decode at the same `tensor_parallel_size`.
- If the thinker requires TP=2, both thinker stages must use TP=2 and be given
-  separate GPU sets, for example `"0,1"` for prefill and `"2,3"` for decode.
- Choose connector ports and addresses that match your deployment. The values
-  shown above are examples only.
@@ -76,6 +76,47 @@ Please refer to the [test instructions](./ci/test_execution_guide.md) for detail
 !!! warning
    Currently, not all unit tests pass when run on CPU platforms. If you don't have access to a GPU platform to run unit tests locally, rely on the continuous integration system to run the tests for now.

+### Using repository skills with coding agents
+
+vLLM-Omni maintains [repository-scale skills](https://github.com/vllm-project/vllm-omni/tree/main/.claude/skills) for common coding, testing, performance, and review workflows. These skills capture repository-specific structure, validation requirements, and evidence standards that a general coding agent may not know.
+
+If your coding agent discovers repository skills automatically, ask it to use the relevant skill by name. Otherwise, point it to the linked `SKILL.md` and ask it to read the file before changing code. Skills can be combined: use a domain skill for the implementation, `vllm-omni-test` for test and CI decisions, and `precheck-pr` before opening the pull request.
+
+| Task | Repository skill |
+| --- | --- |
+| Add or extend a diffusion model | [`add-diffusion-model`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/add-diffusion-model/SKILL.md) |
+| Add or extend a text-to-speech model | [`add-tts-model`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/add-tts-model/SKILL.md) |
+| Add, select, or debug quantization | [`quantization`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/quantization/SKILL.md) |
+| Diagnose or optimize diffusion performance | [`diffusion-perf-opt`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/diffusion-perf-opt/SKILL.md) |
+| Upgrade an NPU model runner | [`vllm-omni-npu-model-runner-upgrade`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/vllm-omni-npu-upgrade/SKILL.md) |
+| Add a regression test, choose L1-L4 coverage, or wire Buildkite | [`vllm-omni-test`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/vllm-omni-test/SKILL.md) |
+| Self-check a branch before opening a pull request | [`precheck-pr`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/precheck-pr/SKILL.md) |
+| Perform a maintainer or reviewer review of an existing pull request or local branch | [`review-pr`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/review-pr/SKILL.md) |
+
+Use the following workflow when collaborating with a coding agent:
+
+1. **Ground the task.** Provide the issue, RFC, or design document that defines the expected behavior. Ask the agent to state its assumptions and identify the affected module or feature contracts before editing code.
+2. **Select the narrowest applicable skills.** Start with the domain skill that owns the change. Add `vllm-omni-test` whenever production behavior or tests change. For a bug fix, ask for the smallest reproducer and a regression test tied to the original symptom.
+3. **Require executable evidence.** Ask the agent to run the narrowest relevant checks and report the exact commands and results. It must identify tests it could not run because of unavailable hardware, model weights, credentials, or dependencies; an unrun check is not a pass.
+4. **Inspect the resulting diff.** Confirm that the implementation stays within the issue or RFC, does not overwrite unrelated work, and includes the required tests, documentation, and benchmark or accuracy evidence.
+5. **Run the pre-submit workflow.** Use `precheck-pr`, then run the applicable local tests and pre-commit hooks before opening the pull request.
+
+For example:
+
+```text
+Use add-diffusion-model and vllm-omni-test to implement the linked issue.
+Read the relevant design documents first, keep the change within the issue,
+and report every test command, result, and hardware validation gap.
+```
+
+```text
+Use vllm-omni-test to turn this bug reproducer into the smallest deterministic
+regression test. Prefer L1 CPU coverage; if the failure requires real weights
+or serving, explain the required L2/L3 environment and provide the exact command.
+```
+
+Repository skills guide the agent, but they do not replace contributor judgment, the accepted issue or RFC, required test evidence, or maintainer review. Review all generated changes before submitting them.
+
 ## Issues

 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.
@@ -11,6 +11,10 @@ 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)
@@ -30,6 +34,10 @@ in the active navigation.
 - [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
@@ -42,14 +50,14 @@ in the active navigation.
 - [Tensor Parallel](feature/tensor_parallel.md)
 - [VAE Patch Parallelism](feature/vae_parallel.md)

-#### Attention Backends
+#### 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:

 - [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)
@@ -0,0 +1,73 @@
+# 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 |
+
+The [Prefill-Decode Disaggregation design reference](pd_disaggregation.md)
+describes the experimental Qwen3-Omni runtime topology and its current
+integration boundaries. It is not yet a supported launch recipe.
+
+Some runtime designs are deliberately not promoted as standalone User Guide
+features yet:
+
+- Generic, OmniConnector-based
+  [Disaggregated Inference](../design/feature/disaggregated_inference.md) is not
+  yet a standalone User Guide feature. Its
+  [OmniConnector implementations](../design/index.md#communication) describe
+  transport contracts, while practical connector 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.
@@ -0,0 +1,140 @@
+# Prefill-Decode (PD) Disaggregation (experimental)
+
+!!! warning "Experimental design reference"
+    PD disaggregation is only partially integrated on the current `main` branch.
+    The runtime contains PD detection and routing scaffolding, but there is no
+    supported deploy overlay or end-to-end validated launch recipe yet. Treat this
+    page as a description of the current design, not as production guidance.
+
+Prefill-decode disaggregation splits the Qwen3-Omni Thinker into two logical
+stages: a prefill worker that processes the prompt and produces KV cache, and a
+decode worker that imports that cache and generates tokens. The Talker and
+Code2Wav stages remain downstream; PD does not disaggregate the entire
+Qwen3-Omni pipeline.
+
+## Current design
+
+### Runtime topology
+
+```mermaid
+flowchart LR
+    client["Client request"] --> orchestrator["Omni Orchestrator"]
+    orchestrator -->|"original prompt<br/>max_tokens = 1"| prefill["Thinker prefill<br/>KV producer"]
+    prefill -->|"KV blocks"| connector[("vLLM KV connector")]
+    prefill -->|"KV routing metadata"| orchestrator
+    orchestrator -->|"original prompt<br/>remote KV parameters"| decode["Thinker decode<br/>KV consumer"]
+    connector -->|"remote KV load"| decode
+    decode -->|"generated text"| client
+```
+
+The orchestrator owns the request lifecycle, while the vLLM KV connector owns
+the bulk KV transfer. The original prompt is submitted to both Thinker stages:
+the decode worker still needs the request tokens and multimodal features to
+construct its request, while the imported KV cache prevents it from repeating
+the completed prefill computation.
+
+For the full Qwen3-Omni pipeline, splitting the Thinker adds one logical stage:
+
+| Stage | Role | Output or handoff |
+| --- | --- | --- |
+| 0 | Thinker prefill | Saves prompt KV and prompt-side multimodal output |
+| 1 | Thinker decode | Loads remote KV, generates text, and produces decode-side conditioning |
+| 2 | Talker | Converts combined Thinker conditioning into codec codes |
+| 3 | Code2Wav | Converts codec codes into audio |
+
+Text remains a final output of the decode stage, and audio remains a final
+output of Code2Wav.
+
+### Request lifecycle
+
+1. At startup, PD detection looks for one stage marked `is_prefill_only` and
+   one downstream stage marked `is_decode_only`. The decode stage must list the
+   prefill stage as an input source.
+2. The orchestrator clones the Thinker sampling parameters for prefill, forces
+   `max_tokens=1`, clears stop conditions, and sets producer-side
+   `kv_transfer_params`. This makes prefill finish by length after exporting KV.
+3. When prefill finishes, the orchestrator captures its KV routing metadata and
+   Qwen multimodal output under the original request ID.
+4. The orchestrator submits the original prompt to decode with consumer-side
+   `kv_transfer_params`. Those parameters identify the transfer, prefill engine,
+   bootstrap endpoint, and, when required by the connector, remote request.
+5. The decode engine imports the KV cache and continues generation. The normal
+   Omni stage path then carries Thinker conditioning through Talker and
+   Code2Wav.
+
+### Control and data paths
+
+PD uses two different connector layers. They solve different handoffs and are
+not interchangeable.
+
+| Path | Payload | Owner |
+| --- | --- | --- |
+| PD control path | Request ID, original prompt, `transfer_id`, bootstrap address, engine ID, and connector-specific request metadata | Omni Orchestrator |
+| PD data path | Thinker attention KV blocks | vLLM KV connector |
+| Downstream model path | Thinker hidden states and embeddings, Talker codec codes, and audio tensors | vLLM-Omni stage connectors and processors |
+
+### Detection and validation
+
+The current runtime accepts one logical prefill/decode pair per pipeline. Each
+stage may use tensor parallelism, but both sides of the pair must satisfy these
+invariants:
+
+- both stages define `kv_transfer_config`
+- prefill uses `kv_producer` or `kv_both`
+- decode uses `kv_consumer` or `kv_both`
+- both stages use the same KV connector
+- `kv_buffer_device` and `kv_buffer_size` agree when set on both stages
+- both stages use the same `tensor_parallel_size`
+
+These checks happen before request processing so an inconsistent PD topology
+fails during engine initialization.
+
+## What is implemented today
+
+| Area | Current state |
+| --- | --- |
+| Pair detection and topology validation | Implemented in the shared PD helper |
+| Prefill sampling preparation | Implemented for sync and async entrypoints |
+| Prefill-to-decode routing | Implemented in the current Orchestrator |
+| Deploy-based configuration | Not yet migrated; the supported `stages:` deploy schema cannot currently expand Qwen3-Omni into the four-stage PD topology |
+| Qwen3 Thinker-to-Talker handoff | Bridge state and embedding-merge helpers exist, but the merge is not wired into the live stage processor |
+| Connector compatibility | Incomplete; the Orchestrator currently requires `remote_request_id`, while the pinned Mooncake connector completes prefill without returning that metadata |
+| Validation coverage | PD entrypoint tests and the PD deploy overlay in online end-to-end tests are temporarily disabled during migration |
+| Performance | No non-regression or throughput/latency result is established for the current path |
+
+Because these pieces are still converging, the previous legacy `stage_args` YAML
+and `vllm serve` command have been removed. The serve CLI now accepts the
+deploy-based `stages:` schema, so retaining the legacy recipe would imply a
+launch path that current `main` rejects.
+
+## Configuration contract under development
+
+The intended deploy integration starts from the
+[default Qwen3-Omni deploy config](gh-file:vllm_omni/deploy/qwen3_omni_moe.yaml)
+and expands its frozen three-stage pipeline into the four stages shown above.
+A complete integration must:
+
+- split the Thinker definition without duplicating the Talker or Code2Wav model
+  contracts
+- remap downstream stage IDs, input sources, and stage connectors
+- inject producer and consumer `kv_transfer_config` values into the two Thinker
+  stages
+- allocate separate device sets for prefill and decode
+- preserve both final outputs: decode text and Code2Wav audio
+- provide connector-specific routing metadata without hard-coding one connector
+  contract
+
+The required number of GPUs depends on tensor-parallel sizes and whether Talker
+and Code2Wav share a device. A basic TP=1 layout uses one device for prefill, one
+for decode, and one for the two downstream audio stages.
+
+## Implementation map
+
+- PD detection, validation, and sampling-parameter preparation:
+  [`PDDisaggregationMixin`](gh-file:vllm_omni/entrypoints/pd_utils.py)
+- startup detection and connector metadata:
+  [`AsyncOmniEngine`](gh-file:vllm_omni/engine/async_omni_engine.py)
+- request-scoped prefill-to-decode routing:
+  [`Orchestrator`](gh-file:vllm_omni/engine/orchestrator.py)
+- Qwen3 downstream conditioning helpers:
+  [Qwen3-Omni stage input processor](gh-file:vllm_omni/model_executor/stage_input_processors/qwen3_omni.py)
@@ -51,6 +51,7 @@ th {
 | `OvisImagePipeline` | Ovis-Image | `OvisAI/Ovis-Image` | ✅︎ | ✅︎ | | ✅︎ | — |
 | `LongcatImagePipeline` | LongCat-Image | `meituan-longcat/LongCat-Image` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
 | `LongCatImageEditPipeline` | LongCat-Image-Edit | `meituan-longcat/LongCat-Image-Edit` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
+| `LongCatVideoAvatarPipeline` | LongCat-Video-Avatar-1.5 A2V/AI2V (native single-speaker and multi-speaker AI2V/AVC) | `meituan-longcat/LongCat-Video-Avatar-1.5` | ✅︎ | | | | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/meituan-longcat/LongCat-Video-Avatar-1.5.md) |
 | `BooguImagePipeline` | Boogu-Image | `Boogu/Boogu-Image-0.1-Base`, `Boogu/Boogu-Image-0.1-Edit` | ✅︎ | | | | — |
 | `StableDiffusionXLPipeline` | Stable-Diffusion-XL | `stabilityai/stable-diffusion-xl-base-1.0` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
 | `StableDiffusion3Pipeline` | Stable-Diffusion-3 | `stabilityai/stable-diffusion-3.5-medium` | ✅︎ | ✅︎ | | ✅︎ | — |
@@ -79,6 +80,7 @@ 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` | ✅︎ | ✅︎ | | | — |
@@ -1,14 +1,18 @@
 # 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 currently supports PEFT-style LoRA adapters, allowing you to customize model behavior without modifying the base model weights.
+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.

 ## Overview

-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 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 Adapter Format

-LoRA adapters must be in **PEFT (Parameter-Efficient Fine-Tuning)** format. A typical LoRA adapter directory structure:
+### PEFT (Parameter-Efficient Fine-Tuning) format (default)
+A typical PEFT-format LoRA adapter directory structure:

 ```
 lora_adapter/
@@ -25,19 +29,20 @@ The `adapter_config.json` file contains metadata about the LoRA adapter, includi

 ### Offline Inference

-#### Pre-loaded LoRA
+#### PEFT backend: pre-loaded LoRA

-Load a LoRA adapter at initialization. This adapter is pre-loaded into the cache and can be activated by requests:
+Load a PEFT-format LoRA adapter at initialization. The adapter is pre-loaded into the cache and can be activated per request:

 ```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_path=lora_path,
+    lora_backend="peft",  # default, can be omitted
 )

 lora_request = LoRARequest(
@@ -53,6 +58,98 @@ 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.

