Merge branch 'main' into fix/omni-weight-snapshot-prefetch

Signed-off-by: Semmer <semmer@live.cn>

.buildkite/pipeline-intel.yaml

@@ -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: "v0.21.0"
+          VLLM_VERSION: "v0.22.0"
         priority: 100
         timeout_in_minutes: 60
         soft_fail: false

.buildkite/pipeline.yml

@@ -1,14 +1,14 @@
 # Document 1: Buildkite loads only this block on first parse. The next step resolves docs-only skip-ci
 # from git diff, then uploads document 2. When docs-only skip applies, image-build still runs for the nightly
-# exception in upload_pipeline_with_skip_ci.sh (nightly-test label or main NIGHTLY=1); otherwise upload-nightly
+# exception in upload_pipeline.py (nightly-test label or main NIGHTLY=1); otherwise upload-nightly
 # can be skipped together with test-ready/test-merge. Weekly uploads also depend on image-build.
 
-# Document 2: appended after `---`; same file, read by upload_pipeline_with_skip_ci.sh (not evaluated as a second pipeline by Buildkite). Child uploads include test-ready.yml, test-merge.yml, test-nightly.yml, and test-weekly.yml.
+# Document 2: appended after `---`; same file, read by upload_pipeline.py (not evaluated as a second pipeline by Buildkite). Child uploads include test-ready.yml, test-merge.yml, test-nightly.yml, and test-weekly.yml.
 steps:
   - label: ":github: Resolve skip-ci & upload pipeline"
     key: upload-ci-pipeline
     commands:
-      - "bash .buildkite/scripts/upload_pipeline_with_skip_ci.sh"
+      - "python3 .buildkite/scripts/upload_pipeline.py --upload .buildkite/pipeline.yml"
     agents:
       queue: "cpu_queue_premerge"
 
@@ -31,7 +31,7 @@ steps:
     key: upload-ready-pipeline
     if: __UPLOAD_READY_IF__
     commands:
-      - buildkite-agent pipeline upload .buildkite/test-ready.yml
+      - python3 .buildkite/scripts/upload_pipeline.py --upload .buildkite/test-ready.yml
     agents:
       queue: "cpu_queue_premerge"
 
@@ -41,7 +41,7 @@ steps:
     key: upload-merge-pipeline
     if: __UPLOAD_MERGE_IF__
     commands:
-      - buildkite-agent pipeline upload .buildkite/test-merge.yml
+      - python3 .buildkite/scripts/upload_pipeline.py --upload .buildkite/test-merge.yml
     agents:
       queue: "cpu_queue_premerge"
 

.buildkite/rebase-pipeline.yaml

@@ -14,7 +14,7 @@ steps:
     depends_on: image-build
     key: upload-ready-pipeline
     commands:
-      - buildkite-agent pipeline upload .buildkite/test-ready.yml
+      - python3 .buildkite/scripts/upload_pipeline.py --upload .buildkite/test-ready.yml
     agents:
       queue: "cpu_queue_premerge"
 
@@ -23,7 +23,7 @@ steps:
     depends_on: image-build
     key: upload-merge-pipeline
     commands:
-      - buildkite-agent pipeline upload .buildkite/test-merge.yml
+      - python3 .buildkite/scripts/upload_pipeline.py --upload .buildkite/test-merge.yml
     agents:
       queue: "cpu_queue_premerge"
 

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

@@ -52,6 +52,7 @@ time timeout -k 30 30m docker run \
     pip install tblib==3.1.0
     cd /workspace/vllm-omni
     pytest -v -s -m "core_model and xpu and B60"
+    pytest -v -s tests/diffusion/quantization/test_mxfp8_config.py
     export VLLM_XPU_USE_SAMPLER_KERNEL=0    # NOTE: Remove this after vLLM v0.21.1 is merged. Fixes Qwen2-5 omni-expansion tests.
     pytest -v -s -m "advanced_model and xpu and B60"
 '

.buildkite/scripts/upload_pipeline.py

@@ -0,0 +1,308 @@
+#!/usr/bin/env python3
+# SPDX-License-Identifier: Apache-2.0
+"""Render and optionally upload Buildkite pipeline YAML with diff-aware logic.
+
+Bootstrap mode (pipeline.yml with __IMAGE_BUILD_IF__ placeholders):
+  - Detect docs-only PR/main changes and substitute skip-ci ``if`` expressions.
+
+Test pipeline mode (e.g. test-merge.yml):
+  - Drop steps or groups whose ``source_file_dependencies`` do not match changed files.
+  - ``source_file_dependencies`` may be set on a leaf step or on a group step.
+  - Always strip ``source_file_dependencies`` from uploaded YAML (Buildkite does not
+    recognize this uploader-only key).
+
+Usage:
+  python3 upload_pipeline.py [--upload] <pipeline.yml>
+
+  # Bootstrap (replaces upload_pipeline_with_skip_ci.sh):
+  python3 upload_pipeline.py --upload .buildkite/pipeline.yml
+
+  # Test pipeline (replaces upload_test_pipeline_with_diff_skip.py):
+  python3 upload_pipeline.py --upload .buildkite/test-merge.yml
+"""
+
+from __future__ import annotations
+
+import argparse
+import os
+import subprocess
+import sys
+from pathlib import Path
+from typing import Any
+
+try:
+    import yaml
+except ImportError:
+    subprocess.check_call(
+        [sys.executable, "-m", "pip", "install", "-q", "pyyaml"],
+    )
+    import yaml
+
+LOG = "upload_pipeline"
+ROOT = Path(__file__).resolve().parent.parent.parent
+DOC_SEP = "\n---\n"
+BOOTSTRAP_MARKER = "__IMAGE_BUILD_IF__"
+
+
+def _log(message: str) -> None:
+    print(f"{LOG}: {message}", file=sys.stderr)
+
+
+def _git(*args: str) -> subprocess.CompletedProcess[str]:
+    return subprocess.run(
+        ["git", *args],
+        check=False,
+        capture_output=True,
+        text=True,
+    )
+
+
+def resolve_changed_files() -> list[str] | None:
+    """Return changed file paths, or None when diff cannot be resolved."""
+    is_pr = os.environ.get("BUILDKITE_PULL_REQUEST", "false") != "false" and os.environ.get(
+        "BUILDKITE_PULL_REQUEST", ""
+    )
+    commit = os.environ.get("BUILDKITE_COMMIT", "")
+
+    if is_pr:
+        base_branch = os.environ.get("BUILDKITE_PULL_REQUEST_BASE_BRANCH", "main")
+        base_ref = f"origin/{base_branch}"
+        if _git("rev-parse", "--verify", base_ref).returncode != 0:
+            _log(f"origin/{base_branch} not found locally; trying fetch")
+            _git("fetch", "--depth=200", "origin", base_branch)
+        if _git("rev-parse", "--verify", base_ref).returncode != 0:
+            if _git("rev-parse", "--verify", base_branch).returncode == 0:
+                base_ref = base_branch
+            else:
+                _log(f"cannot resolve PR base {base_branch}; using safe defaults")
+                return None
+        diff_range = f"{base_ref}...{commit}"
+    elif os.environ.get("BUILDKITE_BRANCH", "") == "main":
+        if _git("rev-parse", "--verify", f"{commit}^").returncode != 0:
+            _log("main commit has no parent; using safe defaults")
+            return None
+        diff_range = f"{commit}^..{commit}"
+    else:
+        _log("not PR/main build; using safe defaults")
+        return None
+
+    result = _git("diff", "--name-only", diff_range)
+    if result.returncode != 0:
+        _log(f"git diff failed ({diff_range}); using safe defaults")
+        return None
+
+    files = [line for line in result.stdout.splitlines() if line.strip()]
+    _log(f"{len(files)} changed file(s)")
+    return files
+
+
+def is_docs_only_change(changed_files: list[str]) -> bool:
+    has_any = False
+    for file_path in changed_files:
+        if not file_path:
+            continue
+        has_any = True
+        if file_path.startswith("docs/"):
+            continue
+        if file_path.endswith(".md"):
+            continue
+        if file_path == "mkdocs.yml":
+            continue
+        return False
+    return has_any
+
+
+def resolve_skip_ci(changed_files: list[str] | None) -> bool:
+    if changed_files is None:
+        _log("skip-ci=0 (could not resolve changed files)")
+        return False
+    if is_docs_only_change(changed_files):
+        _log("docs-only change detected; skip-ci=1")
+        return True
+    _log("non-doc changes detected; skip-ci=0")
+    return False
+
+
+def render_bootstrap_pipeline(text: str, *, skip_ci: bool) -> str:
+    if DOC_SEP in text:
+        _, continuation = text.split(DOC_SEP, 1)
+    else:
+        continuation = text
+
+    nightly_only = (
+        '(build.pull_request.labels includes "nightly-test") || (build.branch == "main" && build.env("NIGHTLY") == "1")'
+    )
+    if skip_ci:
+        image_if = f"'{nightly_only}'"
+        ready_if = "'false'"
+        merge_if = "'false'"
+    else:
+        image_if = "'true'"
+        ready_if = '\'build.branch != "main" && build.pull_request.labels includes "ready"\''
+        merge_if = (
+            '\'(build.branch == "main" && build.env("NIGHTLY") != "1" '
+            '&& build.env("WEEKLY") != "1") || '
+            '(build.branch != "main" && build.pull_request.labels includes "merge-test")\''
+        )
+
+    return (
+        continuation.replace("__IMAGE_BUILD_IF__", image_if)
+        .replace("__UPLOAD_READY_IF__", ready_if)
+        .replace("__UPLOAD_MERGE_IF__", merge_if)
+    )
+
+
+def _matches_dependencies(changed_files: list[str], prefixes: list[str]) -> bool:
+    for path in changed_files:
+        for prefix in prefixes:
+            normalized = prefix.rstrip("/")
+            if path == normalized or path.startswith(f"{normalized}/"):
+                return True
+    return False
+
+
+def _strip_source_file_dependencies(step: dict[str, Any]) -> dict[str, Any]:
+    return {key: value for key, value in step.items() if key != "source_file_dependencies"}
+
+
+def _step_label(step: dict[str, Any]) -> str:
+    return str(step.get("group") or step.get("label") or "<step>")
+
+
+def _filter_steps(steps: list[Any], changed_files: list[str]) -> list[Any]:
+    filtered: list[Any] = []
+    for step in steps:
+        if not isinstance(step, dict):
+            filtered.append(step)
+            continue
+
+        deps = step.get("source_file_dependencies")
+        if deps is not None and not isinstance(deps, list):
+            raise ValueError(
+                f"source_file_dependencies must be a list in step {_step_label(step)!r}",
+            )
+        if deps is not None and not _matches_dependencies(changed_files, deps):
+            _log(
+                f"skip {_step_label(step)!r} (no changes under {deps})",
+            )
+            continue
+
+        nested = step.get("steps")
+        if nested is not None:
+            kept_nested = _filter_steps(nested, changed_files)
+            if not kept_nested:
+                _log(f"omit empty group {_step_label(step)!r}")
+                continue
+            new_step = _strip_source_file_dependencies(step)
+            new_step["steps"] = kept_nested
+            filtered.append(new_step)
+            continue
+
+        if deps is not None:
+            filtered.append(_strip_source_file_dependencies(step))
+        else:
+            filtered.append(step)
+
+    return filtered
+
+
+def _strip_uploader_metadata_from_steps(steps: list[Any]) -> list[Any]:
+    """Remove uploader-only keys while keeping all steps (no diff filtering)."""
+    stripped: list[Any] = []
+    for step in steps:
+        if not isinstance(step, dict):
+            stripped.append(step)
+            continue
+
+        deps = step.get("source_file_dependencies")
+        if deps is not None and not isinstance(deps, list):
+            raise ValueError(
+                f"source_file_dependencies must be a list in step {_step_label(step)!r}",
+            )
+
+        nested = step.get("steps")
+        new_step = _strip_source_file_dependencies(step)
+        if nested is not None:
+            new_step["steps"] = _strip_uploader_metadata_from_steps(nested)
+        stripped.append(new_step)
+
+    return stripped
+
+
+def render_test_pipeline(
+    doc: dict[str, Any],
+    changed_files: list[str] | None,
+) -> dict[str, Any]:
+    steps = doc.get("steps")
+    if not isinstance(steps, list):
+        return doc
+    if changed_files is not None:
+        steps = _filter_steps(steps, changed_files)
+    else:
+        steps = _strip_uploader_metadata_from_steps(steps)
+    return {**doc, "steps": steps}
+
+
+def resolve_pipeline_path(arg: str) -> Path:
+    path = Path(arg)
+    if path.is_absolute():
+        return path
+    return ROOT / path
+
+
+def render_pipeline(path: Path) -> str:
+    text = path.read_text(encoding="utf-8")
+    changed_files = resolve_changed_files()
+
+    if BOOTSTRAP_MARKER in text:
+        rendered = render_bootstrap_pipeline(text, skip_ci=resolve_skip_ci(changed_files))
+        return rendered
+
+    doc = yaml.safe_load(text)
+    if not isinstance(doc, dict):
+        raise ValueError(f"invalid pipeline YAML: {path}")
+
+    doc = render_test_pipeline(doc, changed_files)
+
+    return yaml.safe_dump(doc, sort_keys=False)
+
+
+def upload_to_buildkite(content: str) -> None:
+    subprocess.run(
+        ["buildkite-agent", "pipeline", "upload"],
+        input=content,
+        text=True,
+        check=True,
+    )
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        "pipeline",
+        nargs="?",
+        default=".buildkite/pipeline.yml",
+        help="Pipeline YAML path (default: .buildkite/pipeline.yml)",
+    )
+    parser.add_argument(
+        "--upload",
+        action="store_true",
+        help="Pipe rendered YAML to buildkite-agent pipeline upload",
+    )
+    args = parser.parse_args()
+
+    path = resolve_pipeline_path(args.pipeline)
+    if not path.is_file():
+        _log(f"missing pipeline file: {path}")
+        return 1
+
+    rendered = render_pipeline(path)
+    if args.upload:
+        upload_to_buildkite(rendered)
+    else:
+        sys.stdout.write(rendered)
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

.buildkite/scripts/upload_pipeline_with_skip_ci.sh

@@ -1,146 +0,0 @@
-#!/usr/bin/env bash
-# Evaluate docs-only skip-ci and upload continuation steps from the given pipeline YAML
-# (YAML document after the first `---`). Buildkite `if` is evaluated at upload time.
-#
-# Usage: upload_pipeline_with_skip_ci.sh [pipeline_yaml]
-#   pipeline_yaml: path relative to repo root or absolute (default: .buildkite/pipeline.yml)
-set -euo pipefail
-
-ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/../.." && pwd)"
-PIPELINE_ARG="${1:-.buildkite/pipeline.yml}"
-if [[ "${PIPELINE_ARG}" = /* ]]; then
-  PIPELINE_YML="${PIPELINE_ARG}"
-else
-  PIPELINE_YML="${ROOT}/${PIPELINE_ARG}"
-fi
-
-# Prints a single digit to stdout: 1 = skip image CI, 0 = run. Logs go to stderr.
-is_docs_only_change() {
-  local file_path
-  local has_any=0
-
-  while IFS= read -r file_path; do
-    [[ -z "${file_path}" ]] && continue
-    has_any=1
-
-    if [[ "${file_path}" == docs/* ]]; then
-      continue
-    fi
-    if [[ "${file_path}" == *.md ]]; then
-      continue
-    fi
-    if [[ "${file_path}" == "mkdocs.yml" ]]; then
-      continue
-    fi
-    return 1
-  done
-
-  [[ "${has_any}" -eq 1 ]]
-}
-
-resolve_skip_ci() {
-  local is_pr_build=0
-  local files
-  local base_branch base_ref
-
-  if [[ "${BUILDKITE_PULL_REQUEST:-false}" != "false" && -n "${BUILDKITE_PULL_REQUEST:-}" ]]; then
-    is_pr_build=1
-  fi
-
-  if [[ "${is_pr_build}" -eq 1 ]]; then
-    base_branch="${BUILDKITE_PULL_REQUEST_BASE_BRANCH:-main}"
-    if ! git rev-parse --verify "origin/${base_branch}" >/dev/null 2>&1; then
-      echo "resolve_skip_ci: origin/${base_branch} not found locally; trying fetch" >&2
-      git fetch --depth=200 origin "${base_branch}" >/dev/null 2>&1 || true
-    fi
-
-    base_ref=""
-    if git rev-parse --verify "origin/${base_branch}" >/dev/null 2>&1; then
-      base_ref="origin/${base_branch}"
-    elif git rev-parse --verify "${base_branch}" >/dev/null 2>&1; then
-      base_ref="${base_branch}"
-    else
-      echo "resolve_skip_ci: cannot resolve PR base ${base_branch}; skip-ci=0" >&2
-      echo -n 0
-      return 0
-    fi
-
-    if ! files="$(git diff --name-only "${base_ref}...${BUILDKITE_COMMIT}" 2>/dev/null)"; then
-      echo "resolve_skip_ci: failed to compute PR changed files; skip-ci=0" >&2
-      echo -n 0
-      return 0
-    fi
-  elif [[ "${BUILDKITE_BRANCH:-}" == "main" ]]; then
-    if ! git rev-parse --verify "${BUILDKITE_COMMIT}^" >/dev/null 2>&1; then
-      echo "resolve_skip_ci: commit has no parent on main; skip-ci=0" >&2
-      echo -n 0
-      return 0
-    fi
-    if ! files="$(git diff --name-only "${BUILDKITE_COMMIT}^..${BUILDKITE_COMMIT}" 2>/dev/null)"; then
-      echo "resolve_skip_ci: failed to compute main changed files; skip-ci=0" >&2
-      echo -n 0
-      return 0
-    fi
-  else
-    echo "resolve_skip_ci: not PR/main build; skip-ci=0" >&2
-    echo -n 0
-    return 0
-  fi
-
-  if is_docs_only_change <<< "${files}"; then
-    echo "resolve_skip_ci: docs-only change detected; skip-ci=1" >&2
-    echo -n 1
-    return 0
-  fi
-
-  echo "resolve_skip_ci: non-doc changes detected; skip-ci=0" >&2
-  echo -n 0
-}
-
-SKIP_CI="$(resolve_skip_ci)"
-
-if [[ ! -f "${PIPELINE_YML}" ]]; then
-  echo "upload_pipeline_with_skip_ci: missing ${PIPELINE_YML}" >&2
-  exit 1
-fi
-
-export ROOT SKIP_CI PIPELINE_YML
-python3 <<'PY' | buildkite-agent pipeline upload
-import os
-import pathlib
-
-path = pathlib.Path(os.environ["PIPELINE_YML"])
-text = path.read_text(encoding="utf-8")
-sep = "\n---\n"
-# Two supported layouts:
-#   - multi-doc: doc 1 = bootstrap (loaded by Buildkite), doc 2 (after `---`) = uploaded steps.
-#   - single-doc: whole file is the uploaded steps; caller (UI init step) is the bootstrap.
-if sep in text:
-    _, continuation = text.split(sep, 1)
-else:
-    continuation = text
-
-skip = os.environ.get("SKIP_CI") == "1"
-# When docs-only skip-ci: skip default CI image, but still build for L4 nightly (PR label nightly-test or
-# main NIGHTLY=1), otherwise upload-nightly (depends_on image-build) would be skipped too.
-nightly_only = (
-    '(build.pull_request.labels includes "nightly-test") '
-    '|| (build.branch == "main" && build.env("NIGHTLY") == "1")'
-)
-# Placeholder in pipeline.yml is `if: __IMAGE_BUILD_IF__` (valid YAML); replace value only.
-if skip:
-    rep = f"'{nightly_only}'"
-    ready_rep = "'false'"
-    merge_rep = "'false'"
-else:
-    rep = "'true'"
-    ready_rep = "'build.branch != \"main\" && build.pull_request.labels includes \"ready\"'"
-    merge_rep = "'(build.branch == \"main\" && build.env(\"NIGHTLY\") != \"1\" && build.env(\"WEEKLY\") != \"1\") || (build.branch != \"main\" && build.pull_request.labels includes \"merge-test\")'"
-rendered = (
-    continuation
-    .replace("__IMAGE_BUILD_IF__", rep)
-    .replace("__UPLOAD_READY_IF__", ready_rep)
-    .replace("__UPLOAD_MERGE_IF__", merge_rep)
-)
-print(rendered, end="")
-PY

.buildkite/test-amd-merge.yml

@@ -1,41 +1,111 @@
+env:
+  VLLM_WORKER_MULTIPROC_METHOD: spawn
+  HF_HUB_DOWNLOAD_TIMEOUT: 300
+  HF_HUB_ETAG_TIMEOUT: 60
+
 steps:
 
-- label: "Simple Unit Test"
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - export VLLM_ROCM_USE_AITER=0
-    # ignore test_teacache_extractors.py because it use rocm gemm kernel from vLLM
-    # that is not supported on CPU
-    - "timeout 60m pytest -v -s -m 'core_model and cpu' --ignore=tests/diffusion/cache/test_teacache_extractors.py --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
+- group: ":card_index_dividers: Simple Test"
+  steps:
+    - label: "Simple · Diffusion & Model Executor Test"
+      agent_pool: mi325_1
+      depends_on: amd-build
+      timeout_in_minutes: 50
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - export VLLM_ROCM_USE_AITER=0
+        # ignore test_teacache_extractors.py because it use rocm gemm kernel from vLLM
+        # that is not supported on CPU
+        - "timeout 40m pytest -sv tests/diffusion tests/model_executor -m 'core_model and cpu' --ignore=tests/diffusion/cache/test_teacache_extractors.py --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
 
-- label: "Diffusion Images API LoRA E2E"
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - timeout 20m pytest -s -v tests/diffusion/lora/test_images_generations_lora.py
+    - label: "Simple · Other Test"
+      agent_pool: mi325_1
+      depends_on: amd-build
+      timeout_in_minutes: 50
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - export VLLM_ROCM_USE_AITER=0
+        # ignore test_teacache_extractors.py because it use rocm gemm kernel from vLLM
+        # that is not supported on CPU
+        - "timeout 40m pytest -sv tests/diffusion tests/model_executor -m 'core_model and cpu' --ignore=tests/diffusion/cache/test_teacache_extractors.py --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
 
-- label: "Diffusion Model CPU offloading Test"
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - export VLLM_LOGGING_LEVEL=DEBUG
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - |
-      timeout 20m bash -c '
-        set +e
-        pytest -s -v tests/diffusion/offloader/test_diffusion_cpu_offload.py
-        EXIT1=\$?
-        pytest -s -v tests/diffusion/offloader/test_diffusion_layerwise_offload.py
-        EXIT2=\$?
-        exit \$((EXIT1 | EXIT2))
-      '
+- group: ":card_index_dividers: Diffusion Test"
+  steps:
+    - label: "Diffusion · LoRA Test"
+      agent_pool: mi325_1
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - timeout 20m pytest -s -v tests/diffusion/lora/test_images_generations_lora.py
+
+    - label: "Diffusion Model CPU offloading Test"
+      agent_pool: mi325_1
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - export VLLM_LOGGING_LEVEL=DEBUG
+        - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+        - |
+          timeout 20m bash -c '
+            set +e
+            pytest -s -v tests/diffusion/offloader/test_diffusion_cpu_offload.py
+            EXIT1=\$?
+            pytest -s -v tests/diffusion/offloader/test_diffusion_layerwise_offload.py
+            EXIT2=\$?
+            exit \$((EXIT1 | EXIT2))
+          '
+
+    - label: "Diffusion Sequence Parallelism Test (Need 4 GPUs)"
+      agent_pool: mi325_4
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - export VLLM_LOGGING_LEVEL=DEBUG
+        - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+        - timeout 20m pytest -s -v tests/diffusion/distributed/test_sequence_parallel.py
+        - timeout 20m pytest -s -v tests/diffusion/distributed/test_ulysses_uaa_perf.py
+
+    # merge-only tests
+    - label: "Diffusion Tensor Parallelism Test"
+      agent_pool: mi325_2
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - pytest -s -v tests/diffusion/distributed/test_zimage_parallelism.py
+
+    - label: "Diffusion Model Test"
+      agent_pool: mi325_1
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - timeout 30m pytest -s -v tests/e2e/offline_inference/test_z_image.py
+
+    - label: "Diffusion GPU Worker Test"
+      agent_pool: mi325_2
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - timeout 20m pytest -s -v tests/diffusion/test_diffusion_worker.py
+
+    - label: "Diffusion · Wan22 Test"
+      agent_pool: mi325_1
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - |
+          timeout 40m bash -c "
+            export VLLM_IMAGE_FETCH_TIMEOUT=60
+            pytest -s -v tests/e2e/offline_inference/test_wan22_t2v.py tests/e2e/online_serving/test_wan22_t2v.py -m 'advanced_model and cuda and diffusion' --run-level 'advanced_model'
+          "
 
 ## ISSUE depends on `diffusers` package: https://github.com/huggingface/diffusers/issues/13274
 # - label: "Audio Generation Model Test"
@@ -49,52 +119,6 @@ steps:
 #     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
 #     - timeout 20m pytest -s -v tests/e2e/offline_inference/test_stable_audio_expansion.py -m "advanced_model and diffusion and L4" --run-level advanced_model
 
-- label: "Diffusion Cache Backend Test"
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - export VLLM_LOGGING_LEVEL=DEBUG
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - timeout 15m pytest -s -v -m "core_model and cache and diffusion and not distributed_cuda and L4"
-
-- label: "Diffusion Sequence Parallelism Test (Need 4 GPUs)"
-  agent_pool: mi325_4
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - export VLLM_LOGGING_LEVEL=DEBUG
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - timeout 20m pytest -s -v tests/diffusion/distributed/test_sequence_parallel.py
-    - timeout 20m pytest -s -v tests/diffusion/distributed/test_ulysses_uaa_perf.py
-
-# merge-only tests
-- label: "Diffusion Tensor Parallelism Test"
-  agent_pool: mi325_2
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - pytest -s -v tests/diffusion/distributed/test_zimage_parallelism.py
-
-- label: "Diffusion Model Test"
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - timeout 30m pytest -s -v tests/e2e/offline_inference/test_z_image.py
-
-- label: "Diffusion GPU Worker Test"
-  agent_pool: mi325_2
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - timeout 20m pytest -s -v tests/diffusion/test_diffusion_worker.py
-
 - label: "Engine Test"
   agent_pool: mi325_1
   depends_on: amd-build
@@ -108,50 +132,65 @@ steps:
 # This model seems to be important, so I am trying to run it here.
 # Since this is from test-nightly.yml,
 # I keep this non-blocking.
-- label: "Omni Model Test Qwen2-5-Omni"
-  agent_pool: mi325_4
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  commands:
-    - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
-    - pytest -s -v tests/e2e/*/test_qwen2_5_omni_expansion.py --run-level "core_model"
+- group: ":card_index_dividers: E2E Test"
+  steps:
+    - label: "Omni Model Test Qwen2-5-Omni"
+      agent_pool: mi325_4
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      commands:
+        - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
+        - pytest -s -v tests/e2e/*/test_qwen2_5_omni_expansion.py --run-level "core_model"
 
-- label: "Omni Model Test Qwen3-Omni"
-  agent_pool: mi325_2
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - pytest -s -v tests/e2e/offline_inference/test_qwen3_omni.py tests/e2e/online_serving/test_qwen3_omni.py tests/e2e/online_serving/test_mimo_audio.py -m "advanced_model" --run-level "advanced_model"
+    - label: "Omni Model Test Qwen3-Omni"
+      agent_pool: mi325_2
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+        - pytest -s -v tests/e2e/offline_inference/test_qwen3_omni.py tests/e2e/online_serving/test_qwen3_omni.py -m 'advanced_model and cuda' --run-level 'advanced_model'
 
-- label: "Qwen3-TTS CustomVoice E2E Test"
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - |
-      timeout 20m bash -c '
-        export VLLM_LOGGING_LEVEL=DEBUG
-        export VLLM_WORKER_MULTIPROC_METHOD=spawn
-        export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
-        pytest -s -v tests/e2e/online_serving/test_qwen3_tts_customvoice.py tests/e2e/offline_inference/test_qwen3_tts_customvoice.py -m "advanced_model" --run-level "advanced_model"
-      '
+    - label: "Qwen3-TTS CustomVoice E2E Test"
+      agent_pool: mi325_1
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - |
+          timeout 20m bash -c '
+            export VLLM_LOGGING_LEVEL=DEBUG
+            export VLLM_WORKER_MULTIPROC_METHOD=spawn
+            export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
+            pytest -s -v tests/e2e/online_serving/test_qwen3_tts_customvoice.py tests/e2e/offline_inference/test_qwen3_tts_customvoice.py -m "advanced_model" --run-level "advanced_model"
+          '
 
-- label: "Qwen3-TTS Base E2E Test"
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - |
-      timeout 30m bash -c '
-        export VLLM_LOGGING_LEVEL=DEBUG
-        export VLLM_WORKER_MULTIPROC_METHOD=spawn
-        export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
-        pytest -s -v tests/e2e/online_serving/test_qwen3_tts_base.py tests/e2e/offline_inference/test_qwen3_tts_base.py -m "advanced_model" --run-level "advanced_model"
-      '
+    - label: "Qwen3-TTS Base E2E Test"
+      agent_pool: mi325_1
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - |
+          timeout 30m bash -c '
+            export VLLM_LOGGING_LEVEL=DEBUG
+            export VLLM_WORKER_MULTIPROC_METHOD=spawn
+            export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
+            pytest -s -v tests/e2e/online_serving/test_qwen3_tts_base.py tests/e2e/offline_inference/test_qwen3_tts_base.py -m "advanced_model" --run-level "advanced_model"
+          '
+
+    - label: "Voxtral-TTS E2E Test"
+      agent_pool: mi325_1
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - |
+          timeout 20m bash -c '
+            export VLLM_LOGGING_LEVEL=DEBUG
+            export VLLM_WORKER_MULTIPROC_METHOD=spawn
+            pytest -s -v tests/e2e/online_serving/test_voxtral_tts.py tests/e2e/offline_inference/test_voxtral_tts.py -m "advanced_model" --run-level "advanced_model"
+          '
 
 # TODO: Bagel test on ROCm is very unstable. @tjtanaa
 # Need to debug before reneable numerical changes across large PRs
@@ -192,28 +231,3 @@ steps:
 #     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
 #     - export VLLM_ROCM_USE_AITER_RMSNORM=0
 #     - timeout 40m pytest -s -v tests/e2e/online_serving/test_bagel.py -m "advanced_model" --run-level "advanced_model" -k "rocm"
-
-- label: "Diffusion · Wan22 Test"
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - |
-      timeout 40m bash -c "
-        export VLLM_IMAGE_FETCH_TIMEOUT=60
-        pytest -s -v tests/e2e/offline_inference/test_wan22_t2v.py tests/e2e/online_serving/test_wan22_t2v.py -m 'advanced_model and cuda and diffusion' --run-level 'advanced_model'
-      "
-
-- label: "Voxtral-TTS E2E Test"
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - |
-      timeout 20m bash -c '
-        export VLLM_LOGGING_LEVEL=DEBUG
-        export VLLM_WORKER_MULTIPROC_METHOD=spawn
-        pytest -s -v tests/e2e/online_serving/test_voxtral_tts.py tests/e2e/offline_inference/test_voxtral_tts.py -m "advanced_model" --run-level "advanced_model"
-      '

.buildkite/test-amd-ready.yaml

@@ -1,31 +1,33 @@
+env:
+  VLLM_WORKER_MULTIPROC_METHOD: spawn
+  HF_HUB_DOWNLOAD_TIMEOUT: 300
+  HF_HUB_ETAG_TIMEOUT: 60
+
 steps:
 
-- label: "Simple Unit Test"
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - export VLLM_ROCM_USE_AITER=0
-    # ignore test_teacache_extractors.py because it use rocm gemm kernel from vLLM
-    # that is not supported on CPU
-    - "timeout 60m pytest -v -s -m 'core_model and cpu' --ignore=tests/diffusion/cache/test_teacache_extractors.py --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
+- group: ":card_index_dividers: Simple Test"
+  steps:
+    - label: "Simple · Diffusion & Model Executor Test"
+      agent_pool: mi325_1
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - export VLLM_ROCM_USE_AITER=0
+        # ignore test_teacache_extractors.py because it use rocm gemm kernel from vLLM
+        # that is not supported on CPU
+        - "timeout 40m pytest -sv tests/diffusion tests/model_executor -m 'core_model and cpu' --ignore=tests/diffusion/cache/test_teacache_extractors.py --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
 
-- label: "Voxtral TTS CUDA Unit Test"
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - timeout 10m pytest -s -v tests/model_executor/models/voxtral_tts/test_cuda_graph_acoustic_transformer.py
-
-- label: "Diffusion Batching Test"
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - timeout 20m pytest -s -v tests/diffusion/batching/test_diffusion_batching.py -m "core_model and diffusion" --run-level "core_model"
+    - label: "Simple · Other Test"
+      agent_pool: mi325_1
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - export VLLM_ROCM_USE_AITER=0
+        # ignore test_teacache_extractors.py because it use rocm gemm kernel from vLLM
+        # that is not supported on CPU
+        - "timeout 40m pytest -sv tests/diffusion tests/model_executor -m 'core_model and cpu' --ignore=tests/diffusion/cache/test_teacache_extractors.py --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
 
 - label: "Custom Pipeline Test"
   agent_pool: mi325_1
@@ -35,23 +37,92 @@ steps:
   commands:
     - timeout 20m pytest -s -v tests/e2e/offline_inference/custom_pipeline/ -m "core_model"
 
-- label: "Diffusion Model CPU offloading Test"
+
+- label: "Voxtral TTS CUDA Unit Test"
   agent_pool: mi325_1
   depends_on: amd-build
   mirror_hardwares: [amdproduction]
   grade: Blocking
   commands:
-    - export VLLM_LOGGING_LEVEL=DEBUG
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - |
-      timeout 20m bash -c '
-        set +e
-        pytest -s -v tests/diffusion/offloader/test_diffusion_cpu_offload.py
-        EXIT1=\$?
-        pytest -s -v tests/diffusion/offloader/test_diffusion_layerwise_offload.py
-        EXIT2=\$?
-        exit \$((EXIT1 | EXIT2))
-      '
+    - timeout 10m pytest -s -v tests/model_executor/models/voxtral_tts/test_cuda_graph_acoustic_transformer.py
+
+- group: ":card_index_dividers: Diffusion Test"
+  steps:
+    - label: "Diffusion · Batch Test"
+      agent_pool: mi325_1
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - timeout 20m pytest -sv tests/diffusion/batching -m 'core_model and cuda' --run-level "core_model"
+
+    - label: "Diffusion · Offloader Test"
+      agent_pool: mi325_1
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - export VLLM_LOGGING_LEVEL=DEBUG
+        - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+        - |
+          timeout 20m bash -c '
+            set +e
+            pytest -s -v tests/diffusion/offloader/test_diffusion_cpu_offload.py
+            EXIT1=\$?
+            pytest -s -v tests/diffusion/offloader/test_diffusion_layerwise_offload.py
+            EXIT2=\$?
+            exit \$((EXIT1 | EXIT2))
+          '
+
+    - label: "Diffusion · Cache Test"
+      agent_pool: mi325_1
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - export GPU_ARCHS=gfx942
+        - export VLLM_LOGGING_LEVEL=DEBUG
+        - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+        - timeout 15m pytest -s -v -m "core_model and cache and diffusion and not distributed_cuda and L4"
+
+    - label: "Diffusion Sequence Parallelism Test"
+      agent_pool: mi325_2
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - export VLLM_LOGGING_LEVEL=DEBUG
+        - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+        - timeout 20m pytest -s -v tests/diffusion/distributed/test_sequence_parallel.py -m core_model
+
+    - label: "Diffusion GPU Worker Test"
+      agent_pool: mi325_2
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - timeout 20m pytest -s -v tests/diffusion/test_diffusion_worker.py
+
+    - label: "Diffusion · Model Test"
+      agent_pool: mi325_1
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - timeout 15m pytest -sv tests/diffusion/models/ -m 'core_model and cuda' --run-level "core_model"
+
+    - label: "Diffusion · Wan22 Test"
+      agent_pool: mi325_1
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - |
+          timeout 40m bash -c "
+            export VLLM_TEST_CLEAN_GPU_MEMORY=1
+            export VLLM_IMAGE_FETCH_TIMEOUT=60
+            pytest -s -v tests/e2e/online_serving/test_wan22_t2v.py -m 'core_model' --run-level 'core_model'
+          "
 
 ## ISSUE depends on `diffusers` package: https://github.com/huggingface/diffusers/issues/13274
 # - label: "Audio Generation Model Test"
@@ -65,56 +136,6 @@ steps:
 #     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
 #     - timeout 20m pytest -s -v tests/e2e/offline_inference/test_stable_audio_expansion.py -m "advanced_model and diffusion and L4" --run-level advanced_model
 
-- label: "Diffusion Cache Backend Test"
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - export GPU_ARCHS=gfx942
-    - export VLLM_LOGGING_LEVEL=DEBUG
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - timeout 15m pytest -s -v -m "core_model and cache and diffusion and not distributed_cuda and L4"
-
-- label: "Diffusion Sequence Parallelism Test"
-  agent_pool: mi325_2
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - export VLLM_LOGGING_LEVEL=DEBUG
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - timeout 20m pytest -s -v tests/diffusion/distributed/test_sequence_parallel.py -m core_model
-
-- label: "Diffusion GPU Worker Test"
-  agent_pool: mi325_2
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - timeout 20m pytest -s -v tests/diffusion/test_diffusion_worker.py
-
-- label: "Diffusion · Model Test"
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - timeout 15m pytest -sv tests/diffusion/models/ -m 'core_model and cuda' --run-level "core_model"
-
-- label: "Diffusion · Wan22 Test"
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - |
-      timeout 40m bash -c "
-        export VLLM_TEST_CLEAN_GPU_MEMORY=1
-        export VLLM_IMAGE_FETCH_TIMEOUT=60
-        pytest -s -v tests/e2e/online_serving/test_wan22_t2v.py -m 'core_model' --run-level 'core_model'
-      "
-
 - label: "Engine Test"
   agent_pool: mi325_1
   depends_on: amd-build
@@ -138,43 +159,56 @@ steps:
 #     - timeout 10m pytest -s -v tests/e2e/offline_inference/test_qwen3_omni.py
 #     - timeout 20m pytest -s -v tests/e2e/online_serving/test_qwen3_omni.py -m "core_model" --run-level "core_model"
 
-- label: "MiMo-Audio E2E Test with H100"
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - |
-      timeout 30m bash -c '
-        export VLLM_LOGGING_LEVEL=DEBUG
-        export VLLM_WORKER_MULTIPROC_METHOD=spawn
-        pytest -s -v tests/e2e/online_serving/test_mimo_audio.py -m "core_model" --run-level "core_model"
-      '
+- group: ":card_index_dividers: E2E Test"
+  steps:
+    - label: "MiMo-Audio E2E Test with H100"
+      agent_pool: mi325_1
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - |
+          timeout 30m bash -c '
+            export VLLM_LOGGING_LEVEL=DEBUG
+            export VLLM_WORKER_MULTIPROC_METHOD=spawn
+            pytest -s -v tests/e2e/online_serving/test_mimo_audio.py -m "core_model" --run-level "core_model"
+          '
 
-- label: "Qwen3-TTS E2E Test"
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - export VLLM_LOGGING_LEVEL=DEBUG
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
-    - timeout 30m pytest -s -v tests/e2e/online_serving/test_qwen3_tts_customvoice.py -m "core_model" --run-level "core_model"
+    - label: "Qwen3-TTS E2E Test"
+      agent_pool: mi325_1
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - export VLLM_LOGGING_LEVEL=DEBUG
+        - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+        - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
+        - timeout 30m pytest -s -v tests/e2e/online_serving/test_qwen3_tts_customvoice.py -m "core_model" --run-level "core_model"
 
-- label: "Voxtral-TTS E2E Test"
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - |
-      timeout 20m bash -c '
-        export VLLM_LOGGING_LEVEL=DEBUG
-        export VLLM_WORKER_MULTIPROC_METHOD=spawn
-        pytest -s -v tests/e2e/online_serving/test_voxtral_tts.py -m "advanced_model" --run-level "advanced_model"
-        pytest -s -v tests/e2e/offline_inference/test_voxtral_tts.py -m "advanced_model" --run-level "advanced_model"
-      '
+    - label: "Voxtral-TTS E2E Test"
+      agent_pool: mi325_1
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - |
+          timeout 20m bash -c '
+            export VLLM_LOGGING_LEVEL=DEBUG
+            export VLLM_WORKER_MULTIPROC_METHOD=spawn
+            pytest -s -v tests/e2e/online_serving/test_voxtral_tts.py -m "advanced_model" --run-level "advanced_model"
+            pytest -s -v tests/e2e/offline_inference/test_voxtral_tts.py -m "advanced_model" --run-level "advanced_model"
+          '
+
+    - label: "CosyVoice3-TTS E2E Test"
+      agent_pool: mi325_1
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - |
+          timeout 20m bash -c '
+            pytest -s -v tests/e2e/online_serving/test_cosyvoice3_tts_expansion.py -m "full_model" --run-level "core_model"
+          '
 
 # TODO: Bagel test on ROCm is very unstable. @tjtanaa
 # Need to debug before reneable numerical changes across large PRs
@@ -214,14 +248,3 @@ steps:
 #     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
 #     - export VLLM_ROCM_USE_AITER_RMSNORM=0
 #     - timeout 40m pytest -s -v tests/e2e/online_serving/test_bagel.py -m "core_model" --run-level "core_model" -k "rocm"
-
-- label: "CosyVoice3-TTS E2E Test"
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - |
-      timeout 20m bash -c '
-        pytest -s -v tests/e2e/online_serving/test_cosyvoice3_tts_expansion.py -m "full_model" --run-level "core_model"
-      '

.buildkite/test-merge.yml

@@ -107,7 +107,7 @@ steps:
     depends_on: upload-merge-pipeline
     steps:
       - label: "Entrypoint Test with H100"
-        timeout_in_minutes: 35
+        timeout_in_minutes: 60
         commands:
           - pytest -s -v tests/entrypoints/ -m "advanced_model and cuda and H100" --run-level "advanced_model"
         agents:
@@ -206,7 +206,34 @@ steps:
               volumes:
                 - "/fsx/hf_cache:/fsx/hf_cache"
 
+      - label: "TTS · Ming-Omni-TTS Test"
+        timeout_in_minutes: 20
+        commands:
+          - |
+            timeout 20m bash -c "
+              export VLLM_LOGGING_LEVEL=DEBUG
+              pytest -s -v tests/e2e/online_serving/test_ming_tts.py tests/e2e/offline_inference/test_ming_tts.py -m 'advanced_model and cuda' --run-level 'advanced_model'
+            "
+        agents:
+          queue: "gpu_1_queue"
+        plugins:
+          - docker#v5.2.0:
+              image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+              always-pull: true
+              propagate-environment: true
+              shm-size: "8gb"
+              environment:
+                - "HF_HOME=/fsx/hf_cache"
+              volumes:
+                - "/fsx/hf_cache:/fsx/hf_cache"
+
       - label: "TTS · Voxtral-TTS Test"
+        source_file_dependencies:
+          - tests/e2e/offline_inference/test_voxtral_tts.py
+          - tests/e2e/online_serving/test_voxtral_tts.py
+          - vllm_omni/model_executor/models/voxtral_tts/
+          - vllm_omni/model_executor/stage_input_processors/voxtral_tts.py
+          - vllm_omni/deploy/voxtral_tts.yaml
         timeout_in_minutes: 20
         commands:
           - |
@@ -249,6 +276,12 @@ steps:
                       type: DirectoryOrCreate
 
       - label: "TTS · GLM-TTS Test"
+        source_file_dependencies:
+          - tests/e2e/online_serving/test_glm_tts.py
+          - tests/e2e/offline_inference/test_glm_tts.py
+          - vllm_omni/model_executor/models/glm_tts/
+          - vllm_omni/model_executor/stage_input_processors/glm_tts.py
+          - vllm_omni/deploy/glm_tts.yaml
         timeout_in_minutes: 20
         commands:
           - |
@@ -291,6 +324,12 @@ steps:
                       type: DirectoryOrCreate
 
       - label: "Omni · Qwen3-Omni Test"
+        source_file_dependencies:
+          - tests/e2e/offline_inference/test_qwen3_omni.py
+          - tests/e2e/online_serving/test_qwen3_omni.py
+          - vllm_omni/model_executor/models/qwen3_omni/
+          - vllm_omni/model_executor/stage_input_processors/qwen3_omni.py
+          - vllm_omni/deploy/qwen3_omni_moe.yaml
         timeout_in_minutes: 30
         commands:
           - pytest -s -v tests/e2e/offline_inference/test_qwen3_omni.py tests/e2e/online_serving/test_qwen3_omni.py -m 'advanced_model and cuda' --run-level 'advanced_model'
@@ -329,6 +368,11 @@ steps:
                       type: DirectoryOrCreate
 
       - label: "Omni · Mimo Audio Test"
+        source_file_dependencies:
+          - tests/e2e/online_serving/test_mimo_audio.py
+          - vllm_omni/model_executor/models/mimo_audio/
+          - vllm_omni/model_executor/stage_input_processors/mimo_audio.py
+          - vllm_omni/deploy/mimo_audio.yaml
         timeout_in_minutes: 30
         commands:
           - pytest -s -v tests/e2e/online_serving/test_mimo_audio.py -m 'advanced_model and cuda' --run-level 'advanced_model'
@@ -366,14 +410,39 @@ steps:
                       path: /mnt/hf-cache
                       type: DirectoryOrCreate
 
-      - label: "Omni · VoxCPM2 Test"
-        timeout_in_minutes: 20
+      - label: "TTS · VoxCPM2 Test"
+        timeout_in_minutes: 30
         commands:
           - |
-            timeout 20m bash -c "
+            timeout 30m bash -c "
               export VLLM_LOGGING_LEVEL=DEBUG
               export VLLM_WORKER_MULTIPROC_METHOD=spawn
-              pytest -s -v tests/e2e/offline_inference/test_voxcpm2.py -m 'advanced_model and cuda' --run-level 'advanced_model'
+              pytest -s -v tests/e2e/offline_inference/test_voxcpm2_tts.py tests/e2e/online_serving/test_voxcpm2_tts.py -m 'advanced_model and cuda' --run-level 'advanced_model'
+            "
+        agents:
+          queue: "gpu_1_queue"
+        plugins:
+          - docker#v5.2.0:
+              image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+              always-pull: true
+              propagate-environment: true
+              shm-size: "8gb"
+              environment:
+                - "HF_HOME=/fsx/hf_cache"
+                - "HF_TOKEN"
+              volumes:
+                - "/fsx/hf_cache:/fsx/hf_cache"
+
+      - label: "Omni · Higgs-Audio V2 Test"
+        timeout_in_minutes: 30
+        commands:
+          - |
+            timeout 30m bash -c "
+              export VLLM_LOGGING_LEVEL=DEBUG
+              export VLLM_WORKER_MULTIPROC_METHOD=spawn
+              export VLLM_USE_DEEP_GEMM=0
+              export VLLM_MOE_USE_DEEP_GEMM=0
+              pytest -s -v tests/e2e/online_serving/test_higgs_audio_v2.py tests/e2e/offline_inference/test_higgs_audio_v2.py -m 'advanced_model and cuda' --run-level 'advanced_model'
             "
         agents:
           queue: "gpu_1_queue"
@@ -390,6 +459,9 @@ steps:
                 - "/fsx/hf_cache:/fsx/hf_cache"
 
       - label: "Diffusion · Qwen Image Edit Test"
+        source_file_dependencies:
+          - tests/e2e/online_serving/test_qwen_image_edit.py
+          - vllm_omni/diffusion/models/qwen_image/
         timeout_in_minutes: 20
         commands:
           - pytest -s -v tests/e2e/online_serving/test_qwen_image_edit.py -m 'advanced_model and cuda' --run-level 'advanced_model'
@@ -428,6 +500,9 @@ steps:
                       type: DirectoryOrCreate
 
       - label: "Diffusion · Longcat Image Edit Test"
+        source_file_dependencies:
+          - tests/e2e/online_serving/test_longcat_image_edit.py
+          - vllm_omni/diffusion/models/longcat_image/
         timeout_in_minutes: 20
         commands:
           - pytest -s -v tests/e2e/online_serving/test_longcat_image_edit.py -m 'advanced_model and cuda' --run-level 'advanced_model'
@@ -466,6 +541,9 @@ steps:
                       type: DirectoryOrCreate
 
       - label: "Diffusion · Longcat Image Test"
+        source_file_dependencies:
+          - tests/e2e/online_serving/test_longcat_image.py
+          - vllm_omni/diffusion/models/longcat_image/
         timeout_in_minutes: 20
         commands:
           - pytest -s -v tests/e2e/online_serving/test_longcat_image.py -m 'advanced_model and cuda' --run-level 'advanced_model'
@@ -504,6 +582,13 @@ steps:
                       type: DirectoryOrCreate
 
       - label: "Diffusion · Bagel Test"
+        source_file_dependencies:
+          - tests/e2e/offline_inference/test_bagel.py
+          - tests/e2e/online_serving/test_bagel.py
+          - vllm_omni/model_executor/models/bagel/
+          - vllm_omni/diffusion/models/bagel/
+          - vllm_omni/model_executor/stage_input_processors/bagel.py
+          - vllm_omni/deploy/bagel.yaml
         timeout_in_minutes: 20
         commands:
           - pytest -s -v tests/e2e/offline_inference/test_bagel.py tests/e2e/online_serving/test_bagel.py -m 'advanced_model and cuda' --run-level 'advanced_model'
@@ -543,6 +628,9 @@ steps:
 
 
       - label: "Diffusion · Qwen Image Test"
+        source_file_dependencies:
+          - tests/e2e/online_serving/test_qwen_image.py
+          - vllm_omni/diffusion/models/qwen_image/
         commands:
           - |
             timeout 40m bash -c '
@@ -584,6 +672,9 @@ steps:
                       type: DirectoryOrCreate
 
       - label: "Diffusion · Qwen Image Layered Test"
+        source_file_dependencies:
+          - tests/e2e/online_serving/test_qwen_image_layered.py
+          - vllm_omni/diffusion/models/qwen_image/
         commands:
           - |
             timeout 40m bash -c "
@@ -625,6 +716,10 @@ steps:
                       type: DirectoryOrCreate
 
       - label: "Diffusion · Wan22 Test"
+        source_file_dependencies:
+          - tests/e2e/offline_inference/test_wan22_t2v.py
+          - tests/e2e/online_serving/test_wan22_t2v.py
+          - vllm_omni/diffusion/models/wan2_2/
         commands:
           - |
             timeout 40m bash -c "
@@ -666,6 +761,9 @@ steps:
                       type: DirectoryOrCreate
 
       - label: "Diffusion · HunyuanVideo 1.5 Test"
+        source_file_dependencies:
+          - tests/e2e/online_serving/test_hunyuan_video_15.py
+          - vllm_omni/diffusion/models/hunyuan_video/
         timeout_in_minutes: 40
         commands:
           - |
@@ -741,6 +839,9 @@ steps:
                 - "/fsx/hf_cache:/fsx/hf_cache"
 
       - label: "Diffusion · SD3 Test"
+        source_file_dependencies:
+          - tests/e2e/online_serving/test_sd3.py
+          - vllm_omni/diffusion/models/sd3/
         timeout_in_minutes: 45
         commands:
           - |

.buildkite/test-nightly.yml

@@ -305,6 +305,51 @@ steps:
                       path: /mnt/hf-cache
                       type: DirectoryOrCreate
 
+      - label: ":full_moon: Omni · Perf Test · Multi-Replica"
+        key: nightly-omni-performance-multi-replicas
+        timeout_in_minutes: 180
+        commands:
+          - export BENCHMARK_DIR=tests/dfx/perf/results
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen3_omni_multi_replicas.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
+            exit $$EXIT
+        agents:
+          queue: "mithril-h100-pool"
+        plugins:
+          - kubernetes:
+              podSpec:
+                containers:
+                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+                    resources:
+                      limits:
+                        nvidia.com/gpu: 3
+                    volumeMounts:
+                      - name: devshm
+                        mountPath: /dev/shm
+                      - name: hf-cache
+                        mountPath: /root/.cache/huggingface
+                    env:
+                      - name: HF_HOME
+                        value: /root/.cache/huggingface
+                      - name: HF_TOKEN
+                        valueFrom:
+                          secretKeyRef:
+                            name: hf-token-secret
+                            key: token
+                nodeSelector:
+                  node.kubernetes.io/instance-type: gpu-h100-sxm
+                volumes:
+                  - name: devshm
+                    emptyDir:
+                      medium: Memory
+                  - name: hf-cache
+                    hostPath:
+                      path: /mnt/hf-cache
+                      type: DirectoryOrCreate
+
 
       - label: ":full_moon: Omni · Multi-Replica Startup Test with 4x H100"
         timeout_in_minutes: 45
@@ -349,11 +394,11 @@ steps:
     depends_on: upload-nightly-pipeline
     if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test" || build.pull_request.labels includes "tts-test"
     steps:
-      - label: ":full_moon: TTS · Function Test"
+      - label: ":full_moon: TTS · Function Test with L4"
         timeout_in_minutes: 90
         commands:
           - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
-          - pytest -s -v tests/e2e/ -m "full_model and L4 and tts" --run-level "full_model" --ignore=tests/e2e/accuracy
+          - pytest -s -v tests/e2e/ -m "full_model and L4 and tts" --run-level "full_model" --ignore=tests/e2e/accuracy --ignore=tests/e2e/online_serving/test_voxcpm2_tts_expansion.py
         agents:
           queue: "gpu_1_queue" # g6.12xlarge instance on AWS, has 4 L4 GPU
         plugins:
@@ -368,6 +413,69 @@ steps:
               volumes:
                 - "/fsx/hf_cache:/fsx/hf_cache"
 
+      # - label: ":full_moon: TTS · Function Test with H100"
+      #   timeout_in_minutes: 90
+      #   commands:
+      #     - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
+      #     - pytest -s -v tests/e2e/ -m "full_model and H100 and tts" --run-level "full_model" --ignore=tests/e2e/accuracy
+      #   agents:
+      #     queue: "mithril-h100-pool"
+      #   plugins:
+      #     - kubernetes:
+      #         podSpec:
+      #           containers:
+      #             - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+      #               resources:
+      #                 limits:
+      #                   nvidia.com/gpu: 1
+      #               volumeMounts:
+      #                 - name: devshm
+      #                   mountPath: /dev/shm
+      #                 - name: hf-cache
+      #                   mountPath: /root/.cache/huggingface
+      #               env:
+      #                 - name: HF_HOME
+      #                   value: /root/.cache/huggingface
+      #                 - name: HF_TOKEN
+      #                   valueFrom:
+      #                     secretKeyRef:
+      #                       name: hf-token-secret
+      #                       key: token
+      #           nodeSelector:
+      #             node.kubernetes.io/instance-type: gpu-h100-sxm
+      #           volumes:
+      #             - name: devshm
+      #               emptyDir:
+      #                 medium: Memory
+      #             - name: hf-cache
+      #               hostPath:
+      #                 path: /mnt/hf-cache
+      #                 type: DirectoryOrCreate
+
+      - label: ":full_moon: TTS · Accuracy Test · VoxCPM2"
+        timeout_in_minutes: 30
+        commands:
+          - |
+            timeout 30m bash -c "
+              export VLLM_LOGGING_LEVEL=DEBUG
+              export VLLM_WORKER_MULTIPROC_METHOD=spawn
+              export VLLM_ALLOW_LONG_MAX_MODEL_LEN=1
+              pytest -s -v tests/e2e/online_serving/test_voxcpm2_tts_expansion.py -m 'full_model and cuda and tts' --run-level 'full_model'
+            "
+        agents:
+          queue: "gpu_1_queue"
+        plugins:
+          - docker#v5.2.0:
+              image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+              always-pull: true
+              propagate-environment: true
+              shm-size: "8gb"
+              environment:
+                - "HF_HOME=/fsx/hf_cache"
+                - "HF_TOKEN"
+              volumes:
+                - "/fsx/hf_cache:/fsx/hf_cache"
+
       - label: ":full_moon: TTS · Perf Test"
         key: nightly-tts-performance
         timeout_in_minutes: 180
@@ -414,6 +522,101 @@ steps:
                       path: /mnt/hf-cache
                       type: DirectoryOrCreate
 
+      - label: ":full_moon: TTS · VoxCPM2 · Perf Test"
+        key: nightly-tts-performance-voxcpm2
+        timeout_in_minutes: 180
+        commands:
+          - export BENCHMARK_DIR=tests/dfx/perf/results
+          - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_voxcpm2.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
+            exit $$EXIT
+        agents:
+          queue: "mithril-h100-pool"
+        plugins:
+          - kubernetes:
+              podSpec:
+                containers:
+                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+                    resources:
+                      limits:
+                        nvidia.com/gpu: 1
+                    volumeMounts:
+                      - name: devshm
+                        mountPath: /dev/shm
+                      - name: hf-cache
+                        mountPath: /root/.cache/huggingface
+                    env:
+                      - name: HF_HOME
+                        value: /root/.cache/huggingface
+                      - name: HF_TOKEN
+                        valueFrom:
+                          secretKeyRef:
+                            name: hf-token-secret
+                            key: token
+                nodeSelector:
+                  node.kubernetes.io/instance-type: gpu-h100-sxm
+                volumes:
+                  - name: devshm
+                    emptyDir:
+                      medium: Memory
+                  - name: hf-cache
+                    hostPath:
+                      path: /mnt/hf-cache
+                      type: DirectoryOrCreate
+
+  - label: ":full_moon: TTS · Higgs-Audio-V3 · Perf Test"
+    key: nightly-tts-performance-higgs-audio-v3
+    depends_on: upload-nightly-pipeline
+    if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test" || build.pull_request.labels includes "tts-test"
+    timeout_in_minutes: 180
+    commands:
+      - export BENCHMARK_DIR=tests/dfx/perf/results
+      - export VLLM_USE_DEEP_GEMM="0"
+      - export VLLM_MOE_USE_DEEP_GEMM="0"
+      - |
+        set +e
+        pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_higgs_audio_v3.json
+        EXIT=$$?
+        buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
+        exit $$EXIT
+    agents:
+      queue: "mithril-h100-pool"
+    plugins:
+      - kubernetes:
+          podSpec:
+            containers:
+              - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+                resources:
+                  limits:
+                    nvidia.com/gpu: 1
+                volumeMounts:
+                  - name: devshm
+                    mountPath: /dev/shm
+                  - name: hf-cache
+                    mountPath: /root/.cache/huggingface
+                env:
+                  - name: HF_HOME
+                    value: /root/.cache/huggingface
+                  - name: HF_TOKEN
+                    valueFrom:
+                      secretKeyRef:
+                        name: hf-token-secret
+                        key: token
+            nodeSelector:
+              node.kubernetes.io/instance-type: gpu-h100-sxm
+            volumes:
+              - name: devshm
+                emptyDir:
+                  medium: Memory
+              - name: hf-cache
+                hostPath:
+                  path: /mnt/hf-cache
+                  type: DirectoryOrCreate
+
   # Diffusion X2I suite: x2i / x2a / x2t and related non-video paths; x2v is only in "Diffusion X2V Model Test" below.
   - group: ":card_index_dividers: Diffusion X2I(&A&T) Model Test"
     key: nightly-diffusion-x2iat-group
@@ -437,6 +640,7 @@ steps:
             tests/e2e/online_serving/test_longcat_image_edit_expansion.py
             tests/e2e/online_serving/test_flux_2_dev_expansion.py
             tests/e2e/online_serving/test_bagel_expansion.py
+            tests/e2e/offline_inference/test_sensenova_u1_expansion.py
             -m "full_model and diffusion and H100 and not distributed_cuda"
             --run-level "full_model"
         agents:
@@ -525,6 +729,7 @@ steps:
             tests/e2e/online_serving/test_longcat_image_expansion.py
             tests/e2e/online_serving/test_longcat_image_edit_expansion.py
             tests/e2e/online_serving/test_flux_2_dev_expansion.py
+            tests/e2e/offline_inference/test_glm_image_autoround_w4a16_expansion.py
             -m "full_model and diffusion and H100 and distributed_cuda"
             --run-level "full_model"
         agents:
@@ -578,7 +783,88 @@ steps:
                   - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
                     resources:
                       limits:
-                        nvidia.com/gpu: 3
+                        nvidia.com/gpu: 2
+                    volumeMounts:
+                      - name: devshm
+                        mountPath: /dev/shm
+                      - name: hf-cache
+                        mountPath: /root/.cache/huggingface
+                    env:
+                      - name: HF_HOME
+                        value: /root/.cache/huggingface
+                      - name: HF_TOKEN
+                        valueFrom:
+                          secretKeyRef:
+                            name: hf-token-secret
+                            key: token
+                nodeSelector:
+                  node.kubernetes.io/instance-type: gpu-h100-sxm
+                volumes:
+                  - name: devshm
+                    emptyDir:
+                      medium: Memory
+                  - name: hf-cache
+                    hostPath:
+                      path: /mnt/hf-cache
+                      type: DirectoryOrCreate
+
+      - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with H100 · Multi-GPU DreamZero"
+        timeout_in_minutes: 120
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/online_serving/test_dreamzero_expansion.py
+            -m "full_model and diffusion and H100 and distributed_cuda"
+            --run-level "full_model"
+        agents:
+          queue: "mithril-h100-pool"
+        plugins:
+          - kubernetes:
+              podSpec:
+                containers:
+                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+                    resources:
+                      limits:
+                        nvidia.com/gpu: 2
+                    volumeMounts:
+                      - name: devshm
+                        mountPath: /dev/shm
+                      - name: hf-cache
+                        mountPath: /root/.cache/huggingface
+                    env:
+                      - name: HF_HOME
+                        value: /root/.cache/huggingface
+                      - name: HF_TOKEN
+                        valueFrom:
+                          secretKeyRef:
+                            name: hf-token-secret
+                            key: token
+                nodeSelector:
+                  node.kubernetes.io/instance-type: gpu-h100-sxm
+                volumes:
+                  - name: devshm
+                    emptyDir:
+                      medium: Memory
+                  - name: hf-cache
+                    hostPath:
+                      path: /mnt/hf-cache
+                      type: DirectoryOrCreate
+
+
+      - label: ":full_moon: Diffusion X2I(&A&T) · GLM-Image Function Test with H100"
+        timeout_in_minutes: 120
+        commands:
+          - pytest -sv tests/e2e/online_serving/test_glm_image_expansion.py -m "full_model and diffusion and H100" --run-level "full_model"
+        agents:
+          queue: "mithril-h100-pool"
+        plugins:
+          - kubernetes:
+              podSpec:
+                containers:
+                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+                    resources:
+                      limits:
+                        nvidia.com/gpu: 2
                     volumeMounts:
                       - name: devshm
                         mountPath: /dev/shm
@@ -701,6 +987,46 @@ steps:
                       path: /mnt/hf-cache
                       type: DirectoryOrCreate
 
+      - label: ":full_moon: HunyuanImage3-DIT · Accuracy Test"
+        timeout_in_minutes: 180
+        commands:
+          - export HUNYUAN_IMAGE3_MODEL="tencent/HunyuanImage-3.0-Instruct"
+          - export HUNYUAN_IMAGE3_DEVICES="0,1,2,3"
+          - pytest -s -v tests/e2e/accuracy/test_hunyuan_image3_pixel_accuracy.py -m full_model --run-level full_model
+        agents:
+          queue: "mithril-h100-pool"
+        plugins:
+          - kubernetes:
+              podSpec:
+                containers:
+                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+                    resources:
+                      limits:
+                        nvidia.com/gpu: 4
+                    volumeMounts:
+                      - name: devshm
+                        mountPath: /dev/shm
+                      - name: hf-cache
+                        mountPath: /root/.cache/huggingface
+                    env:
+                      - name: HF_HOME
+                        value: /root/.cache/huggingface
+                      - name: HF_TOKEN
+                        valueFrom:
+                          secretKeyRef:
+                            name: hf-token-secret
+                            key: token
+                nodeSelector:
+                  node.kubernetes.io/instance-type: gpu-h100-sxm
+                volumes:
+                  - name: devshm
+                    emptyDir:
+                      medium: Memory
+                  - name: hf-cache
+                    hostPath:
+                      path: /mnt/hf-cache
+                      type: DirectoryOrCreate
+
       - label: ":full_moon: Diffusion X2I(&A&T) · Perf Test · Qwen-Image"
         key: nightly-diffusion-x2iat-performance-qwen-image
         timeout_in_minutes: 180
@@ -797,8 +1123,8 @@ steps:
                       path: /mnt/hf-cache
                       type: DirectoryOrCreate
 
-      - label: ":full_moon: Diffusion X2I(&A&T) · Perf Test · Qwen-Image-Edit-2509"
-        key: nightly-diffusion-x2iat-performance-qwen-image-edit-2509
+      - label: ":full_moon: Diffusion X2I(&A&T) · Perf Test · Qwen-Image-Edit-2511"
+        key: nightly-diffusion-x2iat-performance-qwen-image-edit-2511
         timeout_in_minutes: 180
         commands:
           - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
@@ -806,7 +1132,7 @@ steps:
           - export CACHE_DIT_VERSION=1.3.0
           - |
             set +e
-            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_edit_2509_vllm_omni.json
+            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
             EXIT=$$?
             buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
             buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
@@ -893,26 +1219,20 @@ steps:
                       path: /mnt/hf-cache
                       type: DirectoryOrCreate
 
-      - label: ":full_moon: Diffusion X2I(&A&T) · HunyuanImage3 · DiT Perf Test"
-        key: nightly-hunyuan-image3-performance
-        soft_fail: true
-        timeout_in_minutes: 120
-        if: build.env("RUN_HUNYUAN_IMAGE3_PERF") == "1"
+      - label: ":full_moon: Diffusion X2I(&A&T) · Perf Test · BAGEL"
+        key: nightly-diffusion-x2iat-performance-bagel
+        timeout_in_minutes: 180
         commands:
           - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
           - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
           - export CACHE_DIT_VERSION=1.3.0
           - |
             set +e
-            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_hunyuan_image_tp4_fp8.json
-            EXIT1=$$?
-            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_hunyuan_image_tp2_fp8_sp2.json
-            EXIT2=$$?
-            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_hunyuan_image_tp2_fp8_cfgp2.json
-            EXIT3=$$?
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_bagel_vllm_omni.json
+            EXIT=$$?
             buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
             buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
-            exit $$((EXIT1 | EXIT2 | EXIT3))
+            exit $$EXIT
         agents:
           queue: "mithril-h100-pool"
         plugins:
@@ -922,7 +1242,7 @@ steps:
                   - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
                     resources:
                       limits:
-                        nvidia.com/gpu: 4
+                        nvidia.com/gpu: 1
                     volumeMounts:
                       - name: devshm
                         mountPath: /dev/shm
@@ -998,6 +1318,8 @@ steps:
         timeout_in_minutes: 180
         commands:
           - pytest -s -v tests/e2e/accuracy/wan22_i2v/test_wan22_i2v_video_similarity.py -m full_model --run-level full_model
+          - pytest -s -v tests/e2e/accuracy/hunyuanvideo15_t2v/test_hunyuanvideo15_t2v_video_similarity.py -m full_model --run-level full_model
+          - pytest -s -v tests/e2e/accuracy/hunyuanvideo15_i2v/test_hunyuanvideo15_i2v_video_similarity.py -m full_model --run-level full_model
           - pytest -s -v tests/e2e/accuracy/test_ltx2_3_video_similarity.py -m full_model --run-level full_model
           - pytest -s -v tests/e2e/accuracy/test_diffusers_backend_similarity.py -k '2v_matches_diffusers' -m full_model --run-level full_model
         agents:
@@ -1188,11 +1510,14 @@ steps:
       - nightly-omni-performance-no-async-chunk
       - nightly-omni-performance-async-chunk
       - nightly-omni-performance-vllm-text
+      - nightly-omni-performance-multi-replicas
       - nightly-tts-performance
+      - nightly-tts-performance-higgs-audio-v3
       - nightly-diffusion-x2iat-performance-qwen-image
       - nightly-diffusion-x2iat-performance-qwen-image-edit
-      - nightly-diffusion-x2iat-performance-qwen-image-edit-2509
+      - nightly-diffusion-x2iat-performance-qwen-image-edit-2511
       - nightly-diffusion-x2iat-performance-qwen-image-layered
+      - nightly-diffusion-x2iat-performance-bagel
       - nightly-diffusion-x2v-performance
       - nightly-testcase-statistics
     if: build.env("NIGHTLY") == "1" && build.env("EMAIL_DISTRIBUTION") == "1"
@@ -1201,13 +1526,16 @@ steps:
       - export DEFAULT_INPUT_DIR=tests/dfx/perf/results
       - export DEFAULT_OUTPUT_DIR=tests/dfx/perf/results
       - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-tts-performance
+      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-tts-performance-higgs-audio-v3
       - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-omni-performance-no-async-chunk
       - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-omni-performance-async-chunk
       - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-omni-performance-vllm-text
+      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-omni-performance-multi-replicas
       - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2iat-performance-qwen-image
       - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2iat-performance-qwen-image-edit
-      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2iat-performance-qwen-image-edit-2509
+      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2iat-performance-qwen-image-edit-2511
       - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2iat-performance-qwen-image-layered
+      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2iat-performance-bagel
       - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2v-performance
       - buildkite-agent artifact download "tests/dfx/perf/results/*.html" . --step nightly-testcase-statistics
       - python tools/nightly/generate_nightly_perf_excel.py

.buildkite/test-ready.yml

@@ -183,7 +183,7 @@ steps:
     depends_on: upload-ready-pipeline
     commands:
       - |
-        timeout 15m bash -c "
+        timeout 20m bash -c "
                   pytest -sv tests/distributed/ -m 'core_model and cuda' --run-level 'core_model'
         "
     agents:
@@ -215,6 +215,43 @@ steps:
                   path: /mnt/hf-cache
                   type: DirectoryOrCreate
 
+  - label: "Entrypoints Test"
+    depends_on: upload-ready-pipeline
+    commands:
+      - |
+        timeout 30m bash -c "
+          export VLLM_WORKER_MULTIPROC_METHOD=spawn
+          pytest -sv tests/entrypoints/ -m 'core_model and cuda' --run-level 'core_model'
+        "
+    agents:
+      queue: "mithril-h100-pool"
+    plugins:
+      - kubernetes:
+          podSpec:
+            containers:
+              - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+                resources:
+                  limits:
+                    nvidia.com/gpu: 2
+                volumeMounts:
+                  - name: devshm
+                    mountPath: /dev/shm
+                  - name: hf-cache
+                    mountPath: /root/.cache/huggingface
+                env:
+                  - name: HF_HOME
+                    value: /root/.cache/huggingface
+            nodeSelector:
+              node.kubernetes.io/instance-type: gpu-h100-sxm
+            volumes:
+              - name: devshm
+                emptyDir:
+                  medium: Memory
+              - name: hf-cache
+                hostPath:
+                  path: /mnt/hf-cache
+                  type: DirectoryOrCreate
+
   - group: ":card_index_dividers: E2E Test"
     depends_on: upload-ready-pipeline
     steps:
@@ -239,6 +276,11 @@ steps:
                 - "/fsx/hf_cache:/fsx/hf_cache"
 
       - label: "Omni · Qwen3-Omni Test"
+        source_file_dependencies:
+          - tests/e2e/online_serving/test_qwen3_omni.py
+          - vllm_omni/model_executor/models/qwen3_omni/
+          - vllm_omni/model_executor/stage_input_processors/qwen3_omni.py
+          - vllm_omni/deploy/qwen3_omni_moe.yaml
         commands:
           - |
             timeout 20m bash -c "
@@ -273,14 +315,14 @@ steps:
                       path: /mnt/hf-cache
                       type: DirectoryOrCreate
 
-      - label: "Omni · VoxCPM2 Test"
-        timeout_in_minutes: 20
+      - label: "TTS · VoxCPM2 Test"
+        timeout_in_minutes: 30
         commands:
           - |
-            timeout 20m bash -c "
+            timeout 30m bash -c "
               export VLLM_LOGGING_LEVEL=DEBUG
               export VLLM_WORKER_MULTIPROC_METHOD=spawn
-              pytest -s -v tests/e2e/offline_inference/test_voxcpm2.py -m 'core_model' --run-level 'core_model'
+              pytest -s -v tests/e2e/offline_inference/test_voxcpm2_tts.py tests/e2e/online_serving/test_voxcpm2_tts.py -m 'core_model' --run-level 'core_model'
             "
         agents:
           queue: "gpu_1_queue"
@@ -340,7 +382,33 @@ steps:
               volumes:
                 - "/fsx/hf_cache:/fsx/hf_cache"
 
+      - label: "TTS · Higgs-Audio-V3 Test"
+        commands:
+          - |
+            timeout 20m bash -c "
+              export VLLM_WORKER_MULTIPROC_METHOD=spawn
+              export VLLM_USE_DEEP_GEMM=0
+              export VLLM_MOE_USE_DEEP_GEMM=0
+              pytest -s -v tests/e2e/online_serving/test_higgs_audio_v3.py -m 'core_model' --run-level 'core_model'
+            "
+        agents:
+          queue: "gpu_4_queue"
+        plugins:
+          - docker#v5.2.0:
+              image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+              always-pull: true
+              propagate-environment: true
+              shm-size: "8gb"
+              environment:
+                - "HF_HOME=/fsx/hf_cache"
+                - "HF_TOKEN"
+              volumes:
+                - "/fsx/hf_cache:/fsx/hf_cache"
+
       - label: "Diffusion · Qwen Image Edit Test"
+        source_file_dependencies:
+          - tests/e2e/online_serving/test_qwen_image_edit.py
+          - vllm_omni/diffusion/models/qwen_image/
         commands:
           - |
             timeout 20m bash -c "
@@ -376,6 +444,12 @@ steps:
                       type: DirectoryOrCreate
 
       - label: "Diffusion · Bagel Test"
+        source_file_dependencies:
+          - tests/e2e/online_serving/test_bagel.py
+          - vllm_omni/model_executor/models/bagel/
+          - vllm_omni/diffusion/models/bagel/
+          - vllm_omni/model_executor/stage_input_processors/bagel.py
+          - vllm_omni/deploy/bagel.yaml
         commands:
           - |
             timeout 40m bash -c "
@@ -417,6 +491,9 @@ steps:
                       type: DirectoryOrCreate
 
       - label: "Diffusion · Wan22 Test"
+        source_file_dependencies:
+          - tests/e2e/online_serving/test_wan22_t2v.py
+          - vllm_omni/diffusion/models/wan2_2/
         commands:
           - |
             timeout 40m bash -c "
@@ -459,6 +536,9 @@ steps:
 
 
       - label: "Diffusion · Qwen Image Test"
+        source_file_dependencies:
+          - tests/e2e/online_serving/test_qwen_image.py
+          - vllm_omni/diffusion/models/qwen_image/
         commands:
           - |
             timeout 40m bash -c "
@@ -500,6 +580,9 @@ steps:
                       type: DirectoryOrCreate
 
       - label: "Diffusion · Qwen Image Layered Test"
+        source_file_dependencies:
+          - tests/e2e/online_serving/test_qwen_image_layered.py
+          - vllm_omni/diffusion/models/qwen_image/
         commands:
           - |
             timeout 40m bash -c "

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

@@ -6,6 +6,13 @@
 {% set docker_image_amd = "rocm/vllm-omni:$BUILDKITE_COMMIT" %}
 {% set default_working_dir = "/app/vllm-omni" %}
 
+{% if env %}
+env:
+{% for name in env %}
+  {{ name }}: "{{ env[name] }}"
+{% endfor %}
+{% endif %}
+steps:
   - group: "AMD Tests"
     depends_on: ~
     steps:
@@ -31,8 +38,15 @@
         agents:
           queue: amd-cpu
 
-    {% for step in steps %}
-    {% if step.mirror_hardwares and mirror_hw in step.mirror_hardwares %}
+{% for step_group in steps %}
+{% if step_group.group %}
+  - group: "{{ step_group.group }}"
+{% if step_group.depends_on %}
+    depends_on: {{ step_group.depends_on }}
+{% endif %}
+    steps:
+{% for step in step_group.steps %}
+{% if step.mirror_hardwares and mirror_hw in step.mirror_hardwares %}
       - label: "{{ step.agent_pool }}: {{ step.label }}"
         depends_on: amd-build
         agents:
@@ -46,17 +60,82 @@
         command: bash .buildkite/scripts/hardware_ci/run-amd-test.sh
         env:
           DOCKER_BUILDKIT: "1"
+{% if step.env %}
+{% for name in step.env %}
+          {{ name }}: "{{ step.env[name] }}"
+{% endfor %}
+{% endif %}
           TEST_COMMAND: |-
             (command rocm-smi || true) && cd {{ (step.working_dir or default_working_dir) | safe }}
+{% if env %}
+{% for name in env %}
+            export {{ name }}="{{ env[name] }}"
+{% endfor %}
+{% endif %}
+{% if step.env %}
+{% for name in step.env %}
+            export {{ name }}="{{ step.env[name] }}"
+{% endfor %}
+{% endif %}
 {% if "mi250" in step.agent_pool %}
             python3 -m pip uninstall -y amd-aiter
 {% endif %}
             {{ indented_cmd | safe }}
         priority: 100
+        {% if step.timeout_in_minutes %}
+        timeout_in_minutes: {{ step.timeout_in_minutes }}
+        {% endif %}
         {% if step.grade and step.grade == "Blocking" %}
         soft_fail: false
         {% else %}
         soft_fail: true
         {% endif%}
+{% endif %}
+{% endfor %}
+{% elif step_group.mirror_hardwares and mirror_hw in step_group.mirror_hardwares %}
+{% set step = step_group %}
+  - label: "{{ step.agent_pool }}: {{ step.label }}"
+    depends_on: amd-build
+    agents:
+      {% if step.agent_pool %}
+      queue: amd_{{ step.agent_pool }}
+      {% else %}
+      queue: amd_mi325_1
+      {% endif %}
+{% set cmd_body = (step.command or (step.commands | join("\n"))) | trim %}
+{% set indented_cmd = cmd_body | replace("\n", "\n        ") %}
+    command: bash .buildkite/scripts/hardware_ci/run-amd-test.sh
+    env:
+      DOCKER_BUILDKIT: "1"
+{% if step.env %}
+{% for name in step.env %}
+      {{ name }}: "{{ step.env[name] }}"
+{% endfor %}
+{% endif %}
+      TEST_COMMAND: |-
+        (command rocm-smi || true) && cd {{ (step.working_dir or default_working_dir) | safe }}
+{% if env %}
+{% for name in env %}
+        export {{ name }}="{{ env[name] }}"
+{% endfor %}
+{% endif %}
+{% if step.env %}
+{% for name in step.env %}
+        export {{ name }}="{{ step.env[name] }}"
+{% endfor %}
+{% endif %}
+{% if "mi250" in step.agent_pool %}
+        python3 -m pip uninstall -y amd-aiter
+{% endif %}
+        {{ indented_cmd | safe }}
+    priority: 100
+    {% if step.timeout_in_minutes %}
+    timeout_in_minutes: {{ step.timeout_in_minutes }}
     {% endif %}
-    {% endfor %}
+    {% if step.grade and step.grade == "Blocking" %}
+    soft_fail: false
+    {% else %}
+    soft_fail: true
+    {% endif%}
+{% endif %}
+{% endfor %}

.claude/skills/diffusion-perf-opt/SKILL.md

@@ -21,6 +21,9 @@ Before proposing changes, ask for the optimization scene if it is not already kn
 - Current enabled strategies: USP/SP, CFG parallel, HSDP/FSDP, VAE patch parallel, torch.compile, profiler options.
 - Optimization target: latency, throughput, memory, cost, or quality-preserving speed.
 - Precision/quality tolerance: bf16/fp8/quantization/sparsity/approximate attention allowed or not.
+- Quality validation method: exact-output comparison when feasible, or metrics
+  such as SSIM, PSNR, LPIPS, cosine similarity, MAE, MSE, temporal flicker
+  checks, baseline self-run variance, and reviewable output artifacts.
 
 ## Workflow
 
@@ -79,7 +82,19 @@ Before proposing changes, ask for the optimization scene if it is not already kn
    - Record latency, stage timings, memory, output quality, and logs.
    - Report comparison tables in ms. Include at least end-to-end client time, server `inference_time`, server stage generation time if available, `vae.encode`, `diffuse`, `vae.decode`, and peak memory.
 
-6. **Collect diagnostic trace only after narrowing hypotheses.**
+6. **Enforce the quality and precision gate for every optimization.**
+   - Do not mark an optimization ready until quality is checked on the same
+     workload: model, prompt/media, seed, shape, steps, scheduler, dtype,
+     backend, parallelism, and output encoding.
+   - For math-preserving changes, require exact/near-exact agreement or show
+     that differences are within baseline self-run variance.
+   - For precision, quantization, approximate attention, sparse/custom kernels,
+     or backend changes, include reviewable artifacts and metrics such as SSIM,
+     PSNR, LPIPS, cosine similarity, MAE, MSE, and temporal flicker checks.
+   - If quality tolerance is not stated, default to quality-preserving behavior.
+     Failed or inconclusive quality validation blocks a ready-to-merge claim.
+
+7. **Collect diagnostic trace only after narrowing hypotheses.**
    - Use torch profiler for a small number of requests.
    - Run two separate diagnostic traces instead of mixing concerns:
      - **Operator/shape trace**: enable `torch_profiler_record_shapes=True` and keep stack collection disabled. Use this to rank CUDA kernels, NCCL collectives, attention/MLP/norm/RoPE work, and shape-specific hot operators.
@@ -91,15 +106,16 @@ Before proposing changes, ask for the optimization scene if it is not already kn
    - Diagnostic reports must be written to disk and preserve: server command, profiler config, warmup/request/polling commands, trace artifact paths, rank analyzed, analyzer output or summary, and the decision about whether additional ranks are necessary.
    - Analyze both rank-level balance and device-level free bubbles when additional ranks are opened.
 
-7. **Analyze host, communication, and operators.**
+8. **Analyze host, communication, and operators.**
    - Find GPU idle/free intervals and map each large gap to the enclosing CPU/Python code.
    - Separate real GPU idle from profiler overhead such as CUPTI `Command Buffer Full`.
    - Compare NCCL kernel time to user annotations; annotations can overcount nested intervals.
    - Rank operator work by total CUDA time and by repeated small-kernel launch count.
 
-8. **Produce an optimization plan.**
+9. **Produce an optimization plan.**
    - Classify candidates as P0/P1/P2.
    - For each candidate, state necessity, expected benefit, implementation path, validation plan, and quality risk.
+   - Include a concrete quality/precision validation gate for each candidate.
    - Do not implement high-risk operator rewrites before proving the operator is a bottleneck for the target shapes.
    - End the plan with a user-facing candidate selection table. The assistant
      should not automatically choose a risky optimization just because it is
@@ -124,7 +140,13 @@ Before proposing changes, ask for the optimization scene if it is not already kn
 - **P1:** meaningful code changes with contained risk. Examples: cross-attention KV caching, VAE gather/broadcast reduction, AdaLayerNorm/RMSNorm/RoPE fusion after trace evidence.
 - **P2:** high implementation or quality risk. Examples: FA to LA replacement, custom Triton/CUDA fused kernels, FP8/quantization, sparsity/Rainfusion-style acceleration.
 
-Every implemented optimization needs A/B validation and quality regression. A/B means same workload and hardware before/after. Quality regression means checking generated image/video stability, artifacts, temporal flicker, and seed behavior when precision or approximate kernels change.
+Every implemented optimization needs A/B validation and a passing
+quality/precision gate, including math-preserving changes such as padding trim,
+layout cleanup, cache reuse, or host/runtime cleanup. Use objective metrics
+when available, keep reviewable artifacts, and compare against baseline
+self-run variance for precision, quantization, approximate kernels, or backend
+changes. If quality validation fails or is inconclusive, do not present the
+optimization as ready to merge.
 
 ## Optimization Layers
 

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

@@ -0,0 +1,97 @@
+---
+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.
+---
+
+# PR Pre-Check
+
+Self-review your branch before creating a PR against `vllm-project/vllm-omni`. Two modes: **quick** catches showstoppers, **full** does a thorough maintainer-grade review. Never posts to GitHub; the report is for the contributor's terminal only.
+
+## Mode Selection
+
+| Mode | When | Time |
+|------|------|------|
+| **Quick** | About to push, final sanity check | ~3 min |
+| **Full** | Ready for review, want maintainer-level scan | ~10 min |
+
+Default to quick if unsure. Run full before marking a PR "ready for review."
+
+## Workflow
+
+### Step 1: Detect Base Branch
+
+```bash
+BASE_SHA=$(git merge-base HEAD origin/main 2>/dev/null \
+         || git merge-base HEAD main 2>/dev/null \
+         || echo origin/main)
+echo "diffing against ${BASE_SHA}"
+git diff --name-only ${BASE_SHA}...HEAD
+```
+
+### Step 2: Validate PR Title
+
+Check the most recent commit message (or branch name if no commit yet) against the project convention. Valid prefixes:
+
+| Prefix | Applies to |
+|--------|-----------|
+| `[Bugfix]` | Bug fixes |
+| `[CI/Build]` | Build or CI improvements |
+| `[Doc]` | Documentation changes |
+| `[Model]` | New/improved models (include model name) |
+| `[Frontend]` | Frontend changes (API server, OmniLLM class, etc.) |
+| `[Kernel]` | CUDA/kernel changes |
+| `[Core]` | Core logic changes (OmniProcessor, OmniARScheduler, etc.) |
+| `[Hardware][Vendor]` | Hardware-specific (e.g., `[Hardware][Ascend]`) |
+| `[Misc]` | Other changes (use sparingly) |
+
+✗ if: missing prefix, wrong case (`[bugfix]`), or WIP/Draft in title.
+⚠ if: `[Model]` prefix without the model identifier (e.g., `[Model] Add new model` — should be `[Model] Add <ModelName> ...`).
+
+### Step 3: Categorize the PR
+
+| Diff contains | PR type |
+|---------------|---------|
+| New files under `vllm_omni/model_executor/models/<name>/` | **New Model** |
+| Changes to `vllm_omni/diffusion/` | **Diffusion Model** |
+| `[Bugfix]` prefix or single-file fix | **Bug Fix** |
+| Perf/benchmark/throughput claims in commit msg or diff | **Performance** |
+| Everything else | **General** |
+
+If multiple rows apply (e.g., a diffusion model is also a new model), union the checklists.
+
+### Step 4: Run Checklist
+
+Ask: "Quick mode or full mode?" Then walk the checklist for the detected PR type from [references/checklists.md](references/checklists.md). Each item produces ✓, ✗, or ⚠.
+
+### Step 5: Print Report
+
+```
+Pre-check report for <branch>
+
+  Mode: quick | full
+  Type: <new-model | diffusion-model | bug-fix | perf | general>
+
+  Dimension          Result
+  ─────────────────  ──────
+  PR title format    ✓
+  PR desc integrity  ✓
+  Registry/config    ✓
+  Dead code          ⚠ 2 warnings
+  Accuracy           ✓
+  Benchmark          ✗ missing software versions
+
+  Verdict: 1 blocking | 2 warnings | recommend fixing ✗ before PR
+```
+
+**Severity:**
+
+| Mark | Meaning |
+|------|---------|
+| ✗ | Blocking — fix before opening PR |
+| ⚠ | Warning — consider fixing |
+| ✓ | Pass |
+| — | Skipped (not applicable) |
+
+### Stop Here
+
+Do not post comments, open PRs, or modify files. The report is for the contributor's terminal only.