@@ -141,6 +238,7 @@ 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
@@ -164,10 +164,12 @@ In `DiffusionParallelConfig`:
 | `ulysses_degree` | int | 1 | Number of GPUs for Ulysses-SP. Uses all-to-all communication. |
 | `ring_degree` | int | 1 | Number of GPUs for Ring-Attention. Uses P2P ring communication. |
 | `ulysses_mode` | str | `"default"` | Ulysses attention mode. Set to `"advanced_uaa"` to handle arbitrary sequence lengths and head counts without padding. |
+| `mask_sp_padding` | bool | `False` | When the sequence length is not divisible by the SP size, tokens are auto-padded with zeros. Set to `True` to mask those padding tokens (strict, but uses the slower varlen attention path); the default `False` leaves them unmasked, keeping the fast path with negligible numerical impact. |

 **Notes:**
 - Total sequence parallel size equals to `ulysses_degree × ring_degree`
 - Degrees must evenly divide the sequence length for optimal performance (or use `ulysses_mode="advanced_uaa"` for Ulysses-SP)
+- `mask_sp_padding` is an experimental feature, currently only supported by `Wan2.2`, `Wan2.2 Vace`, `Qwen-Image`, `Flux 2`, and `HunyuanVideo 1.5`


 ## Best Practices
@@ -0,0 +1,60 @@
+# 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** |
@@ -49,7 +49,7 @@ Parallelism methods distribute computation across GPUs without quality loss (mat

 | Method | Description | Best For |
 |--------|-------------|----------|
-| **[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 |
+| **[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 |

 **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.

@@ -100,7 +100,9 @@ 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:

@@ -119,7 +121,7 @@ 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**   |     ✅     |     ✅      |           ✅           |       ✅        |         ✅         |          ❌          |   ❌    |             ✅             |          ❌           |       ❌        |        ❌         |
@@ -145,6 +147,7 @@ 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

@@ -155,12 +158,16 @@ 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
@@ -189,7 +196,7 @@ 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)** | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❓ | ❓ | ❌ | | | | | |
@@ -200,97 +207,43 @@ The following tables show which models support each feature:

 !!! info

-    1. Tensor Parallel and HSDP are not compatible.
+    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.
    2. TeaCache and Cache-DiT are not compatible.
    3. CPU Offloading (Layerwise) and CPU Offloading (Module-wise) are not compatible.
-    4. CPU Offloading (Layerwise) supports single-card for now.
-    5. Using FP8-Quant as an example of qunatization methods.
+    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.
    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

-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** |
+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.

 ## Learn More

-**Cache Acceleration:**
+The Diffusion Acceleration navigation groups the remaining guides as follows:

- **[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
+| 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) |

-**Parallelism Methods:**
+Related cross-model and runtime features are documented separately:

- **[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
+- [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.
@@ -88,8 +88,9 @@ 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 |
+| `--lora-path` | str | — | Path to PEFT LoRA adapter folder or checkpoint file |
 | `--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:**

@@ -12,6 +12,7 @@ For backend selection and SageAttention usage, see the [Diffusion Attention Back
 | Model | Default Resolution | Default Frames | Default Steps | Guidance | VRAM (BF16) |
 |---|---|---|---|---|---|
 | `Wan-AI/Wan2.2-T2V-A14B-Diffusers` | 720x1280 | 81 | 40 | 4.0 | ~60 GiB |
+| `robbyant/lingbot-video-dense-1.3b` / `robbyant/lingbot-video-moe-30b-a3b` | 192x320 | 9 | 2 | 3.0 | ~68 GiB (MoE smoke) |
 | `Lightricks/LTX-2` | 512x768 | 121 | 40 | video 3.0 / audio 7.0 | Model-dependent |
 | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v` | 480x832 | 121 | 50 | 6.0 | 1×A100 80GB |
 | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v` | 720x1280 | 121 | 50 | 6.0 | FP8 + VAE tiling required |
@@ -35,6 +36,25 @@ python text_to_video.py \
  --output t2v_out.mp4
 ```

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

 ```bash
@@ -125,6 +145,9 @@ 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

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

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

 ## Hardware Support

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

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

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

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

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

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

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


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

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

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

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

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

        # AR stage text — each CompletionOutput.text is already a delta when
@@ -621,7 +621,7 @@ def main():
        images = getattr(output, "images", None)
        if images:
            break
-        req_out = getattr(output, "request_output", None)
+        req_out = output
        images = getattr(req_out, "images", None) if req_out is not None else None
        if images:
            break
@@ -60,8 +60,12 @@ 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,
 )
@@ -83,6 +87,22 @@ 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=(
@@ -294,6 +314,30 @@ 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"),
@@ -487,6 +531,12 @@ 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:
@@ -517,19 +567,39 @@ 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,
@@ -540,7 +610,7 @@ def main():
        num_inference_steps=num_inference_steps,
        num_frames=num_frames,
        frame_rate=frame_rate,
-        extra_args={"sample_solver": args.sample_solver},
+        extra_args=sampling_extra_args,
    )
    if flow_shift is not None:
        sampling_params.extra_args["flow_shift"] = flow_shift
@@ -576,8 +646,8 @@ def main():
        if frames.multimodal_output and "audio" in frames.multimodal_output:
            audio = frames.multimodal_output["audio"]
            audio_sample_rate = frames.multimodal_output.get("audio_sample_rate", audio_sample_rate)
-        if frames.is_pipeline_output and frames.request_output is not None:
-            inner_output = frames.request_output
+        if frames.is_pipeline_output and frames is not None:
+            inner_output = frames
            if isinstance(inner_output, OmniRequestOutput):
                if inner_output.multimodal_output and "audio" in inner_output.multimodal_output:
                    audio = inner_output.multimodal_output["audio"]
@@ -0,0 +1,319 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import argparse
+import json
+import re
+import subprocess
+import tempfile
+import wave
+from functools import lru_cache
+from pathlib import Path
+from typing import Any
+
+import imageio
+import numpy as np
+import torch
+
+from vllm_omni.diffusion.data import DiffusionParallelConfig
+from vllm_omni.diffusion.models.longcat_video.pipeline_longcat_video_avatar import (
+    prepare_longcat_video_avatar_model_for_omni,
+)
+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():
+    parser = argparse.ArgumentParser(description="Generate LongCat-Video-Avatar A2V/AI2V video.")
+    parser.add_argument("--model", default="meituan-longcat/LongCat-Video-Avatar-1.5")
+    parser.add_argument("--base-model-dir", default=None, help="Optional local LongCat-Video base model directory.")
+    parser.add_argument("--stage", choices=["at2v", "ai2v"], default="at2v")
+    parser.add_argument("--prompt", default="A person speaks calmly while facing the camera.")
+    parser.add_argument("--negative-prompt", default="low quality, blurry, watermark, text")
+    parser.add_argument("--audio", default=None, help="Input speech audio path.")
+    parser.add_argument("--image", default=None, help="Input reference image path for AI2V.")
+    parser.add_argument("--input-json", default=None, help="Official LongCat Avatar JSON case for AI2V.")
+    parser.add_argument("--output", default="longcat_avatar_output.mp4")
+    parser.add_argument("--resolution", choices=["480p", "720p"], default="480p")
+    parser.add_argument("--num-frames", type=int, default=93)
+    parser.add_argument("--num-segments", default="1", help="Number of AVC segments, or 'auto' to cover full audio.")
+    parser.add_argument("--num-cond-frames", type=int, default=13)
+    parser.add_argument("--ref-img-index", type=int, default=10)
+    parser.add_argument("--mask-frame-range", type=int, default=3)
+    parser.add_argument("--fps", type=int, default=25)
+    parser.add_argument("--num-inference-steps", type=int, default=8)
+    parser.add_argument("--seed", type=int, default=42)
+    parser.add_argument("--use-distill", action=argparse.BooleanOptionalAction, default=True)
+    parser.add_argument("--use-int8", action=argparse.BooleanOptionalAction, default=True)
+    parser.add_argument(
+        "--build-components-on-gpu",
+        action="store_true",
+        default=False,
+        help="Build large Avatar components directly on GPU for faster startup. Requires more peak VRAM.",
+    )
+    parser.add_argument("--use-kv-cache", action=argparse.BooleanOptionalAction, default=True)
+    parser.add_argument("--offload-kv-cache", action=argparse.BooleanOptionalAction, default=False)
+    parser.add_argument("--enforce-eager", action="store_true")
+    return parser.parse_args()
+
+
+def _extract_frames(output: Any):
+    if isinstance(output, list):
+        output = output[0] if output else None
+    if isinstance(output, OmniRequestOutput):
+        if not output.images:
+            raise ValueError("No video frames found in OmniRequestOutput.")
+        frames = output.images[0] if len(output.images) == 1 else output.images
+        if isinstance(frames, tuple) and len(frames) == 2:
+            frames = frames[0]
+        if isinstance(frames, dict):
+            frames = frames.get("frames") or frames.get("video")
+    else:
+        frames = output
+    if frames is None:
+        raise ValueError("No video frames found in output.")
+    return frames
+
+
+def _frames_to_uint8(frames):
+    frames_np = np.asarray([np.asarray(frame) for frame in frames])
+    if frames_np.ndim == 5:
+        frames_np = frames_np[0]
+    if np.issubdtype(frames_np.dtype, np.floating):
+        frames_np = (np.clip(frames_np, 0.0, 1.0) * 255).round()
+    return np.clip(frames_np, 0, 255).astype(np.uint8)
+
+
+@lru_cache(maxsize=1)
+def _ffmpeg_exe() -> str:
+    try:
+        import imageio_ffmpeg
+
+        return imageio_ffmpeg.get_ffmpeg_exe()
+    except Exception as exc:
+        raise RuntimeError("LongCat Avatar example requires the Python imageio[ffmpeg] dependency.") from exc
+
+
+def _wav_duration(path: Path) -> float:
+    with wave.open(str(path), "rb") as audio:
+        return audio.getnframes() / float(audio.getframerate())
+
+
+def _save_video_with_audio(frames, output_path: Path, audio_path: str, fps: int) -> None:
+    frames_np = _frames_to_uint8(frames)
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    with tempfile.TemporaryDirectory(prefix="longcat_avatar_export_") as tmp:
+        tmp_dir = Path(tmp)
+        temp_video = tmp_dir / "video.mp4"
+        crop_audio = tmp_dir / "audio.wav"
+        crop_video = tmp_dir / "video_cropped.mp4"
+        writer = imageio.get_writer(str(temp_video), fps=fps, quality=5)
+        try:
+            for frame in frames_np:
+                writer.append_data(frame)
+        finally:
+            writer.close()
+
+        duration = len(frames_np) / fps
+        ffmpeg = _ffmpeg_exe()
+        subprocess.run([ffmpeg, "-y", "-i", audio_path, "-t", f"{duration}", str(crop_audio)], check=True)
+        audio_duration = _wav_duration(crop_audio)
+        subprocess.run(
+            [
+                ffmpeg,
+                "-y",
+                "-i",
+                str(temp_video),
+                "-t",
+                f"{audio_duration}",
+                "-c:v",
+                "copy",
+                str(crop_video),
+            ],
+            check=True,
+        )
+        subprocess.run(
+            [
+                ffmpeg,
+                "-y",
+                "-i",
+                str(crop_video),
+                "-i",
+                str(crop_audio),
+                "-c:v",
+                "libx264",
+                "-c:a",
+                "aac",
+                "-shortest",
+                str(output_path),
+            ],
+            check=True,
+        )
+
+
+def _infer_asset_root_from_input_json(input_json: Path) -> Path:
+    resolved = input_json.resolve()
+    parts = resolved.parts
+    if "assets" not in parts:
+        return resolved.parent
+    asset_idx = parts.index("assets")
+    return Path(*parts[:asset_idx])
+
+
+def _resolve_case_path(path: str, asset_root: Path) -> str:
+    raw_path = Path(path)
+    if raw_path.is_absolute():
+        return str(raw_path)
+    candidate = asset_root / raw_path
+    return str(candidate if candidate.exists() else raw_path)
+
+
+def _speaker_sort_key(name: str) -> tuple[int, str]:
+    match = re.search(r"(\d+)$", str(name))
+    return (int(match.group(1)) if match else 10_000, str(name))
+
+
+def _load_input_case(input_json: str) -> tuple[dict[str, Any], Path]:
+    input_json_path = Path(input_json)
+    with input_json_path.open("r", encoding="utf-8") as f:
+        case = json.load(f)
+    return case, _infer_asset_root_from_input_json(input_json_path)
+
+
+def _create_merged_audio_for_case(
+    audio_paths: dict[str, str],
+    audio_type: str,
+    output_path: Path,
+) -> str:
+    sorted_paths = [path for _, path in sorted(audio_paths.items(), key=lambda item: _speaker_sort_key(str(item[0])))]
+    if len(sorted_paths) == 1:
+        return sorted_paths[0]
+    inputs = [arg for path in sorted_paths for arg in ("-i", path)]
+    audio_type = audio_type.lower()
+    if audio_type == "para":
+        filter_complex = f"amix=inputs={len(sorted_paths)}:duration=longest:normalize=0"
+    elif audio_type == "add":
+        filter_complex = "".join(f"[{idx}:a]" for idx in range(len(sorted_paths)))
+        filter_complex += f"concat=n={len(sorted_paths)}:v=0:a=1"
+    else:
+        raise NotImplementedError(f"Unsupported LongCat Avatar audio_type {audio_type!r}.")
+    subprocess.run([_ffmpeg_exe(), "-y", *inputs, "-filter_complex", filter_complex, str(output_path)], check=True)
+    return str(output_path)
+
+
+def main():
+    args = parse_args()
+    input_case = None
+    input_asset_root = None
+    audio_for_mux = args.audio
+    if args.input_json:
+        input_case, input_asset_root = _load_input_case(args.input_json)
+        args.stage = "ai2v"
+        args.prompt = input_case.get("prompt") or args.prompt
+        args.image = _resolve_case_path(input_case["cond_image"], input_asset_root)
+        args.audio = None
+    if not args.input_json and not args.audio:
+        raise ValueError("--audio is required unless --input-json is provided.")
+    if args.stage == "ai2v" and not args.image:
+        raise ValueError("--image is required when --stage=ai2v.")
+    model_for_omni = prepare_longcat_video_avatar_model_for_omni(args.model, args.use_int8)
+
+    additional_config = {
+        "model_type": "avatar-v1.5",
+        "resolution": args.resolution,
+        "use_distill": args.use_distill,
+        "use_int8": args.use_int8,
+        "build_components_on_gpu": args.build_components_on_gpu,
+    }
+    if args.base_model_dir:
+        additional_config["base_model_dir"] = args.base_model_dir
+
+    omni = Omni(
+        model=model_for_omni,
+        model_class_name="LongCatVideoAvatarPipeline",
+        dtype="bfloat16",
+        enforce_eager=args.enforce_eager,
+        additional_config=additional_config,
+        parallel_config=DiffusionParallelConfig(
+            ulysses_degree=1,
+            ring_degree=1,
+            cfg_parallel_size=1,
+            tensor_parallel_size=1,
+            vae_patch_parallel_size=1,
+            pipeline_parallel_size=1,
+        ),
+    )
+
+    audio_input: str | dict[str, str] | None = args.audio
+    additional_information = {"stage": args.stage, "resolution": args.resolution}
+    if input_case is not None and input_asset_root is not None:
+        audio_input = {
+            speaker: _resolve_case_path(path, input_asset_root)
+            for speaker, path in sorted(
+                input_case["cond_audio"].items(),
+                key=lambda item: _speaker_sort_key(str(item[0])),
+            )
+        }
+        additional_information.update(
+            {
+                "input_json": args.input_json,
+                "audio_type": input_case.get("audio_type", "para"),
+                "bbox": input_case.get("bbox", {}),
+            }
+        )
+
+    prompt = {
+        "prompt": args.prompt,
+        "negative_prompt": args.negative_prompt,
+        "multi_modal_data": {"audio": audio_input},
+        "additional_information": additional_information,
+    }
+    if args.stage == "ai2v":
+        prompt["multi_modal_data"]["image"] = args.image
+
+    try:
+        output = omni.generate(
+            prompt,
+            OmniDiffusionSamplingParams(
+                generator=torch.Generator(device=current_omni_platform.device_type).manual_seed(args.seed),
+                seed=args.seed,
+                guidance_scale=1.0 if args.use_distill else 4.0,
+                guidance_scale_2=1.0 if args.use_distill else 4.0,
+                num_inference_steps=args.num_inference_steps,
+                num_frames=args.num_frames,
+                fps=args.fps,
+                extra_args={
+                    "stage": args.stage,
+                    "resolution": args.resolution,
+                    "save_fps": args.fps,
+                    "use_distill": args.use_distill,
+                    "use_int8": args.use_int8,
+                    "input_json": args.input_json,
+                    "num_segments": args.num_segments,
+                    "num_cond_frames": args.num_cond_frames,
+                    "ref_img_index": args.ref_img_index,
+                    "mask_frame_range": args.mask_frame_range,
+                    "use_kv_cache": args.use_kv_cache,
+                    "offload_kv_cache": args.offload_kv_cache,
+                },
+            ),
+            use_tqdm=True,
+        )
+    finally:
+        omni.close()
+
+    with tempfile.TemporaryDirectory(prefix="longcat_avatar_audio_mux_") as tmp:
+        if input_case is not None and isinstance(audio_input, dict):
+            audio_for_mux = _create_merged_audio_for_case(
+                audio_input,
+                input_case.get("audio_type", "para"),
+                Path(tmp) / "merged_audio.wav",
+            )
+        _save_video_with_audio(_extract_frames(output), Path(args.output), audio_for_mux, args.fps)
+    print(f"Saved video to {args.output}")
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,117 @@
+"""
+Offline inference example: MammothModa2 image summarization (single AR stage).
+
+Example:
+  uv run python examples/offline_inference/mammothmodal2_preview/run_mammothmoda2_image_summarize.py \
+    --model path/to/MammothModa2-Preview \
+    --deploy-config vllm_omni/deploy/mammoth_moda2_ar.yaml \
+    --image /path/to/input.jpg \
+    --question "Please summarize the content of this image."
+"""
+
+from __future__ import annotations
+
+import argparse
+import os
+
+from PIL import Image
+from vllm import SamplingParams
+from vllm.multimodal.image import convert_image_mode
+
+from vllm_omni import Omni
+
+DEFAULT_SYSTEM = "You are a helpful assistant."
+DEFAULT_QUESTION = "Please summarize the content of this image."
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="MammothModa2 image summarization (offline, AR only).")
+    parser.add_argument("--model", type=str, required=True, help="Path to model directory or model id.")
+    parser.add_argument("--deploy-config", type=str, required=True, help="Path to deploy config yaml.")
+    parser.add_argument("--image", type=str, required=True, help="Path to input image.")
+    parser.add_argument("--question", type=str, default=DEFAULT_QUESTION, help="Question/instruction for the model.")
+    parser.add_argument("--system", type=str, default=DEFAULT_SYSTEM, help="System prompt.")
+    parser.add_argument(
+        "--max-tokens",
+        type=int,
+        default=512,
+        help="Max new tokens to generate.",
+    )
+    parser.add_argument("--temperature", type=float, default=0.2)
+    parser.add_argument("--top-p", type=float, default=0.9)
+    parser.add_argument("--seed", type=int, default=42)
+    parser.add_argument("--trust-remote-code", action="store_true")
+    parser.add_argument(
+        "--enable-diffusion-pipeline-profiler",
+        action="store_true",
+        help="Enable diffusion pipeline profiler to display stage durations.",
+    )
+    return parser.parse_args()
+
+
+def build_prompt(system: str, question: str) -> str:
+    return (
+        f"<|im_start|>system\n{system}<|im_end|>\n"
+        "<|im_start|>user\n"
+        "<|vision_start|><|image_pad|><|vision_end|>"
+        f"{question}<|im_end|>\n"
+        "<|im_start|>assistant\n"
+    )
+
+
+def main() -> None:
+    args = parse_args()
+
+    if not os.path.exists(args.image):
+        raise FileNotFoundError(f"Image file not found: {args.image}")
+
+    pil_image = Image.open(args.image)
+    image_data = convert_image_mode(pil_image, "RGB")
+    prompt = build_prompt(args.system, args.question)
+
+    omni = Omni(
+        model=args.model,
+        deploy_config=args.deploy_config,
+        trust_remote_code=args.trust_remote_code,
+        enable_diffusion_pipeline_profiler=args.enable_diffusion_pipeline_profiler,
+    )
+    try:
+        sp = SamplingParams(
+            temperature=float(args.temperature),
+            top_p=float(args.top_p),
+            top_k=-1,
+            max_tokens=int(args.max_tokens),
+            seed=int(args.seed),
+            detokenize=True,
+        )
+        # NOTE: omni.generate() returns a Generator[OmniRequestOutput, None, None].
+        # Consume it inside the try block so the worker isn't closed early.
+        outputs = list(
+            omni.generate(
+                [
+                    {
+                        "prompt": prompt,
+                        "multi_modal_data": {"image": image_data},
+                        "additional_information": {"omni_task": ["chat"]},
+                    }
+                ],
+                [sp],
+            )
+        )
+    finally:
+        omni.close()
+
+    lines: list[str] = []
+    for stage_outputs in outputs:
+        ro = stage_outputs
+        text = ro.outputs[0].text if getattr(ro, "outputs", None) else str(ro)
+        lines.append(f"request_id: {getattr(ro, 'request_id', 'unknown')}\n")
+        lines.append("answer:\n")
+        lines.append(text.strip() + "\n")
+        lines.append("\n")
+
+    print("\n".join(lines))
+
+
+if __name__ == "__main__":
+    main()
@@ -297,7 +297,7 @@ def main(args):
    os.makedirs(output_dir, exist_ok=True)