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

@@ -0,0 +1,135 @@
+# Pre-Check Checklists
+
+Each item is a concrete check to run against the diff. ✗ = fix before PR. ⚠ = consider fixing.
+
+---
+
+## All PRs: Title Format (check first)
+
+PR title must follow the project convention documented in `docs/contributing/README.md`:
+
+| Prefix | Applies to |
+|--------|-----------|
+| `[Bugfix]` | Bug fixes |
+| `[CI/Build]` | Build or CI improvements |
+| `[Doc]` | Documentation changes |
+| `[Model]` | New/improved models (include model name) |
+| `[Frontend]` | Frontend changes (API server, OmniLLM class, etc.) |
+| `[Kernel]` | CUDA/kernel changes |
+| `[Core]` | Core logic changes (OmniProcessor, OmniARScheduler, etc.) |
+| `[Hardware][Vendor]` | Hardware-specific (e.g., `[Hardware][Ascend]`) |
+| `[Misc]` | Other changes (use sparingly) |
+
+**Examples:**
+```
+✓ [Model] Add MiniCPM-o 4.5 support
+✓ [Bugfix] Fix Ovis image text encoder dtype
+✓ [Core] Qwen3-Omni performance optimization
+✓ [Hardware][Ascend] Fix VoxCPM2 on Ascend
+✗ Fix Ovis image text encoder dtype              ← missing prefix
+✗ [bugfix] Fix Ovis image text encoder           ← wrong case
+✗ [WIP] Add model                                ← WIP in title
+```
+
+- [ ] **Title has valid prefix** — ✗ if missing prefix, wrong case, or WIP/Draft in title. For `[Model]` PRs, the title MUST also include the model identifier (e.g., `[Model] Add <ModelName> ...`). ⚠ if missing the model name.
+
+---
+
+## Bug Fix PRs
+
+### Quick
+
+- [ ] **PR body has 4 sections:** what broke (error message), repro steps, root cause, fix description
+- [ ] **Fix is minimal:** diff touches only files directly related to the bug. No unrelated refactors or style changes.
+- [ ] **Repro is runnable:** the PR body includes a copy-paste command that reproduces the bug
+- [ ] **Branch is rebased:** `git merge-base HEAD origin/main` is recent, no merge conflicts
+
+### Full (adds)
+
+- [ ] **Regression test exists:** `git diff --name-only` includes at least one `tests/` file
+- [ ] **Regression test reproduces the original error:** the test would fail on main, pass on this branch
+- [ ] **Fix matches root cause exactly:** no "fix the symptom + something else" — if the root cause is dtype, the fix is dtype, not dtype + reformatting
+- [ ] **No silent failure risk:** no bare `except: pass`, no `try: ... except: return None`, no empty fallback added
+- [ ] **Upstream pattern match:** the fix follows the same pattern as existing code for similar cases (grep for analogous `from_pretrained` calls, etc.)
+- [ ] **Environment documented:** torch/transformers/vllm versions listed if the bug is version-dependent
+
+---
+
+## Performance PRs
+
+### Quick
+
+- [ ] **Before/after numbers in PR body:** at minimum, one metric (RTF, RPS, latency, VRAM) with main vs PR values
+- [ ] **Hardware specified:** GPU model, count, VRAM. "L4" or "H20" is sufficient. "tested on GPU" is not.
+- [ ] **Concurrency scaling:** if the optimization claims better throughput, numbers at 2+ concurrency levels
+- [ ] **No unexplained regressions:** if any metric goes down at any concurrency level, explain why
+- [ ] **Branch is rebased:** `git merge-base HEAD origin/main` is recent, no merge conflicts
+
+### Full (adds)
+
+- [ ] **Software versions:** torch, CUDA, vllm, vllm-omni versions in PR body
+- [ ] **Warmup stated:** "1 warmup + 3 measured" or equivalent
+- [ ] **Stddev or range:** not just a single best run. "average of 2 rounds" is minimum.
+- [ ] **VRAM measured:** peak GPU memory before and after
+- [ ] **Benchmark script checked in:** under `tests/` or `examples/`, runnable by reviewer
+- [ ] **Exact command line in PR:** not pseudocode, not "run the benchmark"
+- [ ] **Pytest summary included:** `36 passed, 3 skipped, 17 warnings in 2897.66s` — proves the whole suite passes
+- [ ] **Config changes documented:** if `max_num_seqs`, `gpu_memory_utilization`, or deploy YAML changed, old and new values stated with reasoning
+
+---
+
+## New Model PRs
+
+### Quick
+
+- [ ] **PR body file list matches `git diff --name-only`:** every file path in the description exists in the diff
+- [ ] **Registry entries match claimed count:** count `_OMNI_MODELS` or `_DIFFUSION_MODELS` entries added vs claimed in body
+- [ ] **Pipeline config `custom_process_input_func` paths resolve:** each referenced module.function exists
+- [ ] **`__init__.py` exports match registry:** each registered class appears in `__init__.py` and `__all__`
+- [ ] **Shell script modes match README:** `run_curl.sh` supports every mode the README documents
+- [ ] **Branch is rebased:** `git merge-base HEAD origin/main` is recent, no merge conflicts
+
+### Full (adds)
+
+- [ ] **Dead code scan (5 patterns):**
+  1. Dead `forward()` — defined but never called through the pipeline path
+  2. Dead factory/builder functions — instantiated nowhere
+  3. Dead wrappers — thin wrappers with no callers
+  4. Dead branch guards — `if version > X` where X is always satisfied
+  5. Unused parameters — `__init__` args never accessed
+- [ ] **Copy-paste detection:** no string constant defined in 3+ files, no cross-module validation duplication, no near-identical shape coercion fns
+- [ ] **Import hygiene:** no re-export-only imports, no module-level side effects without docs, no `import os` in function body when already at top. Function-body imports are acceptable when the import is only reachable from a closure or factory return value (i.e., `import` would otherwise execute at module load even though the user may never trigger that path). Either lift to top-of-file or annotate with `# noqa: PLC0415`.
+- [ ] **Accuracy:** at least one test compares output against reference; audio = valid WAV at correct sample rate; determinism verified if seed claimed. ⚠ if missing and not explicitly deferred to a follow-up PR or RFC section; — if explicitly deferred (the deferral target SHOULD be linked in the PR body).
+- [ ] **Performance:** hardware, software versions, warmup, and metrics (RTF, VRAM, RPS, latency) stated. ⚠ if missing and not explicitly deferred; — if explicitly deferred.
+- [ ] **Benchmark settings:** model config (TP, PP, max_model_len, enforce_eager, quant), runtime config (batch, input/output spec), environment versions
+- [ ] **Benchmark script checked in + exact command line + pytest summary**
+
+---
+
+## Diffusion Model PRs
+
+Diffusion models live under `vllm_omni/diffusion/`. In addition to the New Model checklist above, verify:
+
+- [ ] **Transformer adapter follows contract:** inherits from `nn.Module`, implements `load_weights()`, uses `vllm_omni.diffusion.attention.layer.Attention`
+- [ ] **Pipeline has both offline and online paths:** `forward()` for batch generation, streaming path for online serving
+- [ ] **Cache-DiT integration:** at least one acceleration (TeaCache, FBCache, etc.) is wired up. ✗ for new **top-level** pipelines. For **subclass** pipelines that inherit `__call__` from a parent where Cache-DiT is already absent, downgrade to ⚠ and recommend filing a follow-up issue against the parent pipeline family.
+- [ ] **Parallelism config:** TP/SP/USP/CFG-Parallel options exposed in pipeline config
+- [ ] **Docs table updated:** model added to `docs/models/supported_models.md` with correct metadata
+- [ ] **E2E test exists:** at least one test exercises the full generate path
+
+---
+
+## General PRs
+
+### Quick
+
+- [ ] **PR body matches diff:** description doesn't claim changes not in the diff
+- [ ] **Branch is rebased:** `git merge-base HEAD origin/main` is recent, no merge conflicts
+- [ ] **CI gates passing:** DCO, pre-commit, build — check `gh pr view --json statusCheckRollup` if a PR already exists
+
+### Full (adds)
+
+- [ ] **No new dead code in changed files:** scan for unreachable paths, unused imports
+- [ ] **Test coverage:** if core code changed (`engine/`, `stages/`, `connectors/`), tests are added or existing tests cover the path
+- [ ] **Import hygiene:** no redundant imports added
+- [ ] **No unrelated changes:** diff scope matches PR description

.claude/skills/quantization/SKILL.md

@@ -0,0 +1,103 @@
+---
+name: quantization
+description: Work on vLLM-Omni quantization for diffusion, autoregressive, omni, or multi-stage models. Use when choosing or adding methods such as fp8, int8, gguf, mxfp8, mxfp4, mxfp4_dualscale, ModelOpt, AutoRound, INC, msModelSlim, awq, or gptq; debugging quantized loading; or validating memory, speed, and output quality.
+---
+
+# vLLM-Omni Quantization
+
+Use this skill for quantization work in `vllm-omni`. Start from the local
+code and docs, then pick the smallest validated path for the model, method,
+hardware, and quality target.
+
+## First Checks
+
+Before changing code or recommending a command, identify:
+
+- Model and task: AR text, omni/TTS, text-to-image, text-to-video, or multi-stage diffusion.
+- Hardware backend: CUDA generation, Ascend NPU, Intel XPU, or another platform.
+- Quantization mode: online from BF16/FP16 weights, pre-quantized checkpoint, runtime KV/FA quantization, or per-component routing.
+- Scope: transformer/DiT, thinker language model, text encoder, VAE, audio/vision encoder, talker, or a specific stage.
+- Validation target: memory reduction, latency/throughput, output quality, or all three.
+
+Do not assume a method is supported just because the CLI accepts the string.
+Check `docs/user_guide/quantization/` and the closest implementation first.
+
+## Quick Decision
+
+| Task | Start With |
+|------|------------|
+| Choose a method or command | `references/methods.md` and `references/modality-compat.md` |
+| Use `build_quant_config()` or per-component routing | `references/methods.md` |
+| Work on diffusion quantization | `references/diffusion.md` |
+| Add quantization to a new model | `references/adding-models.md` |
+| Convert or load ModelOpt FP8 checkpoints | `references/modelopt-fp8.md` |
+| Debug ModelOpt, GGUF, AutoRound, MXFP, or serialized Int8 loading | `references/diffusion.md` and method docs under `docs/user_guide/quantization/` |
+
+## Current Runtime Shape
+
+The unified entrypoint is:
+
+```python
+from vllm_omni.quantization import build_quant_config
+```
+
+It supports method strings, flat method dictionaries, per-component
+dictionaries, existing `QuantizationConfig` objects, and `None`. The factory
+delegates generic methods to upstream `vllm` and keeps vLLM-Omni overrides for
+diffusion or omni-specific routing:
+
+- `gguf`
+- `int8`
+- `mxfp8`
+- `mxfp4`
+- `mxfp4_dualscale`
+- `inc`, `auto-round`, `auto_round`
+- ModelOpt auto-detected configs: `modelopt`, `modelopt_fp4`, `modelopt_mixed`
+
+Use `ComponentQuantizationConfig` when only one stage or component should be
+quantized. Pre-quantized ModelOpt-style checkpoints should not spill into
+vision/audio encoders that have no corresponding scale tensors.
+
+## Ownership Boundary
+
+- Upstream `vllm` owns generic quantization configs, kernels, loader semantics,
+  hardware capability rules, and generic AR quantization methods.
+- `vllm-omni` owns unified config routing, diffusion-specific wrappers,
+  component scoping, GGUF or ModelOpt checkpoint adapters, model-specific
+  prefix mapping, docs, examples, and validation.
+
+If a new method needs missing generic kernels or loader behavior, fix upstream
+`vllm` first. In `vllm-omni`, add thin integration and model wiring.
+
+## Working Loop
+
+1. Read the local method doc under `docs/user_guide/quantization/`.
+2. Inspect the active factory and config class in `vllm_omni/quantization/`.
+3. Inspect the target model pipeline and transformer for stable prefixes and
+   whether every relevant vLLM linear layer receives `quant_config`.
+4. For pre-quantized checkpoints, inspect `transformer/config.json` and the
+   checkpoint tensor names before running full generation.
+5. Validate with the same prompt, seed, size, scheduler, steps, dtype, eager or
+   non-eager mode, and parallelism as the BF16 baseline.
+6. Report quality, latency, and memory separately. Do not call a method
+   supported until quality is checked.
+
+## Common Mistakes
+
+| Symptom | Likely Cause | Fix |
+|---------|--------------|-----|
+| `--quantization` has no visible effect | Wrong scope or unsupported model path | Check component routing and method docs |
+| Some layers stay BF16 unexpectedly | `quant_config` was not threaded into all vLLM linear layers | Audit transformer constructors and prefixes |
+| Quality collapses but loading succeeds | Too many sensitive layers were quantized | Add model-specific `ignored_layers` and compare to BF16 |
+| ModelOpt checkpoint loads but output is corrupted | Prefixes, packed-module mapping, scale routing, or BF16 fallback is wrong | Use `references/modelopt-fp8.md` |
+| GGUF shape or tensor mismatch | Missing architecture-specific adapter | Add explicit adapter mapping; avoid generic fallback |
+| Online and offline MXFP4 disagree | Wrong `mxfp4` vs `mxfp4_dualscale` mode | Check `docs/user_guide/quantization/mxfp4.md` |
+| Quantized path is slower than BF16 | Kernel path, compile mode, dtype, or shape mismatch | Compare eager-to-eager and non-eager-to-non-eager |
+
+## References
+
+- General methods and config routing: [references/methods.md](references/methods.md)
+- Modality and model scope: [references/modality-compat.md](references/modality-compat.md)
+- Diffusion implementation patterns: [references/diffusion.md](references/diffusion.md)
+- Adding model support: [references/adding-models.md](references/adding-models.md)
+- ModelOpt FP8 conversion and loading: [references/modelopt-fp8.md](references/modelopt-fp8.md)

.claude/skills/quantization/references/adding-models.md

@@ -0,0 +1,70 @@
+# Adding Quantization Support to a Model
+
+Use this checklist when extending quantization to a new architecture or stage.
+
+## Classify Ownership
+
+| Case | Owner |
+|------|-------|
+| New generic AR method, kernel, or loader semantics | upstream `vllm` first |
+| Existing upstream method needs diffusion wiring | `vllm-omni` model and config integration |
+| GGUF or ModelOpt tensor mapping for a diffusion architecture | `vllm-omni` adapter |
+| NPU MXFP or msModelSlim diffusion path | `vllm-omni` platform/method wrapper and docs |
+| Multi-stage omni checkpoint quantizes only the thinker LM | `vllm-omni` component scoping |
+
+Avoid building a private quantization stack in model code.
+
+## Model Wiring Checklist
+
+1. Find the closest supported model implementation.
+2. Confirm the model uses vLLM linear layers that accept `quant_config`.
+3. Add `quant_config` to the transformer constructor.
+4. Pass stable `prefix` values through every linear layer.
+5. Handle fused modules and packed mappings explicitly.
+6. Ensure `load_weights()` and any `WeightsMapper` remap `ignored_layers` or
+   `modules_to_not_convert`.
+7. Keep tokenizer, scheduler, VAE, text encoder, audio/vision encoders, and
+   talker BF16 unless method-specific docs validate them.
+8. Add or update a method doc under `docs/user_guide/quantization/`.
+
+## Checkpoint Adapter Checklist
+
+For pre-quantized checkpoints:
+
+- Inspect `transformer/config.json` first.
+- Check `quant_method`, `method`, `producer.name`, `quant_algo`, serialized
+  flags, `ignored_layers`, and any method-specific fields.
+- Verify tensor name mapping before generation.
+- Verify packed modules such as `to_qkv`, `add_kv_proj`, and `w13`.
+- Confirm scale tensor names and shapes.
+- Allow dequantization back to BF16 for intentionally skipped target layers.
+
+## Sensitive Layer Discovery
+
+Start conservative. Quantize the backbone first and keep output-side or routing
+layers BF16 until quality is proven.
+
+Frequently sensitive areas:
+
+- attention output projections such as `to_out`
+- FFN down projections such as `w2`
+- final projections such as `proj_out`, `final_layer`, and output heads
+- modulation, timestep, position, image, or text embedders
+- refiner blocks
+- MoE routers, experts selected by routing, and layernorm-adjacent modules
+- VAE, text/vision towers, audio encoders, and codec/talker stages
+
+Do not reuse one model family's ignore list blindly.
+
+## Tests and Evidence
+
+Minimum evidence for a PR:
+
+- loader/config test or smoke command
+- fixed-seed BF16 vs quantized output comparison
+- quality metrics and saved outputs for precision-changing work
+- memory and latency comparison under identical execution mode
+- failure behavior for unsupported models or missing checkpoint fields
+
+When the PR adds a method to a support table, include the exact model,
+hardware, command, quality threshold, and artifact path used for validation.

.claude/skills/quantization/references/diffusion.md

@@ -0,0 +1,113 @@
+# Diffusion Quantization Reference
+
+Use this file for DiT quantization, config routing, checkpoint adapters, and
+quality or loader debugging.
+
+## Supported Method Families
+
+The current local diffusion quantization families are:
+
+- CUDA/GPU online or checkpoint: `fp8`, `int8`, `gguf`, ModelOpt
+- Ascend NPU: `int8`, `mxfp8`, `mxfp4`, `mxfp4_dualscale`, msModelSlim paths
+- Intel XPU: AutoRound/INC and online MXFP8 paths where documented
+
+Always cross-check the method doc before claiming model support. The CLI
+accepting a method string is not enough.
+
+## Implementation Starting Points
+
+| Area | Files |
+|------|-------|
+| Unified factory | `vllm_omni/quantization/factory.py` |
+| Per-component routing | `vllm_omni/quantization/component_config.py` |
+| GGUF diffusion method | `vllm_omni/quantization/gguf_config.py` |
+| Int8 diffusion method | `vllm_omni/quantization/int8_config.py` |
+| MXFP8 diffusion method | `vllm_omni/quantization/mxfp8_config.py` |
+| MXFP4 and dual-scale method | `vllm_omni/quantization/mxfp4_config.py` |
+| AutoRound/INC omni extension | `vllm_omni/quantization/inc_config.py` |
+| ModelOpt checkpoint adapters | `vllm_omni/diffusion/model_loader/checkpoint_adapters/modelopt.py` |
+| FP8 scaled-mm runtime patches | `vllm_omni/patch.py` |
+| Similarity tool | `vllm_omni/quantization/tools/compare_diffusion_trajectory_similarity.py` |
+
+## Threading `quant_config`
+
+For a diffusion transformer:
+
+1. Accept `quant_config: QuantizationConfig | None` in the transformer.
+2. Pass `quant_config` and stable `prefix` values to every vLLM linear layer.
+3. Preserve packed-module mappings for fused QKV, FFN, or model-specific fused
+   projections.
+4. Call `apply_vllm_mapper()` on configs that need HF-to-vLLM name remapping.
+5. For component configs, keep prefixes specific enough to avoid quantizing VAE
+   or encoders by accident.
+
+Common skipped areas are `proj_out`, output projections, modulation layers,
+embedders, refiner blocks, MoE routers, and layernorm-adjacent paths.
+
+## Online vs Offline
+
+Online quantization:
+
+- starts from BF16/FP16 checkpoint weights
+- computes or stores quantized weights at load time
+- typically uses `--quantization <method>` or `quantization="<method>"`
+
+Offline or pre-quantized checkpoints:
+
+- include quantized weights and scales on disk
+- usually carry `quantization_config` in `transformer/config.json`
+- should auto-detect when the config is complete
+- may require model-specific adapter logic for tensor names and packed modules
+
+Do not mix online flags with incompatible offline checkpoint configs. If the
+active config and disk config differ, `resolve_quant_config_from_disk()` should
+raise or rebuild rather than silently loading corrupted weights.
+
+## GGUF Rules
+
+- Require `quantization_config.gguf_model`.
+- Use the normal base model for tokenizer, scheduler, text encoder, and VAE.
+- Add explicit architecture adapters for tensor-name mapping.
+- Use `named_parameters()` and `named_buffers()`, not `state_dict()`, to
+  discover loadable names.
+- Guard fused QKV/KV rewrites so tensors are not rewritten twice.
+- Flatten N-D activations around the GGUF matmul and restore shape.
+
+Unsupported architectures should fail clearly instead of falling back to a fake
+generic mapper.
+
+## MXFP Rules
+
+- `mxfp8` is W8A8 microscaling, currently centered on NPU and documented XPU
+  behavior.
+- `mxfp4` is online single-scale W4A4 on NPU.
+- `mxfp4_dualscale` is online/offline dual-scale W4A4 with BF16 fallback; the
+  offline path is the production path when calibrated `mul_scale` exists.
+- Offline dual-scale checkpoints must inject per-transformer `ignored_layers`
+  into config because cascade transformers can have different BF16 fallback
+  sets.
+
+Do not load an offline dual-scale checkpoint with the online single-scale path.
+
+## Common Failures
+
+| Symptom | Likely Cause | Fix |
+|---------|--------------|-----|
+| `Unknown quantization method` | Factory override or upstream registration is missing | Add the right override or upstream method |
+| Missing scale tensors | Wrong scope, wrong adapter, or BF16 layer not allowed to dequantize | Check config scope and adapter scale routing |
+| Output rank collapses from 3-D to 2-D | FP8 scaled-mm kernel ignored output shape | Check the patch in `vllm_omni/patch.py` |
+| Quality degrades silently | Sensitive layers quantized | Add `ignored_layers`; compare to BF16 |
+| Only one cascade transformer works | Disk config differs per transformer but active config is reused | Rebuild from each transformer's config |
+| Quantized path slower than BF16 | Kernel, dtype, compile, or benchmark mode mismatch | Compare eager-to-eager and non-eager-to-non-eager |
+
+## Verification
+
+For a new or changed diffusion quantization path, run:
+
+1. Config and loader smoke test.
+2. One fixed-seed generation against BF16.
+3. Numeric comparison with the similarity tool or equivalent metrics.
+4. Serving test if the path is user-facing online.
+5. Memory and latency check under the same execution mode as the baseline.
+
+Keep output artifacts when precision changes are part of the PR.

.claude/skills/quantization/references/methods.md

@@ -0,0 +1,82 @@
+# General Quantization Methods
+
+Use this file for method selection and unified `vllm-omni` config syntax. For
+diffusion-specific wiring, see `diffusion.md`. For ModelOpt checkpoint export
+and loading details, see `modelopt-fp8.md`.
+
+## Unified Entry Point
+
+`vllm_omni.quantization.build_quant_config()` accepts:
+
+- `None` or `"none"` to disable quantization
+- a method string such as `"fp8"`, `"int8"`, `"mxfp8"`, `"awq"`, or `"auto-round"`
+- a flat dict with `"method"` or `"quant_method"`
+- a per-component dict such as `{"transformer": {"method": "fp8"}, "vae": None}`
+- an already-built `QuantizationConfig`
+
+Per-component dicts are routed by `ComponentQuantizationConfig` using
+longest-prefix matching. Prefix stability matters: if a model remaps HF names
+to vLLM names, apply the mapper to `ignored_layers` or component prefixes.
+
+## Local Overrides
+
+The local factory delegates most methods to upstream `vllm`, but has vLLM-Omni
+overrides for methods that need diffusion, NPU, XPU, or component behavior:
+
+| Method | Local Config | Main Use |
+|--------|--------------|----------|
+| `gguf` | `DiffusionGGUFConfig` | Pre-quantized diffusion transformer weights |
+| `int8` | `DiffusionInt8Config` | Online or serialized W8A8 diffusion transformers |
+| `mxfp8` | `DiffusionMXFP8Config` | W8A8 MXFP8 diffusion on NPU and online XPU path |
+| `mxfp4` | `DiffusionMXFP4Config` | Online W4A4 MXFP4 diffusion on NPU |
+| `mxfp4_dualscale` | `DiffusionMXFP4DualScaleMixedConfig` | Online/offline W4A4 dual-scale plus BF16 fallback on NPU |
+| `inc`, `auto-round`, `auto_round` | `OmniINCConfig` | AutoRound/INC checkpoint loading, including MXFP8 checkpoint metadata |
+| ModelOpt config dicts | upstream ModelOpt configs through auto-detect | FP8, NVFP4, or mixed ModelOpt checkpoints |
+
+Check `SUPPORTED_QUANTIZATION_METHODS` in `vllm_omni/quantization/factory.py`
+before adding a new method name.
+
+## AR and Generic Quantization
+
+For AR-backed models, upstream `vllm` methods usually apply:
+
+- `awq` and `gptq`: 4-bit or 8-bit weight quantization with calibration,
+  commonly used for text and AR stages.
+- `fp8`: FP8 weight path or KV-cache FP8 depending on flags and hardware.
+- `inc` or `auto-round`: pre-quantized checkpoints with Intel Neural
+  Compressor / AutoRound metadata.
+
+Use these for the AR language model stage only when the checkpoint and model
+guide support it. Omni and TTS models often keep audio, vision, talker, and
+codec stages in BF16.
+
+## Diffusion and Offline Methods
+
+For diffusion transformers, prefer the method-specific docs:
+
+- `docs/user_guide/quantization/fp8.md`
+- `docs/user_guide/quantization/int8.md`
+- `docs/user_guide/quantization/gguf.md`
+- `docs/user_guide/quantization/modelopt.md`
+- `docs/user_guide/quantization/mxfp8.md`
+- `docs/user_guide/quantization/mxfp4.md`
+- `docs/user_guide/quantization/autoround.md`
+- `docs/user_guide/quantization/msmodelslim.md`
+
+Pre-quantized checkpoints should normally auto-detect from config. Runtime
+flags such as `--force-cutlass-fp8`, `--linear-backend cutlass`, and
+`--moe-backend cutlass` select kernels; they do not quantize a BF16 checkpoint.
+
+## Validation
+
+Use fixed-seed A/B comparisons and record:
+
+- model path and quantized checkpoint path
+- method config and `ignored_layers`
+- prompt, size, frame count, steps, scheduler, dtype, and seed
+- eager/non-eager mode, compile flags, and parallelism
+- output metrics such as PSNR, MAE, cosine similarity, SSIM, LPIPS, plus visual artifacts
+- latency and peak memory, reported separately from quality
+
+The helper `vllm_omni.quantization.tools.compare_diffusion_trajectory_similarity`
+is the default lightweight image/video comparison tool for diffusion paths.

.claude/skills/quantization/references/modality-compat.md

@@ -0,0 +1,47 @@
+# Quantization Compatibility by Modality
+
+Use this as a routing guide. The source of truth is the local method docs under
+`docs/user_guide/quantization/` and the current model implementation.
+
+## Summary
+
+- AR and omni language-model quantization mostly follows upstream `vllm`, with
+  vLLM-Omni scoping for multi-stage models.
+- Diffusion quantization is method-specific and model-specific. It usually
+  targets the diffusion transformer only.
+- Pre-quantized checkpoints should quantize only the component that has
+  quantized weights and scale tensors.
+
+## Model Type Matrix
+
+| Model Type | Typical Quantization Scope | Notes |
+|------------|----------------------------|-------|
+| AR text models | Language model | Upstream `vllm` methods usually apply |
+| Omni/TTS models | Thinker or AR language-model stage | Audio, vision, talker, and codec stages usually stay BF16 |
+| DiT image models | Diffusion transformer | Check FP8, Int8, GGUF, ModelOpt, AutoRound docs |
+| DiT video models | Diffusion transformer | HunyuanVideo FP8 and Wan2.2 MXFP paths are documented; validate other video paths |
+| Multi-stage diffusion | Selected stage only | Use per-component routing and per-stage validation |
+| Audio diffusion | Usually unsupported | Prefer dtype, offload, cache, or parallelism unless docs say otherwise |
+
+## Method Hints
+
+| Goal | Candidate |
+|------|-----------|
+| AR memory reduction | AWQ, GPTQ, AutoRound, ModelOpt where checkpoint supports it |
+| CUDA diffusion W8A8 | FP8 or Int8 |
+| Pre-quantized CUDA diffusion checkpoint | ModelOpt or GGUF |
+| Ascend NPU diffusion | Int8, MXFP8, MXFP4, MXFP4 DualScale, msModelSlim |
+| Intel XPU checkpoint | AutoRound/INC paths |
+| Conservative image quality | selective FP8/Int8 with `ignored_layers` |
+| Unsupported diffusion model | cache acceleration, CPU/layerwise offload, dtype, TP/SP/CFG parallelism |
+
+## Validation Rule
+
+Compatibility means more than successful loading. A method is compatible only
+after it has:
+
+- correct component scope
+- complete checkpoint/config metadata if offline
+- fixed-seed quality comparison against BF16
+- acceptable latency and memory behavior
+- docs or examples that state model, method, hardware, and caveats

.claude/skills/quantization/references/modelopt-fp8.md

@@ -0,0 +1,122 @@
+# ModelOpt FP8 Conversion and Loading
+
+Use this when exporting or loading ModelOpt FP8 checkpoints for diffusion
+transformers. This reference captures the practical conversion lessons from the
+2026-04-21 ModelOpt FP8 paste and aligns them with the current
+`vllm-omni` ModelOpt adapter.
+
+## Core Rule
+
+Treat ModelOpt FP8 as selective checkpoint quantization, not one-click
+whole-model FP8. Quantize the main transformer or DiT backbone, keep sensitive
+or peripheral modules BF16, serialize a complete ModelOpt checkpoint, and let
+vLLM-Omni auto-detect it from config.
+
+Do not pass `--quantization fp8` to force a ModelOpt checkpoint path. The
+checkpoint's `quantization_config` should select `modelopt`.
+
+## Conversion Strategy
+
+Prefer:
+
+- backbone attention Q/K/V and FFN expansion projections
+- conservative per-model ignore lists
+- MHA quantizers disabled until basic linear quantization is stable
+- full module-name prefixes in ignore/routing logic
+- BF16 fallback for output-side and routing-sensitive layers
+
+Avoid quantizing by default:
+
+- VAE
+- text, vision, or audio encoders
+- embedders and modulation layers
+- final heads and output projections
+- refiner modules
+- MoE routers and layernorm-sensitive blocks
+
+## Model-Specific Starting Points
+
+| Model Family | Quantize First | Keep BF16 First |
+|--------------|----------------|-----------------|
+| Z-Image | attention q/k/v, FFN w1/w3 | `to_out`, `w2`, `noise_refiner`, `context_refiner`, embedders, modulation, final/output |
+| Qwen-Image | transformer backbone linear layers | `proj_out`, `img_in`, `txt_in`, norms, modulation, position/time/embed layers |
+| FLUX2-dev / FLUX2-klein | backbone linear layers | `proj_out`, context/x embedders, `norm_out`, stream modulation, time embeddings |
+| HunyuanImage-3.0 | `model.model` backbone | VAE, vision tower, token/time/image embedders, final image head, LM head, routers, layernorm-sensitive blocks |
+
+These are starting points, not support claims. Validate with BF16 outputs.
+
+## Required Checkpoint Metadata
+
+`transformer/config.json` must include a complete `quantization_config`.
+At minimum it should identify ModelOpt and the algorithm:
+
+```json
+{
+  "quantization_config": {
+    "producer": {"name": "modelopt"},
+    "quant_method": "modelopt",
+    "quant_algo": "FP8"
+  }
+}
+```
+
+Current vLLM-Omni auto-detects:
+
+- `FP8` and `FP8_PER_CHANNEL_PER_TOKEN` -> `modelopt`
+- `NVFP4` -> `modelopt_fp4`
+- `MIXED_PRECISION` -> `modelopt_mixed`
+
+Packed modules and scales must map correctly:
+
+- `to_qkv` from `to_q`, `to_k`, `to_v`
+- `add_kv_proj` from `add_q_proj`, `add_k_proj`, `add_v_proj`
+- `w13` from `w1`, `w3`
+- `.input_scale`, `.weight_scale`, `.weight_scale_2`, `.weight_scale_inv`
+
+For selective FP8, target layers that remain BF16 must be allowed to
+dequantize from FP8 weights using the matching `weight_scale`, or the adapter
+must skip the quantized scale tensors for that BF16 target.
+
+## Runtime Signals
+
+Healthy loading should show ModelOpt auto-detection and a ModelOpt linear
+kernel path. Useful signals include:
+
+- auto-detected quantization from config
+- selected ModelOpt FP8 linear method
+- CUTLASS or FlashInfer FP8 scaled-mm backend when enabled and supported
+- non-eager path compiling the transformer when expected
+
+If online serving fails while offline inference works, inspect warmup and
+serving initialization before changing checkpoint conversion.
+
+## Validation Order
+
+1. Check config completeness.
+2. Run an export checker such as `examples/quantization/check_modelopt_fp8_export.py`.
+3. Confirm actual FP8 tensors and expected disk-size reduction.
+4. Run one offline generation with fixed prompt, seed, size, and steps.
+5. Run serving with `vllm serve --omni` and verify `/v1/images/generations`.
+6. Compare against BF16 using visual review plus metrics such as LPIPS, PSNR,
+   MAE, cosine similarity, and SSIM.
+7. Compare performance only under matched modes:
+   - BF16 eager vs FP8 eager
+   - BF16 non-eager vs FP8 non-eager
+
+FP8 is not automatically faster. Check whether execution reaches FP8 kernels,
+whether compile mode is comparable, and whether the workload shape fits the
+kernel path.
+
+## Triage
+
+| Symptom | First Check |
+|---------|-------------|
+| Startup failure | `config.json`, `model_index.json`, `quantization_config`, missing scales |
+| Loads but output is corrupted | Over-quantized sensitive layers or wrong full-name prefixes |
+| Offline works but online fails | Dummy warmup, serving init, compile path |
+| FP8 is not faster | Kernel selection, dtype, compile mode, workload shape |
+| BF16 fallback fails | Missing `weight_scale` for dequantization or wrong packed-module mapping |
+
+The success criterion is stable, correctly scoped checkpoint loading with
+quality preserved enough for the target use case, not maximizing the number of
+FP8 layers.

.claude/skills/readme.md

@@ -21,11 +21,18 @@ include:
 - `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
 - `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
 
 ## Maintenance Guidelines
 

.github/PULL_REQUEST_TEMPLATE.md

@@ -1,21 +1,15 @@
 <!-- markdownlint-disable -->
-PLEASE FILL IN THE PR DESCRIPTION HERE ENSURING ALL CHECKLIST ITEMS (AT THE BOTTOM) HAVE BEEN CONSIDERED.
+PLEASE FILL IN THE PR DESCRIPTION HERE.
 
 ## Purpose
 
 ## Test Plan
 
+**vLLM Version:**
+
+**vLLM-Omni Commit:**
+
 ## Test Result
 
----
-<details>
-<summary> Essential Elements of an Effective PR Description Checklist </summary>
-
-- [ ] The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
-- [ ] The test plan. Please provide the test scripts & test commands. Please state the reasons if your codes don't require additional test scripts. For test file guidelines, please check the [test style doc](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/ci/tests_style/)
-- [ ] The test results. Please paste the results comparison before and after, or the e2e results.
-- [ ] (Optional) The necessary documentation update, such as updating `supported_models.md` and `examples` for a new model. **Please run `mkdocs serve` to sync the documentation editions to `./docs`.**
-- [ ] (Optional) Release notes update. If your change is user-facing, please update the release notes draft.
-</details>
-
-**BEFORE SUBMITTING, PLEASE READ <https://github.com/vllm-project/vllm-omni/blob/main/CONTRIBUTING.md>** (anything written below this line will be removed by GitHub Actions)
+**BEFORE SUBMITTING:** read [CONTRIBUTING.md](https://github.com/vllm-project/vllm-omni/blob/main/CONTRIBUTING.md) and run the [precheck-pr skill](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/precheck-pr/SKILL.md) with the code agent for a self-check against project conventions.
+(anything written below this line will be removed by GitHub Actions)

.gitignore

@@ -270,3 +270,6 @@ vllm_omni/_version.py
 .worktrees/
 # CI overlay yamls materialized from tests/utils.py:_CI_OVERLAYS at test time
 tests/.ci_generated/
+
+# Local Lance precision-matching dump scripts (not part of the repo)
+lance_compare/

.readthedocs.yml

@@ -10,6 +10,11 @@ build:
   jobs:
     post_checkout:
       - git fetch --unshallow || true
+    pre_install:
+      # 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
 
 mkdocs:
   configuration: mkdocs.yml

CONTRIBUTING.md

@@ -1,3 +1,5 @@
 # Contributing to vLLM-Omni
 
 You may find information about contributing to vLLM-Omni on [Contributing](https://vllm-omni.readthedocs.io/en/latest/contributing/)
+
+Before submitting a PR, run the [precheck-pr skill](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/precheck-pr/SKILL.md) with the code agent for a self-check against project conventions.

README.md

@@ -16,6 +16,7 @@ Easy, fast, and cheap omni-modality model serving for everyone
 ---
 
 *Latest News* 🔥
+- [2026/06] We released [0.22.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.22.0) - an **omnimodal world-model** release aligned with vLLM 0.22, featuring [Nvidia Cosmos3](recipes/cosmos3/Cosmos3-Nano.md)/DreamZero world model support, expanded quantization coverage across Blackwell/NPU/XPU, TTS production improvements, new models including MiniCPM-o 4.5, MOSS-TTS, and Lance, plus RL integration with [VeRL-Omni](https://github.com/verl-project/verl-omni).
 - [2026/05] We released [0.20.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.20.0) - refreshes the serving/runtime stack for large-scale omni workloads, and improves diffusion model performance, quantization, and hardware readiness across CUDA, ROCm, MUSA, NPU, and XPU backends.
 - [2026/03] We released [0.18.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.18.0) - strengthens the core runtime through a large entrypoint refactor and scheduler/runtime cleanups, expands unified quantization and diffusion execution, broadens multimodal model coverage, and improves production readiness across audio, omni, image, video, RL, and multi-platform deployments.
 - [2026/03] Check out our first public [project deepdive](https://youtu.be/sgwNfsNnR9I) at the vLLM Hong Kong Meetup!
@@ -56,8 +57,9 @@ vLLM-Omni is flexible and easy to use with:
 
 vLLM-Omni seamlessly supports most popular open-source models on HuggingFace, including:
 
-- Omni-modality models (e.g. Qwen-Omni)
-- Multi-modality generation models (e.g. Qwen-Image)
+- **Omni-modality models** (e.g. Qwen3-Omni, Cosmos, HunyuanImage, BAGEL)
+- **TTS models** (e.g. Qwen3-TTS, VoxCPM2, Ming-Omni-TTS, CosyVoice3)
+- **Diffusion models** — image, video, and audio generation (e.g. Qwen-Image, Wan2.2, FLUX)
 
 ## Getting Started
 

benchmarks/README.md

@@ -1,44 +1,60 @@
-# Benchmarks Overview and Architecture
+# Benchmarks
 
-This document explains the benchmark architecture across all benchmark assets in this repo. It describes what we measure, and where to find or plug in new scenarios. Per-task details remain in subfolder READMEs (e.g., `benchmarks/<model>/README.md`).
+This directory contains benchmark suites for evaluating different model families and infrastructure components in vLLM-Omni. Each subfolder targets a different benchmark family with its own scripts, configs, and metrics. See the per-subfolder READMEs for detailed usage.
 
-## Scope and goals
-- Establish repeatable latency/throughput measurements for multimodal LLM pipelines.
-- Provide both HF Transformers (offline) and vLLM-Omni (multi-stage/pipeline) baselines.
-- Make it easy to plug in new datasets and models with minimal changes to the runner scripts.
+## Benchmark families
 
-## Dataset and inputs
-- Default example: SeedTTS top-100 prompts (`benchmarks/build_dataset/top100.txt`) via `benchmarks/build_dataset/`.
-- Extensible: drop in new prompt files or modality-aligned payloads; keep the expected format for the consuming scripts (e.g., one prompt per line).
-- If you add a new dataset, document it under `benchmarks/<model>/README.md` and point scripts to your data path.
+### [TTS](tts/README.md) — Text-to-Speech
 
-## Directory layout
-- `benchmarks/build_dataset/` — dataset prep utilities (e.g., SeedTTS top100).
-- `benchmarks/<model>/vllm_omni/` — vLLM-Omni pipeline benchmarks, logs, outputs.
-- `benchmarks/accuracy/` — accuracy benchmark integrations that adapt external
-  benchmark suites to vLLM-Omni serving and evaluation flows.
-- Add new tasks under `benchmarks/<model>/...` with the same pattern: `transformers/`, `vllm_omni/`, task-specific README, and (optionally) dataset prep notes.
+Model-agnostic serving benchmarks for TTS models, including Qwen3-TTS and VoxCPM2.
 
-## Reference workflows
-- **HF Transformers (offline, single process)**  
-  Script (example): `benchmarks/<model>/transformers/eval_qwen3_moe_omni_transformers.sh`  
-  Outputs: `benchmark_results/perf_stats.json`, `benchmark_results/results.json`, `benchmark_results/audio/` (if audio is produced).
+- **Layout**: `tts/bench_tts.py` (serving benchmark driver), `tts/model_configs.yaml` (model registry), `tts/plot_results.py` (result plotting)
+- **Dataset**: Seed-TTS full or text-only datasets, plus bundled smoke/design prompts under `build_dataset/`
+- **Key metrics**: TTFP (time to first audio packet), E2E latency, RTF (real-time factor), throughput (audio seconds / wall-clock second)
 
-- **vLLM-Omni end-to-end pipeline**  
-  Script (example): `benchmarks/<model>/vllm_omni/eval_qwen3_moe_omni.sh`  
-  Outputs: `vllm_omni/logs/*.stats.jsonl` (per-stage/overall latency & TPS), `vllm_omni/logs/stage*.log`, `vllm_omni/outputs/` (text/audio artifacts).
+### [Diffusion](diffusion/README.md) — Image and Video Generation
 
-- **Adding a new task/model**  
-  1) Create `benchmarks/<model>/transformers/` and/or `benchmarks/<model>/vllm_omni/` with scripts referencing your model and dataset.  
-  2) Add a task README describing dataset, configs, and expected outputs.  
-  3) Keep the output/log structure similar for easy comparison (perf_stats/results/audio or text outputs; stats.jsonl/logs for pipeline).
+Online-serving benchmark for diffusion image/video models, sending requests to the configured vLLM serving endpoint (`/v1/chat/completions`, `/v1/images/generations`, or `/v1/videos`, depending on backend/task).
 