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

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

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


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

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

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

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

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

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

@@ -57,8 +57,9 @@ python examples/offline_inference/text_to_image/text_to_image.py \
  --output lingbot_t2i.png
 ```

-The LingBot prompt builder selects the `image` output modality and requests one
-frame. Passing an input image is not supported in LingBot T2I mode.
+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.

 ## Quick Start

@@ -109,8 +110,9 @@ 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 |
+| `--lora-path` | str | — | Path to PEFT LoRA adapter folder or checkpoint file |
 | `--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). |
@@ -115,7 +115,7 @@ def build_demo(args: argparse.Namespace) -> gr.Blocks:
        )
        images_outputs = []
        for output in outputs:
-            req_out = output.request_output
+            req_out = output
            if not isinstance(req_out, OmniRequestOutput) or not hasattr(req_out, "images"):
                raise ValueError("Invalid request_output structure or missing 'images' key")
            images = req_out.images
@@ -8,7 +8,9 @@ import time
 from pathlib import Path
 from typing import Any

+import numpy as np
 import torch
+from diffusers.utils import numpy_to_pil

 from vllm_omni.diffusion.data import logger
 from vllm_omni.diffusion.utils.image_output import extract_images_from_outputs
@@ -19,7 +21,9 @@ 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,
+    build_text_to_image_prompt as build_model_text_to_image_prompt,
+)
+from vllm_omni.model_extras import (
    get_extra_body_params,
    get_model_class_name,
    should_init_extra_args_for_non_diffusion_stages,
@@ -54,6 +58,28 @@ 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 = []
+    for image in images:
+        if isinstance(image, np.ndarray):
+            normalized.extend(numpy_to_pil(image))
+        else:
+            normalized.append(image)
+    return normalized
+
+
 def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser(description="Generate an image with supported diffusion models.")
    parser.add_argument(
@@ -247,6 +273,7 @@ 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.",
    )
@@ -256,6 +283,15 @@ 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,
@@ -391,8 +427,12 @@ def main():
    # Prepare LoRA kwargs for Omni initialization
    lora_args: dict[str, Any] = {}
    if args.lora_path:
-        lora_args["lora_path"] = args.lora_path
-        print(f"Using LoRA from: {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}")

    # Build quantization kwargs: use quantization_config dict when
    # ignored_layers is specified so the list flows through OmniDiffusionConfig
@@ -481,20 +521,27 @@ def main():

    # Build LoRA request when --lora-path is set
    lora_request = None
-    if args.lora_path:
-        lora_request_id = stable_lora_int_id(args.lora_path)
+    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(args.lora_path).stem,
+            lora_name=Path(lora_path).stem,
            lora_int_id=lora_request_id,
-            lora_path=args.lora_path,
+            lora_path=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,
    )
@@ -609,7 +656,7 @@ def main():
        images = getattr(output, "images", None)
        if images:
            break
-        req_out = getattr(output, "request_output", None)
+        req_out = output
        images = getattr(req_out, "images", None) if req_out is not None else None
        if images:
            break
@@ -619,6 +666,7 @@ def main():

    if not images:
        raise ValueError("No images found in request_output")
+    images = _normalize_images_for_save(images)

    output_path = Path(args.output)
    output_path.parent.mkdir(parents=True, exist_ok=True)
@@ -24,6 +24,7 @@ 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
@@ -371,18 +372,10 @@ python examples/offline_inference/text_to_speech/qwen3_tts/end2end.py \
 Streaming requires `async_chunk: true` in the stage config.

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

 ### Batched decoding
 The Code2Wav stage supports batched decoding through the SpeechTokenizer. Pass multiple prompts via `--txt-prompts` and set `--batch-size` accordingly. To raise `max_num_seqs` on either stage, point `--stage-configs-path` at a stage configs YAML with the desired values (see `vllm_omni/model_executor/stage_configs/` for templates):
@@ -437,11 +430,14 @@ Streaming is exposed through the online OpenAI Speech API (`stream=true`). See [

 ---

-## IndexTTS-2
+## IndexTTS-2 and IndexTTS-2.5

-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.
+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.

 ### Quick start
+
+IndexTTS-2:
+
 ```bash
 python examples/offline_inference/text_to_speech/indextts2/end2end.py \
    --model IndexTeam/IndexTTS-2 \
@@ -449,6 +445,24 @@ 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
@@ -474,10 +488,13 @@ python examples/offline_inference/text_to_speech/indextts2/end2end.py \
 ```

 ### Notes
- `--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).
+- `--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.

 ---

@@ -168,7 +168,7 @@ def run_e2e():
    print(f"Received {len(outputs)} outputs.")
    for i, output in enumerate(outputs):
        try:
-            ro = output.request_output
+            ro = output
            if ro is None:
                print("No request_output found.")
                continue
@@ -179,7 +179,7 @@ def main(args):

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

    async for stage_output in omni.generate(prompt, request_id=request_id):
-        mm = stage_output.request_output.outputs[0].multimodal_output
+        mm = stage_output.outputs[0].multimodal_output
        if not stage_output.finished:
            t_now = time.perf_counter()
            sample_rate, n = _collect_audio_chunk(mm, all_audio_chunks, sample_rate)
@@ -1,13 +1,16 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Offline inference example for IndexTTS2 via vLLM-Omni.
+"""Offline inference example for IndexTTS 2.0 and 2.5 via vLLM-Omni.

-Two-stage pipeline: GPT AR (Stage 0) → S2Mel + BigVGAN (Stage 1).
+Two-stage pipeline: GPT AR (Stage 0) → semantic codec + S2Mel + BigVGAN
+(Stage 1).
 Output is 22050 Hz mono WAV.

 Usage:
  python end2end.py \
-    --model /path/to/IndexTeam/IndexTTS-2 \
+    --model /path/to/indextts-2.5 \
+    --model-version 2.5 \
+    --lang zh \
    --text "你好，这是一个语音合成测试。" \
    --ref-audio /path/to/ref.wav

@@ -47,6 +50,10 @@ 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:
@@ -63,12 +70,44 @@ 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_token_ids": build_indextts2_prefill_prompt_ids(
+            model,
+            text,
+            **prompt_kwargs,
+        ),
        "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)
@@ -95,9 +134,14 @@ 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=args.deploy_config,
+        deploy_config=deploy_config,
        stage_init_timeout=args.stage_init_timeout,
    )

@@ -126,7 +170,7 @@ def main(args) -> None:
    print(f"Synthesizing: {args.text!r}")
    if args.ref_audio:
        print(f"  ref_audio: {args.ref_audio}")
-    inputs = build_request(
+    request_kwargs = dict(
        model=args.model,
        text=args.text,
        ref_audio_path=args.ref_audio,
@@ -136,7 +180,17 @@ 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
@@ -154,8 +208,37 @@ def main(args) -> None:


 def parse_args():
-    parser = FlexibleArgumentParser(description="IndexTTS2 offline inference")
-    parser.add_argument("--model", required=True, help="HF model path for IndexTTS2.")
+    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.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.")
@@ -170,7 +253,15 @@ 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)
+    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(
        "--output-dir",
        default=os.path.join(
@@ -180,7 +271,10 @@ def parse_args():
    )
    parser.add_argument("--deploy-config", default=None)
    parser.add_argument("--stage-init-timeout", type=int, default=600)
-    return parser.parse_args()
+    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


 if __name__ == "__main__":
@@ -137,7 +137,7 @@ def main(args) -> None:
    params_list = sampling_params

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


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

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

 ### LingBot-Video

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

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

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

 ### LTX-2

@@ -218,6 +232,9 @@ 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
@@ -14,7 +14,9 @@ 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.model_extras import get_extra_body_params, get_model_class_name
+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.outputs import OmniRequestOutput
 from vllm_omni.platforms import current_omni_platform

@@ -78,6 +80,17 @@ _MODEL_PRESETS = {
        "fps": 16,
        "output": "helios_output.mp4",
    },
+    "lingbot": {
+        "model_class_name": "LingBotVideoPipeline",
+        "height": 192,
+        "width": 320,
+        "num_frames": 9,
+        "num_inference_steps": 2,
+        "guidance_scale": 3.0,
+        "fps": 24,
+        "flow_shift": 3.0,
+        "output": "lingbot_video_output.mp4",
+    },
    "ltx2": {
        "height": 512,
        "width": 768,
@@ -108,27 +121,51 @@ _MODEL_PRESETS = {
 def _detect_preset(model: str, model_class_name: str | None = None) -> dict:
    model_lower = model.lower()
    class_lower = (model_class_name or "").lower()
-    if "distilled" in class_lower or "distilled" in model_lower:
-        return _MODEL_PRESETS["ltx2_distilled"]
-    if "ltx23" in class_lower or "ltx-2.3" in model_lower or "ltx_2.3" in model_lower:
-        return _MODEL_PRESETS["ltx23"]
-    if "ltx2" in class_lower or "ltx-2" in model_lower or "ltx_2" in model_lower:
+    if "lingbot" in model_lower or "lingbotvideo" in class_lower:
+        return _MODEL_PRESETS["lingbot"]
+    if "ltx" in class_lower or "ltx" in model_lower:
+        if "distilled" in class_lower or "distilled" in model_lower:
+            return _MODEL_PRESETS["ltx2_distilled"]
+        if "ltx23" in class_lower or "ltx-2.3" in model_lower or "ltx_2.3" in model_lower:
+            return _MODEL_PRESETS["ltx23"]
        return _MODEL_PRESETS["ltx2"]
-    if "vace" in model_lower:
+    if "vace" in model_lower or "vace" in class_lower:
        return _MODEL_PRESETS["vace"]
    # Edge must be matched before the generic cosmos branch (its "cosmos" substring would
    # otherwise pick up the Nano/Super 720p / gs 6.0 / flow_shift 10.0 preset).
    if ("cosmos" in model_lower or "cosmos" in class_lower) and ("edge" in model_lower or "edge" in class_lower):
        return _MODEL_PRESETS["cosmos3_edge"]
-    if "cosmos" in model_lower:
+    if "cosmos" in model_lower or "cosmos" in class_lower:
        return _MODEL_PRESETS["cosmos"]
-    if "hunyuan" in model_lower:
+    if "hunyuan" in model_lower or "hunyuan" in class_lower:
        return _MODEL_PRESETS["hunyuan"]
-    if "helios" in model_lower:
+    if "helios" in model_lower or "helios" in class_lower:
        return _MODEL_PRESETS["helios"]
    return _MODEL_PRESETS["wan"]


+def build_text_to_video_prompt(prompt: str, negative_prompt: str | None) -> dict[str, Any]:
+    """Build the canonical request envelope for the shared T2V example."""
+    result: dict[str, Any] = {
+        "prompt": prompt,
+        "modalities": ["video"],
+    }
+    if negative_prompt is not None:
+        result["negative_prompt"] = negative_prompt
+    return result
+
+
+def _normalize_float_tensor(tensor: torch.Tensor, source_range: str) -> torch.Tensor:
+    """Normalize decoded frames according to the pipeline's output contract."""
+    if not tensor.is_floating_point():
+        return tensor
+    if source_range == "negative_one_to_one":
+        return tensor.clamp(-1, 1) * 0.5 + 0.5
+    if source_range == "zero_to_one":
+        return tensor.clamp(0, 1)
+    raise ValueError(f"Unsupported floating-point tensor range: {source_range!r}")
+
+
 def parse_profiler_config(value: str) -> dict[str, Any]:
    try:
        config = json.loads(value)
@@ -158,7 +195,7 @@ def parse_extra_body(value: str) -> dict[str, Any]:
 def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser(
        description="Generate a video from a text prompt. "
-        "Supports Wan2.2, HunyuanVideo-1.5, Helios, and other text-to-video models."
+        "Supports Wan2.2, HunyuanVideo-1.5, Helios, LingBot-Video, and other text-to-video models."
    )
    parser.add_argument(
        "--model",
@@ -334,6 +371,29 @@ 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",
@@ -365,7 +425,7 @@ def _extract_peak_memory_mb(result: Any) -> float:
        return 0.0
    val = getattr(result, "peak_memory_mb", 0.0)
    if not val:
-        inner = getattr(result, "request_output", None)
+        inner = result
        if isinstance(inner, list):
            inner = inner[0] if inner else None
        val = getattr(inner, "peak_memory_mb", 0.0)
@@ -383,6 +443,7 @@ def main():
    for key, default_val in preset.items():
        if getattr(args, key.replace("-", "_"), None) is None:
            setattr(args, key.replace("-", "_"), default_val)
+    model_class_name = args.model_class_name

    generator = torch.Generator(device=current_omni_platform.device_type).manual_seed(args.seed)
    # Cache-dit config (Wan2.2 only)
@@ -440,6 +501,12 @@ 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).
@@ -449,6 +516,7 @@ def main():
    omni = Omni(**omni_kwargs)
    model_class_name = get_model_class_name(omni) or model_class_name
    declared_extra_body_params = get_extra_body_params(model_class_name)
+    output_tensor_range = get_output_tensor_range(model_class_name)

    if profiler_enabled:
        print("[Profiler] Starting profiling...")
@@ -469,12 +537,29 @@ def main():
    print(f"  Video size: {args.width}x{args.height}")
    print(f"{'=' * 60}\n")

-    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"]):
+    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")):
        # Preserve the historical empty-prompt behavior for non-LTX examples.
-        prompt_dict["negative_prompt"] = ""
+        negative_prompt = ""
+    prompt_dict = build_text_to_video_prompt(args.prompt, negative_prompt)
+
+    extra_args = {}
+    if lora_request:
+        extra_args["lora_request"] = lora_request
+        extra_args["lora_scale"] = args.lora_scale

    sampling_kwargs = dict(
        height=args.height,
@@ -483,6 +568,7 @@ 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
@@ -525,8 +611,8 @@ def main():
        if frames.multimodal_output and "audio" in frames.multimodal_output:
            audio = frames.multimodal_output["audio"]
            audio_sample_rate = frames.multimodal_output.get("audio_sample_rate", audio_sample_rate)
-        if frames.is_pipeline_output and frames.request_output is not None:
-            inner_output = frames.request_output
+        if frames.is_pipeline_output and frames is not None:
+            inner_output = frames
            if isinstance(inner_output, OmniRequestOutput):
                if inner_output.multimodal_output and "audio" in inner_output.multimodal_output:
                    audio = inner_output.multimodal_output["audio"]
@@ -580,8 +666,7 @@ def main():
                frame_tensor = frame_tensor[0]
            if frame_tensor.dim() == 3 and frame_tensor.shape[0] in (3, 4):
                frame_tensor = frame_tensor.permute(1, 2, 0)
-            if frame_tensor.is_floating_point():
-                frame_tensor = frame_tensor.clamp(-1, 1) * 0.5 + 0.5
+            frame_tensor = _normalize_float_tensor(frame_tensor, output_tensor_range)
            return frame_tensor.float().numpy()
        if isinstance(frame, np.ndarray):
            frame_array = frame
@@ -633,8 +718,7 @@ def main():
                video_tensor = video_tensor[0]
        elif video_tensor.dim() == 4 and video_tensor.shape[0] in (3, 4):
            video_tensor = video_tensor.permute(1, 2, 3, 0)
-        if video_tensor.is_floating_point():
-            video_tensor = video_tensor.clamp(-1, 1) * 0.5 + 0.5
+        video_tensor = _normalize_float_tensor(video_tensor, output_tensor_range)
        video_array = video_tensor.float().numpy()
    elif isinstance(frames, np.ndarray):
        video_array = frames
@@ -1,131 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-from __future__ import annotations
-
-import argparse
-import time
-from pathlib import Path
-from typing import Any
-
-import numpy as np
-import torch
-
-from vllm_omni.diffusion.data import DiffusionParallelConfig
-from vllm_omni.entrypoints.omni import Omni
-from vllm_omni.inputs.data import OmniDiffusionSamplingParams
-from vllm_omni.outputs import OmniRequestOutput
-from vllm_omni.platforms import current_omni_platform
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(description="Generate text-to-video with a LingBot-Video checkpoint.")
-    parser.add_argument("--model", default="robbyant/lingbot-video-dense-1.3b")
-    parser.add_argument("--prompt", default="a robotic arm picks up a red block")
-    parser.add_argument("--negative-prompt", default=None)
-    parser.add_argument("--output", default="lingbot_video_output.mp4")
-    parser.add_argument("--height", type=int, default=192)
-    parser.add_argument("--width", type=int, default=320)
-    parser.add_argument("--num-frames", type=int, default=9)
-    parser.add_argument("--num-inference-steps", type=int, default=2)
-    parser.add_argument("--guidance-scale", type=float, default=3.0)
-    parser.add_argument("--flow-shift", type=float, default=3.0)
-    parser.add_argument("--seed", type=int, default=42)
-    parser.add_argument("--fps", type=int, default=24)
-    parser.add_argument("--output-type", choices=["pt", "np"], default="pt")
-    parser.add_argument("--batch-cfg", action="store_true")
-    return parser.parse_args()
-
-
-def _peak_memory_mb(output: Any) -> float:
-    result = output[0] if isinstance(output, list) and output else output
-    return float(getattr(result, "peak_memory_mb", 0.0) or 0.0)
-
-
-def _extract_video(output: Any) -> Any:
-    if isinstance(output, list):
-        output = output[0] if output else None
-    if isinstance(output, OmniRequestOutput):
-        if output.final_output_type != "image":
-            raise ValueError(f"Unexpected output type {output.final_output_type!r}.")
-        if not output.images:
-            raise ValueError("No video frames found in OmniRequestOutput.")
-        return output.images[0] if len(output.images) == 1 else output.images
-    return output
-
-
-def _to_frame_array(video: Any) -> np.ndarray:
-    if isinstance(video, torch.Tensor):
-        tensor = video.detach().cpu()
-        if tensor.dim() == 5:
-            tensor = tensor[0]
-        if tensor.dim() == 4 and tensor.shape[-1] not in (3, 4):
-            if tensor.shape[0] in (3, 4):
-                tensor = tensor.permute(1, 2, 3, 0)
-            elif tensor.shape[1] in (3, 4):
-                tensor = tensor.permute(0, 2, 3, 1)
-        if tensor.is_floating_point():
-            if float(tensor.min()) < 0.0:
-                tensor = tensor.clamp(-1, 1) * 0.5 + 0.5
-            else:
-                tensor = tensor.clamp(0, 1)
-        return tensor.float().numpy()
-    if isinstance(video, np.ndarray):
-        array = video[0] if video.ndim == 5 else video
-        if np.issubdtype(array.dtype, np.integer):
-            return array.astype(np.float32) / 255.0
-        return array.astype(np.float32, copy=False)
-    if isinstance(video, list):
-        return np.stack([_to_frame_array(frame) for frame in video], axis=0)
-    return np.asarray(video, dtype=np.float32)
-
-
-def main() -> None:
-    args = parse_args()
-    generator = torch.Generator(device=current_omni_platform.device_type).manual_seed(args.seed)
-    omni = Omni(
-        model=args.model,
-        model_class_name="LingBotVideoPipeline",
-        flow_shift=args.flow_shift,
-        parallel_config=DiffusionParallelConfig(),
-    )
-
-    prompt: dict[str, Any] = {
-        "prompt": args.prompt,
-        "modalities": ["video"],
-        "num_frames": args.num_frames,
-    }
-    if args.negative_prompt is not None:
-        prompt["negative_prompt"] = args.negative_prompt
-
-    sampling_params = OmniDiffusionSamplingParams(
-        height=args.height,
-        width=args.width,
-        num_frames=args.num_frames,
-        num_inference_steps=args.num_inference_steps,
-        guidance_scale=args.guidance_scale,
-        generator=generator,
-        output_type=args.output_type,
-        extra_args={"batch_cfg": args.batch_cfg, "shift": args.flow_shift},
-    )
-
-    start = time.perf_counter()
-    output = omni.generate(prompt, sampling_params)
-    elapsed = time.perf_counter() - start
-    print(f"Total generation time: {elapsed:.4f} seconds ({elapsed * 1000:.2f} ms)")
-    peak_mb = _peak_memory_mb(output)
-    if peak_mb:
-        print(f"Worker peak GPU memory (reserved): {peak_mb:.2f} MiB ({peak_mb / 1024:.2f} GiB)")
-
-    video = _to_frame_array(_extract_video(output))
-    output_path = Path(args.output)
-    output_path.parent.mkdir(parents=True, exist_ok=True)
-    from diffusers.utils import export_to_video
-
-    export_to_video(list(video), str(output_path), fps=args.fps)
-    print(f"Saved generated video to {output_path}")
-    omni.close()
-
-
-if __name__ == "__main__":
-    main()
@@ -138,7 +138,7 @@ def _extract_peak_memory_mb(result: Any) -> float:
        return 0.0
    val = getattr(result, "peak_memory_mb", 0.0)
    if not val:
-        inner = getattr(result, "request_output", None)
+        inner = result
        if isinstance(inner, list):
            inner = inner[0] if inner else None
        val = getattr(inner, "peak_memory_mb", 0.0)
@@ -1,6 +1,9 @@
 # Image-To-Video

-This example demonstrates how to deploy the Wan2.2 image-to-video model for online video generation using vLLM-Omni.
+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.

 ## Start Server

@@ -20,9 +23,7 @@ bash run_server.sh

 The script allows overriding:
 - `MODEL` (default: `Wan-AI/Wan2.2-I2V-A14B-Diffusers`)
- `PORT` (default: `8091`)
- `BOUNDARY_RATIO` (default: `0.875`)
- `FLOW_SHIFT` (default: `12.0`)
+- `PORT` (default: `8099`)
 - `CACHE_BACKEND` (default: `none`)
 - `ENABLE_CACHE_DIT_SUMMARY` (default: `0`)

@@ -1,12 +1,12 @@
 #!/bin/bash
-# Wan2.2 image-to-video server start script
+# Shared 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 Wan2.2 I2V server..."
+echo "Starting image-to-video server..."
 echo "Model: $MODEL"
 echo "Port: $PORT"
 echo "Cache backend: $CACHE_BACKEND"
@@ -23,10 +23,10 @@ including `librosa`.

 The deploy config auto-loads via `--omni`.
 The default `vllm_omni/deploy/minicpmo_4_5.yaml` keeps all three stages on
-logical device 0 with memory budgets of 55%, 15%, and 15%, leaving headroom
+logical device 0 with memory budgets of 55%, 15%, and 18%, leaving headroom
 for runtime kernels such as the HiFi-GAN vocoder's cuDNN workspace. The
-profile admits at most four sequences per stage and bounds startup video
-profiling to 32 frames per video. For throughput,
+profile admits at most four sequences per stage and bounds Talker context
+to 4096 tokens. For throughput,
 `minicpmo_4_5_2gpu.yaml` gives the Thinker
 GPU 0 (90%) and colocates the Talker (55%) and Code2Wav (35%) on GPU 1. That
 profile admits at most four concurrent sequences per stage.
@@ -196,6 +196,7 @@ async def run_demo(args: argparse.Namespace) -> dict[str, object]:
            args.model,
            ref_audio=_ref_audio_data_url(args.ref_audio),
            session_id=args.session_id,
+            temperature=args.temperature,
            timeout_s=args.timeout_s,
        )
        stream_event_cursor = len(client.events.events)
@@ -383,6 +384,12 @@ def parse_args() -> argparse.Namespace:
    parser.add_argument("--output-dir", default="/tmp/minicpmo_realtime_duplex_demo")
    parser.add_argument("--chunk-ms", type=int, default=200)
    parser.add_argument("--timeout-s", type=float, default=60.0)
+    parser.add_argument(
+        "--temperature",
+        type=float,
+        default=None,
+        help="Stage0 sampling temperature; response-required soft-interrupt uses 0.0.",
+    )
    parser.add_argument("--no-realtime-pacing", action="store_true")
    parser.add_argument("--require-audio", action="store_true")
    return parser.parse_args()
@@ -1,6 +1,9 @@
 # Text-To-Image

-This example demonstrates how to deploy Qwen-Image model for online image generation service using vLLM-Omni.
+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.

 ## Start Server

@@ -115,7 +118,23 @@ python gradio_demo.py

 ## LoRA

-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).
+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`.

 ### Using Python Client with LoRA

@@ -226,8 +245,8 @@ 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   | 50      | Number of denoising steps      |
-| `true_cfg_scale`         | float | 4.0     | Qwen-Image CFG scale           |
+| `num_inference_steps`    | int   | model   | Number of denoising steps      |
+| `true_cfg_scale`         | float | model   | True CFG scale when supported  |
 | `seed`                   | int   | None    | Random seed (reproducible)     |
 | `negative_prompt`        | str   | None    | Negative prompt                |
 | `num_outputs_per_prompt` | int   | 1       | Number of images to generate   |
@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 """
-Qwen-Image Gradio Demo for online serving.
+Shared text-to-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="Qwen-Image Demo") as demo:
-        gr.Markdown("# Qwen-Image Online Generation")
-        gr.Markdown("Generate images using Qwen-Image model")
+    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.Row():
            with gr.Column(scale=1):
@@ -115,7 +115,6 @@ 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,
                    )
@@ -173,7 +172,7 @@ def create_demo(server_url: str):


 def main():
-    parser = argparse.ArgumentParser(description="Qwen-Image Gradio Demo")
+    parser = argparse.ArgumentParser(description="Shared text-to-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")
@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 """
-Qwen-Image OpenAI-compatible image generation client.
+Shared OpenAI-compatible text-to-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: Qwen-Image CFG scale
+        true_cfg_scale: True CFG scale for models that support it
        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:
+    if negative_prompt is not None:
        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="Qwen-Image chat client")
+    parser = argparse.ArgumentParser(description="Shared text-to-image client")
    parser.add_argument("--prompt", "-p", default="a cup of coffee on the table", help="Text prompt")
-    parser.add_argument("--output", "-o", default="qwen_image_output.png", help="Output file")
+    parser.add_argument("--output", "-o", default="text_to_image_output.png", help="Output file")
    parser.add_argument("--server", "-s", default="http://localhost:8091", help="Server URL")
-    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("--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("--negative", help="Negative prompt")

    parser.add_argument("--lora-path", default=None, help="Server-local LoRA adapter folder (PEFT format)")
@@ -1,10 +1,10 @@
 #!/bin/bash
-# Qwen-Image online serving startup script
+# Shared text-to-image online serving startup script

 MODEL="${MODEL:-Qwen/Qwen-Image}"
 PORT="${PORT:-8091}"

-echo "Starting Qwen-Image server..."
+echo "Starting text-to-image server..."
 echo "Model: $MODEL"
 echo "Port: $PORT"

@@ -16,7 +16,8 @@ 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`) | compat only, non-chunk | uploaded audio voice only; no presets | — |
+| 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 | — |
 | 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) | — | ✓ |
@@ -138,15 +139,29 @@ bash examples/online_serving/text_to_speech/glm_tts/run_gradio_demo.sh

 ---

-## IndexTTS-2
+## IndexTTS-2 and IndexTTS-2.5

-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`.
+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]'
+```

 ### 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
@@ -161,14 +176,35 @@ 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; IndexTTS-2 does not provide a built-in text-only preset voice.
+- 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.
 - 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.
- `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).
+- 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.

 ---

@@ -569,6 +605,13 @@ python qwen3_tts/streaming_speech_client.py \
 The client writes one PCM file per sentence and a matching
 `sentence_XXX_timestamps.json` sidecar.

+Non-streaming requests can also ask for timestamps: pass
+`"word_timestamps": true` to `POST /v1/audio/speech` and read the
+`X-Word-Timestamps` response header (JSON list of `{word, start_ms, end_ms}`,
+ASCII-escaped). Past 4 KB the header is replaced by
+`X-Word-Timestamps-Omitted: oversize; bytes=<n>; limit=4096` and the audio
+still returns — use the WebSocket path for long transcripts.
+
 To *see* the alignment instead of reading a JSON sidecar, run the
 word-timestamp Gradio demo (server must be launched with `--forced-aligner`):
 ```bash
@@ -1,20 +1,37 @@
 #!/bin/bash
-# Launch vLLM-Omni server for IndexTTS2
+# Launch vLLM-Omni server for IndexTTS 2.0 or 2.5.
 #
 # Usage from repository root:
 #   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
+#   MODEL_VERSION=2.5 MODEL=/path/to/native/bundle \
+#     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="${MODEL:-IndexTeam/IndexTTS-2}"
+MODEL_VERSION="${MODEL_VERSION:-2.0}"
 PORT="${PORT:-8092}"
-DEPLOY_CONFIG="${DEPLOY_CONFIG:-$ROOT_DIR/vllm_omni/deploy/indextts2.yaml}"