-## Metrics to watch
-- **Throughput**: `overall_tps`, `*_tps_avg` per stage.
-- **Latency distribution**: look for long tails in `*.stats.jsonl`.
-- **Quality/completeness**: missing outputs or errors in stage logs indicate pipeline failures or misconfigurations.
+- **Tasks**: text-to-image, text-to-video, image-to-image, image-to-video, text+image-to-image, text+image-to-video
+- **Datasets**: `vbench`, `trace`, `random`
+- **Key metrics**: request throughput, latency percentiles, optional SLO attainment
 
-## Troubleshooting
-- Verify GPU/driver/FlashAttention2 requirements for your chosen model/config.
-- Ensure network access for dataset/model downloads (Google Drive, Hugging Face, etc.).
-- If outputs are missing or slow, inspect per-stage logs and `*.stats.jsonl` for errors, stragglers, or contention.
+### [GLM-Image](glm_image/README.md) — Text-to-Image and Image-to-Image
+
+Benchmarks for GLM-Image performance across HuggingFace baseline, vLLM-Omni offline inference, and vLLM-Omni online serving.
+
+- **Layout**: `glm_image/huggingface/` (HF baseline), `glm_image/vllm-omni/` (offline inference), `glm_image/benchmark_glm_image.py` (online serving)
+- **Tasks**: text-to-image and image-to-image
+- **Key metrics**: request/image throughput, latency percentiles, optional per-stage pipeline timings
+
+### [Distributed](distributed/omni_connectors/README.md) — RDMA Connector Testing
+
+RDMA environment setup and transfer tests for `MooncakeTransferEngineConnector`, including pytest-based single-node checks and manual cross-node benchmarks.
+
+- **Transfer modes**: `copy`, `zerocopy`, `gpu` (GPUDirect)
+- **Supports**: single-node pytest suites and manual multi-node/cross-node transfer testing
+
+### [Accuracy](accuracy/README.md) — Image Generation and Editing Quality
+
+Accuracy benchmarks for image generation/editing models, adapting external suites to vLLM-Omni serving and local judge-evaluation flows.
+
+- **Layout**: `accuracy/text_to_image/` (GEBench), `accuracy/image_to_image/` (GEdit-Bench)
+- **Method**: generation and judge scoring both run through local `vllm-omni serve` endpoints
+
+### Common serving metrics framework
+
+`vllm_omni/benchmarks/` extends `vllm bench serve --omni` with Omni-specific datasets, backends, and multimodal metrics. Key metrics include:
+
+- **Text output**: TTFT (time to first token), TPOT (time per output token), ITL (inter-token latency)
+- **Audio output**: TTFP (time to first audio packet), E2E latency, RTF (real-time factor)
+- **Throughput**: request throughput, output token throughput, total token throughput, audio throughput
+
+See `vllm_omni/benchmarks/serve.py` for the `vllm bench serve --omni` runner wrapper and `vllm_omni/benchmarks/metrics/` for Omni metric definitions.
+
+## Adding a new benchmark
+
+1. Create a subfolder under `benchmarks/<name>/` with scripts, configs if needed, and a `README.md`.
+2. If comparing against another runtime, use clear backend subfolders where applicable, such as `huggingface/` and `vllm-omni/`, or follow the shared TTS serving benchmark pattern in `tts/`.
+3. Add reusable dataset or prompt-building utilities to `build_dataset/` if applicable.
+4. Update this README with a link to the new benchmark family.

benchmarks/accuracy/common.py

@@ -151,6 +151,9 @@ class VllmOmniImageClient:
         seed: int | None = None,
         negative_prompt: str | None = None,
         output_compression: int | None = None,
+        bot_task: str | None = None,
+        sys_type: str | None = None,
+        system_prompt: str | None = None,
     ) -> Image.Image:
         if not isinstance(images, list):
             images = [images]
@@ -170,6 +173,12 @@ class VllmOmniImageClient:
             data["negative_prompt"] = negative_prompt
         if output_compression is not None:
             data["output_compression"] = str(output_compression)
+        if bot_task is not None:
+            data["bot_task"] = bot_task
+        if sys_type is not None:
+            data["sys_type"] = sys_type
+        if system_prompt is not None:
+            data["system_prompt"] = system_prompt
 
         files = [
             (

benchmarks/accuracy/image_to_image/gedit_bench.py

@@ -64,6 +64,47 @@ def resolve_model_name(*, model_name: str | None, model: str | None = None, outp
     raise ValueError("model-name is required when it cannot be inferred from model or output-root")
 
 
+def _coerce_score_int(value: Any) -> int:
+    if isinstance(value, bool):
+        raise TypeError(f"Expected numeric score, got bool: {value!r}")
+    if isinstance(value, (int, float)):
+        return int(value)
+    if isinstance(value, str):
+        stripped = value.strip()
+        if stripped.lstrip("-").isdigit():
+            return int(stripped)
+    raise TypeError(f"Expected numeric score, got {type(value).__name__}: {value!r}")
+
+
+def _expand_score_entry(entry: Any) -> list[int]:
+    if isinstance(entry, dict):
+        if "score" in entry:
+            return [_coerce_score_int(entry["score"])]
+        if "value" in entry:
+            return [_coerce_score_int(entry["value"])]
+        numeric_values = [_coerce_score_int(value) for value in entry.values()]
+        if numeric_values:
+            return numeric_values
+        raise TypeError(f"Score dict has no numeric values: {entry!r}")
+    return [_coerce_score_int(entry)]
+
+
+def _normalize_score_list(score: Any) -> list[int]:
+    if isinstance(score, dict):
+        entries: list[Any] = [score]
+    elif isinstance(score, list):
+        entries = score
+    else:
+        entries = [score]
+
+    normalized: list[int] = []
+    for entry in entries:
+        normalized.extend(_expand_score_entry(entry))
+    if not normalized:
+        raise ValueError(f"Empty score list after normalization: {score!r}")
+    return normalized
+
+
 def parse_score_payload(raw_text: str) -> dict[str, Any]:
     try:
         parsed = extract_json_object(raw_text)
@@ -73,10 +114,7 @@ def parse_score_payload(raw_text: str) -> dict[str, Any]:
             parsed = {"score": json.loads(stripped), "reasoning": ""}
         else:
             raise
-    score = parsed.get("score", [])
-    if not isinstance(score, list):
-        score = [score]
-    parsed["score"] = [int(value) for value in score]
+    parsed["score"] = _normalize_score_list(parsed.get("score", []))
     return parsed
 
 
@@ -391,6 +429,9 @@ class GEditBenchRunner:
         num_inference_steps: int = 20,
         guidance_scale: float | None = None,
         seed: int | None = 42,
+        bot_task: str | None = None,
+        sys_type: str | None = None,
+        system_prompt: str | None = None,
     ):
         self.dataset_ref = dataset_ref
         self.output_root = output_root
@@ -400,6 +441,9 @@ class GEditBenchRunner:
         self.num_inference_steps = num_inference_steps
         self.guidance_scale = guidance_scale
         self.seed = seed
+        self.bot_task = bot_task
+        self.sys_type = sys_type
+        self.system_prompt = system_prompt
         self.client = VllmOmniImageClient(base_url=base_url, api_key=api_key)
 
     def generate(
@@ -478,6 +522,9 @@ class GEditBenchRunner:
             num_inference_steps=self.num_inference_steps,
             guidance_scale=self.guidance_scale,
             seed=self.seed,
+            bot_task=self.bot_task,
+            sys_type=self.sys_type,
+            system_prompt=self.system_prompt,
         )
         edited_image.save(output_path)
         return {
@@ -633,6 +680,25 @@ def build_parser() -> argparse.ArgumentParser:
     generate.add_argument("--num-inference-steps", type=int, default=20)
     generate.add_argument("--guidance-scale", type=float, default=None)
     generate.add_argument("--seed", type=int, default=42)
+    generate.add_argument(
+        "--bot-task",
+        type=str,
+        default=None,
+        choices=["think", "recaption", "think_recaption", "vanilla"],
+        help="HunyuanImage-3 prompting mode for /v1/images/edits (optional).",
+    )
+    generate.add_argument(
+        "--sys-type",
+        type=str,
+        default=None,
+        help="Hunyuan system-prompt type (e.g. en_unified). Defaults from --bot-task when omitted.",
+    )
+    generate.add_argument(
+        "--system-prompt",
+        type=str,
+        default=None,
+        help="Custom Hunyuan system prompt body (sys_type=custom).",
+    )
     generate.add_argument("--workers", type=int, default=1)
     generate.add_argument("--max-samples", type=int, default=None)
     generate.add_argument("--samples-per-group", type=int, default=None)
@@ -675,6 +741,9 @@ def main(argv: list[str] | None = None) -> int:
             num_inference_steps=args.num_inference_steps,
             guidance_scale=args.guidance_scale,
             seed=args.seed,
+            bot_task=args.bot_task,
+            sys_type=args.sys_type,
+            system_prompt=args.system_prompt,
         )
         records = runner.generate(
             model_name=model_name,

benchmarks/build_dataset/sound_effect_smoke/en/meta.lst

@@ -0,0 +1,10 @@
+se001|ocean waves crashing on a rocky beach|10.0
+se002|heavy rain on a metal roof at night|8.0
+se003|a dog barking in the distance|6.0
+se004|a crackling campfire in a quiet forest|10.0
+se005|footsteps on dry leaves|6.0
+se006|thunder rolling across a wide valley|8.0
+se007|a busy coffee shop with chatter and espresso machines|10.0
+se008|wind howling through tall pine trees|8.0
+se009|a small stream flowing over pebbles|10.0
+se010|a creaking wooden door slowly opening|6.0

benchmarks/build_dataset/ttsd_smoke/en/meta.lst

@@ -0,0 +1,10 @@
+ttsd001|prompt-wavs/spk1.wav|prompt-wavs/spk2.wav|[S1] Hello and welcome to the show today. [S2] Thanks for having me, it's a pleasure to be here. [S1] Let's dive right in.
+ttsd002|prompt-wavs/spk1.wav|prompt-wavs/spk2.wav|[S1] Did you finish the report? [S2] Almost — I'm just polishing the conclusion. [S1] Great, send it over when you're done.
+ttsd003|prompt-wavs/spk1.wav|prompt-wavs/spk2.wav|[S1] Have you tried that new restaurant downtown? [S2] Yes, the pasta is amazing. [S1] I'll book a table for Friday.
+ttsd004|prompt-wavs/spk1.wav|prompt-wavs/spk2.wav|[S1] What did you think of yesterday's meeting? [S2] Productive, but a bit long. [S1] Agreed, we should set an hour cap next time.
+ttsd005|prompt-wavs/spk1.wav|prompt-wavs/spk2.wav|[S1] The weather is beautiful today. [S2] Perfect for a hike. [S1] Want to drive out to the trails this afternoon?
+ttsd006|prompt-wavs/spk1.wav|prompt-wavs/spk2.wav|[S1] How did the demo go? [S2] Better than expected — the client signed off on the prototype. [S1] Fantastic news!
+ttsd007|prompt-wavs/spk1.wav|prompt-wavs/spk2.wav|[S1] Coffee or tea this morning? [S2] Coffee please, with a splash of milk. [S1] Coming right up.
+ttsd008|prompt-wavs/spk1.wav|prompt-wavs/spk2.wav|[S1] Did you read the email about the deadline change? [S2] I did, we now have until next Friday. [S1] That should give us enough buffer.
+ttsd009|prompt-wavs/spk1.wav|prompt-wavs/spk2.wav|[S1] Are you joining the team dinner tonight? [S2] I'll be there around seven. [S1] See you then.
+ttsd010|prompt-wavs/spk1.wav|prompt-wavs/spk2.wav|[S1] Have we backed up the database? [S2] Last backup ran two hours ago. [S1] Good, I'll trigger a fresh snapshot before the migration.

benchmarks/diffusion/bench_attention_backends.py

@@ -0,0 +1,328 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Standalone diagnostic for the diffusion attention backends added in #3079.
+
+Exercises the same synthetic attention shape through each backend and each
+torch SDPA sub-kernel, with and without an attn_mask, so we can pinpoint why
+CUDNN_ATTN underperforms on some SKUs. Two typical causes:
+
+  (a) cuDNN lacks a tuned kernel for this (SM, head_dim, seq) combination
+      and silently falls back to MATH.
+  (b) attn_mask is non-None and the cuDNN SDPA dispatch rejects it,
+      walking CUDNN -> FLASH -> MATH.
+
+Run:
+    python benchmarks/diffusion/bench_attention_backends.py --preset hv15
+    python benchmarks/diffusion/bench_attention_backends.py --preset wan22
+    python benchmarks/diffusion/bench_attention_backends.py \
+        --batch 1 --heads 24 --seq 14336 --head-dim 128
+
+Optional extras:
+    pip install --pre flash-attn-4        # FA4 is currently pre-release only
+    pip install -U flashinfer              # latest FlashInfer (0.6.9)
+
+The table at the end is the data we want on the PR — surface the row where a
+backend is >1.5x the SDPA baseline, that's the one to gate off the auto-route.
+"""
+
+from __future__ import annotations
+
+import argparse
+import time
+
+import torch
+from torch.nn.attention import SDPBackend, sdpa_kernel
+
+# Presets mirror the DiT hot-path attention call (single-stream block) at the
+# shapes we run in the PR's validation configs.
+_PRESETS = {
+    # HunyuanVideo-1.5 480p/33f: (30 * 52 * 9) latent + ~256 text tokens.
+    "hv15": {"batch": 1, "heads": 24, "seq": 14336, "head_dim": 128},
+    # Wan 2.2 480p/33f rough estimate; adjust when we measure real shapes.
+    "wan22": {"batch": 1, "heads": 40, "seq": 16384, "head_dim": 128},
+    # Smaller image-gen shape for quick smoke tests.
+    "flux": {"batch": 1, "heads": 24, "seq": 4096, "head_dim": 128},
+}
+
+_SDPA_BACKENDS = [
+    ("CUDNN_ATTENTION", [SDPBackend.CUDNN_ATTENTION]),
+    ("FLASH_ATTENTION", [SDPBackend.FLASH_ATTENTION]),
+    ("EFFICIENT_ATTENTION", [SDPBackend.EFFICIENT_ATTENTION]),
+    ("MATH", [SDPBackend.MATH]),
+    # The PR's CUDNN_ATTN impl uses this priority chain; keep it here to show
+    # what torch actually picks when multiple are allowed.
+    ("CUDNN_ATTN_CHAIN", [SDPBackend.CUDNN_ATTENTION, SDPBackend.FLASH_ATTENTION, SDPBackend.MATH]),
+]
+
+
+def _env_report() -> None:
+    if not torch.cuda.is_available():
+        raise SystemExit("CUDA not available; this diagnostic needs a GPU.")
+    cc = torch.cuda.get_device_capability()
+    print("=" * 72)
+    print(f"GPU           : {torch.cuda.get_device_name()}")
+    print(f"SM capability : sm_{cc[0]}{cc[1]}")
+    print(f"torch         : {torch.__version__}")
+    print(f"cuDNN version : {torch.backends.cudnn.version()}")
+    print("flashinfer    : ", end="")
+    try:
+        import flashinfer
+
+        print(getattr(flashinfer, "__version__", "present"))
+    except Exception as e:
+        print(f"not installed ({type(e).__name__})")
+    print("=" * 72)
+
+
+def _make_qkv(batch: int, heads: int, seq: int, head_dim: int, device: str, dtype: torch.dtype):
+    # Layout: (B, S, H, D) — what the PR's backends accept as input.
+    q = torch.randn(batch, seq, heads, head_dim, device=device, dtype=dtype)
+    k = torch.randn(batch, seq, heads, head_dim, device=device, dtype=dtype)
+    v = torch.randn(batch, seq, heads, head_dim, device=device, dtype=dtype)
+    return q, k, v
+
+
+def _make_mask(batch: int, seq: int, device: str, dtype: torch.dtype, pad_tokens: int = 128) -> torch.Tensor:
+    # Mirrors a text-encoder padding mask: last `pad_tokens` positions masked.
+    mask = torch.zeros(batch, 1, seq, seq, device=device, dtype=dtype)
+    if pad_tokens > 0:
+        mask[..., -pad_tokens:] = float("-inf")
+    return mask
+
+
+def _time_call(fn, *args, warmup: int = 3, iters: int = 10) -> tuple[float, str]:
+    """Return (median ms, error string). Skips on any exception so that a
+    backend that rejects our args (wrong dtype, missing JIT module, unsupported
+    kwarg value) doesn't abort the whole sweep."""
+    try:
+        for _ in range(warmup):
+            fn(*args)
+        torch.accelerator.synchronize()
+        times = []
+        for _ in range(iters):
+            torch.accelerator.synchronize()
+            t0 = time.perf_counter()
+            fn(*args)
+            torch.accelerator.synchronize()
+            times.append((time.perf_counter() - t0) * 1000.0)
+        times.sort()
+        return times[len(times) // 2], ""
+    except Exception as e:  # noqa: BLE001 — probe script, keep the whole table going
+        msg = str(e).split("\n", 1)[0][:60]
+        return float("nan"), f"{type(e).__name__}: {msg}" if msg else type(e).__name__
+
+
+def _run_sdpa_variants(q, k, v, attn_mask, scale: float) -> list[tuple[str, float, str]]:
+    # q/k/v come in (B, S, H, D); F.sdpa wants (B, H, S, D).
+    q_t = q.permute(0, 2, 1, 3)
+    k_t = k.permute(0, 2, 1, 3)
+    v_t = v.permute(0, 2, 1, 3)
+    rows: list[tuple[str, float, str]] = []
+    for name, backends in _SDPA_BACKENDS:
+
+        def _call(mask=attn_mask):
+            with sdpa_kernel(backends):
+                return torch.nn.functional.scaled_dot_product_attention(
+                    q_t, k_t, v_t, attn_mask=mask, dropout_p=0.0, is_causal=False, scale=scale
+                )
+
+        ms, err = _time_call(_call)
+        rows.append((name, ms, err))
+    return rows
+
+
+def _run_flashinfer(q, k, v, scale: float, backend: str | None = None, attn_mask=None) -> tuple[float, str]:
+    """Call FlashInfer's dense single-prefill.
+
+    ``backend`` hints at cutlass/fa3/trtllm if the installed version exposes
+    that kwarg. ``attn_mask`` is the same additive 4D mask we pass to SDPA —
+    we convert it to the 2D boolean ``custom_mask`` form FlashInfer accepts.
+    Per FlashInfer docs, ``custom_mask`` only applies when ``causal=False``.
+    """
+    try:
+        import inspect
+
+        from flashinfer.prefill import single_prefill_with_kv_cache
+    except Exception as e:
+        return float("nan"), f"import-{type(e).__name__}"
+
+    kwargs: dict = {"sm_scale": scale, "causal": False, "return_lse": False}
+    if backend is not None:
+        sig = inspect.signature(single_prefill_with_kv_cache)
+        if "backend" not in sig.parameters:
+            return float("nan"), "no-backend-kwarg"
+        kwargs["backend"] = backend
+
+    if attn_mask is not None:
+        # Collapse (B, 1, S, S) additive float mask to (S, S) boolean.
+        # FlashInfer expects True = keep, False = masked out.
+        mask_2d = attn_mask[0, 0]
+        kwargs["custom_mask"] = mask_2d != float("-inf")
+
+    def _call():
+        out = single_prefill_with_kv_cache(q[0], k[0], v[0], **kwargs)
+        return out.unsqueeze(0)
+
+    return _time_call(_call)
+
+
+def _run_fa4(q, k, v, scale: float) -> tuple[float, str]:
+    """Call FlashAttention-4 directly (``pip install flash-attn-4``). FA4
+    ships a Blackwell-native kernel via CuTe-DSL; on sm_120 it should beat
+    cuDNN by ~20%. API lives under ``flash_attn.cute``; the older
+    ``flash_attn.flash_attn_func`` path is FA2/FA3 only."""
+    try:
+        from flash_attn.cute import flash_attn_func
+    except Exception as e:
+        return float("nan"), f"import-{type(e).__name__}"
+
+    def _call():
+        # FA4 accepts (B, S, H, D) directly — same layout the PR's backends use.
+        return flash_attn_func(q, k, v, softmax_scale=scale, causal=False)
+
+    return _time_call(_call)
+
+
+def _run_flashinfer_cudnn_batch(q, k, v, scale: float) -> tuple[float, str]:
+    """Call FlashInfer's direct cuDNN wrapper. Bypasses PyTorch SDPA dispatch
+    (which has a few hundred ns of overhead per call) and talks to cuDNN FMHA
+    straight. Useful as a ceiling for 'pure cuDNN, no SDPA' on Blackwell."""
+    try:
+        from flashinfer.prefill import cudnn_batch_prefill_with_kv_cache
+    except Exception as e:
+        return float("nan"), f"import-{type(e).__name__}"
+
+    b, s, h, d = q.shape
+    qo_indptr = torch.tensor([0, s], dtype=torch.int32, device=q.device)
+    kv_indptr = torch.tensor([0, s], dtype=torch.int32, device=q.device)
+
+    def _call():
+        return cudnn_batch_prefill_with_kv_cache(
+            q.reshape(b * s, h, d),
+            k.reshape(b * s, h, d),
+            v.reshape(b * s, h, d),
+            qo_indptr=qo_indptr,
+            kv_indptr=kv_indptr,
+            max_qo_len=s,
+            max_kv_len=s,
+            sm_scale=scale,
+            causal=False,
+        )
+
+    return _time_call(_call)
+
+
+def _print_table(title: str, rows: list[tuple[str, float, str]], baseline_name: str | None = None) -> None:
+    print(f"\n{title}")
+    print("-" * 88)
+    header = f"{'backend':<24} {'median (ms)':>14}    {'vs baseline':>12}    status"
+    print(header)
+    print("-" * 88)
+    baseline_ms = None
+    if baseline_name is not None:
+        for name, ms, _ in rows:
+            if name == baseline_name and ms == ms:  # not NaN
+                baseline_ms = ms
+                break
+    for name, ms, err in rows:
+        ms_str = f"{ms:>14.3f}" if ms == ms else f"{'n/a':>14}"
+        if baseline_ms is not None and ms == ms:
+            ratio = baseline_ms / ms
+            ratio_str = f"{ratio:>11.2f}x"
+        else:
+            ratio_str = f"{'—':>12}"
+        status = "ok" if not err else f"FAILED ({err})"
+        print(f"{name:<24} {ms_str}    {ratio_str}    {status}")
+
+
+def _pick_winner(rows: list[tuple[str, float, str]]) -> tuple[str, float] | None:
+    """Return (backend, ms) of the fastest non-failing row, or None."""
+    ok_rows = [(n, m) for n, m, err in rows if not err and m == m]
+    if not ok_rows:
+        return None
+    return min(ok_rows, key=lambda x: x[1])
+
+
+def _bench_one_shape(shape: dict, dtype: torch.dtype, device: str) -> tuple[list, list]:
+    torch.manual_seed(0)
+    q, k, v = _make_qkv(**shape, device=device, dtype=dtype)
+    scale = 1.0 / (shape["head_dim"] ** 0.5)
+
+    rows_nomask = _run_sdpa_variants(q, k, v, attn_mask=None, scale=scale)
+    rows_nomask.append(("FLASHINFER (default)", *_run_flashinfer(q, k, v, scale)))
+    # `trtllm-gen` has no sm_120 cubins (NVIDIA/TensorRT-LLM#11799) — skipped.
+    for fi_backend in ("fa2", "fa3", "cutlass", "auto"):
+        rows_nomask.append((f"FLASHINFER ({fi_backend})", *_run_flashinfer(q, k, v, scale, backend=fi_backend)))
+    rows_nomask.append(("FLASHINFER (cudnn-batch)", *_run_flashinfer_cudnn_batch(q, k, v, scale)))
+    rows_nomask.append(("FA4 (direct)", *_run_fa4(q, k, v, scale)))
+
+    mask = _make_mask(shape["batch"], shape["seq"], device=device, dtype=dtype, pad_tokens=256)
+    rows_mask = _run_sdpa_variants(q, k, v, attn_mask=mask, scale=scale)
+    rows_mask.append(("FLASHINFER (dense)", *_run_flashinfer(q, k, v, scale, attn_mask=mask)))
+    rows_mask.append(("FA4 (direct)", float("nan"), "mask-not-supported"))
+
+    return rows_nomask, rows_mask
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    parser.add_argument("--preset", choices=list(_PRESETS.keys()), default="hv15")
+    parser.add_argument("--sweep", action="store_true", help="Run all presets and print a ranking")
+    parser.add_argument("--batch", type=int, default=None)
+    parser.add_argument("--heads", type=int, default=None)
+    parser.add_argument("--seq", type=int, default=None)
+    parser.add_argument("--head-dim", type=int, default=None)
+    parser.add_argument("--dtype", choices=["bf16", "fp16"], default="bf16")
+    parser.add_argument("--device", default="cuda:0")
+    args = parser.parse_args()
+
+    dtype = torch.bfloat16 if args.dtype == "bf16" else torch.float16
+    _env_report()
+    print(f"dtype         : {args.dtype}")
+
+    presets = list(_PRESETS.keys()) if args.sweep else [args.preset]
+    summary: dict[str, tuple[str, float] | None] = {}
+
+    for preset_name in presets:
+        shape = dict(_PRESETS[preset_name])
+        if not args.sweep:
+            for k in ("batch", "heads", "seq", "head_dim"):
+                v = getattr(args, k if k != "head_dim" else "head_dim")
+                if v is not None:
+                    shape[k] = v
+
+        print("\n" + "=" * 88)
+        print(
+            f"Preset: {preset_name}  |  batch={shape['batch']} heads={shape['heads']} "
+            f"seq={shape['seq']} head_dim={shape['head_dim']}"
+        )
+        print("=" * 88)
+
+        rows_nomask, rows_mask = _bench_one_shape(shape, dtype, args.device)
+        _print_table(f"[{preset_name}] No attention mask", rows_nomask, baseline_name="CUDNN_ATTENTION")
+        _print_table(
+            f"[{preset_name}] With attention mask (pad 256 tokens)", rows_mask, baseline_name="CUDNN_ATTENTION"
+        )
+
+        summary[preset_name] = _pick_winner(rows_nomask)
+
+    if args.sweep or len(presets) > 1:
+        print("\n" + "=" * 88)
+        print("Winners per preset (no-mask path)")
+        print("=" * 88)
+        for preset_name, winner in summary.items():
+            if winner is None:
+                print(f"  {preset_name:<10} — no successful backend")
+            else:
+                name, ms = winner
+                print(f"  {preset_name:<10} {name:<24} {ms:>8.3f} ms")
+
+    print("\nNotes:")
+    print("  * Ratios are relative to CUDNN_ATTENTION. >1.0x means faster than cuDNN.")
+    print("  * Mask-path winner inherits CUDNN_ATTN's fallback in the PR's backends.")
+    print("  * `trtllm-gen` and FA4 4.0.0b10 are known-broken on sm_120 as of Apr 2026.")
+    print("  * For e2e timings, run text_to_video.py / text_to_image.py while varying DIFFUSION_ATTENTION_BACKEND.")
+
+
+if __name__ == "__main__":
+    main()

benchmarks/diffusion/bench_sage_attn3_matrix.sh

@@ -0,0 +1,108 @@
+#!/usr/bin/env bash
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+#
+# 2x2 timing matrix (backend x compile mode) for SAGE_ATTN_3 vs TORCH_SDPA,
+# on HunyuanVideo-1.5 480p T2V and Wan 2.2 TI2V-5B. Run from the repo root:
+#
+#     bash benchmarks/diffusion/bench_sage_attn3_matrix.sh
+#
+# Requires: sageattn3 installed (for SAGE_ATTN_3 rows) and a Blackwell GPU.
+
+set -u
+
+STEPS=${STEPS:-50}
+SEED=${SEED:-42}
+PROMPT=${PROMPT:-"A dog running across a field of golden wheat."}
+OUT_DIR=${OUT_DIR:-/tmp/bench_sage3}
+mkdir -p "$OUT_DIR"
+
+declare -A TOTAL PER_STEP TEXT_ENC TRANSFORMER VAE
+
+run() {
+    local name="$1"; shift
+    local model="$1"; shift
+    local h="$1"; shift
+    local w="$1"; shift
+    local f="$1"; shift
+    local g="$1"; shift
+    local log="$OUT_DIR/${name}.log"
+    echo "[$(date +%T)] Running $name ..."
+    env "$@" python examples/offline_inference/text_to_video/text_to_video.py \
+        --model "$model" \
+        --prompt "$PROMPT" \
+        --height "$h" --width "$w" --num-frames "$f" \
+        --num-inference-steps "$STEPS" --seed "$SEED" --guidance-scale "$g" \
+        --enable-diffusion-pipeline-profiler \
+        --output "$OUT_DIR/${name}.mp4" > "$log" 2>&1 || {
+            echo "  FAILED — see $log"
+            TOTAL[$name]="FAIL"; PER_STEP[$name]="FAIL"
+            TEXT_ENC[$name]="-"; TRANSFORMER[$name]="-"; VAE[$name]="-"
+            return
+        }
+
+    local wait_ms
+    wait_ms=$(grep "add_req_and_wait=" "$log" | tail -1 | sed -nE 's/.*add_req_and_wait=([0-9.]+) ms.*/\1/p')
+    local total_s
+    total_s=$(grep -oE "Total generation time: [0-9.]+ seconds" "$log" | tail -1 | sed -nE 's/.*: ([0-9.]+) seconds.*/\1/p')
+    local text_enc_s
+    text_enc_s=$(grep -oE "text_encoder.forward took [0-9.]+s" "$log" | awk '{sub(/s$/,"",$NF); sum+=$NF} END {printf "%.3f", sum+0}')
+    local vae_s
+    vae_s=$(grep -oE "vae.decode took [0-9.]+s" "$log" | awk '{sub(/s$/,"",$NF); sum+=$NF} END {printf "%.3f", sum+0}')
+    local pipeline_s
+    pipeline_s=$(grep -oE "Pipeline\.forward took [0-9.]+s" "$log" | tail -1 | sed -nE 's/.*took ([0-9.]+)s.*/\1/p')
+    local transformer_s
+    transformer_s=$(awk "BEGIN {printf \"%.3f\", ${pipeline_s:-0} - ${vae_s:-0} - ${text_enc_s:-0}}")
+
+    TOTAL[$name]="${total_s:-0}"
+    PER_STEP[$name]=$(awk "BEGIN {printf \"%.3f\", ${wait_ms:-0} / 1000 / $STEPS}")
+    TEXT_ENC[$name]="$text_enc_s"
+    TRANSFORMER[$name]="$transformer_s"
+    VAE[$name]="$vae_s"
+
+    echo "  total=${TOTAL[$name]}s  per-step=${PER_STEP[$name]}s/it  transformer=${TRANSFORMER[$name]}s  vae=${VAE[$name]}s"
+}
+
+# ========== HunyuanVideo-1.5 480p T2V (480x832x33, CFG=6.0) ==========
+HV_MODEL=hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v
+run hv15_sdpa_eager     "$HV_MODEL" 480 832 33 6.0 TORCH_COMPILE_DISABLE=1
+run hv15_sdpa_compiled  "$HV_MODEL" 480 832 33 6.0
+run hv15_sage3_eager    "$HV_MODEL" 480 832 33 6.0 TORCH_COMPILE_DISABLE=1 DIFFUSION_ATTENTION_BACKEND=SAGE_ATTN_3
+run hv15_sage3_compiled "$HV_MODEL" 480 832 33 6.0                         DIFFUSION_ATTENTION_BACKEND=SAGE_ATTN_3
+
+# ========== Wan 2.2 TI2V-5B (704x1280x33, CFG=5.0) ==========
+WAN_MODEL=Wan-AI/Wan2.2-TI2V-5B-Diffusers
+run wan22_sdpa_eager     "$WAN_MODEL" 704 1280 33 5.0 TORCH_COMPILE_DISABLE=1
+run wan22_sdpa_compiled  "$WAN_MODEL" 704 1280 33 5.0
+run wan22_sage3_eager    "$WAN_MODEL" 704 1280 33 5.0 TORCH_COMPILE_DISABLE=1 DIFFUSION_ATTENTION_BACKEND=SAGE_ATTN_3
+run wan22_sage3_compiled "$WAN_MODEL" 704 1280 33 5.0                         DIFFUSION_ATTENTION_BACKEND=SAGE_ATTN_3
+
+print_table() {
+    local title="$1"; shift
+    echo
+    echo "$title (${STEPS} steps, seed=${SEED})"
+    echo
+    printf "| %-23s | %-9s | %-15s | %-12s | %-15s | %-14s |\n" \
+        "Config" "Total (s)" "Per-step (s/it)" "Text Enc (s)" "Transformer (s)" "VAE Decode (s)"
+    printf "| %-23s | %-9s | %-15s | %-12s | %-15s | %-14s |\n" \
+        "-----------------------" "---------" "---------------" "------------" "---------------" "--------------"
+    for row in "$@"; do
+        case "$row" in
+            *_sdpa_eager)     label="SDPA + Eager" ;;
+            *_sdpa_compiled)  label="SDPA + Compiled" ;;
+            *_sage3_eager)    label="SAGE_ATTN_3 + Eager" ;;
+            *_sage3_compiled) label="SAGE_ATTN_3 + Compiled" ;;
+        esac
+        printf "| %-23s | %-9s | %-15s | %-12s | %-15s | %-14s |\n" \
+            "$label" "${TOTAL[$row]}" "${PER_STEP[$row]}" "${TEXT_ENC[$row]}" "${TRANSFORMER[$row]}" "${VAE[$row]}"
+    done
+}
+
+print_table "HunyuanVideo-1.5 480p T2V, 832x480x33" \
+    hv15_sdpa_eager hv15_sdpa_compiled hv15_sage3_eager hv15_sage3_compiled
+
+print_table "Wan 2.2 TI2V-5B, 1280x704x33" \
+    wan22_sdpa_eager wan22_sdpa_compiled wan22_sage3_eager wan22_sage3_compiled
+
+echo
+echo "Logs: $OUT_DIR/*.log"

benchmarks/diffusion/diffusion_benchmark_serving.py

@@ -47,6 +47,13 @@ Usage:
         --endpoint /v1/images/edits --dataset random --task ti2i --num-prompts 10 \
         --bot-task think
 
+    python benchmarks/diffusion/diffusion_benchmark_serving.py \
+      --endpoint /v1/images/edits \
+      --dataset custom \
+      --dataset-path custom_requests.jsonl \
+      --task ti2i \
+      --bot-task think
+
     i2i (chat-based models such as Qwen-Image-Edit):
     python3 benchmarks/diffusion/diffusion_benchmark_serving.py \
         --endpoint /v1/chat/completions --dataset vbench --task i2i --num-prompts 10
@@ -566,6 +573,135 @@ class TraceDataset(BaseDataset):
         return [self[i] for i in range(len(self))]
 
 
+class CustomDataset(BaseDataset):
+    """
+    Custom dataset that loads requests from a JSONL file.
+
+    Each line in the JSONL file should be a JSON object with the following fields:
+    - prompt (required): The text prompt for the request
+    - width (optional): Image/video width
+    - height (optional): Image height
+    - num_inference_steps (optional): Number of diffusion steps
+    - seed (optional): Random seed
+    - image_paths (optional): List of input image paths for i2i/i2v/ti2i tasks
+    - image_urls (optional): List of input image URLs (alternative to image_paths)
+
+    Example JSONL for ti2i:
+    {"prompt": "Add sunset lighting", "width": 1024, "height": 1024, "image_urls": ["https://example.com/image.jpg"]}
+    """
+
+    def __init__(self, args, api_url: str, model: str):
+        super().__init__(args, api_url, model)
+        self.dataset_path = args.dataset_path
+        if not self.dataset_path:
+            raise ValueError("--dataset-path must be provided when using 'custom' dataset")
+        self.load_data()
+
+    def load_data(self) -> None:
+        """Load data from JSONL file."""
+        import pandas as pd
+
+        if not self.dataset_path.endswith(".jsonl"):
+            raise ValueError("Custom dataset must be a JSONL file")
+
+        try:
+            df = pd.read_json(path_or_buf=self.dataset_path, lines=True)
+        except Exception as e:
+            raise ValueError(f"Failed to load JSONL file {self.dataset_path}: {e}")
+
+        if "prompt" not in df.columns:
+            raise ValueError("JSONL file must contain a 'prompt' column")
+
+        self.data = df.to_dict("records")
+        print(f"Loaded {len(self.data)} requests from {self.dataset_path}")
+
+    def _resolve_image_paths(self, item: dict) -> list[str] | None:
+        # Handle local file paths
+        if "image_paths" in item and item["image_paths"]:
+            paths = item["image_paths"]
+            if isinstance(paths, str):
+                paths = [paths]
+
+            # Verify all paths exist
+            valid_paths = []
+            for path in paths:
+                if os.path.exists(path):
+                    valid_paths.append(path)
+                else:
+                    raise ValueError(f"Image file not found: {path}")
+
+            return valid_paths if valid_paths else None
+
+        # Handle URLs - download to temp files
+        if "image_urls" in item and item["image_urls"]:
+            urls = item["image_urls"]
+            if isinstance(urls, str):
+                urls = [urls]
+
+            downloaded_paths = []
+            for url in urls:
+                try:
+                    response = requests.get(url, timeout=30)
+                    response.raise_for_status()
+
+                    # Create temp file with appropriate extension
+                    suffix = os.path.splitext(url)[1] or ".png"
+                    with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as tmp:
+                        tmp.write(response.content)
+                        downloaded_paths.append(tmp.name)
+                except Exception as e:
+                    raise ValueError(f"Failed to download image from {url}: {e}")
+
+            return downloaded_paths if downloaded_paths else None
+
+        return None
+
+    def __len__(self) -> int:
+        return len(self.data)
+
+    def __getitem__(self, idx: int) -> RequestFuncInput:
+        item = self.data[idx]
+
+        # Extract fields with fallback to args defaults
+        prompt = item["prompt"]
+        width = item.get("width", self.args.width)
+        height = item.get("height", self.args.height)
+        num_inference_steps = item.get("num_inference_steps", self.args.num_inference_steps)
+        seed = item.get("seed", self.args.seed)
+        reserved_keys = {"prompt", "width", "height", "num_inference_steps", "seed", "image_paths", "image_urls"}
+        extra_body = {k: v for k, v in item.items() if k not in reserved_keys}
+
+        # Handle image paths/URLs
+        image_paths = self._resolve_image_paths(item)
+
+        return RequestFuncInput(
+            prompt=prompt,
+            api_url=self.api_url,
+            model=self.model,
+            seed=seed,
+            image_paths=image_paths,
+            extra_body=extra_body,
+            width=width,
+            height=height,
+            num_inference_steps=num_inference_steps,
+        )
+
+    def get_requests(self) -> list[RequestFuncInput]:
+        """Get all requests, cycling through data if num_prompts > dataset size."""
+        num_requests = getattr(self.args, "num_prompts", len(self.data))
+
+        if num_requests <= len(self.data):
+            return [self[i] for i in range(num_requests)]
+
+        # Cycle through the dataset to reach num_prompts
+        requests = []
+        for i in range(num_requests):
+            idx = i % len(self.data)
+            requests.append(self[idx])
+
+        return requests
+
+
 class RandomDataset(BaseDataset):
     def __init__(self, args, api_url: str, model: str, enable_negative_prompt: bool = False):
         super().__init__(args, api_url, model)
@@ -950,6 +1086,8 @@ async def benchmark(args):
         dataset = TraceDataset(args, api_url, args.model)
     elif args.dataset == "random":
         dataset = RandomDataset(args, api_url, args.model, args.enable_negative_prompt)
+    elif args.dataset == "custom":
+        dataset = CustomDataset(args, api_url, args.model)
     else:
         raise ValueError(f"Unknown dataset: {args.dataset}")
 
@@ -1097,7 +1235,7 @@ if __name__ == "__main__":
         "--dataset",
         type=str,
         default="vbench",
-        choices=["vbench", "trace", "random"],
+        choices=["vbench", "trace", "random", "custom"],
         help="Dataset to use.",
     )
     parser.add_argument(

benchmarks/tts/bench_tts.py

@@ -53,6 +53,8 @@ _TASK_TO_DATASET: dict[str, str] = {
     "voice_clone": "seed-tts",
     "default_voice": "seed-tts-text",
     "voice_design": "seed-tts-design",
+    "dialogue": "ttsd",
+    "sound_effect": "sound-effect",
 }
 
 # Default design dataset path (bundled with the repo)
@@ -81,6 +83,7 @@ def build_bench_args(
     output_dir: str | None,
     result_filename: str | None,
     extra_cli_args: list[str],
+    output_len: int | None = None,
 ) -> list[str]:
     """Build the ``vllm bench serve --omni`` command for one (task, concurrency) run."""
     dataset_name = _TASK_TO_DATASET[task]
@@ -130,12 +133,18 @@ def build_bench_args(
     if task_extra_body:
         cmd += ["--extra-body", json.dumps(task_extra_body, separators=(",", ":"))]
 
+    if model_cfg.get("trust_remote_code"):
+        cmd.append("--trust-remote-code")
+
     if concurrency is not None:
         cmd += ["--max-concurrency", str(concurrency), "--request-rate", "inf"]
 
     if wer_eval:
         cmd.append("--seed-tts-wer-eval")
 
+    if output_len is not None:
+        cmd += ["--hf-output-len", str(output_len)]
+
     if output_dir or result_filename:
         out_dir = output_dir or "."
         os.makedirs(out_dir, exist_ok=True)
@@ -235,6 +244,15 @@ def main() -> None:
     parser.add_argument("--host", default="localhost")
     parser.add_argument("--port", type=int, default=8000)
     parser.add_argument("--model-configs", default=str(_DEFAULT_MODEL_CONFIGS), help="Path to model_configs.yaml")
+    parser.add_argument(
+        "--output-len",
+        type=int,
+        default=None,
+        help="Override per-request max_new_tokens forwarded to the server "
+        "(seed-tts datasets default to 2048; the MOSS-TTS full talker takes "
+        "this as 'max_new_frames' so smaller values cap audio length per "
+        "request and keep bench runtime tractable).",
+    )
     parser.add_argument("extra", nargs=argparse.REMAINDER, help="Extra args passed directly to vllm bench serve")
     args = parser.parse_args()
 
@@ -289,6 +307,7 @@ def main() -> None:
                 output_dir=args.output_dir,
                 result_filename=result_filename,
                 extra_cli_args=args.extra or [],
+                output_len=args.output_len,
             )
             result = run_one_benchmark(cmd)
             if result is not None:

benchmarks/tts/model_configs.yaml

@@ -14,6 +14,7 @@ models:
     supported_tasks: [default_voice, voice_design]
     backend: openai-audio-speech
     endpoint: /v1/audio/speech
+    trust_remote_code: true
     task_extra_body:
       default_voice:
         voice: Vivian
@@ -27,13 +28,85 @@ models:
     supported_tasks: [voice_clone]
     backend: openai-audio-speech
     endpoint: /v1/audio/speech
+    trust_remote_code: true
     task_extra_body:
       voice_clone:
         task_type: Base
 
+  bosonai/higgs-audio-v3-tts-4b:
+    supported_tasks: [default_voice, voice_clone]
+    backend: openai-audio-speech
+    endpoint: /v1/audio/speech
+    trust_remote_code: true
+    task_extra_body:
+      default_voice: {}
+      voice_clone: {}
+
   openbmb/VoxCPM2:
+    supported_tasks: [voice_clone, default_voice]
+    trust_remote_code: true
+    backend: openai-audio-speech
+    endpoint: /v1/audio/speech
+    trust_remote_code: true
+    task_extra_body:
+      voice_clone: {}
+      default_voice:
+        voice: default
+
+  # MOSS-TTS family. After the serving_speech.py variant-detection refactor
+  # all five variants now go through OpenAI /v1/audio/speech with the
+  # variant-specific request fields (ambient_sound, ref_audio_2, instructions).
+  OpenMOSS-Team/MOSS-TTS:
     supported_tasks: [voice_clone]
     backend: openai-audio-speech
     endpoint: /v1/audio/speech
     task_extra_body:
       voice_clone: {}
+
+  # MOSS-TTS-v1.5: continued-training upgrade of MOSS-TTS 1.0 — same
+  # MossTTSDelay-8B architecture and API, so identical wiring. Adds 31-language
+  # synthesis via the `language` field and inline `[pause Xs]` markers.
+  OpenMOSS-Team/MOSS-TTS-v1.5:
+    supported_tasks: [voice_clone]
+    backend: openai-audio-speech
+    endpoint: /v1/audio/speech
+    task_extra_body:
+      voice_clone: {}
+
+  OpenMOSS-Team/MOSS-TTS-Realtime:
+    supported_tasks: [voice_clone]
+    backend: openai-audio-speech
+    endpoint: /v1/audio/speech
+    task_extra_body:
+      voice_clone: {}
+
+  # MOSS-TTSD synthesizes [S1]/[S2] dialogue. The seed-tts voice_clone dataset
+  # has single-speaker rows; bench piggybacks on it (both speakers share the
+  # same ref_audio). For real dialogue evaluation use the ttsd dataset.
+  OpenMOSS-Team/MOSS-TTSD-v1.0:
+    supported_tasks: [voice_clone, dialogue]
+    backend: openai-audio-speech
+    endpoint: /v1/audio/speech
+    task_extra_body:
+      voice_clone: {}
+      dialogue: {}
+
+  # MOSS-SoundEffect ignores text/ref_audio; expects ambient_sound +
+  # duration_seconds. The sound_effect dataset module emits those.
+  OpenMOSS-Team/MOSS-SoundEffect:
+    supported_tasks: [sound_effect]
+    backend: openai-audio-speech
+    endpoint: /v1/audio/speech
+    task_extra_body:
+      sound_effect:
+        duration_seconds: 10
+
+  # MOSS-VoiceGenerator: instructions-driven (no ref_audio). seed-tts-design
+  # dataset already emits {instructions, target_text}, so voice_design works
+  # out of the box once the serving validator accepts instructions.
+  OpenMOSS-Team/MOSS-VoiceGenerator:
+    supported_tasks: [voice_design]
+    backend: openai-audio-speech
+    endpoint: /v1/audio/speech
+    task_extra_body:
+      voice_design: {}

docker/Dockerfile.ci

@@ -1,5 +1,8 @@
+# Target vllm version: v0.21.0 (releases/v0.21.0 branch).
+# The pinned VLLM_PRECOMPILED_WHEEL_COMMIT and VLLM_BASE_TAG below must
+# be updated on every rebase to match the new upstream vllm commit/tag.
 ARG VLLM_BASE_IMAGE=vllm/vllm-openai
-ARG VLLM_BASE_TAG=v0.21.0
+ARG VLLM_BASE_TAG=v0.22.0
 FROM ${VLLM_BASE_IMAGE}:${VLLM_BASE_TAG}
 ARG APP_DIR=/workspace/vllm-omni
 WORKDIR ${APP_DIR}

docker/Dockerfile.cuda

@@ -1,4 +1,4 @@
-ARG BASE_IMAGE=vllm/vllm-openai:v0.20.0
+ARG BASE_IMAGE=vllm/vllm-openai:v0.22.0
 FROM ${BASE_IMAGE}
 
 ARG COMMON_WORKDIR=/app

docker/Dockerfile.rocm

@@ -1,4 +1,4 @@
-ARG BASE_IMAGE=vllm/vllm-openai-rocm:v0.21.0
+ARG BASE_IMAGE=vllm/vllm-openai-rocm:v0.22.0
 FROM ${BASE_IMAGE} AS base
 
 # Declare a variable to know if we want to use the nightly build or the stable build.

docker/Dockerfile.xpu

@@ -88,7 +88,7 @@ ENV UV_EXTRA_INDEX_URL=${PIP_EXTRA_INDEX_URL}
 ENV UV_INDEX_STRATEGY="unsafe-best-match"
 ENV UV_LINK_MODE="copy"
 
-ARG VLLM_VERSION=v0.21.0
+ARG VLLM_VERSION=v0.22.0
 RUN git clone -b ${VLLM_VERSION} https://github.com/vllm-project/vllm
 WORKDIR /workspace/vllm
 

docs/.nav.yml

@@ -87,6 +87,8 @@ nav:
       - AutoRound: user_guide/quantization/autoround.md
       - msModelSlim: user_guide/quantization/msmodelslim.md
     - ComfyUI: features/comfyui.md
+  - Fault Tolerance:
+    - Failure Modes: user_guide/fault_injection_reliability_matrix.md
 - Developer Guide:
   - General:
     - contributing/README.md

docs/api/README.md

@@ -92,7 +92,6 @@ Core scheduling and caching components.
 Configuration classes.
 
 - [vllm_omni.config.model.OmniModelConfig][]
-- [vllm_omni.config.stage_config.ModelPipeline][]
 - [vllm_omni.config.stage_config.StageConfig][]
 - [vllm_omni.config.stage_config.StageConfigFactory][]
 - [vllm_omni.config.stage_config.StageType][]

docs/cli/bench/serve.md

@@ -2,29 +2,29 @@
 The vllm bench command launches the vLLM-Omni benchmark to evaluate the performance of multimodal models.
 
 ## Notes
-We currently only support using the "openai-chat-omni" backend.
+We currently only support using the "openai-chat-omni" and "openai-image-edits-omni" backend.
 
 ## Basic Parameter Description
 You can use `vllm bench serve --omni --help=all` to get descriptions of all parameters. The commonly used parameters are described below:
-- `--omni`  
+- `--omni`
   Enable Omni (multimodal) mode, supporting multimodal inputs and outputs such as images, videos, and audio.
 
-- `--backend`  
+- `--backend`
   Specify the backend adapter as openai-chat-omni, using OpenAI Chat compatible API behavior as the protocol. Currently only openai-chat-omni is supported.
 
-- `--model`  
+- `--model`
   The model identifier to load, filled according to the models supported by vLLM-Omni.
 
-- `--endpoint`  
+- `--endpoint`
   The API endpoint exposed externally, to which clients send their requests.
 
-- `--dataset-name`  
+- `--dataset-name`
   The name of the dataset used; random-mm indicates generating random multimodal inputs (images, videos, audio).
 
-- `--num-prompts`  
+- `--num-prompts`
   The total number of requests to send, an integer.
 
-- `--max-concurrency`  
+- `--max-concurrency`
   "Maximum number of concurrent requests. This can be used "
         "to help simulate an environment where a higher level component "
         "is enforcing a maximum number of concurrent requests. While the "
@@ -34,69 +34,73 @@ You can use `vllm bench serve --omni --help=all` to get descriptions of all para
         "actual request rate may be lower than specified with --request-rate, "
         "if the server is not processing requests fast enough to keep up."
 
-- `--request-rate`  
+- `--request-rate`
   "Number of requests per second. If this is inf, "
         "then all the requests are sent at time 0. "
         "Otherwise, we use Poisson process or gamma distribution "
         "to synthesize the request arrival times."
 
-- `--ignore-eos`  
+- `--ignore-eos`
   "Set ignore_eos flag when sending the benchmark request."
 
-- `--metric-percentiles`  
+- `--metric-percentiles`
   Comma-separated list of percentiles for selected metrics. "
         "To report 25-th, 50-th, and 75-th percentiles, use \"25,50,75\". "
         "Default value is \"99\"."
         "Use \"--percentile-metrics\" to select metrics.
 
-- `--percentile-metrics`  
+- `--percentile-metrics`
         "Comma-separated list of selected metrics to report percentiles."
                     "This argument specifies the metrics to report percentiles."
-                    'Allowed metric names are "ttft", "tpot", "itl", "e2el", "audio_ttfp", "audio_rtf", "audio_duration". '
+                    'Allowed metric names are "ttft", "tpot", "itl", "ttfc", "tpoc", "icl", '
+                    '"tpop", "e2el", "audio_ttfp", "audio_rtf", "audio_duration". '
 
-- `--save-result`  
+- `-print-stage`
+Print per-stage benchmark metrics for --omni serving when stage metrics are returned by the server. Disabled by default.
+
+- `--save-result`
 Specify to save benchmark results to a json file
 
-- `--save-detailed`  
+- `--save-detailed`
 "When saving the results, whether to include per request "
         "information such as response, error, ttfs, tpots, etc."
 
-- `--result-dir`  
+- `--result-dir`
  "Specify directory to save benchmark json results."
         "If not specified, results are saved in the current directory."
 
-- `--result-filename`  
+- `--result-filename`
 "Specify the filename to save benchmark json results."
         "If not specified, results will be saved in "
         "{label}-{args.request_rate}qps-{base_model_id}-{current_dt}.json"
 
-- `--random-prefix-len`  
+- `--random-prefix-len`
   Number of fixed prefix tokens before the random context in a request.
   The total input length is the sum of random-prefix-len and a random
   context length sampled from [input_len * (1 - range_ratio),
   input_len * (1 + range_ratio)].Only the random and random-mm modes
   support this parameter.
 
-- `--random-input-len`  
+- `--random-input-len`
   Number of input tokens per request.Only the random and random-mm modes support this parameter.
 
-- `--random-output-len`  
+- `--random-output-len`
   Number of output tokens per request.Only the random and random-mm modes support this parameter.
 
-- `--random-range-ratio`  
+- `--random-range-ratio`
   Range ratio for sampling input/output length,
   used only for random sampling. Must be in the range [0, 1) to define
   a symmetric sampling range
   [length * (1 - range_ratio), length * (1 + range_ratio)].
   Only the random and random-mm modes support this parameter.
 
-- `--random-mm-base-items-per-request`  
+- `--random-mm-base-items-per-request`
   Base number of multimodal items per request for random-mm.
   Actual per-request count is sampled around this base using
   --random-mm-num-mm-items-range-ratio.
   Only the random-mm mode supports this parameter.
 
-- `--random-mm-limit-mm-per-prompt`  
+- `--random-mm-limit-mm-per-prompt`
   Per-modality hard caps for items attached per request, e.g.
   '{"image": 3, "video": 1, "audio": 1}'. The sampled per-request item
   count is clamped to the sum of these limits. When a modality
@@ -104,7 +108,7 @@ Specify to save benchmark results to a json file
   renormalized.
   Only the random-mm mode supports this parameter.
 
-- `--random-mm-num-mm-items-range-ratio`  
+- `--random-mm-num-mm-items-range-ratio`
   Range ratio r in [0, 1] for sampling items per request.
   We sample uniformly from the closed integer range
   [floor(n*(1-r)), ceil(n*(1+r))]
@@ -115,7 +119,7 @@ Specify to save benchmark results to a json file
   An error is raised if the computed min exceeds the max.
   Only the random-mm mode supports this parameter.
 
-- `--random-mm-bucket-config`  
+- `--random-mm-bucket-config`
   The bucket config is a dictionary mapping a multimodal item
   sampling configuration to a probability.
   Currently allows for 3 modalities: audio, images and videos.
@@ -276,7 +280,9 @@ Generate synthetic image、video、audio inputs alongside random text prompts to
 
 Notes:
 
-- Works only with online benchmark via the OpenAI backend (`--backend openai-chat-omni`) and endpoint `/v1/chat/completions`.
+- Works only with online benchmark via:
+  - the OpenAI chat backend (`--backend openai-chat-omni`) and endpoint `/v1/chat/completions`.
+  - the OpenAI edit image backend (`--backend openai-image-edits-omni`) and endpoint `/v1/images/edits`.
 
 Start the server (example):
 
@@ -357,3 +363,105 @@ How sampling works:
 This should be seen as an edge case, and if this behavior can be avoided by setting `--random-mm-limit-mm-per-prompt` to a large number. Note that this might result in errors due to engine config `--limit-mm-per-prompt`.
 - The resulting request contains synthetic image data in `multi_modal_data` (OpenAI Chat format). When `random-mm` is used with the OpenAI Chat backend, prompts remain text and MM content is attached via `multi_modal_data`.
 </details>
+
+### Multi-Stage Benchmark
+
+<details class="admonition abstract" markdown="1">
+<summary>Show more</summary>
+
+Start the server (change the port and model):
+
+```bash
+vllm serve --omni --port 29999 --model ~/Qwen3-Omni-30B-A3B-Instruct
+```
+
+Then run the benchmarking script (remember to add the flag `--print-stage`):
+```bash
+vllm bench serve --omni \
+  --dataset-name random \
+  --port 29999 \
+  --max-concurrency 1 \
+  --num-warmups 2 \
+  --model ~/Qwen3-Omni-30B-A3B-Instruct \
+  --endpoint /v1/chat/completions \
+  --backend openai-chat-omni \
+  --num-prompts 3 \
+  --random-input-len 2500 \
+  --ignore-eos \
+  --percentile-metrics ttft,tpot,itl,e2el,audio_ttfp,audio_rtf,ttfc,tpoc,icl \
+  --random-output-len 900 \
+  --extra_body '{"modalities": ["text","audio"]}' \
+  --print-stage
+```
+
+Besides the "Serving Benchmark Result", there will be "Stage Benchmark Result" below:
+```text
+============= Stage Benchmark Result =============
+=============== Stage 0 (thinker) ================
+-------------------Stage Timing-------------------
+Mean stage_gen_time (ms):                18243.16
+Median stage_gen_time (ms):              18220.83
+P99 stage_gen_time (ms):                 18296.75
+================== Text Result ===================
+Stage generated tokens:                  2700
+-----------Serving Time to First Token------------
+Mean Serving TTFT (ms):                  816.31
+Median Serving TTFT (ms):                818.94
+P99 Serving TTFT (ms):                   829.76
+-----Time per Output Token (excl. 1st token)------
+Mean TPOP (ms):                          19.41
+Median TPOP (ms):                        19.40
+P99 TPOP (ms):                           19.47
+---------------Inter-token Latency----------------
+Mean ITL (ms):                           19.41
+Median ITL (ms):                         19.12
+P99 ITL (ms):                            23.58
+================ Stage 1 (talker) ================
+-------------------Stage Timing-------------------
+Mean stage_gen_time (ms):                18849.64
+Median stage_gen_time (ms):              13641.15
+P99 stage_gen_time (ms):                 33655.58
+============= Internal Stream Result =============
+-----------Serving Time to First Chunk------------
+Mean Serving TTFC (ms):                  1029.14
+Median Serving TTFC (ms):                1027.98
+P99 Serving TTFC (ms):                   1032.59
+-----Time per Output Chunk (excl. 1st chunk)------
+Mean TPOP (ms):                          17.53
+Median TPOP (ms):                        18.70
+P99 TPOP (ms):                           18.88
+---------------Inter-chunk Latency----------------
+Mean ICL (ms):                           16.27
+Median ICL (ms):                         18.19
+P99 ICL (ms):                            25.86
+=============== Stage 2 (code2wav) ===============
+-------------------Stage Timing-------------------
+Mean stage_gen_time (ms):                18860.74
+Median stage_gen_time (ms):              13655.84
+P99 stage_gen_time (ms):                 33659.79
+================== Audio Result ==================
+Stage audio duration generated(s):       260.11
+Stage audio frames generated:            6242730
+-----------Serving Time to First Packet-----------
+Mean Serving AUDIO_TTFP (ms):            1366.16
+Median Serving AUDIO_TTFP (ms):          1367.20
+P99 Serving AUDIO_TTFP (ms):             1368.26
+-----------------Real Time Factor-----------------
+Mean AUDIO_RTF:                          0.24
+Median AUDIO_RTF:                        0.26
+P99 AUDIO_RTF:                           0.27
+```
+Explanation:
+- stage_gen_time: Time from submitting a request to a specific stage to that stage finishing generation.
+
+- Serving TTFC (Time to First Chunk): Time from the HTTP request being accepted by the serving frontend to
+  the stage producing its first non-empty output chunk. Keep the name TTFT in text stage, TTFP in audio stage
+  for easier mapping.
+
+- TPOP (Time per Output Chunk): Average time from the first output chunk to stage completion, divided by the
+  number of remaining output chunks. The TPOP abbreviation follows Qwen3.5-Omni Technical Report.
+
+- ICL (Inter-Chunk Latency): Time between two consecutive output chunks produced by the same stage. Keep the
+  name ITL in text stage for easier mapping.
+
+</details>

docs/contributing/README.md

@@ -116,6 +116,16 @@ Only specific types of PRs will be reviewed. The PR title is prefixed appropriat
 !!! note
     If the PR spans more than one category, please include all relevant prefixes.
 
+### Pre-Check Before Submitting
+
+Before submitting a PR, run the [precheck-pr skill](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/precheck-pr/SKILL.md) with the code agent for a self-review against project conventions:
+
+The skill offers two modes:
+- **Quick (~3 min):** catches showstoppers — PR title format, missing benchmark claims, rebase status
+- **Full (~10 min):** thorough maintainer-grade review — dead code scan, copy-paste detection, import hygiene
+
+The precheck covers five PR types: Bug Fix, Performance, New Model, Diffusion Model, and General. Each type has a tailored checklist that validates evidence quality (repro steps, A/B benchmarks, registry entries, etc.). See the [precheck-pr skill](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/precheck-pr/SKILL.md) for the full checklist.
+
 ### Local Test
 Please run the L1 and L2 test cases locally first and attach the results before contacting us to add the "ready" label. Please refer to the [test instructions](./ci/test_guide.md) for running the test cases.
 

docs/contributing/ci/CI_5levels.md

@@ -268,6 +268,101 @@ Before entering specific testing levels, the project establishes two common spec
 1.  ***PR Checklist ([Tests Style](../ci/tests_style.md))***: This template defines the self-check items that must be completed before submitting a code review (Pull Request). It ensures that each code change meets basic requirements such as code style, dependency updates, and documentation synchronization before entering the automated testing pipeline, serving as the first manual line of defense for quality assurance.
 2.  ***CI Failure Explanation ([CI Failures](../ci/failures.md))***: This document archives and explains common failure patterns in the Continuous Integration (CI) pipeline, error log interpretation, and preliminary troubleshooting steps. It helps developers and testers quickly diagnose the causes of automated test failures, improving problem-solving efficiency.
 
+<<<<<<< ready
+## Notes
+### Diff-aware Buildkite uploads (`source_file_dependencies`)
+
+L2 (`.buildkite/test-ready.yml`) and L3 (`.buildkite/test-merge.yml`) pipelines can **skip unrelated GPU jobs at upload time** based on the PR diff. This is implemented by `.buildkite/scripts/upload_pipeline.py`, which filters steps before calling `buildkite-agent pipeline upload`.
+
+#### What `source_file_dependencies` is
+
+- A **uploader-only** YAML key on a Buildkite step or group. **Buildkite does not understand it**; `upload_pipeline.py` always **removes** it from the YAML that is uploaded.
+- A list of path **prefixes** (directories or individual files). If **any** changed file in the diff equals a prefix or starts with `prefix/`, the step is kept; otherwise the step (or entire group) is **omitted** from the uploaded pipeline.
+
+#### When filtering runs
+
+| Build context | Changed files used for matching |
+| --- | --- |
+| Pull request | `git diff --name-only origin/<base>...<BUILDKITE_COMMIT>` |
+| `main` branch push | `git diff --name-only <commit>^..<commit>` |
+| Other (e.g. local dry-run, non-PR branch) | Diff cannot be resolved → **no filtering**; all steps are uploaded and `source_file_dependencies` is still stripped |
+
+Docs-only PRs are handled earlier in bootstrap (`pipeline.yml`) via skip-ci logic; `source_file_dependencies` applies only to the **uploaded** L2/L3 test pipelines.
+
+#### Where it is configured
+
+| Level | Pipeline file | Upload entry |
+| --- | --- | --- |
+| L2 | `.buildkite/test-ready.yml` | `upload_pipeline.py --upload .buildkite/test-ready.yml` (from `pipeline.yml`) |
+| L3 | `.buildkite/test-merge.yml` | `upload_pipeline.py --upload .buildkite/test-merge.yml` (from `pipeline.yml`) |
+
+Steps **without** `source_file_dependencies` are always uploaded (subject to the usual label conditions: `ready` for L2, `merge-test` for L3).
+
+#### Current skip policy (L2 / L3)
+
+To balance CI cost and coverage:
+
+- **Always run** (no `source_file_dependencies`): baseline groups such as Simple Test, Diffusion unit tests, Engine/Model Executor, Distributed, Custom Pipeline, Entrypoints (L2), LoRA / Entrypoints (L3), and **all E2E jobs on L4 docker queues** (`gpu_1_queue`, `gpu_4_queue`).
+- **Diff-gated** (`source_file_dependencies` set): **H100 E2E jobs only** (`queue: mithril-h100-pool` under the E2E Test group)—typically the model’s pytest file(s) plus related `vllm_omni/` model, deploy, and stage paths.
+
+Adding a new expensive H100 E2E step: list the test file(s) and the smallest set of source prefixes that should trigger it. Prefer **per-step** deps on leaf jobs rather than a broad group-level list unless every child shares the same paths.
+
+#### YAML example
+
+```yaml
+      - label: "Diffusion · Qwen Image Test"
+        source_file_dependencies:
+          - tests/e2e/online_serving/test_qwen_image.py
+          - vllm_omni/diffusion/models/qwen_image/
+        commands:
+          - pytest -s -v tests/e2e/online_serving/test_qwen_image.py -m 'core_model' ...
+        agents:
+          queue: "mithril-h100-pool"
+```
+
+A **group** may also define `source_file_dependencies`; nested steps inherit filtering as a unit—the whole group is dropped if no prefix matches.
+
+#### Local dry-run
+
+```bash
+# Render filtered YAML to stdout (no upload)
+python3 .buildkite/scripts/upload_pipeline.py .buildkite/test-ready.yml
+
+# Confirm uploader-only keys are stripped
+python3 .buildkite/scripts/upload_pipeline.py .buildkite/test-merge.yml | grep source_file_dependencies
+# (no output expected)
+```
+
+On a PR build, Buildkite logs from `upload_pipeline.py` include lines such as `skip '…' (no changes under …)` for omitted steps.
+
+#### Related
+
+- Implementation: `.buildkite/scripts/upload_pipeline.py`
+- L2/L3 diff skip does **not** replace label-based triggers (`ready`, `merge-test`); it only reduces which steps appear **after** the pipeline is already scheduled.
+=======
+### Test helper environment variables
+
+Some shared helpers under `tests/helpers/` honor optional environment variables for local debugging. These are **not** set in CI by default.
+
+| Variable | Accepted values | Description |
+| -------- | --------------- | ----------- |
+| `VLLM_OMNI_KEEP_REQUEST_MEDIA` | `1`, `true`, `yes` (case-insensitive) | When enabled, temporary WAV files created by `tests.helpers.media.convert_audio_bytes_to_text` are **not** deleted when the pytest process exits. By default, each call writes a unique file under the system temp directory via `tempfile.mkstemp` and registers `atexit` cleanup. Use this when debugging audio output validation (Whisper transcription, keyword checks, text–audio similarity). The saved path is logged as `audio data is saved: <path>`. |
+
+Example (Linux / macOS):
+
+```bash
+export VLLM_OMNI_KEEP_REQUEST_MEDIA=1
+pytest -s -v tests/e2e/online_serving/test_qwen3_omni.py -k test_mix_to_text_audio
+```
+
+Example (Windows PowerShell):
+
+```powershell
+$env:VLLM_OMNI_KEEP_REQUEST_MEDIA = "1"
+pytest -s -v tests/e2e/online_serving/test_qwen3_omni.py -k test_mix_to_text_audio
+```
+>>>>>>> main
+
 ## Chapter 1: L1 & L2 Level Testing - Unit Testing and Basic End-to-End Verification
 
 ### 1.1 Testing Purpose
@@ -302,6 +397,7 @@ A clear directory structure is key to managing test cases efficiently.
 ### 1.4 Execution Method and Example
 
 -   ***Trigger Timing***: **`PR with ready label`**. That is, when a developer adds a "ready for review" or similar label to a PR on platforms like GitHub, L1 and L2 tests are automatically triggered.
+-   ***Diff-aware step skipping***: On L2, expensive **H100 E2E** jobs may be omitted at pipeline upload when the PR diff does not touch their [`source_file_dependencies`](#diff-aware-buildkite-uploads-source_file_dependencies) paths; L4 docker jobs and non-E2E steps still run. See [Diff-aware Buildkite uploads](#diff-aware-buildkite-uploads-source_file_dependencies).
 -   ***Execution Environment***: L1 uses ***CPU*** environment; L2 requires ***GPU*** environment.
 -   ***Script Example***:
 
@@ -404,7 +500,8 @@ L3 level testing executes after code is merged into the main branch. Its core pu
 
 ### 2.4 Execution Method and Example
 
--   ***Trigger Timing***: **`PR Merged`**. Automatically triggered after code review is approved and merged into the main branch.
+-   ***Trigger Timing***: **`PR Merged`**. Automatically triggered after code review is approved and merged into the main branch (typically via `merge-test` label on the PR before merge).
+-   ***Diff-aware step skipping***: Same [`source_file_dependencies`](#diff-aware-buildkite-uploads-source_file_dependencies) mechanism as L2—only **H100 E2E** steps are diff-gated; L4 docker E2E and other L3 groups always upload. See [Diff-aware Buildkite uploads](#diff-aware-buildkite-uploads-source_file_dependencies).
 -   ***Execution Environment***: ***GPU*** servers.
 -   ***Script Example***:
 
@@ -520,6 +617,8 @@ L3 level testing executes after code is merged into the main branch. Its core pu
 
     **Implicit Validation**: The `send_omni_request` and `send_diffusion_request` methods internally includes validation logic dynamically selected based on the --run-level parameter: core_model performs basic validation, while advanced_model and full_model perform deep validation.
 
+    **Audio output debugging**: Deep validation may transcribe returned audio via `convert_audio_bytes_to_text` (Whisper). If an audio keyword or text–audio similarity assertion fails, set `VLLM_OMNI_KEEP_REQUEST_MEDIA=1` before running pytest to keep the intermediate WAV files for inspection (see [Test helper environment variables](#test-helper-environment-variables)).
+
 -   ***Run Command (L3 merge)***: `pytest -s -v /tests/e2e/online_serving/test_{model_name}.py -m advanced_model --run-level=advanced_model`
 
 -   ***Run Command (L4 nightly expansion)***: `pytest -s -v /tests/e2e/online_serving/test_{model_name}_expansion.py -m full_model --run-level=full_model`

docs/contributing/ci/failures.md

@@ -69,6 +69,8 @@ If the failure is already tracked, leave a comment on your PR noting that the fa
 
 If the failure appears to be new, investigate whether your changes caused it.
 
+For resilience behavior under injected process signals/OOM pressure (validation scenarios, not root-cause equivalence), see [Failure Mode Injection Scenarios and Expected Behavior Matrix](../../user_guide/fault_injection_reliability_matrix.md).
+
 ### Reading Buildkite Logs
 
 1. Click **Details** next to the Buildkite check on your PR.

docs/design/feature/disaggregated_inference.md

@@ -8,6 +8,7 @@ Backend-specific setup lives in separate docs:
 - [SharedMemoryConnector](omni_connectors/shared_memory_connector.md)
 - [MooncakeStoreConnector](omni_connectors/mooncake_store_connector.md)
 - [MooncakeTransferEngineConnector](omni_connectors/mooncake_transfer_engine_connector.md)
+- [MoriTransferEngineConnector](omni_connectors/mori_transfer_engine_connector.md)
 - [YuanrongConnector](omni_connectors/yuanrong_connector.md)
 - [YuanrongTransferEngineConnector](omni_connectors/yuanrong_transfer_engine_connector.md)
 
@@ -23,6 +24,7 @@ Current connectors operate in D2H2D (device to host to device) mode.
 | Single node | SharedMemoryConnector | Auto-configured if no connector is specified. |
 | Multi node (Mooncake Store) | MooncakeStoreConnector | TCP-based, requires Mooncake Master + metadata server. |
 | Multi node (Mooncake RDMA) | MooncakeTransferEngineConnector | RDMA/TCP direct transfer with managed memory pool. Fastest. |
+| Multi node (Mori RDMA) | MoriTransferEngineConnector | RDMA direct transfer via Mori IOEngine. |
 | Multi node (Yuanrong) | YuanrongConnector | Requires Yuanrong Datasystem + etcd. |
 | Ascend NPU P2P (Yuanrong TE) | YuanrongTransferEngineConnector | Uses Yuanrong TransferEngine directly. Configure NPU device IPv4 and `memory_pool_device: "npu"`. |
 

docs/design/feature/omni_connectors/mori_transfer_engine_connector.md

@@ -0,0 +1,76 @@
+# MoriTransferEngineConnector
+
+## When to Use
+
+Currently supports intra-node deployment with Mori.
+
+As noted in #1742, inter-node support will be added back in a future
+refactor.
+
+## Mechanism
+
+Uses Mori's `IOEngine` / `MemoryDesc` API for zero-copy RDMA transfers.
+
+- Data Plane: RDMA (InfiniBand/RoCE) with managed memory pool.
+- Control Plane: ZMQ for pull-request handshake and async completion.
+
+## Installation
+
+See the [Mori repository](https://github.com/ROCm/mori) for installation instructions.
+
+## Configuration
+
+Mori is configured through the new deploy-config schema (see
+[`docs/configuration/stage_configs.md`](../../../configuration/stage_configs.md)).
+Define the connector at the top level of the deploy YAML and reference it
+by name from each stage's `input_connectors` / `output_connectors`:
+
+```yaml
+connectors:
+  mori_connector:
+    name: MoriTransferEngineConnector
+    extra:
+      host: "auto"
+      zmq_port: 50051
+      device_name: ""
+      memory_pool_size: 536870912
+      memory_pool_device: "cuda"
+
+stages:
+  - stage_id: 0
+    output_connectors:
+      to_stage_1: mori_connector
+
+  - stage_id: 1
+    input_connectors:
+      from_stage_0: mori_connector
+```
+
+A ready-to-run intra-node example for Qwen3-Omni-MoE on AMD MI300X lives
+at `vllm_omni/deploy/qwen3_omni_moe_mori_intranode.yaml` and can be loaded
+with:
+
+```bash
+vllm-omni serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --log-stats \
+    --deploy-config vllm_omni/deploy/qwen3_omni_moe_mori_intranode.yaml
+```
+
+The yaml wires `MoriTransferEngineConnector` (with `backend_type: xgmi`) to
+the chunk_transfer_adapter path (`async_chunk: true`) so stage-to-stage
+hidden-state and codec-frame streams ship GPU-to-GPU over AMD Infinity
+Fabric instead of SHM.  Qwen2.5-Omni + Mori is not yet functional on
+the chunk path: it needs `thinker2talker_async_chunk` /
+`talker2code2wav_async_chunk` input processors that do not exist yet
+(the orchestrator-level path the upstream PR originally targeted was
+lost during the entrypoints → engine refactor and removed in #1742).
+
+Parameters:
+
+- host: local RDMA IP (`"auto"` for auto-detect).
+- zmq_port: ZMQ base port for control-plane communication.
+- device_name: RDMA device (e.g., `"mlx5_0"`), empty for auto-detect.
+- memory_pool_size: RDMA memory pool size in bytes.
+- memory_pool_device: `"cpu"` (pinned) or `"cuda"` (GPUDirect / XGMI RDMA).
+
+For more details, refer to the
+[Mori repository](https://github.com/ROCm/mori).

docs/design/index.md

@@ -13,6 +13,10 @@ This section contains design documents and architecture specifications for vLLM-
 - [Adding Step Execution Support for Diffusion Pipelines](feature/diffusion_step_execution.md)
 - [Continuous Batching for Step-Wise Diffusion](feature/diffusion_continuous_batching.md)
 
+## Infrastructure Design Documents
+
+- [Prometheus Metrics](metrics.md)
+
 ## Module Design Documents
 
 - [AR Module](module/ar_module.md)

docs/design/metrics.md

@@ -0,0 +1,208 @@
+# Prometheus Metrics Design
+
+This document describes how vLLM-Omni exposes Prometheus metrics for multi-stage pipelines, the constraints that shaped the design, and how the pipeline-level metrics coexist with upstream vLLM per-engine metrics. This iteration covers the text and audio surface; diffusion / image / video families land in a follow-up.
+
+## Objectives
+
+- Expose pipeline-level request and latency metrics that span the full multi-stage execution (orchestrator scope).
+- Preserve all upstream vLLM per-engine metrics (`vllm:*`) for stages backed by an AR LLM engine, and reshape their `engine` label into `stage` + `replica` so multi-replica deployments gain per-replica visibility automatically.
+- Expose per-modality SLO metrics that the upstream `vllm:*` families do not capture — audio TTFP / RTF / duration / frames / streaming continuity / silent-loss.
+- Expose per-replica-edge cross-stage transfer metrics so the slack between E2E latency and the sum of per-stage `gen_time` (queueing, serialization, network) becomes attributable.
+- Keep the metrics collection overhead low enough that it does not regress TTFA or throughput.
+
+## Background
+
+### Upstream vLLM Metrics
+
+Upstream vLLM defines ~37 Prometheus metric families under the `vllm:` prefix. These are registered by `PrometheusStatLogger` and cover engine-level state: KV cache usage, running/waiting request counts, token throughput, TTFT, inter-token latency, e2e latency, and so on. They are served via the `/metrics` HTTP endpoint provided by `prometheus_fastapi_instrumentator` and the default `prometheus_client` WSGI handler.
+
+vLLM's `unregister_vllm_metrics()` function strips every `prometheus_client` collector whose `_name` attribute contains the substring `"vllm"`. This runs during engine initialization to clean up stale collectors from prior instantiations within the same process.
+
+### The Problem
+
+vLLM-Omni runs multiple engine instances (stages × replicas) within a single process, coordinated by an Orchestrator. The pipeline needs its own metrics — aggregate request counts, end-to-end latency across all stages, per-modality SLO signals, and cross-stage transfer attribution — that do not exist in upstream vLLM. All pipeline-level metrics use the `vllm:omni_` prefix to distinguish them from upstream per-engine metrics. At import time (see `vllm_omni/patch.py`) we replace `unregister_vllm_metrics()` with a scoped version that still strips upstream `vllm:*` collectors before each new `PrometheusStatLogger` registers (so multi-engine processes don't crash on duplicate registration), but preserves anything prefixed `vllm:omni_` / `vllm_omni`.
+
+Upstream per-engine metrics retain the `vllm:` prefix but are now registered by `OmniPrometheusStatLogger`, a thin subclass of upstream's `PrometheusStatLogger` that reshapes the single `engine` label into a `stage` + `replica` pair (see "OmniPrometheusStatLogger wrap" below).
+
+## Architecture
+
+### Component Overview
+
+```text
+                       +------------------------+
+                       |  API Server (FastAPI)  |
+                       |   GET /metrics         |
+                       +-----------+------------+
+                                   |
+                  prometheus_client default registry
+                                   |
+        +--------+--------+--------+--------+--------+
+        |                                            |
+   vllm:omni_*                                    vllm:*
+   collectors                                  collectors
+        |                                            |
+   +----+--------+   +-----------+   +----------+   +-----------+
+   | OmniPromet- |   | OmniMod-  |   | OmniTra- |   | OmniProm- |
+   | heusMetrics |   | alityMet- |   | nsferMe- |   | etheusSt- |
+   |             |   | rics      |   | trics    |   | atLogger  |
+   +----+--------+   +-----+-----+   +----+-----+   +----+------+
+        |                  |              |              |
+     OmniBase           OmniBase     Orchestrator    Orchestrator
+   (request life-     (finalize +   (record_trans-  (per-(stage,
+    cycle, success/    streaming     fer_tx/rx        replica)
+    fail counter)      hooks via     hooks via        scheduler/
+                       observe_*     emit hook in     iteration
+                       APIs)         OrchestratorAg-  stats)
+                                     gregator)
+```
+
+### Data Flow
+
+There are four independent paths for metric collection.
+
+**Path 1: Pipeline-level metrics (`vllm:omni_*`)**
+
+`OmniPrometheusMetrics` registers the Gauge / Counter / Histogram collectors at import time. It is instantiated once per entrypoint, labeled with the model name. The entrypoint calls its methods as requests progress:
+
+- `set_running(n)` / `set_waiting(n)` — updated after each request completes. The running count comes from `OmniRequestCounter`, a simple counter incremented/decremented by the Orchestrator as it tracks requests. Waiting is derived as `total - running`.
+- `request_succeeded(e2e_seconds, finished_reason="stop")` — recorded when a request finishes at the final stage. `finished_reason` is extracted from `engine_outputs.outputs[0].finish_reason` (vLLM `CompletionOutput` convention) and increments `vllm:omni_requests_success_total{finished_reason}`.
+- `request_failed()` — recorded by the cleanup path when a request exits without natural completion. Internally maps to `finished_reason="abort"` so a single Counter family covers both natural and aborted completion.
+
+**Path 2: Audio modality metrics (`vllm:omni_audio_*`)**
+
+`OmniModalityMetrics` registers seven audio families with `{model_name, stage, replica}` (plus an extra `threshold_ms` / `reason` label on the two extra-cardinality Counters). Three observation sites:
+
+- `observe_modality_at_finalize(...)` — called from `omni_base._process_single_result` inside the existing `e2e_done` finalize guard. For `output_type == "audio"` it emits `audio_frames_total`, `audio_duration_s`, `audio_rtf` (or `audio_skipped_requests_total{reason="no_audio_data"}` when no audio was produced). Sample rate is resolved from `engine_outputs.multimodal_output` via `definitions.resolve_audio_sample_rate(...)` (fallback chain mirrors `serving_chat.py`'s audio response path).
+- `observe_audio_first_packet(...)` — called from the OpenAI SSE audio branch in `serving_chat.py` on the first audio packet for a request. The once-per-request guard is held by `ClientRequestState.first_audio_ts`. The `request_arrival_ts` anchor is stored in `ClientRequestState` by `async_omni.generate()`, computed at request entry.
+- `observe_audio_streaming_finalize(...)` — called from `serving_chat.py` after the streaming chunk loop exhausts. It runs the per-chunk player simulation from `vllm_omni/benchmarks/audio_continuity.py` to compute the worst-case underrun and emits `audio_underrun_s` plus (when the request stayed below the threshold) `audio_continuity_ok_total{threshold_ms}`. Per-chunk PCM byte counts and arrival timestamps are recorded by the same audio branch that updates `first_audio_ts`.
+
+**Path 3: Cross-stage transfer metrics (`vllm:omni_transfer_*`)**
+
+`OmniTransferMetrics` registers four Histogram families with `{model_name, from_stage, from_replica, to_stage, to_replica}` labels. Each observation corresponds to one physical transfer hop (one chunk between adjacent stages), not the per-request accumulated total — so the histograms track per-transfer distribution.
+
+The hook lives in `OrchestratorAggregator.record_transfer_tx` and `record_transfer_rx`. After the existing `TransferEdgeStats` accumulation, the aggregator calls `_emit_transfer_tx` / `_emit_transfer_rx`. Those:
+
+1. Resolve `from_replica` / `to_replica` via a `replica_resolver` callback supplied by `async_omni.py`. The resolver delegates to `stage_pool.get_bound_replica_id(request_id)` — i.e. the orchestrator's existing sticky-routing binding is the source of truth.
+2. Convert the underlying `_ms` accumulators to seconds and call the `_s`-suffixed observe methods on `OmniTransferMetrics`.
+
+Defensive fail-safe: if `transfer_emitter` or `replica_resolver` is missing, or the resolver returns `None` for either side, the emit is skipped silently (the underlying `TransferEdgeStats` accumulation is unaffected).
+
+**Path 4: Per-engine metrics (`vllm:*`, stage/replica wrap)**
+
+The Orchestrator instantiates `OmniPrometheusStatLogger` (a thin subclass of upstream `vllm.v1.metrics.loggers.PrometheusStatLogger`) and feeds it scheduler stats and iteration stats after processing each batch of engine outputs. This populates the standard ~37 vLLM metric families (TTFT, ITL, TPOT, KV cache usage, etc.) using the same upstream code path — but with the `engine` label reshaped into `stage` + `replica` so multi-replica deployments produce distinct series per replica. See the next section for the wrap mechanics.
+
+### Shared State Between Threads
+
+The Orchestrator runs in a background thread. The API server (OmniBase) runs in the asyncio event loop thread. `OmniRequestCounter` bridges them — a plain Python object with an `int` field. The Orchestrator increments/decrements it; the entrypoint reads it for gauge updates. No lock is needed because the counter is advisory (a stale read by one Prometheus scrape interval is acceptable). It is created by `AsyncOmniEngine.__init__()` and passed to the Orchestrator at construction time.
+
+### Metric Registration and Lifecycle
+
+All `vllm:omni_*` collectors are registered once when their owning class (`OmniPrometheusMetrics` / `OmniModalityMetrics` / `OmniTransferMetrics`) is imported. Per-`(stage, replica)` labels are bound lazily on first observation to avoid registering label sets for combinations that never produce data.
+
+The `prometheus_client` default registry holds all collectors. FastAPI's `/metrics` endpoint serves the default registry, so `vllm:omni_*` and the wrapped `vllm:*` metrics appear in the same scrape response alongside `http_*` and `process_*` metrics from the instrumentator and the Python client runtime.
+
+## OmniPrometheusStatLogger Wrap
+
+Upstream `PrometheusStatLogger.__init__` hard-codes `labelnames = ["model_name", "engine"]` as a local variable, references it across ~37 metric-family construction sites, and uses the `engine` label value in five different `.labels()` call shapes (kwarg with int engine, kwarg with str engine, positional with str engine in the middle, plus a `metrics_info["engine"] = str(...)` dict pattern). To reshape `engine` into `stage` + `replica` without forking the entire upstream `__init__`, the wrap uses three coordinated mechanisms:
+
+1. **Class-level metric class slot overrides.** `OmniPrometheusStatLogger` overrides `_gauge_cls`, `_counter_cls`, `_histogram_cls` (which upstream calls via `self._gauge_cls(...)` etc.) with `_RelabelGauge` / `_RelabelCounter` / `_RelabelHistogram` wrapper classes. These intercept the `labelnames` kwarg at metric family creation time and replace `engine` with `("stage", "replica")`.
+2. **Property descriptor for `per_engine_labelvalues`.** Upstream builds `self.per_engine_labelvalues = {idx: [model_name, str(idx)]}` inside `__init__` and then captures it into a local variable for `create_metric_per_engine` calls. By making `per_engine_labelvalues` a Python property on the subclass, the setter intercepts upstream's assignment and rewrites each 2-tuple into a 3-tuple `[model_name, stage, replica]` using the `stage_replica_map` supplied at construction time. The captured local then sees the rewritten dict.
+3. **Override of `.labels()` on the wrapper classes.** For the five call sites that pass `engine` directly (kwarg or positional, int or str), `_RelabelMixin.labels()` translates the engine value back to `(stage, replica)` via a process-level `_ENGINE_INDEX_MAP` populated by `OmniPrometheusStatLogger.__init__`. This handles `gauge_engine_sleep_state.labels(engine=idx, ...)`, `counter_request_success_base.labels(model_name, str(idx), str(reason))`, `info_gauge.labels(**metrics_info)`, etc.
+
+The three sub-helpers that upstream `PrometheusStatLogger.__init__` constructs (`spec_decoding_prom` / `kv_connector_prom` / `perf_metrics_prom`) use their own `_counter_cls` / `_gauge_cls` / `_histogram_cls` slots and would otherwise build families with the raw 2-element labelnames. `_OmniPerfMetricsProm` / `_OmniSpecDecodingProm` / `_OmniKVConnectorProm` subclass each helper to route the same relabel mixin through their internal family construction.
+
+The `Orchestrator` constructs `stage_replica_map` from the static `stage_pools` configuration at startup:
+
+```python
+stage_replica_map = {
+    flat_idx: (str(stage_id), str(replica_id))
+    for flat_idx, (stage_id, replica_id) in enumerate(
+        (s, r)
+        for s, pool in enumerate(stage_pools)
+        for r in range(pool.num_replicas)
+    )
+}
+```
+
+A reverse map `(stage_id, replica_id) -> flat_idx` is maintained on the Orchestrator so the per-replica `record(engine_idx=...)` call site can look up the right flat index.
+
+> Dynamic add/remove of replicas at runtime is intentionally out of scope — the upstream `PrometheusStatLogger` materializes per-engine_idx child metrics at init time, and supporting hot-add would require non-trivial intervention into upstream's per-family child dictionaries.
+
+## Gating: `--log-stats`
+
+All metrics — both the 15 `vllm:omni_*` families and the ~65 upstream `vllm:*` wrap families — are gated by the user's `--log-stats` CLI flag (default off). The flag is plumbed from `OmniBase.__init__(log_stats=...)` through `AsyncOmniEngine` to the stage-spawn helpers and to the three Prometheus metric classes (`OmniPrometheusMetrics` / `OmniModalityMetrics` / `OmniTransferMetrics`), and is forwarded to `Orchestrator._init_metrics_state(...)`.
+
+Behavior with `--log-stats=off` (default):
+
+- The three `Omni*Metrics` classes register their module-level Gauge / Counter / Histogram families at import time (prometheus_client requires up-front registration), but each `observe / inc / set` method early-returns. The per-label child series for `vllm:omni_*` stay materialized but never have data written to them.
+- `OmniPrometheusStatLogger` is not constructed in `_init_metrics_state`, so the ~65 upstream `vllm:*` wrap families are not registered in the default registry at all.
+- The engine core's `Scheduler.make_stats()` also short-circuits inside upstream (`if not self.log_stats: return None`), so no `SchedulerStats` is produced per step — the per-iteration cost is bounded by the existing upstream gate.
+
+Behavior with `--log-stats=on`: all metric paths fire normally; the orchestrator's per-replica recording is bounded only by `OmniSchedulerMixin.make_stats()`'s per-scheduler 1 Hz throttle (see next section).
+
+The overhead with the flag on is small enough that an A/B benchmark on Qwen3-Omni-30B single replica (30 sequential audio requests) showed a mean latency delta of +0.6% (Welch's t = 0.318, n=30, not statistically significant at α=0.05); the `/metrics` line count drops from 1358 to 124 lines when the flag is off.
+
+## Throttling: `make_stats()` Override
+
+Upstream vLLM's `Scheduler.make_stats()` runs on every AR generation step, returning a SchedulerStats object for the orchestrator. Under vLLM's architecture, this is fine. But since vLLM-Omni requires that the object be serialized and transferred over ZMQ, receiving a SchedulerStats object on every step can introduce unacceptable overhead to the system.
+
+`OmniSchedulerMixin.make_stats()` (in `vllm_omni/core/sched/omni_scheduler_mixin.py`) throttles stats emission to at most once per second **per scheduler** — i.e. per `(stage, replica)` since each replica owns its own scheduler instance. Between intervals it returns `None`, which the engine core skips serializing. This keeps gauges fresh enough for Prometheus scrapes (typically 15-30s intervals) while eliminating the per-step overhead.
+
+The orchestrator side does not add its own throttle on top: the per-replica recording loop gates only on `raw_outputs.scheduler_stats is not None` (i.e. this replica's scheduler passed its own 1 Hz gate). A previous global `_last_stats_ts` on the orchestrator was removed because it starved every replica other than the first to emit within each second.
+
+## Metric Definitions
+
+### Pipeline (4)
+
+| Metric | Type | Labels | Description |
+|--------|------|--------|-------------|
+| `vllm:omni_num_requests_running` | Gauge | `model_name` | Requests currently executing across all stages |
+| `vllm:omni_num_requests_waiting` | Gauge | `model_name` | Requests queued but not yet scheduled |
+| `vllm:omni_requests_success_total` | Counter | `model_name`, `finished_reason` | Total requests by completion reason ({stop, length, abort, ...}); aborts cover client-disconnect / cancellation paths in addition to upstream `FinishReason.ABORT` |
+| `vllm:omni_e2e_request_latency_s` | Histogram | `model_name` | Pipeline-global end-to-end latency in seconds |
+
+### Audio (7)
+
+Labels: `{model_name, stage, replica}` plus the listed extra label.
+
+| Metric | Type | Extra label | Description |
+|--------|------|-------------|-------------|
+| `vllm:omni_audio_ttfp_s` | Histogram | — | Time from request arrival to first audio packet/frame |
+| `vllm:omni_audio_duration_s` | Histogram | — | Audio content duration (`audio_frames / sample_rate`) |
+| `vllm:omni_audio_rtf` | Histogram | — | Real-time factor `stage_gen_time_s / audio_duration_s` (SLO `< 1`); uses `RTF_BUCKETS` |
+| `vllm:omni_audio_frames_total` | Counter | — | Cumulative audio frames generated |
+| `vllm:omni_audio_underrun_s` | Histogram | — | Per-request worst-case player deficit; `> 0` indicates listener heard silent gaps |
+| `vllm:omni_audio_continuity_ok_total` | Counter | `threshold_ms` | Incremented when the request's worst underrun stayed below `threshold_ms` |
+| `vllm:omni_audio_skipped_requests_total` | Counter | `reason` | Silent-loss counter — code2wav rejected malformed codec input and returned `200 OK` with empty audio |
+
+### Cross-stage transfer (4)
+
+Labels: `{model_name, from_stage, from_replica, to_stage, to_replica}`.
+
+| Metric | Type | Description |
+|--------|------|-------------|
+| `vllm:omni_transfer_size_bytes` | Histogram | Per-transfer payload size in bytes |
+| `vllm:omni_transfer_tx_s` | Histogram | Sender-side time (serialize + submit to connector) |
+| `vllm:omni_transfer_rx_s` | Histogram | Receiver-side time (recv + deserialize) |
+| `vllm:omni_transfer_in_flight_s` | Histogram | Network in-flight time (TX done → RX recv start) |
+
+### LLM stage-level (wrapped `vllm:*`)
+
+After the wrap, every upstream `vllm:*` family — TTFT, ITL, TPOT, e2e latency, KV cache usage, scheduler running/waiting, request success counts, etc. — carries `{model_name, stage, replica}` labels. For the full upstream catalog see [the vLLM docs](https://github.com/vllm-project/vllm/blob/main/docs/usage/metrics.md); note that metrics depending on features unsupported in vLLM-Omni (e.g. speculative decoding, LoRA) will not be available.
+
+## Naming Convention
+
+- All time-bearing metrics use the `_s` suffix (values in seconds). Two bucket families are used:
+  - `SECONDS_BUCKETS` (0.05 s – 300 s) for e2e / generation / TTFP style values.
+  - `SECONDS_FAST_BUCKETS` (0.001 s – 60 s) for fine-grained cross-stage transfer and audio-underrun values that need millisecond-level resolution.
+- Counters use the `_total` suffix (auto-appended by `prometheus_client`).
+- Sizes use the `_bytes` suffix.
+- All omni-specific families are prefixed `vllm:omni_`. The upstream `unregister_vllm_metrics()` function is monkey-patched to a scoped version that still strips upstream `vllm:*` collectors (so multi-engine init within one process does not crash on duplicate registration) but preserves anything prefixed `vllm:omni_` / `vllm_omni`.
+
+## Logging vs. Prometheus
+
+`OrchestratorAggregator` (in `vllm_omni/metrics/stats.py`) is the logging-oriented metrics path. It collects detailed per-request, per-stage, and per-transfer statistics and prints formatted tables to the `INFO` log. This is designed for development and debugging — individual request traces, transfer bandwidth, inter-stage timing.
+
+`OmniPrometheusMetrics` / `OmniModalityMetrics` / `OmniTransferMetrics` form the Prometheus-oriented path. They record aggregate counters, gauges, and histograms suitable for time-series monitoring and alerting. Both paths share the same source data (`StageRequestStats`, `TransferEdgeStats`) — `OrchestratorAggregator.record_transfer_tx/rx` in particular calls both the existing accumulator code and the Prometheus emit hook in the same method body. The two consumption models can run simultaneously without coupling.
+
+The separation follows upstream vLLM's pattern of `LoggingStatLogger` vs. `PrometheusStatLogger` — same underlying data, different consumption models.