-echo "Starting IndexTTS2 server with model: $MODEL"
+if [[ "$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"

 FLASHINFER_DISABLE_VERSION_CHECK=1 \
 vllm serve "$MODEL" \
@@ -1,6 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""OpenAI-compatible client for IndexTTS2 TTS via /v1/audio/speech endpoint.
+"""OpenAI-compatible client for IndexTTS 2.0/2.5 via /v1/audio/speech.

 Examples:
    # With reference audio for voice cloning
@@ -54,13 +54,44 @@ 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", type=str, default="IndexTeam/IndexTTS-2")
+    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("--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")
@@ -97,6 +128,10 @@ 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

@@ -77,6 +77,7 @@ plugins:
        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
        user_guide/quantization/index.md: user_guide/quantization/overview.md
+        configuration/pd_disaggregation.md: features/pd_disaggregation.md
        design/feature/diffusion_request_level_batching.md: design/feature/diffusion_continuous_batching.md
        design/feature/diffusion_step_execution.md: design/feature/diffusion_continuous_batching.md
  - glightbox
@@ -111,6 +112,7 @@ plugins:
        - "vllm_omni.entrypoints.openai"  # avoid importing vllm in mkdocs building
        - "vllm_omni.entrypoints.openai.protocol"  # avoid importing vllm in mkdocs building
        - "vllm_omni.experimental"  # avoid importing optional serving deps (openai, fastapi, uvicorn, httpx) in mkdocs building
+        - "vllm_omni.model_executor.models.nemotron_voicechat.nemo_vendored"  # vendored NeMo code; upstream docstrings trip griffe strict warnings
  - mkdocstrings:
      handlers:
        python:
@@ -84,11 +84,26 @@ 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",
 ]

+# LongCat-Video-Avatar A2V/AI2V runtime dependencies for vocal
+# separation and loudness normalization.
+longcat-video-avatar = [
+    "audio-separator==0.44.2",
+    "pyloudnorm==0.1.1",
+    "soundfile>=0.13.1",
+]
+
 # Optional Blackwell-only fused-bias FP8 GEMM; package is `quack-kernels` (imported
 # as `quack`), not the unrelated `quack`. See docs/user_guide/quantization/fp8.md.
 quack = [
@@ -191,6 +206,12 @@ ignore = [
 # These files keep upstream conventions (star imports, long lines, math-symbol
 # argument names) so they stay easy to diff against upstream releases.
 "vllm_omni/model_executor/models/minicpmo_4_5/minicpmo_4_5_omni_llm.py" = ["E402", "E501", "F403", "F405", "N803"]
+# NemotronVoiceChat inference modules vendored from NVIDIA-NeMo/Speech (branch
+# nemotron-labs-voicechat). Numerics-bearing code is kept byte-close to
+# upstream to stay diffable and parity-safe, so upstream style (long lines,
+# legacy typing, whitespace, unused-import shims) is exempted. TID251 and the
+# F-correctness rules still apply.
+"vllm_omni/model_executor/models/nemotron_voicechat/nemo_vendored/**" = ["E501", "E721", "E741", "F401", "F841", "I001", "N803", "N814", "UP006", "UP007", "UP008", "UP011", "UP032", "UP035", "W291", "W293"]
 "vllm_omni/experimental/fullduplex/joyvl/decision/prompts.py" = ["E501"]  # System/summarizer prompts are long prose lines