docs/design/module/dit_module.md

@@ -169,15 +169,13 @@ class DiffusionRequestStatus(enum.IntEnum):
 
 @dataclass
 class DiffusionRequestState:
-    sched_req_id: str
+    request_id: str
     req: OmniDiffusionRequest
     status: DiffusionRequestStatus = DiffusionRequestStatus.WAITING
 ```
 
 **Design Features**:
 
-- **Scheduler-owned ID**: Each `OmniDiffusionRequest` is tracked by an internal `sched_req_id`, separated from public `request_id` values.
-
 - **Explicit lifecycle**: Requests move through waiting, running, optional preemption, and terminal states.
 
 - **Centralized error handling**: Completion, abort, and error states are all normalized in the scheduler layer.
@@ -188,7 +186,6 @@ class DiffusionRequestState:
 class _BaseScheduler(SchedulerInterface):
     def __init__(self) -> None:
         self._request_states = {}
-        self._request_id_to_sched_req_id = {}
         self._waiting = deque()
         self._running = []
         self._finished_req_ids = set()
@@ -199,7 +196,8 @@ class _BaseScheduler(SchedulerInterface):
 
 - **Common state storage**: Shared request maps and waiting/running sets live in the base class.
 
-- **Shared cleanup logic**: Request-id registration, finish handling, and state removal are centralized instead of duplicated in each policy.
+- **Shared cleanup logic**: Duplicate-request checks, finish handling, and state removal are centralized instead of
+  duplicated in each policy.
 
 - **Current constraint boundary**: `_BaseScheduler` derives `max_num_running_reqs` from `max_num_seqs`, but request-mode diffusion is still clamped back to `1` by the engine. The step-wise path can keep this above `1` for compatible-request batching.
 
@@ -219,12 +217,12 @@ class RequestScheduler(_BaseScheduler):
 
 - **Single-request admission**: `RequestScheduler` still admits one active request at a time because request-mode execution completes a whole request per dispatch.
 
-- **Executor result feedback**: `update_from_output()` converts executor output into `FINISHED_COMPLETED` or `FINISHED_ERROR` and returns finished scheduler ids.
+- **Executor result feedback**: `update_from_output()` converts executor output into `FINISHED_COMPLETED` or `FINISHED_ERROR` and returns finished request ids.
 
 #### 2.5 Engine-Driven Execution Loop
 
 ```python
-sched_req_id = scheduler.add_request(request)
+request_id = scheduler.add_request(request)
 while True:
     sched_output = scheduler.schedule()
     output = executor.add_req(req)
@@ -307,8 +305,8 @@ class GPUWorker:
         )
 
         # Load model
-        model_loader = DiffusersPipelineLoader(load_config)
-        self.pipeline = model_loader.load_model(od_config, load_device=f"cuda:{rank}")
+        model_loader = DiffusersPipelineLoader(load_config, self.od_config)
+        self.pipeline = model_loader.load_model(load_device=f"cuda:{rank}")
 
         # Setup cache backend
         from vllm_omni.diffusion.cache.selector import get_cache_backend

docs/design/qwen3_omni_tts_performance_optimization.md

@@ -411,6 +411,8 @@ Notes:
 - `runtime.max_batch_size` controls stage-level batching.
 - Thinker/Talker commonly use `enforce_eager: false` for CUDA Graph paths.
 - Code2Wav often remains eager (`enforce_eager: true`) depending on runtime behavior.
+- Qwen3-Omni defaults `VLLM_USE_FLASHINFER_MOE_FP16=0`. The Triton has been more stable & faster
+ than the FlashInfer CUTLASS unquantized MoE backend on recent vLLM rebases.
 
 #### 2) Enable async chunk
 

docs/getting_started/installation/gpu.md

@@ -34,7 +34,7 @@ vLLM-Omni is a Python library that supports the following GPU variants. The libr
 
 ### Pre-built wheels
 
-Note: Pre-built wheels are currently available for vLLM-Omni 0.11.0rc1, 0.12.0rc1, 0.14.0rc1, 0.14.0, 0.16.0, 0.18.0, 0.20.0, and 0.21.0. If you need a newer unreleased revision, please [build from source](https://docs.vllm.ai/projects/vllm-omni/en/latest/getting_started/installation/gpu/#build-wheel-from-source).
+Note: Pre-built wheels are currently available for vLLM-Omni 0.11.0rc1, 0.12.0rc1, 0.14.0rc1, 0.14.0, 0.16.0, 0.18.0, 0.20.0, 0.21.0, and 0.22.0. If you need a newer unreleased revision, please [build from source](https://docs.vllm.ai/projects/vllm-omni/en/latest/getting_started/installation/gpu/#build-wheel-from-source).
 
 === "NVIDIA CUDA"
 

docs/getting_started/installation/gpu/cuda.inc.md

@@ -20,7 +20,7 @@ Therefore, it is recommended to install vLLM and vLLM-Omni with a **fresh new**
 
 vLLM-Omni is built based on vLLM. Please install it with command below.
 ```bash
-uv pip install vllm==0.21.0 --torch-backend=auto
+uv pip install vllm==0.22.0 --torch-backend=auto
 ```
 
 #### Installation of vLLM-Omni
@@ -39,13 +39,13 @@ uv pip install 'vllm-omni[demo]'
 # --8<-- [start:build-wheel-from-source]
 
 #### Installation of vLLM
-If you do not need to modify source code of vLLM, you can directly install the stable 0.21.0 release version of the library
+If you do not need to modify source code of vLLM, you can directly install the stable 0.22.0 release version of the library
 
 ```bash
-uv pip install vllm==0.21.0 --torch-backend=auto
+uv pip install vllm==0.22.0 --torch-backend=auto
 ```
 
-The 0.21.0 release of vLLM ships CUDA 13.0-compatible binaries by default. If you need a different CUDA variant or want to reuse an existing PyTorch installation, build vLLM from source instead.
+The 0.22.0 release of vLLM ships CUDA 12.9-compatible binaries by default. If you need a different CUDA variant or want to reuse an existing PyTorch installation, build vLLM from source instead.
 
 #### Installation of vLLM-Omni
 Since vllm-omni is rapidly evolving, it's recommended to install it from source
@@ -66,12 +66,12 @@ If you want to check, modify or debug with source code of vLLM, install the libr
 ```bash
 git clone https://github.com/vllm-project/vllm.git
 cd vllm
-git checkout v0.21.0
+git checkout v0.22.0
 ```
 Set up environment variables to get pre-built wheels. If there are internet problems, just download the whl file manually. And set `VLLM_PRECOMPILED_WHEEL_LOCATION` as your local absolute path of whl file.
 ```bash
-#For CUDA 13.0 (the default for v0.21.0; the wheel filename has no `+cu130` suffix)
-export VLLM_PRECOMPILED_WHEEL_LOCATION=https://github.com/vllm-project/vllm/releases/download/v0.21.0/vllm-0.21.0-cp38-abi3-manylinux_2_35_x86_64.whl
+#For CUDA 12.9 (the default for v0.22.0; the wheel filename has no `+cu129` suffix)
+export VLLM_PRECOMPILED_WHEEL_LOCATION=https://github.com/vllm-project/vllm/releases/download/v0.22.0/vllm-0.22.0-cp38-abi3-manylinux_2_28_x86_64.whl
 ```
 Install vllm with command below (If you have no existing PyTorch).
 ```bash
@@ -102,7 +102,7 @@ docker run --runtime nvidia --gpus 2 \
     --env "HF_TOKEN=$HF_TOKEN" \
     -p 8091:8091 \
     --ipc=host \
-    vllm/vllm-omni:v0.21.0 \
+    vllm/vllm-omni:v0.22.0 \
     vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091
 ```
 

docs/getting_started/installation/gpu/rocm.inc.md

@@ -13,7 +13,7 @@ vLLM-Omni current recommends the steps in under setup through Docker Images.
 
 vLLM-Omni is built based on vLLM. Please install it with command below.
 ```bash
-uv pip install vllm==0.21.0+rocm722 --extra-index-url https://wheels.vllm.ai/rocm/0.21.0/rocm722
+uv pip install vllm==0.22.0+rocm722 --extra-index-url https://wheels.vllm.ai/rocm/0.22.0/rocm722
 ```
 
 #### Installation of vLLM-Omni
@@ -34,13 +34,13 @@ uv pip install onnxruntime-rocm
 # --8<-- [start:build-wheel-from-source]
 
 #### Installation of vLLM
-If you do not need to modify source code of vLLM, you can directly install the stable 0.21.0 release version of the library
+If you do not need to modify source code of vLLM, you can directly install the stable 0.22.0 release version of the library
 
 ```bash
-uv pip install vllm==0.21.0+rocm722 --extra-index-url https://wheels.vllm.ai/rocm/0.21.0/rocm722
+uv pip install vllm==0.22.0+rocm722 --extra-index-url https://wheels.vllm.ai/rocm/0.22.0/rocm722
 ```
 
-The pre-built 0.21.0 vLLM wheel targets ROCm 7.2.2. If you need a different ROCm stack or want to reuse an existing PyTorch installation, build vLLM from source instead.
+The pre-built 0.22.0 vLLM wheel targets ROCm 7.2.2. If you need a different ROCm stack or want to reuse an existing PyTorch installation, build vLLM from source instead.
 
 #### Installation of vLLM-Omni
 Since vllm-omni is rapidly evolving, it's recommended to install it from source
@@ -58,7 +58,7 @@ If you want to check, modify or debug with source code of vLLM, install the libr
 ```bash
 git clone https://github.com/vllm-project/vllm.git
 cd vllm
-git checkout v0.21.0
+git checkout v0.22.0
 python3 -m pip install -r requirements/rocm.txt
 python3 setup.py develop
 ```
@@ -130,7 +130,7 @@ docker run --rm \
   -v ~/.cache/huggingface:/root/.cache/huggingface \
   --env "HF_TOKEN=$HF_TOKEN" \
   -p 8091:8091 \
-  vllm/vllm-omni-rocm:v0.21.0 \
+  vllm/vllm-omni-rocm:v0.22.0 \
   --model Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091
 ```
 
@@ -149,7 +149,7 @@ docker run --rm -it \
   -v ~/.cache/huggingface:/root/.cache/huggingface \
   --env "HF_TOKEN=$HF_TOKEN" \
   --entrypoint bash \
-  vllm/vllm-omni-rocm:v0.21.0
+  vllm/vllm-omni-rocm:v0.22.0
 ```
 
 # --8<-- [end:pre-built-images]

docs/getting_started/quickstart.md

@@ -19,10 +19,10 @@ uv venv --python 3.12 --seed
 source .venv/bin/activate
 
 # On CUDA
-uv pip install vllm==0.21.0 --torch-backend=auto
+uv pip install vllm==0.22.0 --torch-backend=auto
 
 # On ROCm
-uv pip install vllm==0.21.0+rocm721 --extra-index-url https://wheels.vllm.ai/rocm/0.21.0/rocm721
+uv pip install vllm==0.22.0+rocm722 --extra-index-url https://wheels.vllm.ai/rocm/0.22.0/rocm722
 
 git clone https://github.com/vllm-project/vllm-omni.git
 cd vllm-omni
@@ -35,7 +35,7 @@ For additional installation methods — please see the [installation guide](inst
 !!! note
     It is important to install the same major & minor version of vLLM and vLLM Omni, otherwise things may not work as expected. If the versions are misaligned, you will see a warning when you import vLLM Omni.
 
-    If you are seeing strange behavior with the `vllm` command not handling the `--omni` flag correctly, you most likely have a version mismatch with vLLM < `0.21.0` and vLLM Omni `0.21.0`, as vLLM Omni no longer hijacks the vLLM entrypoint. Updating vLLM should resolve this issue.
+    If you are seeing strange behavior with the `vllm` command not handling the `--omni` flag correctly, you most likely have a version mismatch with vLLM < `0.22.0` and vLLM Omni `0.22.0`, as vLLM Omni no longer hijacks the vLLM entrypoint. Updating vLLM should resolve this issue.
 
 ## Offline Inference
 

docs/mkdocs/hooks/generate_argparse.py

@@ -119,9 +119,10 @@ def extract_omni_serve_subparser_init():
                     exec_globals = {
                         "_FlexibleArgumentParser": _FlexibleArgumentParser,
                         "FlexibleArgumentParser": _FlexibleArgumentParser,
+                        "TrackingArgumentParser": _FlexibleArgumentParser,  # only needs add_argument, not tracking
+                        "TrackingNamespace": None,  # type annotation only
                         "make_arg_parser": lambda parser: parser,  # no-op for doc
                         "_ensure_vllm_platform": lambda: None,  # no-op for doc
-                        "nullify_stage_engine_defaults": lambda parser: None,  # no-op for doc
                         "VLLM_SUBCMD_PARSER_EPILOG": "",
                         "logger": logger,
                         "DummySubparsers": DummySubparsers,

docs/mkdocs/hooks/generate_examples.py

@@ -16,6 +16,7 @@ ROOT_DIR_RELATIVE = "../../../../.."
 EXAMPLE_DIR = ROOT_DIR / "examples"
 EXAMPLE_DOC_DIR = ROOT_DIR / "docs/user_guide/examples"
 NAV_FILE = ROOT_DIR / "docs/.nav.yml"
+MAX_INLINE_MATERIAL_SIZE = 8 * 1024
 
 
 def fix_case(text: str) -> str:
@@ -191,18 +192,21 @@ class Example:
 
         return re.sub(link_pattern, replace_link, content)
 
+    def github_url(self, path: Path) -> str:
+        url = "https://github.com/vllm-project/vllm-omni/"
+        url += "tree/main" if path.is_dir() else "blob/main"
+        return f"{url}/{path.relative_to(ROOT_DIR).as_posix()}"
+
     def generate(self) -> str:
         content = f"# {self.title}\n\n"
-        url = "https://github.com/vllm-project/vllm-omni/"
-        url += "tree/main" if self.path.is_dir() else "blob/main"
-        content += f"Source <{url}/{self.path.relative_to(ROOT_DIR)}>.\n\n"
+        content += f"Source <{self.github_url(self.path)}>.\n\n"
 
         # Use long code fence to avoid issues with
         # included files containing code fences too
         code_fence = "``````"
 
         if self.is_code:
-            main_file_rel = self.main_file.relative_to(ROOT_DIR)
+            main_file_rel = self.main_file.relative_to(ROOT_DIR).as_posix()
             content += f'{code_fence}{self.main_file.suffix[1:]}\n--8<-- "{main_file_rel}"\n{code_fence}\n'
         else:
             with open(self.main_file, encoding="utf-8") as f:
@@ -216,10 +220,15 @@ class Example:
 
         content += "## Example materials\n\n"
         for file in sorted(self.other_files):
-            content += f'??? abstract "{file.relative_to(self.path)}"\n'
+            content += f'??? abstract "{file.relative_to(self.path).as_posix()}"\n'
+            if file.stat().st_size > MAX_INLINE_MATERIAL_SIZE:
+                content += (
+                    f"    Large file omitted from the rendered docs. View it on GitHub: <{self.github_url(file)}>.\n\n"
+                )
+                continue
             if file.suffix != ".md":
                 content += f"    {code_fence}{file.suffix[1:]}\n"
-            content += f'    --8<-- "{file.relative_to(ROOT_DIR)}"\n'
+            content += f'    --8<-- "{file.relative_to(ROOT_DIR).as_posix()}"\n'
             if file.suffix != ".md":
                 content += f"    {code_fence}\n"
 
@@ -291,7 +300,7 @@ def update_nav_file(examples: list[Example]):
         for example in category_examples:
             doc_path = EXAMPLE_DOC_DIR / example.category / f"{example.path.stem}.md"
             rel_path = doc_path.relative_to(ROOT_DIR / "docs")
-            category_items.append({example.title: str(rel_path)})
+            category_items.append({example.title: rel_path.as_posix()})
 
         if category_items:
             # Format category name (e.g., "offline_inference" -> "Offline Inference")

docs/mkdocs/hooks/search_index.py

@@ -0,0 +1,29 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Reduce browser search-index memory for generated API reference pages."""
+
+from material.plugins.search.plugin import SearchIndex
+
+_add_entry_from_context = SearchIndex.add_entry_from_context
+
+
+def add_entry_from_context(self: SearchIndex, page) -> None:
+    search = page.meta.get("search") or {}
+    if search.get("exclude"):
+        return
+
+    if str(page.url).startswith("api/"):
+        entry = {
+            "location": page.url,
+            "title": str(page.meta.get("title", page.title)),
+            "text": "",
+        }
+        if "boost" in search:
+            entry["boost"] = search["boost"]
+        self.entries.append(entry)
+        return
+
+    _add_entry_from_context(self, page)
+
+
+SearchIndex.add_entry_from_context = add_entry_from_context

docs/mkdocs/javascript/mathjax.js

@@ -1,20 +1,44 @@
-// Enables MathJax rendering
+// Enables MathJax rendering only on pages that contain math.
 window.MathJax = {
-    tex: {
-      inlineMath: [["\\(", "\\)"]],
-      displayMath: [["\\[", "\\]"]],
-      processEscapes: true,
-      processEnvironments: true
-    },
-    options: {
-      ignoreHtmlClass: ".*|",
-      processHtmlClass: "arithmatex"
-    }
-  };
+  tex: {
+    inlineMath: [["\\(", "\\)"]],
+    displayMath: [["\\[", "\\]"]],
+    processEscapes: true,
+    processEnvironments: true,
+  },
+  options: {
+    ignoreHtmlClass: ".*|",
+    processHtmlClass: "arithmatex",
+  },
+};
 
-  document$.subscribe(() => {
-    MathJax.startup.output.clearCache()
-    MathJax.typesetClear()
-    MathJax.texReset()
-    MathJax.typesetPromise()
-  })
+let mathJaxLoading;
+
+function loadMathJax() {
+  if (window.MathJax.typesetPromise) {
+    return Promise.resolve();
+  }
+  if (!mathJaxLoading) {
+    mathJaxLoading = new Promise((resolve, reject) => {
+      const script = document.createElement("script");
+      script.src = "https://unpkg.com/mathjax@3.2.2/es5/tex-mml-chtml.js";
+      script.async = true;
+      script.onload = resolve;
+      script.onerror = reject;
+      document.head.appendChild(script);
+    });
+  }
+  return mathJaxLoading;
+}
+
+document$.subscribe(() => {
+  if (!document.querySelector(".arithmatex")) {
+    return;
+  }
+  loadMathJax().then(() => {
+    MathJax.startup.output.clearCache();
+    MathJax.typesetClear();
+    MathJax.texReset();
+    MathJax.typesetPromise();
+  });
+});

docs/mkdocs/javascript/memory.js

@@ -0,0 +1,2 @@
+// Opt out of bfcache so large generated API pages are released after navigation.
+window.addEventListener("unload", () => {});

docs/mkdocs/javascript/mermaid.js

@@ -1,21 +1,42 @@
-// Initialize Mermaid for diagram rendering
-mermaid.initialize({
-  startOnLoad: false,
-  theme: 'default',
-  securityLevel: 'loose',
-  flowchart: {
-    useMaxWidth: true,
-    htmlLabels: true
-  }
-});
+// Load Mermaid only on pages that contain diagrams.
+let mermaidLoading;
 
-// Render Mermaid diagrams when page content is ready
-document$.subscribe(() => {
-  const mermaidElements = document.querySelectorAll('.mermaid');
-  if (mermaidElements.length > 0) {
-    mermaid.run({
-      querySelector: '.mermaid',
-      nodes: mermaidElements
+function loadMermaid() {
+  if (window.mermaid) {
+    return Promise.resolve();
+  }
+  if (!mermaidLoading) {
+    mermaidLoading = new Promise((resolve, reject) => {
+      const script = document.createElement("script");
+      script.src = "https://unpkg.com/mermaid@10/dist/mermaid.min.js";
+      script.async = true;
+      script.onload = () => {
+        mermaid.initialize({
+          startOnLoad: false,
+          theme: "default",
+          securityLevel: "loose",
+          flowchart: {
+            useMaxWidth: true,
+            htmlLabels: true,
+          },
+        });
+        resolve();
+      };
+      script.onerror = reject;
+      document.head.appendChild(script);
     });
   }
+  return mermaidLoading;
+}
+
+document$.subscribe(() => {
+  if (!document.querySelector(".mermaid")) {
+    return;
+  }
+  loadMermaid().then(() => {
+    mermaid.run({
+      querySelector: ".mermaid",
+      nodes: document.querySelectorAll(".mermaid"),
+    });
+  });
 });

docs/models/supported_models.md

@@ -19,6 +19,7 @@ th {
 |--------------|--------|-------------------|------------|---------|-----|-----------|
 | `Qwen3OmniMoeForConditionalGeneration` | Qwen3-Omni | `Qwen/Qwen3-Omni-30B-A3B-Instruct` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
 | `Qwen2_5OmniForConditionalGeneration` | Qwen2.5-Omni | `Qwen/Qwen2.5-Omni-7B`, `Qwen/Qwen2.5-Omni-3B` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
+| `MingFlashOmniForConditionalGeneration` + `MingImagePipeline` | Ming-flash-omni-2.0 (omni-speech + imagegen<sup>1</sup>) | `Jonathan1909/Ming-flash-omni-2.0` | ✅︎ |   |   |   |
 | `BagelForConditionalGeneration` | BAGEL (DiT-only) | `ByteDance-Seed/BAGEL-7B-MoT` | ✅︎ | ✅︎ | | ✅︎ |
 | `InternVLAA1Pipeline` | InternVLA-A1 | `InternRobotics/InternVLA-A1-3B` | ✅︎ | ✅︎ | | |
 | `HunyuanImage3ForCausalMM` | HunyuanImage3.0 (DiT-only) | `tencent/HunyuanImage-3.0`, `tencent/HunyuanImage-3.0-Instruct` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
@@ -32,6 +33,7 @@ th {
 | `ZImagePipeline` | Z-Image | `Tongyi-MAI/Z-Image-Turbo` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
 | `WanPipeline` | Wan2.1-T2V, Wan2.2-T2V, Wan2.2-TI2V | `Wan-AI/Wan2.1-T2V-1.3B-Diffusers`, `Wan-AI/Wan2.1-T2V-14B-Diffusers`, `Wan-AI/Wan2.2-T2V-A14B-Diffusers`, `Wan-AI/Wan2.2-TI2V-5B-Diffusers` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
 | `WanImageToVideoPipeline` | Wan2.2-I2V | `Wan-AI/Wan2.2-I2V-A14B-Diffusers` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
+| `Cosmos3OmniDiffusersPipeline` | Cosmos3 T2I, T2V, I2V, V2V, T2V with sound, action policy | `nvidia/Cosmos3-Nano` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
 | `WanSpeechToVideoPipeline` | Wan2.2-S2V | `Wan-AI/Wan2.2-S2V-14B` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
 | `Wan22VACEPipeline` | Wan2.1-VACE | `Wan-AI/Wan2.1-VACE-1.3B-diffusers`, `Wan-AI/Wan2.1-VACE-14B-diffusers` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
 | `LTX2Pipeline` | LTX-2-T2V | `Lightricks/LTX-2` | ✅︎ | ✅︎ | | |
@@ -40,13 +42,16 @@ th {
 | `LTX2ImageToVideoTwoStagesPipeline` | LTX-2-I2V | `rootonchair/LTX-2-19b-distilled` | ✅︎ | ✅︎ | | |
 | `LTX23Pipeline` | LTX-2.3-T2V | `dg845/LTX-2.3-Diffusers` | ✅︎ | ✅︎ | | |
 | `LTX23ImageToVideoPipeline` | LTX-2.3-I2V | `dg845/LTX-2.3-Diffusers` | ✅︎ | ✅︎ | | |
+| `DreamZeroPipeline` | DreamZero-DROID | `GEAR-Dreams/DreamZero-DROID` | ✅︎ | | | |
 | `HeliosPipeline`, `HeliosPyramidPipeline` | Helios | `BestWishYsh/Helios-Base`, `BestWishYsh/Helios-Mid`, `BestWishYsh/Helios-Distilled` | ✅︎ | ✅︎ | ✅︎ | |
 | `MagiHumanPipeline` | MagiHuman | `SII-GAIR/daVinci-MagiHuman-Base-1080p` | ✅︎ | ✅︎ | | |
 | `OvisImagePipeline` | Ovis-Image | `OvisAI/Ovis-Image` | ✅︎ | ✅︎ | | ✅︎ |
 | `LongcatImagePipeline` | LongCat-Image | `meituan-longcat/LongCat-Image` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
 | `LongCatImageEditPipeline` | LongCat-Image-Edit | `meituan-longcat/LongCat-Image-Edit` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
 | `StableDiffusion3Pipeline` | Stable-Diffusion-3 | `stabilityai/stable-diffusion-3.5-medium` | ✅︎ | ✅︎ | | ✅︎ |
-| `CosyVoice3Model` | CosyVoice3 | `FunAudioLLM/Fun-CosyVoice3-0.5B-2512` | ✅︎ | ✅︎ | | |
+| `CosyVoice3Model` | CosyVoice3 | `FunAudioLLM/Fun-CosyVoice3-0.5B-2512` | ✅︎ | ✅︎ | | ✅︎ |
+| `OmniVoiceModel` | OmniVoice | `k2-fsa/OmniVoice` | ✅︎ | | | |
+| `VoxCPM2TalkerForConditionalGeneration` | VoxCPM2 | `openbmb/VoxCPM2` | ✅︎ | | | |
 | `MammothModa2ForConditionalGeneration` | MammothModa2-Preview | `bytedance-research/MammothModa2-Preview` | ✅︎ | ✅︎ | | |
 | `Flux2KleinPipeline` | FLUX.2-klein | `black-forest-labs/FLUX.2-klein-4B`, `black-forest-labs/FLUX.2-klein-9B` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
 | `FluxKontextPipeline` | FLUX.1-Kontext-dev | `black-forest-labs/FLUX.1-Kontext-dev` | ✅︎ | ✅︎ | | |
@@ -58,7 +63,12 @@ th {
 | `Qwen3TTSForConditionalGeneration` | Qwen3-TTS-12Hz-1.7B-CustomVoice | `Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
 | `Qwen3TTSForConditionalGeneration` | Qwen3-TTS-12Hz-1.7B-VoiceDesign | `Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
 | `Qwen3TTSForConditionalGeneration` | Qwen3-TTS-12Hz-1.7B-Base | `Qwen/Qwen3-TTS-12Hz-0.6B-Base` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
+| `MingTTSForConditionalGeneration` | Ming-omni-tts dense 0.5B | `inclusionAI/Ming-omni-tts-0.5B` | ✅︎ | | | |
 | `GLMTTSForConditionalGeneration` | GLM-TTS | `zai-org/GLM-TTS` | ✅︎ | | | |
+| `MossTTSNanoForCausalLM` | MOSS-TTS-Nano | `OpenMOSS-Team/MOSS-TTS-Nano` | ✅︎ | | | |
+| `MossTTSDelayModel` | MOSS-TTS, MOSS-TTSD, MOSS-SoundEffect, MOSS-VoiceGenerator | `OpenMOSS-Team/MOSS-VoiceGenerator` | ✅︎ | | | |
+| `MossTTSRealtime` | MOSS-TTS-Realtime | `OpenMOSS-Team/MOSS-TTS-Realtime` | ✅︎ | | | |
+| `HiggsAudioV2ForConditionalGeneration` | Higgs-Audio v2 | `bosonai/higgs-audio-v2-generation-3B-base` | ✅︎ | | | |
 | `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` | ✅︎ | ✅︎ | | |
@@ -66,11 +76,13 @@ th {
 | `FishSpeechSlowARForConditionalGeneration` | Fish Speech S2 Pro | `fishaudio/s2-pro` | ✅︎ | ✅︎ | | |
 | `DreamIDOmniPipeline` | DreamID-Omni | `XuGuo699/DreamID-Omni` | ✅︎ | ✅︎ | | |
 | `SenseNovaU1Pipeline` | SenseNova-U1 (DiT-only) | `SenseNova/SenseNova-U1-8B-MoT` | ✅︎ | | | |
+| `LancePipeline` | Lance | `bytedance-research/Lance` | ✅︎ | | | |
 | `HunyuanVideo15Pipeline` | HunyuanVideo-1.5-T2V | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v`, `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v` | ✅︎ | ✅︎ | | |
 | `HunyuanVideo15ImageToVideoPipeline` | HunyuanVideo-1.5-I2V | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_i2v`, `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_i2v` | ✅︎ | ✅︎ | | |
 | `VoxtralTTSForConditionalGeneration` | Voxtral TTS | `mistralai/Voxtral-4B-TTS-2603` | ✅︎ | ✅︎ | | |
 | `CovoAudioForConditionalGeneration` | Covo-Audio-Chat | `tencent/Covo-Audio-Chat` | ✅︎ | | | |
 |`DyninOmniForConditionalGeneration` | Dynin-Omni | `snu-aidas/Dynin-Omni` | ✅︎ | | | |
+| `MiniCPMO45OmniForConditionalGeneration` | MiniCPM-o 4.5 | `openbmb/MiniCPM-o-4_5` | ✅︎ | | ✅︎ | |
 | `ErnieImagePipeline` | ERNIE-Image | `baidu/ERNIE-Image`, `baidu/ERNIE-Image-Turbo` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
 |`HiDreamImagePipeline` | HiDream-I1-Full | `HiDream-ai/HiDream-I1-Full` | ✅︎ | ✅︎ | | |
 

docs/serving/image_edit_api.md

@@ -13,7 +13,7 @@ For example...
 ```bash
 # Qwen-Image
 vllm serve Qwen/Qwen-Image-Edit-2511 --omni --port 8000
-
+```
 
 ### Generate Images
 
@@ -53,7 +53,7 @@ result = client.images.edit(
     output_format='jpeg',
     # url格式
     extra_body={
-        "url": [input_image_url1,input_image_url],
+        "url": [input_image_url,input_image_url],
         "num_inference_steps": 50,
         "guidance_scale": 1,
         "seed": 777,
@@ -92,7 +92,8 @@ Content-Type: multipart/form-data
 | `user` | string | null | User identifier for tracking |
 | `output_format` | string | "png" | The format in which the generated images are returned. Must be one of "png", "jpg", "jpeg", "webp". |
 | `output_compression` | integer | 100 | The compression level (0-100%) for the generated images. |
-| `background` | string or null | "auto" | Allows to set transparency for the background of the generated image(s).
+| `background` | string or null | "auto" | Allows to set transparency for the background of the generated image(s). |
+| `stream` | boolean | false | Return Server-Sent Events instead of a single JSON response. Currently supported only by multi-stage image edit pipelines that produce AR text before the final image, such as HunyuanImage3 IT2I AR+DiT. |
 
 #### vllm-omni Extension Parameters
 
@@ -109,6 +110,8 @@ Content-Type: multipart/form-data
 
 ### Response Format
 
+When `stream=false` or omitted, the endpoint returns the standard image edit response:
+
 ```json
 {
   "created": 1701234567,
@@ -120,10 +123,56 @@ Content-Type: multipart/form-data
     }
   ],
   "output_format": null,
-  "size": null,
+  "size": null
 }
 ```
 
+### Streaming Response Format
+
+Set `stream=true` when you want to receive the HunyuanImage3 IT2I AR
+recaption text before the final edited image is ready. Streaming is only
+available for multi-stage image edit pipelines where stage 0 produces AR text
+and a later diffusion stage produces the image. Single-stage diffusion image
+edit pipelines reject `stream=true` with HTTP 400.
+
+The response uses Server-Sent Events with `Content-Type: text/event-stream`.
+Each event is sent as one `data:` line. The stream order is:
+
+1. One or more AR text delta chunks.
+2. One final image chunk.
+3. `data: [DONE]`.
+
+AR text chunks expose only the generated text delta and the AR completion
+`index`. They do not expose token ids, ratio tokens, KV cache data, or internal
+prompt token ids.
+
+Example AR delta event:
+
+```text
+data: {"object":"image.edit.chunk","type":"ar_delta","delta":"A close-up product photo...","index":0,"created":1701234567,"model":"tencent/HunyuanImage-3.0-Instruct"}
+```
+
+Example final image event:
+
+```text
+data: {"object":"image.edit.chunk","type":"image","data":[{"b64_json":"<base64-encoded PNG>","url":null,"revised_prompt":null}],"output_format":"png","size":"1024x1024","created":1701234567,"model":"tencent/HunyuanImage-3.0-Instruct"}
+```
+
+Terminal event:
+
+```text
+data: [DONE]
+```
+
+If the engine fails after the SSE response has started, the stream emits an
+error chunk followed by `[DONE]`:
+
+```text
+data: {"object":"error","created":1701234567,"model":"tencent/HunyuanImage-3.0-Instruct","error":{"message":"<error message>","type":"server_error","code":500}}
+
+data: [DONE]
+```
+
 ## Examples
 
 ### Multiple Images input
@@ -134,12 +183,28 @@ curl -s -D >(grep -i x-request-id >&2) \
   -X POST "http://localhost:8000/v1/images/edits" \
   -F "model=xxx" \
   -F "image=@xx.png" \
-  -F "image=@xx.png"
+  -F "image=@xx.png" \
   -F "prompt='this bear is wearing sportwear. holding a basketball, and bending one leg.'" \
   -F "size=1024x1024" \
   -F "output_format=png"
 ```
 
+### Streaming HunyuanImage3 IT2I AR Text
+
+```bash
+curl -N -X POST "http://localhost:8000/v1/images/edits" \
+  -F "model=tencent/HunyuanImage-3.0-Instruct" \
+  -F "image=@./input.png" \
+  -F "prompt=Turn the product into a clean studio advertisement." \
+  -F "size=1024x1024" \
+  -F "output_format=png" \
+  -F "stream=true"
+```
+
+Use the `ar_delta` events for progressive display of the AR-generated
+recaption. Decode the `b64_json` field from the final `image` event to get the
+edited image.
+
 
 ## Parameter Handling
 

docs/serving/speech_api.md

@@ -114,9 +114,10 @@ Content-Type: application/json
 | `instructions` | string | "" | Voice style/emotion instructions |
 | `max_new_tokens` | integer | 2048 | Maximum tokens to generate |
 | `initial_codec_chunk_frames` | integer | null | Per-request initial chunk size override for TTFA tuning. When null, IC is computed dynamically based on server load. |
+| `non_streaming_mode` | bool | null | Qwen3-TTS prompt construction mode override. Does not affect HTTP response streaming or async-chunk pipelining. When null, Qwen3-TTS uses model defaults: Base=false, CustomVoice/VoiceDesign=true. |
 | `stream` | bool | false | Stream raw PCM chunks as they are decoded (requires `response_format="pcm"`) |
 
-**Supported languages:** Auto, Chinese, English, Japanese, Korean, German, French, Russian, Portuguese, Spanish, Italian
+**Supported languages:** Only applicable to Qwen3-TTS. Derived from the model configuration (`talker_config.codec_language_id` in the checkpoint's `config.json`), plus `Auto`, which is always accepted. Official Qwen3-TTS checkpoints support: Auto, Chinese, English, Japanese, Korean, German, French, Russian, Portuguese, Spanish, Italian.
 