 [tool.ruff.lint.flake8-tidy-imports.banned-api]
@@ -299,6 +320,10 @@ extend-ignore-identifiers-re = [
    ".*[Nn][Dd]3.*",
    ".*updator.*",
    "byt5.*",
+    # NemotronVoiceChat vendored triton kernel: dout/DOUT abbreviate d_out
+    # (output feature dim), not "doubt".
+    ".*dout.*",
+    ".*DOUT.*",
 ]
 extend-ignore-re = [
    "\\.\\.\\.n,d->\\.\\.\\.nd",
@@ -319,3 +344,10 @@ 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"
@@ -0,0 +1,308 @@
+# 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.
@@ -0,0 +1,169 @@
+# MiniMax-H3 on RTX 4090
+
+This recipe uses BF16 weights, tiled VAE decode, tensor parallelism, optional
+Ulysses sequence parallelism, and distributed layerwise offload (DLO). It is the
+24 GiB sibling of [MiniMax-H3-5090.md](MiniMax-H3-5090.md): the two-GPU topology
+matches that recipe, but the resident DiT-block count is lower so the per-rank
+peak fits in 24 GiB. Lower resident counts reduce HBM use and increase
+CPU-to-GPU transfer time.
+
+## Capacity requirements
+
+| Resource | Two RTX 4090s | Four RTX 4090s |
+| --- | ---: | ---: |
+| GPU HBM | 24 GiB per GPU | 24 GiB per GPU |
+| Checkpoint storage | 135 GiB per partition | 135 GiB per partition |
+| Available system RAM | 200 GiB minimum | 200 GiB minimum |
+| Recommended system RAM | 384 GiB | 384 GiB |
+
+`FL2VA` and `Ref2VA` are separate 135 GiB checkpoint partitions. Start one
+server at a time. DLO keeps rank-local weights in pinned host memory; increasing
+`--dlo-resident-layers` improves latency but does **not** reduce host RAM in the
+current implementation because resident layers retain pinned CPU master copies.
+
+A single RTX 4090 is not covered here. The one-GPU DLO profile in the RTX 5090
+recipe peaked at 26.50 GiB with 12 resident layers, which exceeds 24 GiB. On one
+4090, use the model-level CPU offload command in
+[MiniMax-H3.md](MiniMax-H3.md#single-gpu-accuracy-and-memory-first) instead, or
+lower the resident count and re-measure before trusting it.
+
+> **Modular H3:** after #5720 lands, preserve this recipe's one-partition
+> behavior with `--task-type fl2va` or `--task-type ref2va`.
+
+## Two RTX 4090s: 1024x576, 5 seconds
+
+Use TP2 and 12 resident DiT layers. This is the `rtx4090` profile from the main
+recipe. On two RTX 4090s it peaked at 15,620 MiB per rank for T2VA and
+14,918 MiB per rank for Ref2VA, leaving roughly 9 GiB of headroom per card. See
+[Target-hardware validation](#target-hardware-validation) for the full numbers.
+
+```bash
+export VLLM_WORKER_MULTIPROC_METHOD=spawn
+export VLLM_OMNI_VIDEO_SYNC_TIMEOUT=14400
+
+CUDA_VISIBLE_DEVICES=0,1 vllm serve /path/to/MiniMax-H3/FL2VA \
+  --omni --trust-remote-code --host 0.0.0.0 --port 8000 \
+  --num-gpus 2 --tensor-parallel-size 2 --text-encoder-tp-size 2 \
+  --usp 1 --ring 1 --vae-patch-parallel-size 2 \
+  --vae-parallel-mode tile --vae-use-tiling \
+  --enable-distributed-layerwise-offload --dlo-no-use-allgather \
+  --dlo-resident-layers 12 --enforce-eager \
+  --diffusion-attention-backend CUDNN_ATTN
+```
+
+For Ref2VA, stop the FL2VA server and restart the same command with
+`/path/to/MiniMax-H3/Ref2VA`. Ref2VA reference image count and prompt length can
+increase activation memory; begin with one request at a time.
+
+`VLLM_OMNI_VIDEO_SYNC_TIMEOUT` matters more here than on larger cards. DLO
+streams non-resident DiT blocks over PCIe on every denoising step, so a 60-step
+request takes several minutes and would otherwise hit the 600-second default.
+
+## Four RTX 4090s: 1024x576, 5 seconds
+
+Keep TP2 and raise Ulysses sequence parallel to 2 so the world size is
+`TP × USP = 4`. Text-encoder TP and VAE patch parallel follow the GPU count.
+Resident layers stay at 12: adding USP does not shard DiT weights further, so
+per-GPU peak HBM stays close to the two-GPU profile while wall-clock improves.
+
+```bash
+export VLLM_WORKER_MULTIPROC_METHOD=spawn
+export VLLM_OMNI_VIDEO_SYNC_TIMEOUT=14400
+
+CUDA_VISIBLE_DEVICES=0,1,2,3 vllm serve /path/to/MiniMax-H3/FL2VA \
+  --omni --trust-remote-code --host 0.0.0.0 --port 8000 \
+  --num-gpus 4 --tensor-parallel-size 2 --text-encoder-tp-size 4 \
+  --usp 2 --ring 1 --vae-patch-parallel-size 4 \
+  --vae-parallel-mode tile --vae-use-tiling \
+  --enable-distributed-layerwise-offload --dlo-no-use-allgather \
+  --dlo-resident-layers 12 --enforce-eager \
+  --diffusion-attention-backend CUDNN_ATTN
+```
+
+For Ref2VA, stop the FL2VA server and restart the same command with
+`/path/to/MiniMax-H3/Ref2VA`. Pin four dedicated GPUs; if the launcher exposes
+fewer than four devices, startup fails with
+`Stage 0 requires 4 device(s) based on parallel_config`.
+
+## Target-hardware validation
+
+Measured on RTX 4090 (24,564 MiB each, driver 580.126.09) with the serve
+commands above, at vLLM-Omni `0.26.1.dev55+g81b48e83e`, vLLM `0.26.0`, and
+PyTorch `2.11.0+cu130`. GPUs used by each server were dedicated. `Client E2E`
+and `Peak per GPU` come from the `/v1/videos/sync` response headers
+`x-inference-time-s` and `x-peak-memory-mb`; the latter is the rank-0 CUDA
+reserved high-water mark, so it is a per-GPU figure.
+
+| GPUs | Topology | Task | Shape | Frames | Steps | Client E2E | Peak per GPU | Output validation |
+| ---: | --- | --- | ---: | ---: | ---: | ---: | ---: | --- |
+| 2 | TP2 × USP1 | T2VA (`FL2VA`) | 1024x576 | 124 at 24 FPS | 60 | 7 min 9 s | 15.3 GiB | H.264 video + 32 kHz stereo AAC; full `ffmpeg` decode passed |
+| 2 | TP2 × USP1 | Ref2VA | 1024x576 | 124 at 24 FPS | 60 | 14 min 52 s | 14.6 GiB | H.264 video + 32 kHz stereo AAC; full `ffmpeg` decode passed |
+| 4 | TP2 × USP2 | T2VA (`FL2VA`) | 1024x576 | 124 at 24 FPS | 60 | 4 min 29 s | 15.2 GiB | H.264 video + 32 kHz stereo AAC; full `ffmpeg` decode passed |
+| 4 | TP2 × USP2 | Ref2VA | 1024x576 | 124 at 24 FPS | 60 | 9 min 5 s | 16.1 GiB | H.264 video + 32 kHz stereo AAC; successful `/v1/videos/sync` 200 |
+
+All rows use `seed=1101`, `flow_shift=12`, and `audio_flow_shift=3.0`.
+
+Two-GPU repeats: T2VA measured 434.9 s and 429.3 s; Ref2VA measured 891.9 s and
+892.1 s. Peak memory was identical across repeats for both tasks, and the two
+outputs of each pair agreed to within 115 bytes.
+
+Four-GPU T2VA repeats: 273.8 s and 269.5 s, peaking at 15,560 MiB and
+15,612 MiB. Four-GPU Ref2VA is a single successful end-to-end run (545.2 s,
+16,448 MiB). Follow-up Ref2VA requests on this build completed diffusion but
+then hit the engine's hardcoded 30 s async output wait
+(`_ASYNC_OUTPUT_TIMEOUT` in `diffusion_engine.py`) during post-compute D2H,
+so they are not reported as repeats.
+
+Relative to the two-GPU baseline on the same shape, four-GPU TP2×USP2 was about
+1.6× faster for both tasks, while per-GPU peak HBM stayed in the mid-15 GiB
+range because USP does not further shard resident DiT weights. These are
+single-request validation runs, not concurrent throughput benchmarks.
+
+## Request examples
+
+`t2va` requires an explicit `aspect_ratio`. Without it the request fails with
+`t2va requires an explicit aspect_ratio`, even when `width` and `height` are set.
+
+Text to video and audio, against the `FL2VA` server:
+
+```bash
+curl -sS -D headers_t2va.txt -o out_t2va.mp4 --max-time 14400 \
+  -X POST "http://127.0.0.1:8000/v1/videos/sync" \
+  -F 'prompt=傍晚的小厨房，夕阳余晖从窗边洒进来，旧木桌上放着洗到一半的马克杯和起雾的玻璃瓶，悬挂的抹布轻轻晃动。画面带有手持拍摄的轻微晃动和逆光曝光波动，生活感十足。环境音是安静的厨房底噪。' \
+  -F 'aspect_ratio=16:9' \
+  -F 'width=1024' -F 'height=576' -F 'fps=24' \
+  -F 'num_inference_steps=60' -F 'flow_shift=12' -F 'seed=1101' \
+  -F 'extra_params={"task":"t2va","duration":5,"audio_flow_shift":3.0}'
+```
+
+Reference to video and audio, against the `Ref2VA` server:
+
+```bash
+curl -sS -D headers_ref2va.txt -o out_ref2va.mp4 --max-time 14400 \
+  -X POST "http://127.0.0.1:8000/v1/videos/sync" \
+  -F 'prompt=傍晚的小厨房，夕阳余晖从窗边洒进来，旧木桌上放着洗到一半的马克杯和起雾的玻璃瓶，悬挂的抹布轻轻晃动。画面带有手持拍摄的轻微晃动和逆光曝光波动，生活感十足。环境音是安静的厨房底噪。' \
+  -F 'input_reference=@/path/to/reference.jpg;type=image/jpeg' \
+  -F 'audio_reference=</path/to/audio_reference.json' \
+  -F 'width=1024' -F 'height=576' -F 'fps=24' \
+  -F 'num_inference_steps=60' -F 'flow_shift=12' -F 'seed=1101' \
+  -F 'extra_params={"task":"ref2va","duration":5,"audio_flow_shift":3.0}'
+```
+
+`audio_reference` is a JSON object, which is why the example loads it from a file
+with curl's `<` syntax. Its `audio_url` must be an `http(s)` URL or a data URL;
+a bare filesystem path is rejected with `Invalid audio_reference.audio_url`.
+
+```json
+{"audio_url": "https://example.com/reference.wav"}
+```
+
+Reference images are validated before inference: the short edge must be at least
+256 pixels, the long edge at most 5760, and the aspect ratio must fall between
+0.4 and 2.5.
+
+The response headers carry the measurements used in the table above:
+
+```bash
+cat headers_t2va.txt
+ffmpeg -v error -i out_t2va.mp4 -f null - && echo DECODE_OK
+```
@@ -0,0 +1,198 @@
+# 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
+```
@@ -0,0 +1,113 @@
+# NemotronLabs VoiceChat 11B
+
+> Offline speech-to-speech: a 16 kHz user utterance in, the agent's spoken reply
+> (text + 22.05 kHz WAV) out, on a 3-stage vLLM-Omni pipeline.
+
+## Summary
+
+- Vendor: NVIDIA
+- Model: [`nvidia/NVIDIA-NemotronLabs-VoiceChat-11B`](https://huggingface.co/nvidia/NVIDIA-NemotronLabs-VoiceChat-11B)
+- Task: Speech-to-speech voice chat. The model runs a frame-locked 12.5 Hz
+  timeline: a Conformer + NemotronH hybrid-Mamba thinker emits one text token
+  per acoustic frame, a Gemma3-1B EAR-TTS talker turns the text timeline into
+  31-quantizer RVQ code stacks, and an RVQ-VAE codec decodes them to audio.
+- Mode: Offline single-turn inference (batch=1). Online/duplex serving,
+  function calling, and batch>1 are documented follow-ups.
+- Maintainer: [`@yuekaizhang`](https://github.com/yuekaizhang)
+
+## When to use this recipe
+
+Use this recipe to run a single-turn voice-chat exchange with
+`NVIDIA-NemotronLabs-VoiceChat-11B` on one GPU: you provide a user utterance as
+a WAV file (any sample rate; it is resampled to 16 kHz mono) plus an optional
+spoken-style system prompt, and get back the agent's reply as text and a
+22.05 kHz WAV. The integration is NeMo-free at runtime — the perception
+Conformer, EAR-TTS talker, and RVQ-VAE codec are vendored, dependency-stripped
+NeMo modules (`nemo_vendored/`), so no `nemo_toolkit` install is needed.
+
+## References
+
+- Offline example:
+  [`examples/offline_inference/nemotron_voicechat/end2end.py`](../../examples/offline_inference/nemotron_voicechat/end2end.py)
+- Model modules (thinker / talker / code2wav / vendored NeMo):
+  [`vllm_omni/model_executor/models/nemotron_voicechat/`](../../vllm_omni/model_executor/models/nemotron_voicechat/)
+- Staged pipeline config:
+  [`vllm_omni/deploy/nemotron_labs_voicechat.yaml`](../../vllm_omni/deploy/nemotron_labs_voicechat.yaml)
+- Upstream model card:
+  [`nvidia/NVIDIA-NemotronLabs-VoiceChat-11B`](https://huggingface.co/nvidia/NVIDIA-NemotronLabs-VoiceChat-11B)
+- Reference implementation: NVIDIA-NeMo/Speech, branch `nemotron-labs-voicechat`
+
+## Pipeline
+
+| stage | arch | dtype | role |
+|---|---|---|---|
+| 0 thinker | `NemotronVoiceChatThinkerForConditionalGeneration` (LLM_AR) | fp32 | WAV + system prompt -> frame-locked text-token timeline (+ function channel) |
+| 1 talker | `NemotronVoiceChatTalker` (LLM_AR) | fp32 | text timeline -> 31-quantizer RVQ code stacks (one per 80 ms frame) |
+| 2 code2wav | `NemotronVoiceChatCode2Wav` (LLM_GENERATION) | fp32 | RVQ-VAE decode -> 22.05 kHz PCM |
+
+Stages 0/1 default to fp32 for exact parity with the NeMo reference
+implementation (greedy decoding matches it token for token on the acceptance
+fixture). The deploy yaml documents a ~2x-faster bf16 thinker option whose
+output stayed within one word of the reference in testing.
+
+## Hardware Support
+
+## GPU
+
+### 1x H100 80GB
+
+#### Environment
+
+- OS: Linux
+- Python: 3.12
+- vLLM version: 0.26.0
+- vLLM-Omni version or commit: this PR / current `main`
+
+#### Command
+
+```bash
+# Tokenizer: the checkpoint ships no HF tokenizer; it resolves from the
+# nvidia/NVIDIA-Nemotron-Nano-9B-v2 HF id automatically. For air-gapped runs,
+# point NEMOTRON_VOICECHAT_LLM_PATH at a local snapshot of that repo instead.
+python examples/offline_inference/nemotron_voicechat/end2end.py \
+    --checkpoint /path/to/NVIDIA-NemotronLabs-VoiceChat-11B \
+    --wav /path/to/user_question.wav \
+    --output-dir results/nemotron_voicechat
+```
+
+#### Verification
+
+```bash
+ls results/nemotron_voicechat
+# <stem>_output.txt          the agent reply as text
+# <stem>_output.wav          the agent reply as 22.05 kHz audio
+# <stem>_text_tokens.json    the frame-locked text-token timeline
+```
+
+The reply text should read as a coherent spoken-style answer to the question in
+the input WAV, and the WAV should transcribe to (approximately) the same text
+with any ASR model.
+
+#### Notes
+
+- Memory usage: the shipped yaml runs all three stages on one GPU
+  (`gpu_memory_utilization` 0.62 / 0.12 / 0.06); peak usage is dominated by the
+  fp32 thinker. The fp32 default has a hard floor of roughly 43 GB of thinker
+  weights alone (9B backbone + 587M `embed_tokens` + 587M `function_head` +
+  0.6B Conformer), so 48 GB cards cannot run it — use the bf16 thinker option
+  documented in the deploy yaml on anything smaller than an 80 GB part.
+- Input sizing: the timeline is frame-locked, so the reply budget IS the input
+  duration. The acoustic channel trails the text channel; if the WAV does not
+  carry enough trailing silence for the reply to finish, the spoken answer is
+  truncated silently. Leave generous trailing silence (a question ending at
+  ~4.5 s truncated in an 8 s WAV but completed cleanly in 16 s); the offline
+  example warns when the text channel is still speaking near the last frame.
+- Key flags: sampling is greedy end to end. The thinker is frame-locked —
+  `max_tokens` equals the acoustic frame count with `ignore_eos=True`. Do NOT
+  set `min_tokens` on the thinker: the tokenizer's EOS token is also the
+  frame-locked PAD/silence token, so masking it forces the model to babble
+  instead of pausing.
+- The talker's `max_tokens` is 16383 (its stage prompt is one placeholder
+  token, and the stage context is 16384).
+- Known limitations: batch=1 only; offline single turn (no online/duplex
+  serving yet); the function-call channel is decoded but not yet acted on.
@@ -30,8 +30,9 @@ also provided.
  `hf_config.version="4.5"`):
  - Default single-GPU compatibility layout (auto-loaded):
    [`vllm_omni/deploy/minicpmo_4_5.yaml`](../../vllm_omni/deploy/minicpmo_4_5.yaml)
-  - 2-GPU and 3-GPU layouts:
+  - Recommended 2-GPU continuous-batching layout:
    [`vllm_omni/deploy/minicpmo_4_5_2gpu.yaml`](../../vllm_omni/deploy/minicpmo_4_5_2gpu.yaml),
+  - 3-GPU layout:
    [`vllm_omni/deploy/minicpmo_4_5_3gpu.yaml`](../../vllm_omni/deploy/minicpmo_4_5_3gpu.yaml)
  - 8x RTX 4090 layout:
    [`vllm_omni/deploy/minicpmo_4_5_8x4090.yaml`](../../vllm_omni/deploy/minicpmo_4_5_8x4090.yaml)
@@ -50,11 +50,13 @@ 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 |
 | [`LTX/LTX-2.md`](./LTX/LTX-2.md) | LTX-2/LTX-2.3 text-to-video and image-to-video with synchronized audio | H200 141GB / 96GB-class GPU |
 | [`MammothModa2/MammothModa2.md`](./MammothModa2/MammothModa2.md) | Preview and Dev text-to-image (AR → DiT); Dev text/image understanding | Preview: 1x L40S 48GB / 1x ≥40GB GPU; Dev: 1x NVIDIA GPU with sufficient cache headroom |
+| [`meituan-longcat/LongCat-Video-Avatar-1.5.md`](./meituan-longcat/LongCat-Video-Avatar-1.5.md) | Audio-driven avatar video generation (AT2V / AI2V, single- and multi-speaker, AVC continuation) | 1x H100 80GB |
 | [`mistralai/Voxtral-TTS.md`](./mistralai/Voxtral-TTS.md) | Online serving for TTS | 1x RTX 4090 24GB |
 | [`Robbyant/LingBot-Video.md`](./Robbyant/LingBot-Video.md) | Native dense and MoE text-to-video serving | Dense: 1x L20X; MoE: 1x L20X (~67.7 GiB peak) |
 | [`cosmos3/Cosmos3-Nano.md`](./cosmos3/Cosmos3-Nano.md) | Text-to-image, text-to-video, image-to-video, video-to-video generation, text to video with sound, action policy | 1x H200 141GB / B300 |
@@ -64,6 +66,7 @@ recipes/
 | [`OpenBMB/VoxCPM2.md`](./OpenBMB/VoxCPM2.md) | Online + offline TTS with native AR pipeline (48 kHz, 30+ languages) | 1x RTX 4090 24GB |
 | [`OpenMOSS/MOSS-TTS.md`](./OpenMOSS/MOSS-TTS.md) | Online + offline multilingual TTS (MOSS-TTS family, 8B) | 1x H100 80GB |
 | [`NVIDIA/Nemotron-Labs-Audex.md`](./NVIDIA/Nemotron-Labs-Audex.md) | TTS / text-to-audio / audio understanding / cascaded S2S (Audex 2B + 30B-A3B) | 1x H100 80GB |
+| [`NVIDIA/NemotronLabs-VoiceChat.md`](./NVIDIA/NemotronLabs-VoiceChat.md) | Offline speech-to-speech voice chat (11B, frame-locked 12.5 Hz, 3-stage thinker/talker/code2wav) | 1x H100 80GB |
 | [`Qwen/Qwen-Image.md`](./Qwen/Qwen-Image.md) | Text-to-image serving with step-wise continuous batching replay and ModelOpt mixed FP8/NVFP4 | 1x A100 80GB / 2x B200 |
 | [`Qwen/Qwen-Image-2512.md`](./Qwen/Qwen-Image-2512.md) | Text-to-image serving with step-wise continuous batching replay and ModelOpt FP8 / mixed FP8/NVFP4 | 1x A800 80GB / 2x B200 |
 | [`Qwen/Qwen-Image-Edit.md`](./Qwen/Qwen-Image-Edit.md) | Text-guided single-image editing | 1x or 2x H200 141GB |
@@ -27,7 +27,7 @@ parallel, cache, quantized, or expert-kernel backends.
 - Dense checkpoint: <https://huggingface.co/robbyant/lingbot-video-dense-1.3b>
 - MoE checkpoint: <https://huggingface.co/robbyant/lingbot-video-moe-30b-a3b>
 - Upstream project: <https://github.com/Robbyant/lingbot-video>
- Related offline example: [`examples/offline_inference/text_to_video/text_to_video_lingbot.py`](../../examples/offline_inference/text_to_video/text_to_video_lingbot.py)
+- Related offline example: [`examples/offline_inference/text_to_video/text_to_video.py`](../../examples/offline_inference/text_to_video/text_to_video.py)
 - Related online video API docs: [`docs/serving/videos_api.md`](../../docs/serving/videos_api.md)