 #### Voice Clone Parameters (Base task)
 
@@ -332,7 +333,8 @@ curl -X POST http://localhost:8091/v1/audio/speech \
         "input": "Hello, this is a cloned voice",
         "task_type": "Base",
         "ref_audio": "https://example.com/reference.wav",
-        "ref_text": "Original transcript of the reference audio"
+        "ref_text": "Original transcript of the reference audio",
+        "non_streaming_mode": true
     }' --output cloned.wav
 ```
 
@@ -381,6 +383,44 @@ are cached in-process with a shared LRU so repeated requests with the same
 all TTS model types; deleting a voice invalidates every model-type slot at
 once.
 
+### Precomputed Custom Voices
+
+Qwen3-TTS Base and VoxCPM2 can load offline-precomputed voices at startup.
+Generate a directory containing `custom_voice_manifest.json` plus one
+`.safetensors` file per voice, then set the pipeline-wide deploy config field:
+
+```yaml
+custom_voice_dir: /path/to/custom_voices
+```
+
+Qwen3-TTS profiles are created with:
+
+```bash
+python examples/online_serving/text_to_speech/qwen3_tts/precompute_custom_voice.py \
+  --model Qwen/Qwen3-TTS-12Hz-1.7B-Base \
+  --voice-name alice \
+  --ref-audio /path/to/reference.wav \
+  --ref-text "Original transcript of the reference audio" \
+  --mode icl \
+  --output-dir /path/to/custom_voices
+```
+
+VoxCPM2 profiles are created with:
+
+```bash
+python examples/online_serving/text_to_speech/voxcpm2/precompute_custom_voice.py \
+  --model openbmb/VoxCPM2 \
+  --voice-name alice \
+  --ref-audio /path/to/reference.wav \
+  --mode ref_continuation \
+  --prompt-text "Original transcript of the reference audio" \
+  --output-dir /path/to/custom_voices
+```
+
+Only profiles whose safetensors payload can be loaded and validated are exposed
+by `GET /v1/audio/voices`. Valid precomputed voices can be used in
+`POST /v1/audio/speech` by passing `voice="alice"` without `ref_audio`.
+
 **Configuration (environment variables):**
 
 | Variable | Default | Description |
@@ -416,6 +456,7 @@ Content-Type: application/json
 | `ref_audio` | string | null | Default reference audio (Base task) |
 | `ref_text` | string | null | Default reference transcript (Base task) |
 | `max_new_tokens` | integer | null | Default max tokens |
+| `non_streaming_mode` | bool | null | Default Qwen3-TTS prompt construction mode override. Does not affect HTTP response streaming or async-chunk pipelining. When null, Qwen3-TTS uses model defaults: Base=false, CustomVoice/VoiceDesign=true. |
 
 Each item in the `items` array requires only `input` (the text). All other fields are optional and override the batch-level defaults when set:
 
@@ -431,6 +472,7 @@ Each item in the `items` array requires only `input` (the text). All other field
 | `ref_audio` | string | Override reference audio |
 | `ref_text` | string | Override reference transcript |
 | `max_new_tokens` | integer | Override max tokens |
+| `non_streaming_mode` | bool | Override Qwen3-TTS prompt construction mode. Does not affect HTTP response streaming or async-chunk pipelining. When null, inherits the batch-level value (then the model default). |
 
 ### Response Format
 

docs/serving/videos_api.md

@@ -67,8 +67,10 @@ curl -L "http://localhost:8091/v1/videos/${video_id}/content" -o output.mp4
 
 | Parameter | Type | Default | Description |
 |-----------|------|---------|-------------|
-| `input_reference` | file | null | Uploaded reference image for image-to-video requests |
-| `image_reference` | string | null | JSON-encoded reference image payload; do not combine with `input_reference` |
+| `input_reference` | file | null | Uploaded reference image or video for image-to-video/video-to-video requests |
+| `image_reference` | string | null | JSON-encoded reference image payload; do not combine with `input_reference` or `video_reference` |
+| `video_reference` | string | null | JSON-encoded reference video payload; do not combine with `input_reference` or `image_reference` |
+| `audio_reference` | string | null | JSON-encoded audio reference for speech-to-video: `{"audio_url": "..."}` — supports HTTP(s) URLs or base64 data URLs |
 | `width` | integer | model default | Output video width |
 | `height` | integer | model default | Output video height |
 | `num_frames` | integer | model default | Number of generated frames |
@@ -80,6 +82,8 @@ curl -L "http://localhost:8091/v1/videos/${video_id}/content" -o output.mp4
 | `flow_shift` | number | null | Scheduler flow-shift value |
 | `true_cfg_scale` | number | null | True CFG scale when supported by the model |
 | `seed` | integer | null | Random seed for reproducibility |
+| `generate_sound` | boolean | false | Request model-generated audio for video models that support sound generation |
+| `sound_duration` | number | null | Duration in seconds for generated audio; defaults to generated video duration |
 | `negative_prompt` | string | null | Text describing what to avoid in the generated video |
 | `enable_frame_interpolation` | boolean | null | Enable post-generation frame interpolation |
 | `frame_interpolation_exp` | integer | null | Interpolation exponent; `1=2x`, `2=4x`, and so on |
@@ -123,6 +127,60 @@ curl -s http://localhost:8091/v1/videos \
   -F "fps=16"
 ```
 
+### Video-to-Video
+
+For models that support video conditioning, upload the reference video with
+`input_reference`:
+
+```bash
+curl -s http://localhost:8091/v1/videos \
+  -F "prompt=continue this motion with consistent subjects and lighting" \
+  -F "input_reference=@input.mp4;type=video/mp4" \
+  -F "width=1280" \
+  -F "height=720" \
+  -F "num_frames=80" \
+  -F "fps=16"
+```
+
+You can also pass a JSON-safe video URL or `data:video/...;base64,...` payload
+through `video_reference`. Do not send `video_reference` together with
+`input_reference` or `image_reference`.
+
+```bash
+curl -s http://localhost:8091/v1/videos \
+  -F "prompt=continue this motion with consistent subjects and lighting" \
+  -F 'video_reference={"video_url":"https://example.com/input.mp4"}' \
+  -F "width=1280" \
+  -F "height=720" \
+  -F "num_frames=80" \
+  -F "fps=16"
+```
+
+JSON references currently support `image_url`/`video_url`; `file_id` references
+are not implemented yet. Models may expose additional V2V controls through
+`extra_params`. For example, Cosmos3 supports
+`condition_frame_indexes_vision` and `condition_video_keep` to select which
+decoded reference frames are used as clean conditioning.
+
+### Speech-to-Video
+
+For models that support audio-driven generation (e.g., Wan2.2-S2V), pass both
+an image reference and an audio reference. The `audio_reference` field accepts a
+JSON string with `audio_url` pointing to an HTTP(s) URL or base64 data URL.
+
+```bash
+curl -s http://localhost:8091/v1/videos \
+  -F "prompt=A person singing" \
+  -F 'image_reference={"image_url": "https://example.com/face.png"}' \
+  -F 'audio_reference={"audio_url": "https://example.com/speech.mp3"}' \
+  -F "width=832" \
+  -F "height=480" \
+  -F "num_inference_steps=40" \
+  -F "guidance_scale=4.5" \
+  -F "fps=16"
+```
+
+
 ### Synchronous Generation
 
 ```bash
@@ -146,7 +204,12 @@ export VLLM_OMNI_STORAGE_PATH=/var/tmp/vllm-omni-videos
 
 ## Model-Specific Examples
 
-For complete text-to-video and image-to-video walkthroughs, see:
+For complete text-to-video, image-to-video, and model-specific video-to-video
+walkthroughs, see:
 
 - [Text-to-Video](../user_guide/examples/online_serving/text_to_video.md)
 - [Image-to-Video](../user_guide/examples/online_serving/image_to_video.md)
+- [Speech-to-Video](../user_guide/examples/online_serving/speech_to_video.md)
+  for Wan2.2-S2V audio-driven lip-sync generation
+- [Cosmos3 recipes](https://github.com/vllm-project/vllm-omni/blob/main/recipes/cosmos3/Cosmos3-Nano.md)
+  for model-specific video-to-video examples and conditioning controls

docs/usage/metrics.md

@@ -0,0 +1,98 @@
+# Production Metrics
+
+vLLM-Omni exposes Prometheus metrics via the `/metrics` endpoint on the OpenAI-compatible API server. This page covers the text and audio surface; diffusion / image / video metrics are tracked in a follow-up PR.
+
+```bash
+vllm-omni serve Qwen/Qwen3-Omni-30B-A3B-Instruct --port 8000 --log-stats
+curl http://localhost:8000/metrics
+```
+
+**`--log-stats` is required to populate metric data.** Without the flag, the endpoint still returns `200 OK` but the upstream `vllm:*` wrap is not registered at all, and the 15 `vllm:omni_*` families are registered as placeholders with no data written to them. This keeps the runtime cost essentially zero for deployments that don't need monitoring. With the flag, all ~80 families populate.
+
+## Metric Namespaces
+
+| Prefix | Source | Present when |
+|--------|--------|--------------|
+| `vllm:omni_` | vLLM-Omni orchestrator / audio modality / cross-stage transfer | Pipeline-dependent |
+| `vllm:` | Upstream vLLM engine, wrapped by `OmniPrometheusStatLogger` to expose `{stage, replica}` | Pipeline includes an LLM (AR) stage |
+| `http_` / `process_` | Uvicorn / Python runtime | Always |
+
+## Pipeline-Level Metrics (`vllm:omni_`)
+
+Defined in `vllm_omni/metrics/prometheus.py`. Track request lifecycle across the full multi-stage pipeline.
+
+### Request counts
+
+| Metric | Type | Labels | Description |
+|--------|------|--------|-------------|
+| `vllm:omni_num_requests_running` | Gauge | `model_name` | Pipeline-global in-flight requests (dispatched to engine, not yet finalized) |
+| `vllm:omni_num_requests_waiting` | Gauge | `model_name` | Requests waiting in the Orchestrator queue |
+| `vllm:omni_requests_success_total` | Counter | `model_name`, `finished_reason` | Total requests by completion reason. `finished_reason` ∈ {`stop`, `length`, `abort`, ...} mirroring upstream `vllm:request_success_total`; aborts cover client disconnect / cancellation paths in addition to upstream `FinishReason.ABORT` |
+
+### Latency
+
+| Metric | Type | Labels | Description |
+|--------|------|--------|-------------|
+| `vllm:omni_e2e_request_latency_s` | Histogram | `model_name` | Pipeline-global end-to-end request latency in seconds |
+
+## Audio Modality Metrics (`vllm:omni_`)
+
+Emitted at request finalize, except for `audio_ttfp_s` (streaming-hook at the first audio packet) and `audio_underrun_s` / `audio_continuity_ok_total` (streaming finalize, after the chunk stream is exhausted). All carry `{model_name, stage, replica}` plus the listed extra label.
+
+| Metric | Type | Extra label | Description |
+|--------|------|-------------|-------------|
+| `vllm:omni_audio_ttfp_s` | Histogram | — | Time from request arrival to first audio packet/frame |
+| `vllm:omni_audio_duration_s` | Histogram | — | Audio content duration (`audio_frames / sample_rate`) |
+| `vllm:omni_audio_rtf` | Histogram | — | Real-time factor (`stage_gen_time_s / audio_duration_s`); streaming TTS SLO red line `< 1`; uses `RTF_BUCKETS` |
+| `vllm:omni_audio_frames_total` | Counter | — | Cumulative audio frame count; throughput via `rate()` |
+| `vllm:omni_audio_underrun_s` | Histogram | — | Per-request worst-case player deficit; `> 0` indicates listener heard silent gaps |
+| `vllm:omni_audio_continuity_ok_total` | Counter | `threshold_ms` | Incremented when the request's worst underrun stayed below `threshold_ms` |
+| `vllm:omni_audio_skipped_requests_total` | Counter | `reason` | Silent-loss counter — code2wav rejected malformed codec input and returned `200 OK` with empty audio |
+
+The continuity math comes from `vllm_omni/benchmarks/audio_continuity.py::compute_continuity_stats` so the server-side observation aligns with the bench-side definition.
+
+## Cross-Stage Transfer Metrics (`vllm:omni_`)
+
+Per-physical-transfer histograms tracking the data hop between adjacent stages. Labels `{model_name, from_stage, from_replica, to_stage, to_replica}` let dashboards attribute latency to specific replica edges. `from_replica` / `to_replica` are resolved from the orchestrator's sticky-routing binding (`stage_pool.get_bound_replica_id(request_id)`), so no extra plumbing through `TransferEdgeStats` is needed.
+
+| Metric | Type | Description |
+|--------|------|-------------|
+| `vllm:omni_transfer_size_bytes` | Histogram | Per-transfer payload size in bytes |
+| `vllm:omni_transfer_tx_s` | Histogram | Sender-side time (serialize + submit to connector) |
+| `vllm:omni_transfer_rx_s` | Histogram | Receiver-side time (recv + deserialize) |
+| `vllm:omni_transfer_in_flight_s` | Histogram | Network in-flight time (TX done → RX recv start) |
+
+## vLLM Engine Metrics (`vllm:`)
+
+When the pipeline includes an LLM stage, the upstream vLLM engine exposes its full set of ~37 metric families under the `vllm:` prefix.
+
+vLLM-Omni wraps the upstream `vllm.v1.metrics.loggers.PrometheusStatLogger` with `OmniPrometheusStatLogger` so that the original `engine` single label is reshaped into `stage` + `replica`. Every `vllm:*` family — TTFT, ITL, TPOT, e2e latency, KV cache usage, scheduler running/waiting, request success counts, etc. — therefore gains per-`(stage, replica)` visibility automatically. No omni-side duplicate is needed for the text path.
+
+```text
+# Before wrap:
+vllm:num_requests_running{model_name="...", engine="1"}              3.0
+
+# After wrap:
+vllm:num_requests_running{model_name="...", stage="1", replica="0"}  2.0
+vllm:num_requests_running{model_name="...", stage="1", replica="1"}  1.0
+```
+
+For the full list of upstream metrics, see [the vLLM docs](https://github.com/vllm-project/vllm/blob/main/docs/usage/metrics.md).
+
+## Metric Availability by Pipeline Type
+
+| Metric group | Multi-stage LLM (Qwen3-Omni) |
+|---|---|
+| `vllm:omni_` request tracking + latency | With `--log-stats` |
+| `vllm:omni_` audio modality | With `--log-stats`, if pipeline has a talker stage |
+| `vllm:omni_` transfer | With `--log-stats`, if pipeline has ≥ 2 stages |
+| `vllm:` engine metrics (per `(stage, replica)`) | With `--log-stats` |
+| `vllm:` MFU metrics | With `--log-stats --enable-mfu-metrics` |
+
+## Naming Convention
+
+- All time-bearing metrics use the `_s` suffix (values in seconds). Buckets are `SECONDS_BUCKETS` for e2e / generation-style values and `SECONDS_FAST_BUCKETS` (1 ms → 60 s) for the fine-grained transfer and audio-underrun values.
+- Counters use the `_total` suffix (auto-appended by `prometheus_client`).
+- Sizes use the `_bytes` suffix.
+- All omni-specific families are prefixed `vllm:omni_`. The upstream `unregister_vllm_metrics()` function is monkey-patched (see `vllm_omni/patch.py`) to a scoped version that still strips upstream `vllm:*` collectors so multi-engine init within one process does not crash on duplicate registration, but preserves anything prefixed `vllm:omni_` / `vllm_omni`.
+- Text and audio first-output use distinct families (`vllm:time_to_first_token_seconds` reused from upstream for text; `vllm:omni_audio_ttfp_s` for audio) rather than a single metric with a `modality` label.

docs/user_guide/diffusion/attention_backends.md

@@ -1,28 +1,26 @@
 # Diffusion Attention Backends
 
-This document describes the diffusion attention backends available in vLLM-Omni, how to select them globally and per-role, and how to use SageAttention.
+This document describes the diffusion attention backends available in vLLM-Omni, how to select them globally and per-role, the per-platform defaults, and how to use SageAttention.
 
 ## Overview
 
 Diffusion attention backend selection is resolved in `vllm_omni.diffusion.attention.selector`. It looks up the backend from a structured `AttentionConfig` carried on `OmniDiffusionConfig` and falls back to the platform default when nothing is configured.
 
-This backend is used by diffusion attention layers such as the DiT attention in video and image generation models.
+This backend is used by diffusion attention layers such as the DiT attention in video and image generation models. It does **not** affect autoregressive (LLM) attention paths — those go through vLLM's own attention backend selector.
 
-On CUDA, the practical choices today are:
-
-- `FLASH_ATTN`: FlashAttention backend. This is the default on supported CUDA systems when FlashAttention is installed.
-- `TORCH_SDPA`: PyTorch `scaled_dot_product_attention`.
-- `SAGE_ATTN`: SageAttention backend, if `sageattention` is installed.
-
-If no attention backend is configured, vLLM-Omni asks the current platform to choose the default. On CUDA, that normally means `FLASH_ATTN` when available, otherwise `TORCH_SDPA`.
+The full set of backends and their platform defaults is in the **Backend Options** and **Platform Defaults** sections below. If no attention backend is configured, vLLM-Omni asks the current platform to choose the default.
 
 ## Backend Options
 
 | Value | Notes |
 |---|---|
-| `FLASH_ATTN` | Default on CUDA when FlashAttention is available. Good default for most diffusion workloads. |
-| `TORCH_SDPA` | Most conservative fallback. Useful for debugging or compatibility. |
-| `SAGE_ATTN` | Requires `sageattention`. Can improve performance on some workloads, but output quality must be validated model-by-model. |
+| `FLASH_ATTN` | Wraps FlashAttention 2. Default on Hopper / Ada / Ampere when `flash-attn` is installed. |
+| `CUDNN_ATTN` | Pins `sdpa_kernel([CUDNN_ATTENTION])`. Default on Blackwell (sm_10x / sm_12x) with cuDNN ≥ 9.5. Wins on mask-heavy DiTs (HunyuanVideo-1.5: 2× e2e vs SDPA). |
+| `FLASHINFER_ATTN` | Calls FlashInfer's dense `single_prefill_with_kv_cache` directly with `custom_mask` for non-causal masked attention. Used as Blackwell fallback when cuDNN is unavailable. Requires `flashinfer`. |
+| `TORCH_SDPA` | PyTorch `scaled_dot_product_attention` with the default backend dispatcher. Most conservative; always available. |
+| `SAGE_ATTN` | SageAttention 2.2 — INT8-quantized attention with FP16 accumulation. Lossy but typically visually indistinguishable on diffusion outputs. Requires `sageattention`. |
+| `SAGE_ATTN_3` | Requires `sageattn3` from `SageAttention/sageattention3_blackwell`. CUDA only, intended for Blackwell GPUs, with GQA/MQA requests falling back to PyTorch SDPA. |
+
 
 ## Configuration
 
@@ -95,6 +93,80 @@ config = OmniDiffusionConfig(
 
 A plain dict is also accepted and normalized to `AttentionConfig`.
 
+## Platform Defaults
+
+### Blackwell (sm_100 / sm_103 / sm_120 / sm_121)
+
+Auto-route preference, in order:
+
+1. `CUDNN_ATTN` — when cuDNN ≥ 9.5 is available (ships in PyTorch 2.5+ wheels)
+2. `FLASHINFER_ATTN` — when `flashinfer` is installed but cuDNN < 9.5
+3. `FLASH_ATTN` — when `flash-attn` is installed with the Blackwell CUTE kernel
+4. `TORCH_SDPA` — last resort
+
+The startup log line `Defaulting to diffusion attention backend CUDNN_ATTN (Blackwell sm_120, cuDNN 91002)` confirms the route.
+
+**Why CUDNN_ATTN by default**: on mask-heavy diffusion models (HunyuanVideo-1.5, Qwen-Image), cuDNN's pinned FMHA kernel sidesteps a PyTorch SDPA dispatch quirk where the unpinned dispatcher picks `EFFICIENT_ATTENTION` (~25 ms) for masked calls instead of cuDNN (~11 ms). The pin gives 2× e2e on HV-1.5 with no regression on lighter models.
+
+### Hopper (sm_90) / Ada (sm_89) / Ampere (sm_80–sm_86)
+
+Auto-route preference:
+
+1. `FLASH_ATTN` — when `flash-attn` is installed
+2. `TORCH_SDPA` — fallback
+
+`CUDNN_ATTN` and `FLASHINFER_ATTN` are still selectable via env var on these GPUs but are not in the auto-route — FlashAttention 2 is the well-tuned path on pre-Blackwell hardware.
+
+## End-to-End Benchmark (BF16, sm_120 RTX Pro 6000 Blackwell)
+
+Same prompt and seed across runs. `Total generation time` from `text_to_video.py` / `text_to_image.py`.
+
+| Model | Shape | TORCH_SDPA | CUDNN_ATTN | FLASHINFER_ATTN |
+|---|---|---|---|---|
+| HunyuanVideo-1.5 (T2V) | 480p / 33f / 50 steps | 147.05 s | **73.02 s** | 127.84 s |
+| Wan 2.2 14B (T2V) | 480p / 33f / 40 steps | 117.75 s | 117.17 s | **115.07 s** |
+| Qwen-Image (T2I) | 1024² / 50 steps | 17.41 s | **15.14 s** | 16.02 s |
+| FLUX.2-dev (T2I) | 1024² / 50 steps, TP=2 | 53.62 s | **53.30 s** | 54.94 s |
+
+Pattern: mask-heavy DiTs (HV-1.5, Qwen-Image) favor `CUDNN_ATTN`; lighter-mask DiTs and TP-saturated configs (Wan 2.2, FLUX.2 TP=2) tie within noise.
+
+## Known Limitations
+
+### LTX-2.0: `CUDNN_ATTN` crashes under torch.compile
+
+LTX-2's audio attention has a symbolic head_dim under torch.compile tracing. cuDNN's SDPA backend selector rejects symbolic dims and Dynamo aborts compilation. Tracked in [#3121](https://github.com/vllm-project/vllm-omni/issues/3121).
+
+**Workaround**: explicitly select `FLASHINFER_ATTN` or `TORCH_SDPA` for LTX-2.0:
+
+```bash
+DIFFUSION_ATTENTION_BACKEND=FLASHINFER_ATTN python examples/offline_inference/text_to_video/text_to_video.py \
+    --model Lightricks/LTX-2 ...
+```
+
+### FA4 not yet integrated
+
+FlashAttention-4 (released March 2026) targets Blackwell natively and reportedly beats cuDNN by ~20% on B200. As of this writing the `flash-attn-4 4.0.0b10` wheel crashes with `AttributeError: 'NoneType' object has no attribute '_trait'` during JIT on sm_120. Not yet wired into vLLM-Omni; revisit when stable lands.
+
+## Choosing a Backend Manually
+
+### When to override the default
+
+- **Quality validation**: compare a new backend against `TORCH_SDPA` as the reference, since SDPA's default dispatcher is the most extensively tested.
+- **Lossy speedup hunting**: try `SAGE_ATTN` (INT8 quantized) on diffusion outputs — typically indistinguishable visually but always validate.
+- **Workaround for known issues**: see Known Limitations above.
+
+### Verifying which backend is in use
+
+The startup log prints one of:
+
+```
+Using diffusion attention backend 'CUDNN_ATTN'           # explicit override
+Defaulting to diffusion attention backend CUDNN_ATTN ... # auto-route
+Defaulting to diffusion attention backend SDPA           # nothing else available
+```
+
+If you don't see one of these, the model didn't reach diffusion stage init — check earlier logs for failures.
+
 ## SageAttention Installation
 
 vLLM-Omni expects SageAttention to be installed into the same Python environment as vLLM-Omni.
@@ -115,11 +187,56 @@ Quick check:
 python -c "import sageattention; print(sageattention.__file__)"
 ```
 
-## Usage
+## SageAttention3 Installation
 
-### Enable SageAttention
+vLLM-Omni expects SageAttention3 to be installed into the same Python environment as vLLM-Omni.
 
-Example: HunyuanVideo-1.5 text-to-video
+Build from source:
+
+```bash
+git clone https://github.com/thu-ml/SageAttention.git
+cd SageAttention/sageattention3_blackwell
+python setup.py install
+```
+
+Quick check:
+
+```bash
+python -c "import sageattn3; print(sageattn3.__file__)"
+```
+
+Notes:
+
+- `SAGE_ATTN_3` is only selected on CUDA when `sageattn3` is importable and the GPU is Blackwell-class.
+- SageAttention3's Blackwell kernel assumes `Hq == Hkv`. In vLLM-Omni, GQA/MQA diffusion requests fall back to PyTorch SDPA for correctness.
+
+## Usage Examples
+
+### Default (auto-route)
+
+```bash
+python examples/offline_inference/text_to_video/text_to_video.py \
+    --model hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v \
+    --prompt "A dog running across a field of golden wheat." \
+    --height 480 --width 832 --num-frames 33 \
+    --num-inference-steps 50 --seed 42 --guidance-scale 6.0 \
+    --output hv15.mp4
+```
+
+On Blackwell this picks `CUDNN_ATTN` automatically. Check the log for the `Defaulting to ...` line.
+
+### Explicit backend selection
+
+```bash
+DIFFUSION_ATTENTION_BACKEND=FLASHINFER_ATTN python examples/offline_inference/text_to_video/text_to_video.py \
+    --model Lightricks/LTX-2 \
+    --prompt "A dog running across a field of golden wheat." \
+    --height 480 --width 832 --num-frames 33 \
+    --num-inference-steps 40 --seed 42 --guidance-scale 4.0 \
+    --output ltx2.mp4
+```
+
+### SageAttention (lossy)
 
 ```bash
 DIFFUSION_ATTENTION_BACKEND=SAGE_ATTN python examples/offline_inference/text_to_video/text_to_video.py \
@@ -128,7 +245,7 @@ DIFFUSION_ATTENTION_BACKEND=SAGE_ATTN python examples/offline_inference/text_to_
     --height 480 --width 832 --num-frames 33 \
     --num-inference-steps 30 --seed 42 --guidance-scale 6.0 \
     --tensor-parallel-size 2 \
-    --output ../tmp/hv15_modelopt_sage.mp4
+    --output hv15_sage.mp4
 ```
 
 Example: Wan2.2 TI2V 5B
@@ -143,6 +260,20 @@ DIFFUSION_ATTENTION_BACKEND=SAGE_ATTN python examples/offline_inference/text_to_
     --output outputs/wan22_sage.mp4
 ```
 
+### Enable SageAttention3
+
+Example:
+
+```bash
+DIFFUSION_ATTENTION_BACKEND=SAGE_ATTN_3 python examples/offline_inference/text_to_video/text_to_video.py \
+    --model hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v \
+    --prompt "A dog running across a field of golden wheat." \
+    --height 480 --width 832 --num-frames 33 \
+    --num-inference-steps 30 --seed 42 --guidance-scale 6.0 \
+    --tensor-parallel-size 2 \
+    --output outputs/hv15_sage3.mp4
+```
+
 ### Mixed backends across roles
 
 Use `FLASH_ATTN` for self-attention and `TORCH_SDPA` for cross-attention:
@@ -173,13 +304,13 @@ python examples/offline_inference/text_to_video/text_to_video.py \
 
 ## Validation Guidance
 
-Do not assume that a faster attention backend is numerically interchangeable with `FLASH_ATTN`.
+Don't assume a faster attention backend is numerically interchangeable with `TORCH_SDPA`.
 
 Always compare:
 
 - End-to-end runtime
-- DiT / diffusion stage runtime
-- Output quality against a known-good baseline
+- Diffusion-stage runtime (`add_req_and_wait` line in DiffusionEngine.step breakdown)
+- Output quality against a known-good baseline (CLIP similarity, frame-level diff, or visual review)
 
 At minimum, keep the same:
 
@@ -187,6 +318,19 @@ At minimum, keep the same:
 - prompt
 - seed
 - resolution
-- frame count
-- inference steps
-- parallel config
+- frame count / step count
+- parallel config (TP / CFG-parallel / Ulysses degrees)
+
+## Reproducing the Benchmark Table
+
+The end-to-end numbers above were collected by running `text_to_video.py` /
+`text_to_image.py` with the same prompt and seed while varying
+`DIFFUSION_ATTENTION_BACKEND`. For a quick kernel-level comparison of the
+backends without loading a model:
+
+```bash
+python benchmarks/diffusion/bench_attention_backends.py --preset hv15
+```
+
+It runs all three BF16 backends on representative DiT attention shapes and
+prints a ranking table at the end.

docs/user_guide/diffusion_features.md

@@ -113,7 +113,7 @@ The following tables show which models support each feature:
 | **FLUX.1-schnell**       |     ❌     |     ✅      |           ❌           |       ✅        |         ✅         |          ❌          |   ✅    |             ❌             |          ❌           |       ✅        |        ❌         |
 | **FLUX.2-klein**         |     ✅     |     ✅      |           ✅           |       ✅        |         ✅         |          ❌          |   ✅    |             ❌             |          ❌           |       ✅        |        ❌         |
 | **FLUX.1-Kontext-dev**   |     ❌     |     ❌      |           ❌           |       ❌        |         ✅         |          ❌          |   ✅    |             ❌             |          ❌           |       ❌        |        ❌         |
-| **FLUX.2-dev**           |     ✅     |     ✅      |           ❌           |       ✅        |         ✅         |          ❌          |   ✅    |             ❌             |          ❌           |       ❌        |        ❌         |
+| **FLUX.2-dev**           |     ✅     |     ✅      |           ✅           |       ✅        |         ✅         |          ❌          |   ✅    |             ❌             |          ❌           |       ❌        |        ❌         |
 | **GLM-Image**            |     ❌     |     ❌      |           ❌           |       ✅        |         ✅         |          ❌          |   ✅    |             ❌             |          ❌           |       ❌        |        ❌         |
 | **Hidream-I1-Full**        |     ❌     |     ❌      |           ❌           |       ❌        |         ✅         |          ❌          |   ❌    |             ❌             |          ❌           |       ❌        |        ❌         |
 | **HunyuanImage3**        |     ❌     |     ✅      |           ❌           |       ❌        |         ✅         |          ❌          |   ❌    |             ❌             |          ❌           |       ✅        |        ❌         |
@@ -129,26 +129,31 @@ The following tables show which models support each feature:
 | **Qwen-Image-Edit**      |     ✅     |     ✅      |           ✅           |       ✅        |         ✅         |          ❌          |   ✅    |             ✅             |      ✅ (decode)      |       ❌        |        ❌         |
 | **Qwen-Image-Edit-2509** |     ✅     |     ✅      |           ✅           |       ✅        |         ✅         |          ❌          |   ✅    |        ✅ (decode)         |          ✅           |       ❌        |        ❌         |
 | **Qwen-Image-Layered**   |     ✅     |     ✅      |           ✅           |       ✅        |         ✅         |          ❌          |   ✅    |             ✅             |      ✅ (decode)      |       ❌        |        ❌         |
-| **SenseNova-U1**         |     ❌     |     ❌      |           ❌           |       ❌        |         ✅         |          ❌          |   ❌    |             ✅             |          ❌           |       ❌        |        ❌         |
+| **SenseNova-U1**         |     ❌     |     ✅      |           ❌           |       ❌        |         ✅         |          ❌          |   ❌    |             ✅             |          ❌           |       ❌        |        ❌         |
 | **Stable-Diffusion3.5**  |     ❌     |     ✅      |           ❌           |       ✅        |         ✅         |          ❌          |   ❌    |             ✅             |      ✅ (decode)      |       ❌        |        ❌         |
 | **Z-Image**              |     ✅     |     ✅      |           ✅           |       ❓        |   ✅ (TP=2 only)   |          ❌          |   ✅    |             ❌             |      ✅ (decode)      |       ✅        |        ❌         |
 | **ERNIE-Image**          |     ❌     |     ✅      |           ✅           |       ❓        |         ✅         |          ❌          |   ✅    |             ✅             |          ❌           |       ❌        |        ❌         |
+| **Cosmos3**              |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |          ❌          |   ✅    |             ✅             |      ✅ (decode)      |       ✅        |        ❌         |
 
 > Notes:
 > 1. Nextstep_1(T2I) does not support cache acceleration methods such as TeaCache or Cache-DiT.
 > 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 is not supported; use layerwise offload.
 
 ### VideoGen
 
 | Model                        | ⚡TeaCache | ⚡Cache-DiT | 🔀SP (Ulysses & Ring) | 🔀CFG-Parallel | 🔀Tensor-Parallel | Pipeline-Parallel | 🔀HSDP | 💾CPU Offload (Layerwise) | 💾VAE-Patch-Parallel | 💾Quantization | 🔄Step Execution |
 |------------------------------|:---------:|:----------:|:---------------------:|:--------------:|:-----------------:|:-----------------:|:------:|:-------------------------:|:--------------------:|:--------------:|:----------------:|
 | **Wan2.2**                   |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ✅         |   ✅    |             ✅             |  ✅ (encode/decode)   |       ❌        |        ❌         |
+| **Wan2.2-S2V**               |     ❌     |     ✅      |           ❌           |       ❌        |         ✅         |         ❌         |   ❌    |             ✅             |  ✅ (encode/decode)   |       ❌        |        ❌         |
 | **Wan2.1-VACE**              |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ✅    |             ✅             |      ✅ (decode)      |       ❌        |        ❌         |
 | **LTX-2**                    |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ✅    |             ✅             |          ❌           |       ❌        |        ❌         |
-| **LTX-2.3**                  |     ❌     |     ✅      |           ✅           |       ❌        |         ✅         |         ❌         |   ❌    |             ❌             |          ❌           |       ❌        |        ❌         |
+| **LTX-2.3**                  |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ❌    |             ✅             |      ✅ (decode)      |       ❌        |        ❌         |
 | **Helios**                   |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ✅    |             ✅             |          ❌           |       ❌        |        ❌         |
-| **HunyuanVideo-1.5 T2V I2V** |     ❌     |     ✅      |           ❌           |       ✅        |         ✅         |         ❌         |   ✅    |             ✅             |      ✅ (decode)      |       ✅        |        ❌         |
+| **HunyuanVideo-1.5 T2V I2V** |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ✅    |             ✅             |  ✅ (encode/decode)   |       ✅        |        ❌         |
 | **DreamID-Omni**             |     ❌     |     ❌      |           ❌           |       ✅        |         ❌         |         ❌         |   ✅    |             ✅             |          ❌           |       ❌        |        ❌         |
+| **Cosmos3**                  |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ✅    |             ✅             |  ✅ (encode/decode)   |       ✅        |        ❌         |
+
 
 **Frame Interpolation Support**
 

docs/user_guide/examples/offline_inference/text_to_speech.md

@@ -23,6 +23,7 @@ list of supported architectures across all modalities, see
 | OmniVoice | `k2-fsa/OmniVoice` | 2 (gen + dec) | ✓ | — | voice design (`--instruct`), language (`--lang`) | 24 kHz |
 | Qwen3-TTS | `Qwen/Qwen3-TTS-12Hz-1.7B-{CustomVoice,VoiceDesign,Base}` | 2 (talker + code2wav) | ✓ (Base) | ✓ | 3 task variants (`--query-type`) | 24 kHz |
 | Voxtral TTS | `mistralai/Voxtral-4B-TTS-2603` | varies | ✓ | ✓ | voice presets (`--voice`) | 24 kHz |
+| higgs-audio v2 | `bosonai/higgs-audio-v2-generation-3B-base` (+ `k2-fsa/OmniVoice/audio_tokenizer/` codec) | 2 (talker + code2wav, DualFFN) | ✓ (`--ref-audio` + `--ref-text`) | — | — | 24 kHz |
 
 ## Common Quick Start
 
@@ -333,6 +334,46 @@ Available voice presets are listed on the HF model card (`mistralai/Voxtral-4B-T
 - `--concurrency M` requires `--streaming` and must evenly divide `--num-prompts`.
 - Run `--help` for the full argument surface.
 
+---
+
+## higgs-audio v2
+
+2-stage TTS at 24 kHz: a vLLM-native Llama-3.2-3B talker with a DualFFN audio expert (Stage 0) feeding a HiggsAudio codec decoder (Stage 1) over the shared-memory connector. Stage 1 builds on the HiggsAudio decoder kernel at `vllm_omni/model_executor/models/higgs_audio_v2/higgs_audio_decoder.py`.
+
+### Prerequisites
+
+Voice clone uses HF's `HiggsAudioV2TokenizerModel`, instantiated from `k2-fsa/OmniVoice/audio_tokenizer/` — the boson-ai standalone tokenizer Hub repo's `model.safetensors` is actually the 3B talker LM, so we point HF at k2's repackaged codec weights instead. Only the `audio_tokenizer/` subdir (~806 MB) is downloaded.
+
+```bash
+pip install -U "transformers>=5.3.0"
+```
+
+### Quick start (plain TTS)
+
+```bash
+python examples/offline_inference/text_to_speech/higgs_audio_v2/end2end.py \
+    --texts "Hello world." "The quick brown fox jumps over the lazy dog." \
+    --output-dir results/wavs
+```
+
+### Voice cloning
+
+Pass both `--ref-audio` and `--ref-text` together:
+
+```bash
+python examples/offline_inference/text_to_speech/higgs_audio_v2/end2end.py \
+    --texts "Hello, this is a cloned voice." \
+    --ref-audio /path/to/reference.wav \
+    --ref-text  "Exact transcript spoken in reference.wav." \
+    --output-dir results/wavs
+```
+
+### Notes
+
+- Deploy config auto-loads from `vllm_omni/deploy/higgs_audio_v2.yaml`.
+- `--ref-text` must be the real transcript of `--ref-audio`; mismatched text degrades cloned-voice quality.
+- Out of scope (rejected with 4xx by the request validator): multi-speaker `[SPEAKERn]` dialogue, `profile:` text-only speaker descriptions, the `ref_audio_in_system_message` system-block variant, chunked long-form generation, and per-request `voice` / `instructions` / `task_type` / `language` / `speed != 1.0` / `x_vector_only_mode` / `speaker_embedding`.
+
 ## Example materials
 
 ??? abstract "cosyvoice3/end2end.py"
@@ -343,6 +384,9 @@ Available voice presets are listed on the HF model card (`mistralai/Voxtral-4B-T
     ``````py
     --8<-- "examples/offline_inference/text_to_speech/fish_speech/end2end.py"
     ``````
+??? abstract "higgs_audio_v2/end2end.py"
+    ``````py
+    --8<-- "examples/offline_inference/text_to_speech/higgs_audio_v2/end2end.py"
 ??? abstract "glm_tts/end2end.py"
     ``````py
     --8<-- "examples/offline_inference/text_to_speech/glm_tts/end2end.py"

docs/user_guide/examples/online_serving/qwen3_omni.md

@@ -17,6 +17,9 @@ vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091
 
 The default deployment configuration situated at `vllm_omni/deploy/qwen3_omni_moe.yaml` is resolved and loaded
 automatically via the model registry, obviating the necessity for the `--deploy-config` flag in standard deployment topologies.
+The bundled Qwen3-Omni setup defaults `VLLM_USE_FLASHINFER_MOE_FP16=0`. This keeps the Thinker & Talker on vLLM's
+Triton unquantized MoE path and avoids the performance regression observed with the FlashInfer CUTLASS unquantized MoE
+backend.
 Asynchronous chunk streaming is **enabled by default** within the bundled configuration.
 
 To explicitly utilize a custom deployment YAML, specify the configuration path:
@@ -72,6 +75,12 @@ vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091 \
     --stage-overrides '{"1": {"gpu_memory_utilization": 0.5}}'
 ```
 
+To experiment with the FlashInfer FP16 MoE path, set `VLLM_USE_FLASHINFER_MOE_FP16=1` before launching the server:
+```bash
+VLLM_USE_FLASHINFER_MOE_FP16=1 \
+vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091
+```
+
 For the stage-based CLI, you usually do **not** need `--stage-overrides` for
 that kind of change. Since each command launches one stage, just pass the knob
 directly on that stage command:

docs/user_guide/examples/online_serving/speech_to_video.md

@@ -0,0 +1,128 @@
+# Speech-To-Video
+
+Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/online_serving/speech_to_video>.
+
+This example demonstrates how to deploy the Wan2.2 speech-to-video (S2V) model for online video generation using vLLM-Omni.
+
+## Supported Models
+
+| Model | Model ID |
+|-------|----------|
+| Wan2.2 S2V (14B) | `Wan-AI/Wan2.2-S2V-14B` |
+
+## Start Server
+
+### Basic Start
+
+```bash
+VLLM_WORKER_MULTIPROC_METHOD=spawn \
+vllm serve Wan-AI/Wan2.2-S2V-14B --omni \
+  --model-class-name WanS2VPipeline \
+  --tensor-parallel-size 2 \
+  --flow-shift 3.0 \
+  --vae-use-slicing --vae-use-tiling \
+  --cache-backend cache_dit \
+  --port 8091
+```
+
+### Start with Script
+
+```bash
+bash run_server.sh
+```
+
+The script allows overriding:
+- `MODEL` (default: `Wan-AI/Wan2.2-S2V-14B`)
+- `PORT` (default: `8091`)
+- `FLOW_SHIFT` (default: `3.0`)
+- `TP` (default: `2`)
+- `CACHE_BACKEND` (default: `cache_dit`)
+
+### Start with 4 GPUs
+
+```bash
+TP=4 bash run_server.sh
+```
+
+## API Endpoints
+
+The server provides OpenAI-compatible video generation endpoints:
+
+- `POST /v1/videos`: create a video generation job (async)
+- `POST /v1/videos/sync`: generate a video and return raw bytes (sync, for benchmarks)
+- `GET /v1/videos/{video_id}`: retrieve the current job status and metadata
+- `GET /v1/videos`: list stored video jobs
+- `GET /v1/videos/{video_id}/content`: download the generated video file
+- `DELETE /v1/videos/{video_id}`: delete the job and any stored output
+
+## Sync API (Recommended for Testing)
+
+`POST /v1/videos/sync` blocks until generation completes and returns the raw
+video bytes (`video/mp4`) directly in the response body.
+
+```bash
+bash run_curl_speech_to_video.sh
+```
+
+## Async Job API
+
+`POST /v1/videos` creates an asynchronous job and returns immediately.
+
+```bash
+no_proxy=127.0.0.1 \
+create_response=$(curl -sS -X POST http://127.0.0.1:8091/v1/videos \
+  -H "Accept: application/json" \
+  -F "prompt=A person singing" \
+  -F 'image_reference={"image_url": "https://raw.githubusercontent.com/Wan-Video/Wan2.2/main/examples/Five%20Hundred%20Miles.png"}' \
+  -F 'audio_reference={"audio_url": "https://raw.githubusercontent.com/Wan-Video/Wan2.2/main/examples/Five%20Hundred%20Miles.MP3"}' \
+  -F "width=832" -F "height=480" \
+  -F "num_inference_steps=40" \
+  -F "guidance_scale=4.5" \
+  -F "fps=16")
+
+video_id=$(echo "$create_response" | jq -r '.id')
+
+# Poll until complete
+while true; do
+  status=$(curl -s "http://127.0.0.1:8091/v1/videos/${video_id}" | jq -r '.status')
+  if [ "$status" = "completed" ]; then
+    break
+  fi
+  if [ "$status" = "failed" ]; then
+    echo "Video generation failed"
+    exit 1
+  fi
+  sleep 2
+done
+
+# Download
+curl -L "http://127.0.0.1:8091/v1/videos/${video_id}/content" -o s2v_output.mp4
+```
+
+## Request Parameters
+
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `prompt` | str | - | Text description of the desired video |
+| `image_reference` | JSON str | - | Image reference: `{"image_url": "..."}` — supports HTTP(s) URLs or base64 data URLs |
+| `audio_reference` | JSON str | - | Audio reference: `{"audio_url": "..."}` — supports HTTP(s) URLs or base64 data URLs |
+| `width` | int | None | Video width in pixels |
+| `height` | int | None | Video height in pixels |
+| `fps` | int | None | Output video frame rate |
+| `num_inference_steps` | int | None | Number of denoising steps (40 recommended) |
+| `guidance_scale` | float | None | CFG guidance scale |
+| `seed` | int | None | Random seed for reproducibility |
+
+## Notes
+
+- S2V requires both a reference image (`image_reference`) and an audio reference
+  (`audio_reference`). The generated video will show a person matching the reference
+  image with lip movements synchronized to the audio.
+- `audio_reference` accepts a JSON string: `{"audio_url": "..."}` where the URL can be:
+  - An HTTP/HTTPS URL (e.g., `https://example.com/audio.mp3`)
+  - A base64 data URL (e.g., `data:audio/mp3;base64,...`)
+- `--model-class-name WanS2VPipeline` is required on the server to select the
+  S2V pipeline (distinct from the T2V/I2V pipelines).
+- `--cache-backend cache_dit` enables DiT caching for ~2x speedup on cached steps.
+- Audio is muxed into the output MP4 automatically.
+- Always pass `fps=16` to ensure correct video/audio alignment.

docs/user_guide/examples/online_serving/text_to_speech.md

@@ -19,6 +19,7 @@ For the full list of supported architectures across all modalities, see
 |---|---|---|---|---|---|
 | CosyVoice3 | `FunAudioLLM/Fun-CosyVoice3-0.5B-2512` | ✓ (`ref_audio`+`ref_text`) | ✓ (PCM stream) | — | — |
 | Fish Speech S2 Pro | `fishaudio/s2-pro` | ✓ (`ref_audio`+`ref_text`) | ✓ (PCM stream) | — | ✓ |
+| higgs-audio v2 | `bosonai/higgs-audio-v2-generation-3B-base` | ✓ (`ref_audio`+`ref_text`) | ✓ (codec_streaming) | — | — |
 | GLM-TTS | `zai-org/GLM-TTS` | ✓ (`ref_audio`+`ref_text`, required) | ✓ (PCM stream) | — | ✓ |
 | OmniVoice | `k2-fsa/OmniVoice` | (offline only) | — | — | — |
 | Qwen3-TTS | `Qwen/Qwen3-TTS-12Hz-1.7B-{CustomVoice,VoiceDesign,Base}` | ✓ (Base) | ✓ (PCM + WebSocket) | ✓ (presets + `/v1/audio/voices` upload) | ✓ (standard + FastRTC) |
@@ -193,6 +194,45 @@ python fish_speech/gradio_demo.py --api-base http://localhost:8091  # if server
 
 ---
 
+## higgs-audio v2
+
+2-stage TTS at 24 kHz: a vLLM-native Llama-3.2-3B talker with a DualFFN audio expert (Stage 0) feeding a HiggsAudio codec decoder (Stage 1) that streams chunks back to the client.
+
+### Prerequisites
+
+Voice clone uses HF's `HiggsAudioV2TokenizerModel` loaded from `k2-fsa/OmniVoice/audio_tokenizer/` (~806 MB subdir; the boson-ai standalone tokenizer Hub repo's `model.safetensors` is the 3B talker LM, not the codec):
+
+```bash
+pip install -U "transformers>=5.3.0"
+```
+
+### Launch
+```bash
+GPUS=6,7 PORT=8094 bash examples/online_serving/text_to_speech/higgs_audio_v2/run_server.sh
+```
+Deploy config auto-loads from `vllm_omni/deploy/higgs_audio_v2.yaml`.
+
+### Sending requests
+```bash
+# Plain TTS
+python higgs_audio_v2/batch_speech_client.py \
+    --base-url http://localhost:8094 \
+    --output-dir /tmp/higgs_out \
+    --prompts "Hello world." "The quick brown fox jumps over the lazy dog."
+
+# Voice cloning — pass a reference clip and its transcript together
+python higgs_audio_v2/batch_speech_client.py \
+    --base-url http://localhost:8094 \
+    --output-dir /tmp/higgs_clone \
+    --ref-audio /path/to/reference.wav \
+    --ref-text  "Exact transcript spoken in reference.wav." \
+    --prompts "Hello, this is a cloned voice."
+```
+
+### Notes
+- Output: 24 kHz mono.
+- `--ref-text` must be the real transcript of `--ref-audio`; mismatched text degrades cloned-voice quality.
+- Out of scope (rejected with explicit 4xx): multi-speaker `[SPEAKERn]` tags inside `input`, `profile:` text-only speaker descriptions, the `ref_audio_in_system_message` system-block variant, chunked long-form generation, and per-request `voice` / `instructions` / `task_type` / `language` / `speed != 1.0` / `x_vector_only_mode` / `speaker_embedding`.
 ## GLM-TTS
 
 2-stage TTS (AR + DiT flow-matching) at 24 kHz. Every request requires `ref_audio` + `ref_text`.
@@ -289,6 +329,16 @@ Single-GPU serves now default to the uniproc executor (lower IPC overhead, the B
 
 To opt out of chunked streaming, pass `--no-async-chunk` — the pipeline auto-dispatches to the end-to-end codec processor.
 
+### Tuning stage 1 `max_num_seqs` per task type
+The bundled `qwen3_tts.yaml` ships stage 1 (Code2Wav) at `max_num_seqs: 10`, tuned for Base voice cloning: stage-1 lifetimes are long (~3 s/req), so admitting up to 10 concurrent codec sequences lets requests progress in parallel in the scheduler — ~2× TTFA p95 at c=4 / c=8 (1× H100, 1.7B-Base, seed-tts) at an 8–12 % audio-throughput cost.
+
+CustomVoice / VoiceDesign have much shorter stage-1 lifetimes (~50–200 ms) and are TTFA-optimal at `max_num_seqs: 1`. Override the default when serving those task types:
+
+```bash
+vllm serve Qwen/Qwen3-TTS-12Hz-1.7B-Base --omni \
+    --stage-overrides '{"1": {"max_num_seqs": 1}}'
+```
+
 ### Sending requests
 ```bash
 # CustomVoice with a predefined speaker
@@ -330,6 +380,31 @@ curl -X POST http://localhost:8091/v1/audio/voices \
 ```
 Uploaded voices are then usable as `voice="custom_voice_1"` on subsequent requests.
 
+### Precomputed custom voices
+For reused Base voice-cloning speakers, precompute the reference artifacts once and load them at server startup:
+```bash
+python qwen3_tts/precompute_custom_voice.py \
+    --model Qwen/Qwen3-TTS-12Hz-1.7B-Base \
+    --voice-name alice \
+    --ref-audio /path/to/reference.wav \
+    --ref-text "Original transcript of the reference audio" \
+    --mode icl \
+    --output-dir /path/to/custom_voices
+```
+`--mode icl` stores both `speaker_embedding` and `ref_code`; `--mode xvec` stores only the speaker embedding. Add the output directory to a deploy config:
+```yaml
+custom_voice_dir: /path/to/custom_voices
+```
+Then start the server with that config and call the Speech API with only the voice name:
+```bash
+vllm serve Qwen/Qwen3-TTS-12Hz-1.7B-Base --omni --deploy-config /path/to/qwen3_tts_custom_voice.yaml
+
+curl -X POST http://localhost:8091/v1/audio/speech \
+    -H "Content-Type: application/json" \
+    -d '{"input":"Hello from a precomputed voice.","voice":"alice","task_type":"Base"}' \
+    --output alice.wav
+```
+
 ### Streaming PCM
 ```bash
 curl -X POST http://localhost:8091/v1/audio/speech \
@@ -369,8 +444,10 @@ python qwen3_tts/gradio_fastrtc_demo.py --api-base http://localhost:8000
 `qwen3_tts/batch_speech_client.py` issues many concurrent requests for throughput measurement.
 