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

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

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

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

 HunyuanImage-3.0 sets the diffusion attention backend to `TORCH_SDPA`
@@ -0,0 +1,282 @@
+# LongCat-Video-Avatar-1.5
+
+> Audio-driven avatar video generation (AT2V / AI2V)
+
+## Summary
+
+- Vendor: meituan-longcat
+- Model: `meituan-longcat/LongCat-Video-Avatar-1.5`
+- Task: Audio-to-video (AT2V) and audio-and-image-to-video (AI2V) avatar generation
+- Mode: Offline inference
+- Maintainer: Community
+
+## When to use this recipe
+
+Use this recipe to drive a talking or singing avatar from a speech clip, either
+from text alone (AT2V) or from a reference image (AI2V), with the native
+LongCat-Video-Avatar-1.5 pipeline in vLLM-Omni. It covers:
+
+1. **Single-speaker AT2V / AI2V** — one 93-frame segment from one audio track.
+2. **AVC continuation** — chained segments that cover a longer audio track.
+3. **Multi-speaker AI2V** — several audio tracks placed on a reference image
+   through speaker bounding boxes.
+
+The example saves an mp4 with the generated frames muxed with the input audio.
+
+## References
+
+- Upstream model card: <https://huggingface.co/meituan-longcat/LongCat-Video-Avatar-1.5>
+- Upstream repository and example assets: <https://github.com/meituan-longcat/LongCat-Video>
+- Related example under `examples/`: [`longcat_video/end2end.py`](../../examples/offline_inference/longcat_video/end2end.py)
+
+## Hardware Support
+
+## CUDA
+
+### 1× NVIDIA H100 (80 GB)
+
+#### Environment
+
+- OS: Linux
+- Python: 3.10+
+- Driver: NVIDIA driver with CUDA 12.x
+- vLLM version: Match the repository requirements for your checkout
+- vLLM-Omni version or commit: Use the commit you are deploying from
+
+#### Prerequisites
+
+Install the LongCat Avatar optional runtime dependencies:
+
+```bash
+pip install "vllm-omni[longcat-video-avatar]"
+```
+
+The example uses the `imageio_ffmpeg` executable installed by vLLM-Omni's
+`imageio[ffmpeg]` dependency.
+
+#### Command
+
+Audio-to-video (AT2V):
+
+```bash
+python examples/offline_inference/longcat_video/end2end.py \
+  --model meituan-longcat/LongCat-Video-Avatar-1.5 \
+  --stage at2v \
+  --audio /path/to/speech.wav \
+  --prompt "A person speaks calmly while facing the camera." \
+  --num-frames 93 \
+  --num-inference-steps 8 \
+  --output longcat_avatar_at2v.mp4
+```
+
+Audio-and-image-to-video (AI2V):
+
+```bash
+python examples/offline_inference/longcat_video/end2end.py \
+  --model meituan-longcat/LongCat-Video-Avatar-1.5 \
+  --stage ai2v \
+  --audio /path/to/speech.wav \
+  --image /path/to/reference.jpg \
+  --prompt "A person speaks calmly while facing the camera." \
+  --num-frames 93 \
+  --num-inference-steps 8 \
+  --output longcat_avatar_ai2v.mp4
+```
+
+#### Official Example Assets
+
+Download the official Avatar example assets:
+
+```bash
+mkdir -p longcat_avatar_assets/assets/avatar/single longcat_avatar_assets/assets/avatar/multi
+
+curl -L -o longcat_avatar_assets/assets/avatar/single/man.mp3 \
+  https://raw.githubusercontent.com/meituan-longcat/LongCat-Video/main/assets/avatar/single/man.mp3
+curl -L -o longcat_avatar_assets/assets/avatar/single/man.png \
+  https://raw.githubusercontent.com/meituan-longcat/LongCat-Video/main/assets/avatar/single/man.png
+curl -L -o longcat_avatar_assets/assets/avatar/single_example_1.json \
+  https://raw.githubusercontent.com/meituan-longcat/LongCat-Video/main/assets/avatar/single_example_1.json
+
+curl -L -o longcat_avatar_assets/assets/avatar/multi/sing.png \
+  https://raw.githubusercontent.com/meituan-longcat/LongCat-Video/main/assets/avatar/multi/sing.png
+curl -L -o longcat_avatar_assets/assets/avatar/multi/sing_man.WAV \
+  https://raw.githubusercontent.com/meituan-longcat/LongCat-Video/main/assets/avatar/multi/sing_man.WAV
+curl -L -o longcat_avatar_assets/assets/avatar/multi/sing_woman.WAV \
+  https://raw.githubusercontent.com/meituan-longcat/LongCat-Video/main/assets/avatar/multi/sing_woman.WAV
+curl -L -o longcat_avatar_assets/assets/avatar/multi/introduce.png \
+  https://raw.githubusercontent.com/meituan-longcat/LongCat-Video/main/assets/avatar/multi/introduce.png
+curl -L -o longcat_avatar_assets/assets/avatar/multi/introduce_man.mp3 \
+  https://raw.githubusercontent.com/meituan-longcat/LongCat-Video/main/assets/avatar/multi/introduce_man.mp3
+curl -L -o longcat_avatar_assets/assets/avatar/multi/introduce_woman.mp3 \
+  https://raw.githubusercontent.com/meituan-longcat/LongCat-Video/main/assets/avatar/multi/introduce_woman.mp3
+curl -L -o longcat_avatar_assets/assets/avatar/multi_example_1.json \
+  https://raw.githubusercontent.com/meituan-longcat/LongCat-Video/main/assets/avatar/multi_example_1.json
+curl -L -o longcat_avatar_assets/assets/avatar/multi_example_2.json \
+  https://raw.githubusercontent.com/meituan-longcat/LongCat-Video/main/assets/avatar/multi_example_2.json
+
+export LONGCAT_VIDEO_ASSET_DIR="$PWD/longcat_avatar_assets/assets/avatar"
+export AVATAR_MODEL=meituan-longcat/LongCat-Video-Avatar-1.5
+export PROMPT="$(python -c 'import json, os; print(json.load(open(os.environ["LONGCAT_VIDEO_ASSET_DIR"] + "/single_example_1.json"))["prompt"])')"
+```
+
+`--base-model-dir` points at a local LongCat-Video base checkpoint for the
+tokenizer, text encoder and VAE; omit it to download those components from
+Hugging Face.
+
+```bash
+export BASE_MODEL_DIR=/cache/longcat_models/LongCat-Video
+```
+
+#### Official Asset Smoke Test
+
+Audio-to-video with the official audio:
+
+```bash
+python examples/offline_inference/longcat_video/end2end.py \
+  --model "$AVATAR_MODEL" \
+  --base-model-dir "$BASE_MODEL_DIR" \
+  --stage at2v \
+  --audio "$LONGCAT_VIDEO_ASSET_DIR/single/man.mp3" \
+  --prompt "$PROMPT" \
+  --num-frames 93 \
+  --num-inference-steps 8 \
+  --output official_asset_longcat_avatar_at2v.mp4
+```
+
+Audio-and-image-to-video with the official audio and image:
+
+```bash
+python examples/offline_inference/longcat_video/end2end.py \
+  --model "$AVATAR_MODEL" \
+  --base-model-dir "$BASE_MODEL_DIR" \
+  --stage ai2v \
+  --audio "$LONGCAT_VIDEO_ASSET_DIR/single/man.mp3" \
+  --image "$LONGCAT_VIDEO_ASSET_DIR/single/man.png" \
+  --prompt "$PROMPT" \
+  --num-frames 93 \
+  --num-inference-steps 8 \
+  --output official_asset_longcat_avatar_ai2v.mp4
+```
+
+#### Official Single-Speaker AVC Continuation
+
+`--num-segments` chains AVC segments; `auto` covers the full input audio.
+
+```bash
+python examples/offline_inference/longcat_video/end2end.py \
+  --model "$AVATAR_MODEL" \
+  --base-model-dir "$BASE_MODEL_DIR" \
+  --input-json "$LONGCAT_VIDEO_ASSET_DIR/single_example_1.json" \
+  --num-frames 93 \
+  --num-cond-frames 13 \
+  --num-segments 5 \
+  --num-inference-steps 8 \
+  --output official_asset_longcat_avatar_single_example_1_ai2v_5seg.mp4
+```
+
+Replace `--num-segments 5` with `--num-segments auto` to run until the full
+official audio is covered. That is a slow archival validation path rather than a
+regular smoke test: about 24m28s on a Modal H100 for the full
+`single_example_1.json` audio with 8 inference steps.
+
+#### Official Multi-Speaker AI2V
+
+The multi-speaker cases use several audio tracks with speaker masks and optional
+bounding boxes. The command below generates the first 93-frame segment, matching
+the first call to the official `generate_ai2v()`.
+
+```bash
+python examples/offline_inference/longcat_video/end2end.py \
+  --model "$AVATAR_MODEL" \
+  --base-model-dir "$BASE_MODEL_DIR" \
+  --input-json "$LONGCAT_VIDEO_ASSET_DIR/multi_example_1.json" \
+  --num-frames 93 \
+  --num-inference-steps 8 \
+  --output official_asset_longcat_avatar_multi_example_1_ai2v.mp4
+```
+
+Use `multi_example_2.json` for the sequential-speaking case, and add
+`--num-cond-frames 13 --num-segments auto` to cover the full audio with AVC
+continuation.
+
+#### Verification
+
+```bash
+ffprobe -hide_banner longcat_avatar_ai2v.mp4
+```
+
+The output must report one video stream and one audio stream: the example muxes
+the generated frames with the input audio.
+
+#### Notes
+
+**Support matrix**
+
+| Mode | Speakers | Image required | AVC continuation |
+| --- | --- | --- | --- |
+| AT2V | Single | No | Yes |
+| AI2V | Single | Yes | Yes |
+| AT2V | Multi | Not supported | Not supported |
+| AI2V | Multi | Yes | Yes |
+
+Multi-speaker Avatar requires AI2V because speaker bounding boxes and masks are
+defined on a reference image; AT2V with several tracks fails with an explicit
+error. AVC continuation uses full-audio embeddings only when `--num-segments` is
+greater than `1` or set to `auto`; single-segment AT2V/AI2V keeps the shorter
+first-segment audio path.
+
+**Sampling parameters**
+
+| Parameter | LongCat pipeline value | Description |
+| --- | --- | --- |
+| `--num-frames` | `num_frames` | Frames generated per segment. The default is 93 frames, or about 3.72 seconds at 25 FPS. LongCat normalizes other values to the temporal-VAE-compatible `4k+1` form. |
+| `--num-inference-steps` | `steps` | Denoising steps. The distilled Avatar path uses 8 steps. |
+| `--fps` | `save_fps` | Output frame rate, and the rate used to align audio embeddings with video frames. The default is 25 FPS. |
+| `sampling_params.guidance_scale` | `text_guidance_scale` | Classifier-free guidance applied to the text prompt. |
+| `sampling_params.guidance_scale_2` | `audio_guidance_scale` | Classifier-free guidance applied to the speech audio. |
+
+With the distilled LoRA enabled (the default), the pipeline uses 8 inference
+steps and sets both guidance scales to `1.0`. With `--no-use-distill`, the
+example sets both to `4.0`. For AVC continuation every segment holds
+`num_frames` frames, but each segment after the first reuses `num_cond_frames`
+conditioning frames, so the unique frame count is
+`num_frames + (num_segments - 1) * (num_frames - num_cond_frames)`.
+
+**Key flags**
+
+- `--stage`: `at2v` for audio-to-video, `ai2v` for audio-and-image-to-video.
+- `--resolution`: `480p` or `720p`.
+- `--input-json`: official LongCat Avatar JSON case, used for the single-speaker
+  and multi-speaker AI2V examples.
+- `--num-segments`: number of AVC segments, or `auto` to cover the full audio.
+- `--num-cond-frames`: previous-frame conditioning window for AVC continuation.
+- `--use-int8` / `--no-use-int8`: load the official INT8 Avatar DiT weights by
+  default, or full precision weights.
+- `--use-distill` / `--no-use-distill`: enable the official distilled LoRA path
+  by default.
+- `--build-components-on-gpu`: build large Avatar components directly on GPU for
+  faster startup, at a higher peak VRAM cost. Disabled by default.
+- `--base-model-dir`: local LongCat-Video base model directory for the
+  tokenizer, text encoder and VAE components.
+
+**Memory usage**
+
+Measured on a Modal H100 running the official 93-frame AI2V example with the
+INT8 DiT, distilled LoRA and 8 inference steps:
+
+| Component build | CLI option | Peak GPU memory | Model loading time |
+| --- | --- | ---: | ---: |
+| CPU (default) | None | About 41.0 GiB | About 130s |
+| GPU | `--build-components-on-gpu` | About 56.8 GiB | About 33s |
+
+The reported peak covers the end-to-end run. Building components on CPU only
+reduces initialization memory; denoising still runs on GPU. These numbers are
+memory guidance rather than a performance benchmark.
+
+**Known limitations**
+
+- Single GPU only. Cache acceleration, sequence/CFG/tensor parallelism, HSDP,
+  CPU offload, VAE patch parallelism and quantization are not wired up for this
+  pipeline yet; see the diffusion feature matrix in
+  [`docs/user_guide/diffusion_features.md`](../../docs/user_guide/diffusion_features.md).
+- Offline inference only. Online serving is not supported yet.
@@ -16,7 +16,7 @@ diffusers==0.38.0
 safetensors>=0.8.0
 accelerate==1.12.0
 soundfile>=0.13.1
-cache-dit==1.3.0
+cache-dit==1.5.0
 tqdm>=4.66.0
 torchsde>=0.2.6
 openai-whisper>=20250625
@@ -34,3 +34,5 @@ pydantic-settings>=2.0.0
 # base image does not ship it, so declare it explicitly. The loader also guards
 # this import so a missing gguf no longer breaks all diffusion model loading.
 gguf>=0.10.0
+# Required for running the Cosmos3 model with guardrails enabled (default behavior).
+cosmos-guardrail>=0.3.1
Author	SHA1	Message	Date
Hongsheng Liu	fab29e2873	[Docs] Redirect the former PD guide URL Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-13 12:48:43 +08:00
Hongsheng Liu	1ade14d147	[Docs] Document current PD design Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-13 12:44:42 +08:00
Hongsheng Liu	b6313918d6	Merge branch 'main' into codex-pd-navigation	2026-08-13 12:23:56 +08:00
NATUREandAlicia	d77a35a32b	[Bugfix] Scope Bagel FP8 config to diffusion stage (#6085 ) Signed-off-by: natureofnature <wzliu@connect.hku.hk> Co-authored-by: Alicia <115451386+congw729@users.noreply.github.com>	2026-08-13 11:23:47 +08:00
liwenerandamy-why-3459	877beeaa49	[Perf][MiniCPM-O-4.5]Add CUDA Graph For CFM DiT estimator (#6082 ) Signed-off-by: liwener <liwener.c@gmail.com> Co-authored-by: amy-why-3459 <wuhaiyan17@huawei.com>	2026-08-13 09:36:17 +08:00
Hongsheng Liu	8b8d3f56db	[Misc] Consolidate LingBot text-to-video runner (#6076 ) Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-13 08:42:37 +08:00
Hongsheng Liu	c57178c0e1	[Dependency] Upgrade Cache-DiT to 1.5.0 (#6065 ) Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-13 08:41:59 +08:00
Hongsheng Liu	f27e70dce7	[Docs] Move PD guide into Features Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-13 06:54:34 +08:00
mjZhaoElaineandClaude Opus 5	9a59475015	[CI/Build] Fix ReadTheDocs build under RTD's seeded pip 23.1 (#6129 ) Signed-off-by: Mengjie Zhao <zmj0129@gmail.com> Co-authored-by: Claude Opus 5 <noreply@anthropic.com>	2026-08-13 06:52:01 +08:00
Hongsheng Liu	c46abfef59	[Docs] Mark PD disaggregation as experimental Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-13 06:50:41 +08:00
Nick Cao	12f8168179	[Tests] Enable tiny model testing for Qwen-Image Edit and EditPlus (#5656 ) Signed-off-by: Nick Cao <ncao@redhat.com>	2026-08-12 16:03:49 -04:00
Zhenghan Song	72a02b492a	[Bugfix] Fix diffusion TTS adapter lookup in speech serving (#6121 ) Signed-off-by: HaningZS <resossr0v0@gmail.com>	2026-08-12 15:20:12 -04:00
Juan Pablo ZuluagaandYueqian Lin	5358f5db01	[Perf][TTS] omnivoice hot path: D2H batching, mask caching, cached text embeddings (#5174 ) Signed-off-by: JuanPZuluaga <juanz9312@gmail.com> Co-authored-by: Yueqian Lin <70319226+linyueqian@users.noreply.github.com>	2026-08-12 17:15:35 +00:00
MaciejBalaNV	e677360168	Add cosmos-guardrail dependency and update error message (#6107 ) Signed-off-by: Maciej Bala <mbala@nvidia.com>	2026-08-12 17:04:07 +00:00
Juan Pablo ZuluagaandYueqian Lin	208c6776a8	[Perf][TTS] glm_tts: hoist loop-invariant text embed, RoPE, mask and CFG batch out of Euler loop (#5068 ) Signed-off-by: JuanPZuluaga <juanz9312@gmail.com> Co-authored-by: Yueqian Lin <70319226+linyueqian@users.noreply.github.com>	2026-08-12 17:02:02 +00:00
Shi Boao Bowie 汪志鹏 ruirui yang amy-why-3459 Gao Han Hongsheng Liu	fcf1085395	[Refactor][OutputProcessor 2/8]: OmniRequestOutput should inherit RequestOutput and no nested wrap-up (#5146 ) Signed-off-by: Boao Shi <aoibosh@connect.hku.hk> Signed-off-by: bowieshi <aoibosh@connect.hku.hk> Signed-off-by: princepride <wangzhipeng628@gmail.com> Signed-off-by: R2-Y <ruiruyang2@gmail.com> Signed-off-by: amy-why-3459 <wuhaiyan17@huawei.com> Signed-off-by: Shi Boao Bowie <aoibosh@connect.hku.hk> Co-authored-by: 汪志鹏 <wangzhipeng628@gmail.com> Co-authored-by: ruirui(rein) yang <73573651+R2-Y@users.noreply.github.com> Co-authored-by: amy-why-3459 <wuhaiyan17@huawei.com> Co-authored-by: Gao Han <hgaoaf@connect.ust.hk> Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-12 22:58:23 +08:00
Wenjia LiandHongsheng Liu	38a2500bdf	[Perf] MiniMax H3 Qwen3-VL support fused RMSNorm on NPU (#5915 ) Signed-off-by: Wenjia Li <wjialish@gmail.com> Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com> Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-12 19:12:54 +08:00
amy-why-3459	7cd826d28c	[Bugfix][MiniCPM-o] Align Daily-Omni offline loading and duplex soft-… (#6095 ) Signed-off-by: amy-why-3459 <wuhaiyan17@huawei.com>	2026-08-12 18:36:31 +08:00
wangyuandHongsheng Liu	31384cb39c	[CI/Build] Add nightly DockerHub publish and cleanup to release pipeline (#6048 ) Signed-off-by: wangyu <410167048@qq.com> Signed-off-by: [Your Name] [Your Email] Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-12 17:30:55 +08:00
Yuekai ZhangandClaude Fable 5	5be7541d01	[Model] Support NVIDIA-NemotronLabs-VoiceChat-11B offline speech-to-speech (#5842 ) Signed-off-by: Yuekai Zhang <zhangyuekai@foxmail.com> Co-authored-by: Claude Fable 5 <noreply@anthropic.com>	2026-08-12 16:25:48 +08:00
王金旭 xurui Claude Opus 4.8	9b0df3909a	[Feature] timestamps for TTS via a forced-aligner pooling (#4795 ) Signed-off-by: wjinxu <1299461899@qq.com> Co-authored-by: xurui <xurui1@modelbest.onaliyun.com> Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>	2026-08-12 05:33:34 +00:00
Hongsheng Liu	a15b5b4bcd	[CI/Build] Add examples policy to PR skills (#6046 ) Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-12 11:37:34 +08:00
Hongsheng Liu	3c3989ed04	docs: align quantization overview with navigation (#6074 ) Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-12 11:37:01 +08:00
Rahul SteigerandRahul Steiger	854bff7fe5	[Bugfix] Fix Wan spatial reshard boundary (#6062 ) Signed-off-by: Rahul Steiger <rsteiger@nvl72d106-T18.cm.cluster> Co-authored-by: Rahul Steiger <rsteiger@nvl72d106-T18.cm.cluster>	2026-08-12 11:36:24 +08:00
amy-why-3459	b792578d89	[CI][MiniCPM-o] Add MiniCPM-o 4.5 perf coverage to the ready gate (#6079 ) Signed-off-by: amy-why-3459 <wuhaiyan17@huawei.com>	2026-08-12 11:32:36 +08:00
zzh	bdb1d996be	[Bugfix] Fix HunyuanImage3 accuracy test (#5981 ) Signed-off-by: zzh <943967662@qq.com>	2026-08-12 02:40:05 +00:00
Hongsheng Liu	8b0a6779d4	[Misc] Build canonical image task prompts (#6049 ) Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-12 08:30:34 +08:00
Sy03andYueqian Lin	58007c0084	[New Model] Support IndexTTS 2.5 (#5957 ) Signed-off-by: Sysy <1370724210@qq.com> Co-authored-by: Yueqian Lin <70319226+linyueqian@users.noreply.github.com>	2026-08-11 23:00:35 +00:00
Hongsheng Liu	1ff2753469	docs: align User Guide feature taxonomy (#6045 ) Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-12 00:20:56 +08:00
SYLAR	4697bd1be0	[Docs] Add MiniMax-H3 recipe for RTX PRO 5000 (#5857 ) Signed-off-by: lishunyang12 <lishunyang12@163.com>	2026-08-11 23:53:58 +08:00
Zhou TaichangandClaude Opus 5	2ce15ab7a0	[Rebase] Rebase to vllm 0.27.0 (#5976 ) Signed-off-by: tzhouam <tzhouam@connect.ust.hk> Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>	2026-08-11 15:23:46 +00:00
Nick Cao	584d78c673	[Test] Add tiny model builder for FluxKontextPipeline (#5823 ) Signed-off-by: Nick Cao <ncao@redhat.com>	2026-08-11 10:59:09 -04:00
Jerry Song	89015d5cad	[Feature]: Adds distilled LoRA support for diffusion models (#2783 ) Signed-off-by: Songrui625 <songrui625@gmail.com>	2026-08-11 14:23:43 +00:00
amy-why-3459	348cf95a89	[CI][MiniCPM-o] Align MiniCPM-o 4.5 online serving tests with minicpm… (#6056 ) Signed-off-by: amy-why-3459 <wuhaiyan17@huawei.com>	2026-08-11 21:11:39 +08:00
R0CKSTAR	22dd96ec5c	[Hardware][MUSA] Complete Omni platform interfaces (#6058 ) Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com>	2026-08-11 12:46:42 +00:00
R0CKSTAR Hongsheng Liu amy-why-3459	e04210d690	[Hardware][MUSA] Enable Qwen3-Omni ModelOpt FP8 inference (#5671 ) Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com> Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com> Co-authored-by: amy-why-3459 <wuhaiyan17@huawei.com>	2026-08-11 17:19:09 +08:00
Kristoffer Didan Deng Hongsheng Liu	4ab1b4ff9c	[Perf][Flux2][HunyuanVideo1.5] Skip attention-mask to avoid varlen path (#4645 ) Signed-off-by: kTorp <Kristoffer.Torp@amd.com> Signed-off-by: Didan Deng <33117903+wtomin@users.noreply.github.com> Co-authored-by: Didan Deng <33117903+wtomin@users.noreply.github.com> Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-11 15:31:02 +08:00
Weiyan Didan Deng Hongsheng Liu	06e733d3cd	[Model] Add LongCat-Video-Avatar-1.5 ai2v, at2v support (#4099 ) Signed-off-by: weiyan <weiyanlin117@gmail.com> Signed-off-by: Didan Deng <33117903+wtomin@users.noreply.github.com> Co-authored-by: Didan Deng <33117903+wtomin@users.noreply.github.com> Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-11 15:17:17 +08:00
Wang fuyinandHongsheng Liu	af3faa6c1e	[Diffusion] Add Worker contracts and RPC plumbing for Scheduler-managed paged KV cache (#5550 ) Signed-off-by: Acerak01-fy <wfy2003324@163.com> Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com> Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-11 14:55:42 +08:00
Ruirui Yang \| Rein psv666 Cursor amy-why-3459	322600bfb2	[CI][MiniCPM-o] Add MiniCPM-o 4.5 accuracy and performance coverage (#5524 ) Signed-off-by: R2-Y <ruiruyang2@gmail.com> Signed-off-by: psv666 <2693925048@qq.com> Co-authored-by: psv666 <2693925048@qq.com> Co-authored-by: Cursor <cursoragent@cursor.com> Co-authored-by: amy-why-3459 <wuhaiyan17@huawei.com>	2026-08-11 14:53:49 +08:00
Hongsheng Liu	c4a14091c9	[Doc] Explain repository skills for agentic contributions (#6029 ) Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-11 14:18:41 +08:00
yiminghub2024	6d4e4ffc7c	Add MiniMax-H3 recipe for RTX 4090 setup (#5850 ) Signed-off-by: yiminghub2024 <482890@qq.com>	2026-08-11 14:08:51 +08:00
Zhou248	f97af75d1d	[Model] Batch MiniCPM-o Talker codec sampling (#5792 ) Signed-off-by: Zhou248 <62542847+Zhou248@users.noreply.github.com>	2026-08-11 14:05:02 +08:00
Hongsheng Liu	73a312d5b6	[Bugfix] Normalize NumPy image outputs before saving (#6031 ) Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-11 13:48:03 +08:00
Zijun HuangandWeiQing Chen	1a56953d7a	[NPU][Model] Add distilled 4-step sigma schedule support for MiniMax H3 t2va (#5991 ) Signed-off-by: Zijun Huang <zijunhua@usc.edu> Co-authored-by: WeiQing Chen <40507679+david6666666@users.noreply.github.com>	2026-08-11 13:46:10 +08:00
WeiQing Chen	1f65814fab	[CI] Align MiniMax H3 Ref2VA inputs and add I2VA/Ref2VA accuracy coverage (#5978 ) Signed-off-by: david6666666 <530634352@qq.com>	2026-08-11 13:36:37 +08:00
boatmanandYueqian Lin	4f9b65074d	[Perf][Qwen3-TTS]Cached Incremental Decode (#5202 ) Signed-off-by: boatman <109857087+sphinxkkkbc@users.noreply.github.com> Co-authored-by: Yueqian Lin <70319226+linyueqian@users.noreply.github.com>	2026-08-11 05:24:04 +00:00
Juan Pablo ZuluagaandYueqian Lin	12a5f6fbf3	[Perf][TTS] step_audio2: keep streaming tokens on-device and batch audio-feature length syncs (#5067 ) Signed-off-by: JuanPZuluaga <juanz9312@gmail.com> Co-authored-by: Yueqian Lin <70319226+linyueqian@users.noreply.github.com>	2026-08-10 21:24:25 -07:00
Juan Pablo ZuluagaandYueqian Lin	53afbf3a0c	[Perf][TTS] voxtral_tts hot path: cudagraph opt-out, host-sync removal, kernel caches (#5175 ) Signed-off-by: JuanPZuluaga <juanz9312@gmail.com> Co-authored-by: Yueqian Lin <70319226+linyueqian@users.noreply.github.com>	2026-08-10 21:03:32 -07:00
Shenglei Fu Claude Opus 4.8 Yueqian Lin	ee118ab492	[Bugfix] Per-replica fault isolation: keep API server alive on single-stage death (#4285 ) (#4583 ) Signed-off-by: Shenglei Fu <sfu@confluent.io> Signed-off-by: Shenglei Fu <117230642+ShengleiFu@users.noreply.github.com> Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Co-authored-by: Yueqian Lin <70319226+linyueqian@users.noreply.github.com>	2026-08-10 23:00:18 -04:00