 ### Notes
-- Base voice cloning has per-request reference-audio cost; the uniproc default keeps IPC overhead off the critical path. See the executor-backend section above for background.
+- Base voice cloning has uniproc-vs-mp tradeoffs depending on per-request reference audio cost; see the executor-backend section above.
+- With async chunking, Qwen3-TTS Base voice cloning sends the full reference context in the first Code2Wav packet, then caches that prefix on the Code2Wav stage for follow-up chunks in the same request.
 - `vllm_omni/deploy/qwen3_tts.yaml` is the default deploy config (loaded by HF `model_type`); per-stage runtime overrides are available via `--stage-N-<field> <value>`.
+- Under vocoder-bound overload (single-stream `rtf_p99 ≥ 1` at the target concurrency), set `active_stream_window: 2` at the top of the deploy yaml to cap simultaneously active Stage 1 streams. Off by default; trades TTFP for streaming continuity. See [#3592](https://github.com/vllm-project/vllm-omni/pull/3592) for the mechanism and tradeoff numbers.
 
 ---
 
@@ -398,6 +475,23 @@ python voxcpm2/openai_speech_client.py \
 ```
 The `ref_audio` field accepts local file paths (auto-base64), HTTP URLs, or `data:audio/wav;base64,...` data URIs.
 
+### Precomputed custom voices
+For repeated VoxCPM2 speakers, precompute the prompt cache and load it through `custom_voice_dir`:
+```bash
+python voxcpm2/precompute_custom_voice.py \
+    --model openbmb/VoxCPM2 \
+    --voice-name alice \
+    --ref-audio /path/to/reference.wav \
+    --mode ref_continuation \
+    --prompt-text "Original transcript of the reference audio" \
+    --output-dir /path/to/custom_voices
+```
+Add the output directory to the deploy config:
+```yaml
+custom_voice_dir: /path/to/custom_voices
+```
+After startup, `/v1/audio/voices` lists `alice`, and `/v1/audio/speech` can use `voice="alice"` without sending `ref_audio`.
+
 ### Gradio demo (gapless streaming via AudioWorklet)
 ```bash
 python voxcpm2/gradio_demo.py
@@ -459,6 +553,13 @@ The demo handles voice-preset selection and reference-audio upload. `voxtral_tts
     ``````py
     --8<-- "examples/online_serving/text_to_speech/fish_speech/speech_client.py"
     ``````
+??? abstract "higgs_audio_v2/batch_speech_client.py"
+    ``````py
+    --8<-- "examples/online_serving/text_to_speech/higgs_audio_v2/batch_speech_client.py"
+    ``````
+??? abstract "higgs_audio_v2/run_server.sh"
+    ``````sh
+    --8<-- "examples/online_serving/text_to_speech/higgs_audio_v2/run_server.sh"
 ??? abstract "glm_tts/gradio_demo.py"
     ``````py
     --8<-- "examples/online_serving/text_to_speech/glm_tts/gradio_demo.py"

docs/user_guide/fault_injection_reliability_matrix.md

@@ -0,0 +1,117 @@
+# vLLM-Omni Failure Mode Injection Scenarios and Expected Behavior Matrix
+
+## Background and Goals
+
+The online serving pipeline of vLLM-Omni can be abstracted as: `Serve` (service entry process) -> `Engine` (model orchestration unit) -> `Worker` (execution process) -> `GPU` (compute resource).
+
+- `Serve` provides external APIs, accepts requests, and manages lifecycle.
+- `Engine` handles request scheduling and inference orchestration; the number and organization of engines can vary by model type.
+- `Worker` performs concrete model computation, usually bound to underlying GPU resources.
+- `GPU` is the resource layer that ultimately carries inference compute and memory usage.
+
+<div markdown style="text-align: center">
+
+```mermaid
+%%{init: {'flowchart': {'useMaxWidth': false}}}%%
+flowchart TD
+    R(request)--> A(serve)
+    A --> B(engine)
+    A --> C(engine)
+    A --> D(engine)
+    B --> E(worker)
+    C --> F(worker)
+    D --> I(worker)
+    E --> G(GPU)
+    F --> H(GPU)
+    I --> L(GPU)
+```
+
+</div>
+
+The diagram above shows an abstract relationship. Omni models and Diffusion models share the same hierarchy; differences mainly come from engine decomposition, worker count, and scheduling strategy.
+
+In real customer environments, the system will inevitably encounter accidental operations or environmental disturbances, such as unintended process termination, container/node jitter, or short-term resource contention. From the system perspective, many of these events map to two fault categories: "process receives abnormal signals" or "GPU memory is squeezed." For example, pressing `Ctrl+C` essentially sends a `SIGINT` signal to the `serve` process; running `kill -15 <pid>` maps to graceful termination via `SIGTERM`; running `kill -9 <pid>` maps to forced termination via `SIGKILL`; when `docker kill` is executed or a Pod is deleted, the container runtime usually sends `SIGKILL`.
+
+This document currently covers two core failure modes, and the system is expected to show predictable and observable behavior under each mode:
+
+- **Failure Mode 1: Process receives abnormal signals (`SIGINT` / `SIGTERM` / `SIGKILL`)**  
+  Focus on three aspects: whether processes exit as expected and complete cleanup, whether requests fail fast or connections are interrupted, and whether GPU resources are eventually fully released with no residue.
+- **Failure Mode 2: OOM (occupy all free GPU memory via an extra process)**  
+  Focus on two aspects: whether service health degrades to the expected state (for example, `503`), and whether requests fail within an acceptable time without hanging.
+
+More failure modes (for example, network jitter and network interruption) will be added in future iterations to improve end-to-end reliability validation coverage.
+
+> Note: independent fault injection on the `engine` component is not covered in the current version and will be added gradually in future versions.
+
+## Fault Injection Scenario Matrix
+
+| Scenario | Fault Type | System Behavior | Current Status |
+|------|----------------------------|----------|----------|
+| No load | Send `SIGKILL` to Worker | Worker process is killed immediately; main service detects child-process loss and turns unavailable; API enters stable 5xx |  |
+| No load | Send `SIGTERM` to Worker | Worker exits after receiving termination signal; main service is marked unavailable; API enters stable 5xx |  |
+| No load | Send `SIGKILL` to serve main process | serve main process exits instantly; request connections are interrupted; related child processes are cleaned up with no residue; GPU memory is released quickly | [#3725](https://github.com/vllm-project/vllm-omni/issues/3725) <br>[#43060](https://github.com/vllm-project/vllm/issues/43060) |
+| No load | Send `SIGTERM` to serve main process | serve enters graceful shutdown and stops serving; then exits and completes cleanup; GPU memory is released |  |
+| No load | Send `SIGINT` to serve main process (equivalent to `Ctrl+C`) | Triggers serve shutdown path; service stops responding and becomes unavailable; related child processes exit and resources are released |  |
+| No load | Send `SIGKILL` to all related processes | All related processes terminate immediately; service becomes unavailable at once; no residual processes remain; GPU memory is released quickly |  |
+| No load | Send `SIGTERM` to all related processes | All processes enter exit flow and complete shutdown; service becomes unavailable; resource release is completed |  |
+| Under load | Send `SIGKILL` to Worker | In-flight requests are hard interrupted (5xx/connection drop); main service becomes unavailable | [#3683](https://github.com/vllm-project/vllm-omni/issues/3683) |
+| Under load | Send `SIGTERM` to Worker | In-flight requests are canceled or fail fast; main service becomes unavailable |  |
+| Under load | Send `SIGKILL` to serve main process | serve is hard-killed and current connections are interrupted; in-flight requests fail; after cleanup there are no residual processes and GPU memory is released | [#3683](https://github.com/vllm-project/vllm-omni/issues/3683) |
+| Under load | Send `SIGTERM` to serve main process | serve stops accepting new requests and executes shutdown flow; in-flight requests fail; no residue remains and GPU memory is released | [#3683](https://github.com/vllm-project/vllm-omni/issues/3683) |
+| Under load | Send `SIGINT` to serve main process (equivalent to `Ctrl+C`) | `Ctrl+C`-style serve shutdown; in-flight requests fail (5xx/connection interruption); service unavailable; after exit there is no residue and GPU memory is released | [#3683](https://github.com/vllm-project/vllm-omni/issues/3683) |
+| Under load | Send `SIGKILL` to all related processes | All processes terminate instantly; all in-flight requests fail; service becomes unavailable immediately; GPU memory is released quickly |  |
+| Under load | Send `SIGTERM` to all related processes | All processes exit gracefully; in-flight requests fail; service unavailable; GPU memory is released | [#3683](https://github.com/vllm-project/vllm-omni/issues/3683) |
+| OOM | Occupy all free GPU memory via an extra process | After OOM injection process starts, GPU memory is continuously saturated; service enters unavailable/degraded state and health check drops to 503; different request types (chat/speech, etc.) fail fast within a fixed time and return 500 (no hanging) | [#4285](https://github.com/vllm-project/vllm-omni/issues/4285) |
+
+## Source of Conclusions
+
+The behaviors and conclusions above are summarized from current fault injection validation results on `Qwen3-Omni` and `Wan2.2`.
+
+### Example: `SIGTERM` fault injection log (Qwen3-Omni)
+
+The following full log is from a real run where a `SIGTERM` signal was sent to the serve root process.
+
+```text
+Launching OmniServer with: /workspace/.venv/bin/python3 -m vllm_omni.entrypoints.cli.main serve Qwen/Qwen3-Omni-30B-A3B-Instruct --host 127.0.0.1 --port 60675 --omni --async-chunk --stage-init-timeout 600 --init-timeout 900 --log-stats --stage-configs-path vllm-omni/vllm_omni/deploy/qwen3_omni_moe.yaml
+Server ready on 127.0.0.1:60675 (OmniServer startup took 206.023s)
+OmniServer started successfully
+[reliability][process-kill] current_server_proc pid=556855 name=python3 cmdline=/workspace/.venv/bin/python3 -m vllm_omni.entrypoints.cli.main serve Qwen/Qwen3-Omni-30B-A3B-Instruct --host 127.0.0.1 --port 60675 --omni --async-chunk --stage-init-timeout 600 --init-timeout 900 --log-stats --stage-configs-path vllm-omni/vllm_omni/deploy/qwen3_omni_moe.yaml
+[reliability][process-kill] current_server_proc pid=557118 name=python3 cmdline=/workspace/.venv/bin/python3 -c from multiprocessing.resource_tracker import main;main(57)
+[reliability][process-kill] current_server_proc pid=557119 name=VLLM::StageEngineCoreProc_noid_replica0_DP0 cmdline=VLLM::StageEngineCoreProc_noid_replica0_DP0
+[reliability][process-kill] current_server_proc pid=557122 name=VLLM::StageEngineCoreProc_noid_replica0_DP0 cmdline=VLLM::StageEngineCoreProc_noid_replica0_DP0
+[reliability][process-kill] current_server_proc pid=558201 name=VLLM::StageEngineCoreProc_noid_replica0_DP0 cmdline=VLLM::StageEngineCoreProc_noid_replica0_DP0
+
+[reliability][process-kill] root-kill pid=556855 name=python3 signal=SIGTERM cmdline=/workspace/.venv/bin/python3 -m vllm_omni.entrypoints.cli.main serve Qwen/Qwen3-Omni-30B-A3B-Instruct --host 127.0.0.1 --port 60675 --omni --async-chunk --stage-init-timeout 600 --init-timeout 900 --log-stats --stage-configs-path vllm-omni/vllm_omni/deploy/qwen3_omni_moe.yaml
+[0;36m(APIServer pid=556855)[0;0m INFO 05-19 12:24:51 [omni_base.py:463] [AsyncOmni] Shutting down
+[0;36m(APIServer pid=556855)[0;0m INFO 05-19 12:24:51 [async_omni_engine.py:2397] [AsyncOmniEngine] Shutting down Orchestrator
+[0;36m(APIServer pid=556855)[0;0m INFO 05-19 12:24:51 [orchestrator.py:351] [Orchestrator] Received shutdown signal
+[0;36m(APIServer pid=556855)[0;0m INFO 05-19 12:24:51 [orchestrator.py:1359] [Orchestrator] Shutting down all 3 client(s)
+
+[0;36m(StageEngineCoreProc pid=557119)[0;0m [32mINFO[0m [90m05-19 12:24:51[0m [90m[core.py:1242][0m Shutdown initiated (timeout=0)
+[0;36m(StageEngineCoreProc pid=557119)[0;0m [32mINFO[0m [90m05-19 12:24:51[0m [90m[core.py:1265][0m Shutdown complete
+[0;36m(APIServer pid=556855)[0;0m INFO:     Shutting down
+[0;36m(APIServer pid=556855)[0;0m INFO:     Waiting for application shutdown.
+[0;36m(APIServer pid=556855)[0;0m INFO:     Application shutdown complete.
+[0;36m(APIServer pid=556855)[0;0m INFO:     Finished server process [556855]
+[0;36m(APIServer pid=556855)[0;0m INFO 05-19 12:24:51 [omni_base.py:463] [AsyncOmni] Shutting down
+[rank0]:[W519 12:24:53.427894833 ProcessGroupNCCL.cpp:1575] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+[0;36m(APIServer pid=556855)[0;0m INFO 05-19 12:24:56 [stage_pool.py:731] [StagePool] Stage 0 replica 0 shut down
+[0;36m(StageEngineCoreProc pid=557122)[0;0m [32mINFO[0m [90m05-19 12:24:56[0m [90m[core.py:1242][0m Shutdown initiated (timeout=0)
+[0;36m(StageEngineCoreProc pid=557122)[0;0m [32mINFO[0m [90m05-19 12:24:56[0m [90m[core.py:1265][0m Shutdown complete
+[rank0]:[W519 12:24:57.569425065 ProcessGroupNCCL.cpp:1575] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+[0;36m(APIServer pid=556855)[0;0m INFO 05-19 12:25:00 [stage_pool.py:731] [StagePool] Stage 1 replica 0 shut down
+[0;36m(StageEngineCoreProc pid=558201)[0;0m [32mINFO[0m [90m05-19 12:25:00[0m [90m[core.py:1242][0m Shutdown initiated (timeout=0)
+[0;36m(StageEngineCoreProc pid=558201)[0;0m [32mINFO[0m [90m05-19 12:25:00[0m [90m[core.py:1265][0m Shutdown complete
+[0;36m(APIServer pid=556855)[0;0m WARNING 05-19 12:25:01 [async_omni_engine.py:2406] [AsyncOmniEngine] Orchestrator thread did not exit in time
+[rank0]:[W519 12:25:01.867242016 ProcessGroupNCCL.cpp:1575] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+```
+
+### How this maps to the matrix
+
+- This log corresponds to the `Send SIGTERM to serve main process` scenario in the matrix.
+- The `root-kill ... signal=SIGTERM` line confirms the fault injection action is applied as expected.
+- The sequence from `Shutting down Orchestrator` to stage-level `Shutdown complete` and `Stage ... shut down` matches the expected graceful shutdown path and child-process cleanup behavior described in the matrix.
+
+## Related Documentation
+
+- For practical CI debugging procedures and triage guidance, see [CI Failures](../contributing/ci/failures.md).

docs/user_guide/quantization/autoround.md

@@ -52,8 +52,8 @@ vLLM-Omni's runtime module names.
 
 | Model | Scope | Status | Notes |
 |-------|-------|--------|-------|
+| GLM-Image | Diffusion transformer  | ✅ | `Intel/GLM-Image-int4-AutoRound` |
 | BAGEL | Checkpoint-defined diffusion or transformer stage | Not validated | Requires a compatible AutoRound checkpoint |
-| GLM-Image | Checkpoint-defined diffusion or transformer stage | Not validated | Requires a compatible AutoRound checkpoint |
 
 ## Configuration
 

docs/user_guide/quantization/fp8.md

@@ -28,6 +28,50 @@ in deep DiT blocks.
 Legend: `✅` supported, `❌` unsupported, `⭕` not verified in this
 guide. FP8 on Ampere may use a weight-only path where available.
 
+### Faster FP8 GEMM on Blackwell (quack)
+
+On Blackwell (SM 100+), vLLM runs FP8 linears through the FlashInfer kernel, which
+applies the bias as a separate kernel after the GEMM. On the small GEMMs in video
+DiTs this bias add is a significant overhead. Installing the optional `quack` kernel
+lets vLLM-Omni fuse `alpha * (A @ B) + bias` into a single CuteDSL GEMM, recovering
+that overhead (e.g. HunyuanVideo-1.5 FP8 goes from slower-than-BF16 to faster).
+
+```bash
+# CUDA 12.9
+pip install vllm-omni[quack]
+
+# CUDA 13.x
+pip install 'quack-kernels[cu13]' --extra-index-url https://download.pytorch.org/whl/cu130
+```
+
+It is enabled automatically once installed (no flag needed) and is **Blackwell-only**:
+on Hopper/Ada the CUTLASS FP8 kernel already fuses bias, so quack is not used there.
+Set `VLLM_OMNI_USE_QUACK_FP8=0` to force the FlashInfer path. If `quack-kernels` is
+not installed, FP8 still works — it just keeps the unfused FlashInfer path.
+
+#### Compile cache and warmup
+
+quack JIT-compiles its kernel once per distinct GEMM shape (tens of seconds, longer
+the first time across all autotuned configs). The compiled `.o` files are cached on
+disk and reused on later runs, so this is a one-time cost — **not** per request.
+
+vLLM-Omni points that cache at `~/.cache/vllm_omni/quack` (override with
+`QUACK_CACHE_DIR`) instead of quack's default under `/tmp`, so it survives restarts.
+In containers, set `QUACK_CACHE_DIR` to a mounted/persistent path — or bake it into
+the image — so the first cold start does not recompile. The engine's startup dummy
+run already exercises the kernels, so with a warm cache the first real request is fast.
+
+To pre-warm specific shapes (e.g. at image build time):
+
+```python
+from vllm_omni.quantization.quack_fp8 import warmup_quack_fp8
+# (M, K, N) per linear; M = number of tokens for your resolution/frame count
+warmup_quack_fp8([(14040, 2048, 6144), (14040, 2048, 2048)])
+```
+
+> The PyPI package is `quack-kernels` (imported as `quack`); plain `pip install
+> quack` is an unrelated statistics library. Requires CUDA 12.9+ and Python 3.12.
+
 ## Model Type Support
 
 ### Diffusion Model (Qwen-Image, Wan2.2)

docs/user_guide/quantization/gguf.md

@@ -74,7 +74,7 @@ Online:
 vllm serve Qwen/Qwen-Image \
   --omni \
   --port 8000 \
-  --quantization-config '{"method":"gguf","gguf_model":"QuantStack/Qwen-Image-GGUF/Qwen_Image-Q4_K_M.gguf"}'
+  --diffusion-quantization-config '{"method":"gguf","gguf_model":"QuantStack/Qwen-Image-GGUF/Qwen_Image-Q4_K_M.gguf"}'
 ```
 
 ## Parameters

docs/user_guide/quantization/mxfp8.md

@@ -7,26 +7,28 @@ using the OCP MX format: groups of 32 K-dimension elements share a single
 `float8_e8m0fnu` exponent scale. This gives better accuracy than channel-wise
 FP8 while keeping the same 8-bit weight footprint.
 
-This method supports two modes:
+This method supports three modes:
 
 | Mode | Description |
 |------|-------------|
 | **Online** | BF16 weights are quantized to MXFP8 at load time — no pre-processing needed |
-| **Offline** | msModelSlim-exported MXFP8 weights converted to diffusers format via `merge_mxfp8_checkpoint.py` — weights and scales are loaded directly from the preprocessed checkpoint |
+| **Offline (Native)** | msModelSlim-exported MXFP8 weights converted to diffusers format via `merge_mxfp8_checkpoint.py` — weights and scales are loaded directly from the preprocessed checkpoint |
+| **Offline (AutoRound)** | AutoRound MXFP8 checkpoints with `data_type="mx_fp"` — auto-detected from `config.json` |
 
 ## Hardware Support
 
-| Device | Support |
-|--------|---------|
-| NVIDIA Blackwell GPU (SM 100+) | ⭕ |
-| NVIDIA Ada/Hopper GPU (SM 89+) | ⭕ |
-| NVIDIA Ampere GPU (SM 80+) | ⭕ |
-| AMD ROCm | ⭕ |
-| Intel XPU | ⭕ |
-| Ascend NPU (Atlas 950 A5) | ✅ |
+| Device | Online | Offline (Native) | Offline (AutoRound) |
+|--------|--------|------------------|---------------------|
+| NVIDIA Blackwell GPU (SM 100+) | ⭕ | ⭕ | ⭕ |
+| NVIDIA Ada/Hopper GPU (SM 89+) | ⭕ | ⭕ | ⭕ |
+| NVIDIA Ampere GPU (SM 80+) | ⭕ | ⭕ | ⭕ |
+| AMD ROCm | ⭕ | ⭕ | ⭕ |
+| Intel XPU | ✅ | ❌ | ✅ |
+| Ascend NPU (Atlas 950 A5) | ✅ | ✅ | ⭕ |
 
-Legend: `✅` supported, `❌` unsupported, `⭕` not verified in this
-guide.
+Legend: `✅` supported, `❌` unsupported, `⭕` not verified in this guide.
+
+**Note**: Intel XPU only supports AutoRound MXFP8 for offline mode. Use AutoRound quantized checkpoints or online mode for XPU.
 
 ## Model Type Support
 
@@ -82,9 +84,9 @@ python text_to_video.py --model <your-model> --quantization mxfp8
 vllm serve <your-model> --omni --quantization mxfp8
 ```
 
-### Offline Mode
+### Offline Mode (Native)
 
-Offline mode loads a pre-quantized checkpoint from msModelSlim. A preprocessing
+Native offline mode loads a pre-quantized checkpoint from msModelSlim. A preprocessing
 step converts the raw quantized output to the diffusers format expected by
 vLLM-Omni and injects the quantization config into `transformer/config.json` so
 that vLLM-Omni auto-detects the offline path without a `--quantization` flag.
@@ -159,10 +161,41 @@ omni = Omni(model="/path/to/Wan2.2-TI2V-5B-MXFP8")
 ```
 
 !!! note
-    No `--quantization` flag is needed for offline mode. The preprocessing
+    No `--quantization` flag is needed for native offline mode. The preprocessing
     script injects `quantization_config` into each `transformer/config.json`,
     which vLLM-Omni reads automatically to activate the offline MXFP8 method.
 
+### Offline Mode (AutoRound)
+
+AutoRound MXFP8 checkpoints declare `quant_method="auto-round"` with `data_type="mx_fp"` in their `config.json`. These are automatically detected and use the `IncMxfp8OfflineLinearMethod` backend.
+
+To use an AutoRound MXFP8 checkpoint:
+
+```bash
+python text_to_video.py --model <autoround-mxfp8-model>
+
+# Online serving
+vllm serve <autoround-mxfp8-model> --omni
+```
+
+Python API:
+
+```python
+omni = Omni(model="<autoround-mxfp8-model>")
+```
+
+!!! note
+    AutoRound MXFP8 checkpoints are auto-detected from `config.json` and do not require a `--quantization` flag. The config must include:
+    ```json
+    {
+      "quantization_config": {
+        "quant_method": "auto-round",
+        "data_type": "mx_fp",
+        ...
+      }
+    }
+    ```
+
 ## Parameters
 
 | Parameter | Type | Default | Description |

docs/user_guide/quantization/online.md

@@ -21,7 +21,7 @@ by msModelSlim and the merge tools.
 | NVIDIA Ada/Hopper GPU (SM 89+) | ✅ | ✅ | ⭕ | ⭕ |
 | NVIDIA Ampere GPU (SM 80+) | ✅ | ✅ | ⭕ | ⭕ |
 | AMD ROCm | ⭕ | ⭕ | ⭕ | ⭕ |
-| Intel XPU | ⭕ | ⭕ | ⭕ | ⭕ |
+| Intel XPU | ⭕ | ⭕ | ✅ | ⭕ |
 | Ascend NPU | ❌ | ✅ | ✅ | ✅ |
 
 Legend: `✅` supported, `❌` unsupported, `⭕` not verified in this
@@ -36,7 +36,7 @@ MXFP4 are documented for the Ascend NPU path.
 |--------|-------|----------------|--------|
 | FP8 W8A8 | [FP8](fp8.md) | Qwen-Image; Wan2.2 is not validated | Validated for Qwen-Image family and other DiT models |
 | Int8 W8A8 | [Int8](int8.md) | Qwen-Image; Wan2.2 is not validated | Validated for Qwen-Image and Z-Image |
-| MXFP8 W8A8 | [MXFP8](mxfp8.md) | Wan2.2-T2V-A14B, Wan2.2-I2V-A14B, Wan2.2-TI2V-5B | Ascend NPU only |
+| MXFP8 W8A8 | [MXFP8](mxfp8.md) | Wan2.2-T2V-A14B, Wan2.2-I2V-A14B, Wan2.2-TI2V-5B | Validated on Ascend NPU and Intel XPU |
 | MXFP4 W4A4 | [MXFP4](mxfp4.md) | Wan2.2-T2V-A14B, Wan2.2-I2V-A14B | Ascend NPU only; TI2V-5B is not supported |
 
 ### Multi-Stage Omni/TTS Model (Qwen3-Omni, Qwen3-TTS)

examples/offline_inference/bagel/README.md

@@ -195,6 +195,101 @@ stages:
     devices: "0,1"
 ```
 
+### VAE Patch Parallelism
+
+[VAE Patch Parallelism](https://docs.vllm.ai/projects/vllm-omni/en/latest/user_guide/diffusion/parallelism/vae_patch_parallel.html) splits Bagel VAE **decode/encode** tiles across multiple GPUs on the **DiT stage**, reducing **per-GPU peak memory during VAE decode**. Use it when high-resolution `text2img` or `img2img` hits VAE OOM or large decode spikes.
+
+**Bagel-specific notes:**
+
+- Implemented in `BagelPipeline` via `DistributedAutoEncoder` (DiT stage only).
+- **Single-stage** is the simplest path: one DiT process with TP + VAE patch parallel.
+- **Two-stage**: enable on **stage 1 (DiT)** only; stage 0 (Thinker) keeps encoder-only `VAEEncoder` and does not use VAE patch parallel.
+- You need a DiT `world_size` ≥ `vae_patch_parallel_size` (typically `tensor_parallel_size=2` on that stage). VAE PP reuses the DiT process group; it is not a standalone second-GPU VAE worker.
+
+**Single-stage via deploy YAML** (recommended for `end2end.py`):
+
+```yaml
+pipeline: bagel_single_stage
+async_chunk: false
+
+stages:
+  - stage_id: 0
+    max_num_batched_tokens: 32768
+    max_num_seqs: 1
+    enforce_eager: true
+    trust_remote_code: true
+    enable_prefix_caching: false
+    devices: "0,1"
+    vae_use_tiling: true
+    parallel_config:
+      tensor_parallel_size: 2
+      vae_patch_parallel_size: 2
+    default_sampling_params:
+      seed: 52
+```
+
+```bash
+cd examples/offline_inference/bagel
+
+CUDA_VISIBLE_DEVICES=0,1 python end2end.py \
+    --model /path/to/BAGEL-7B-MoT \
+    --deploy-config /path/to/bagel_single_stage_vae_pp.yaml \
+    --modality text2img \
+    --prompts "A cute cat" \
+    --steps 10 \
+    --output ./out_vae_pp
+```
+
+**Single-stage via `Omni` kwargs** (same flags as online serving):
+
+```python
+from vllm_omni.entrypoints.omni import Omni
+
+omni = Omni(
+    model="ByteDance-Seed/BAGEL-7B-MoT",
+    deploy_config="vllm_omni/deploy/bagel_single_stage.yaml",
+    tensor_parallel_size=2,
+    vae_patch_parallel_size=2,
+    vae_use_tiling=True,
+)
+# Then call omni.generate(...) as in end2end.py
+```
+
+**Two-stage (VAE PP on DiT only):**
+
+```yaml
+stages:
+  - stage_id: 0
+    devices: "0"
+    # AR Thinker — no vae_patch_parallel here
+
+  - stage_id: 1
+    devices: "0,1"
+    vae_use_tiling: true
+    parallel_config:
+      tensor_parallel_size: 2
+      vae_patch_parallel_size: 2
+```
+
+```bash
+python end2end.py --model ByteDance-Seed/BAGEL-7B-MoT \
+    --deploy-config /path/to/bagel_vae_pp.yaml \
+    --modality text2img \
+    --prompts "A cute cat"
+```
+
+**Startup log checks:**
+
+```text
+INFO ... vae_patch_parallel_size=2 requires vae_use_tiling; automatically enabling it.
+```
+
+| Setting | Role |
+| :------ | :--- |
+| `parallel_config.tensor_parallel_size` | DiT world size / TP (must be ≥ `vae_patch_parallel_size`) |
+| `parallel_config.vae_patch_parallel_size` | Number of ranks for distributed VAE tiles (`1` = off) |
+| `vae_use_tiling` | Enable spatial tiling (auto-enabled when `vae_patch_parallel_size > 1`) |
+
 #### Hybrid Sharded Data Parallel (HSDP)
 
 For larger Bagel deployments on multiple GPUs, you can enable HSDP (Hybrid Sharded Data Parallel) by modifying the stage configuration (for example, [`bagel.yaml`](../../../vllm_omni/deploy/bagel.yaml)). HSDP shards transformer weights across GPUs to reduce per-GPU memory usage.

examples/offline_inference/bagel/end2end.py

@@ -121,9 +121,6 @@ def parse_args():
         help="Temperature for text generation sampling (default: 0.3).",
     )
 
-    from vllm_omni.engine.arg_utils import nullify_stage_engine_defaults
-
-    nullify_stage_engine_defaults(parser)
     args = parser.parse_args()
     return args
 
@@ -154,6 +151,7 @@ def main():
     from vllm_omni.entrypoints.omni import Omni
 
     omni_kwargs = vars(args).copy()
+    omni_kwargs.pop("model", None)
     deploy_config = args.deploy_config
     if args.think and deploy_config is None:
         deploy_config = "vllm_omni/deploy/bagel_think.yaml"
@@ -164,9 +162,7 @@ def main():
     if args.quantization:
         omni_kwargs["quantization_config"] = args.quantization
 
-    # Override CLI --model with the derived model_name.
-    omni_kwargs["model"] = model_name
-    omni = Omni(**omni_kwargs)
+    omni = Omni(model=model_name, **omni_kwargs)
 
     formatted_prompts = []
     for p in prompts:

examples/offline_inference/cosmos3/inputs/v2v.json

@@ -0,0 +1,14 @@
+{
+    "prompt": "A robotic arm, primarily white with black joints and cables, is shown in a clean, modern indoor setting with a white tabletop. The arm, equipped with a gripper holding a small, light green pitcher, is positioned above a clear glass containing a reddish-brown liquid and a spoon. The robotic arm is in the process of pouring a transparent liquid into the glass. To the left of the pitcher, there is an opened jar with a similar reddish-brown substance visible through its transparent body. In the background, a vase with white flowers and a brown couch are partially visible, adding to the contemporary ambiance. The lighting is bright, casting soft shadows on the table. The robotic arm's movements are smooth and controlled, demonstrating precision in its task. As the video progresses, the robotic arm completes the pour, leaving the glass half-filled with the reddish-brown liquid. The jar remains untouched throughout the sequence, and the spoon inside the glass remains stationary. The other robotic arm on the right side also stays stationary throughout the video. The final frame captures the robotic arm with the pitcher finishing the pour, with the glass now filled to a higher level, while the pitcher is slightly tilted but still held securely by the gripper.",
+    "negative_prompt": "",
+    "vision_path": "https://github.com/nvidia-cosmos/cosmos-dependencies/raw/refs/heads/assets/cosmos3/inputs/vision/robot_pouring.mp4",
+    "height": 720,
+    "width": 1280,
+    "num_frames": 189,
+    "num_inference_steps": 35,
+    "guidance_scale": 6.0,
+    "flow_shift": 10.0,
+    "fps": 24,
+    "condition_frame_indexes_vision": [0, 1],
+    "condition_video_keep": "first"
+}

examples/offline_inference/dreamzero/export_prediction_video.py

@@ -0,0 +1,304 @@
+#!/usr/bin/env python3
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from __future__ import annotations
+
+import argparse
+import uuid
+from pathlib import Path
+
+import cv2
+import numpy as np
+import torch
+from PIL import Image
+
+from vllm_omni import Omni
+from vllm_omni.inputs.data import OmniDiffusionSamplingParams
+from vllm_omni.outputs import OmniRequestOutput
+
+WORKER_EXTENSION = "vllm_omni.diffusion.models.dreamzero.video_export_worker.DreamZeroVideoExportWorkerExtension"
+ASSET_REPO_ID = "YangshenDeng/vllm-omni-dreamzero-assets"
+DEFAULT_SESSION_PREFIX = "dreamzero-export"
+RELATIVE_OFFSETS = [-23, -16, -8, 0]
+ACTION_HORIZON = 24
+CAMERA_FILES = {
+    "observation/exterior_image_0_left": "exterior_image_1_left.mp4",
+    "observation/exterior_image_1_left": "exterior_image_2_left.mp4",
+    "observation/wrist_image_left": "wrist_image_left.mp4",
+}
+
+
+def _parse_args() -> argparse.Namespace:
+    repo_root = Path(__file__).resolve().parents[3]
+    parser = argparse.ArgumentParser(description="Export DreamZero prediction video from downloaded example inputs.")
+    parser.add_argument("--model", default="GEAR-Dreams/DreamZero-DROID")
+    parser.add_argument("--deploy-config", type=Path, required=True)
+    parser.add_argument(
+        "--video-dir",
+        type=Path,
+        default=repo_root / "outputs" / "dreamzero" / "assets",
+        help="Directory containing the three camera MP4 files.",
+    )
+    parser.add_argument(
+        "--output-dir", type=Path, default=repo_root / "outputs" / "dreamzero" / "generated_predictions"
+    )
+    parser.add_argument("--output-stem", default="dreamzero_prediction")
+    parser.add_argument(
+        "--prompt",
+        default="Move the pan forward and use the brush in the middle of the plates to brush the inside of the pan",
+    )
+    parser.add_argument("--session-id", default=None)
+    parser.add_argument("--save-input-video", action="store_true")
+    parser.add_argument("--save-gif", action="store_true")
+    parser.add_argument("--save-actions", action="store_true")
+    parser.add_argument("--fps", type=int, default=5)
+    return parser.parse_args()
+
+
+def _load_all_frames(video_path: Path) -> np.ndarray:
+    cap = cv2.VideoCapture(str(video_path))
+    frames = []
+    while True:
+        ok, frame = cap.read()
+        if not ok:
+            break
+        frames.append(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+    cap.release()
+    if not frames:
+        raise RuntimeError(f"No frames loaded from {video_path}")
+    return np.stack(frames, axis=0)
+
+
+def _load_camera_frames(video_dir: Path) -> dict[str, np.ndarray]:
+    camera_frames: dict[str, np.ndarray] = {}
+    for camera_key, file_name in CAMERA_FILES.items():
+        video_path = video_dir / file_name
+        if not video_path.exists():
+            raise FileNotFoundError(
+                f"Missing DreamZero example asset: {video_path}. "
+                "Download the example videos with: "
+                f"`hf download {ASSET_REPO_ID} --repo-type dataset --local-dir {video_dir}`"
+            )
+        camera_frames[camera_key] = _load_all_frames(video_path)
+    return camera_frames
+
+
+def _build_frame_schedule(total_frames: int, num_chunks: int) -> list[list[int]]:
+    chunks: list[list[int]] = []
+    current_frame = 23
+    for _ in range(num_chunks):
+        indices = [max(current_frame + offset, 0) for offset in RELATIVE_OFFSETS]
+        if indices[-1] >= total_frames:
+            break
+        chunks.append(indices)
+        current_frame += ACTION_HORIZON
+    return chunks
+
+
+def _make_obs_from_video(
+    camera_frames: dict[str, np.ndarray],
+    frame_indices: list[int],
+    *,
+    prompt: str,
+    session_id: str,
+) -> dict:
+    obs: dict = {}
+    for camera_key, all_frames in camera_frames.items():
+        selected = all_frames[frame_indices]
+        obs[camera_key] = selected[0] if len(frame_indices) == 1 else selected
+
+    obs["observation/joint_position"] = np.zeros(7, dtype=np.float32)
+    obs["observation/cartesian_position"] = np.zeros(6, dtype=np.float32)
+    obs["observation/gripper_position"] = np.zeros(1, dtype=np.float32)
+    obs["prompt"] = prompt
+    obs["session_id"] = session_id
+    return obs
+
+
+def _build_observations(video_dir: Path, prompt: str, session_id: str) -> tuple[dict[str, np.ndarray], list[dict]]:
+    camera_frames = _load_camera_frames(video_dir)
+    total_frames = min(frames.shape[0] for frames in camera_frames.values())
+    chunks = _build_frame_schedule(total_frames, 1)
+    observations = [
+        _make_obs_from_video(camera_frames, [0], prompt=prompt, session_id=session_id),
+    ]
+    if chunks:
+        observations.append(
+            _make_obs_from_video(
+                camera_frames,
+                chunks[0],
+                prompt=prompt,
+                session_id=session_id,
+            )
+        )
+    if len(observations) < 2:
+        raise RuntimeError("Need at least two DreamZero example observations to export a prediction video.")
+    return camera_frames, observations[:2]
+
+
+def _extract_latents(output: OmniRequestOutput) -> torch.Tensor:
+    if not isinstance(output, OmniRequestOutput):
+        raise TypeError(f"Expected OmniRequestOutput, got {type(output)!r}")
+    if not output.images:
+        raise RuntimeError("DreamZero output does not contain video latents in `images`.")
+
+    latents = output.images[0]
+    if not isinstance(latents, torch.Tensor):
+        raise TypeError(f"Expected tensor latents, got {type(latents)!r}")
+
+    latents = latents.detach().cpu()
+    if latents.dim() == 4:
+        latents = latents.unsqueeze(0)
+    if latents.dim() != 5:
+        raise ValueError(f"Unexpected latent shape: {tuple(latents.shape)}")
+
+    if latents.shape[1] < latents.shape[2]:
+        latents = latents.transpose(1, 2).contiguous()
+    return latents
+
+
+def _write_mp4(path: Path, frames: np.ndarray, fps: int) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    height, width = frames.shape[1:3]
+    writer = cv2.VideoWriter(
+        str(path),
+        cv2.VideoWriter_fourcc(*"mp4v"),
+        float(fps),
+        (width, height),
+    )
+    if not writer.isOpened():
+        raise RuntimeError(f"Failed to open video writer for {path}")
+    try:
+        for frame in frames:
+            writer.write(cv2.cvtColor(frame, cv2.COLOR_RGB2BGR))
+    finally:
+        writer.release()
+
+
+def _write_gif(path: Path, frames: np.ndarray, fps: int) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    images = [Image.fromarray(frame) for frame in frames]
+    duration_ms = max(int(round(1000 / max(fps, 1))), 1)
+    images[0].save(
+        path,
+        save_all=True,
+        append_images=images[1:],
+        duration=duration_ms,
+        loop=0,
+    )
+
+
+def _stitch_input_frames(camera_frames: dict[str, np.ndarray]) -> np.ndarray:
+    total_frames = min(frames.shape[0] for frames in camera_frames.values())
+    stitched = []
+    for frame_index in range(total_frames):
+        left = camera_frames["observation/exterior_image_0_left"][frame_index]
+        right = camera_frames["observation/exterior_image_1_left"][frame_index]
+        wrist = camera_frames["observation/wrist_image_left"][frame_index]
+        pad = np.zeros((left.shape[0], left.shape[1], 3), dtype=np.uint8)
+        canvas = np.concatenate([left, right], axis=1)
+        bottom = np.concatenate([wrist, pad], axis=1)
+        stitched.append(np.concatenate([canvas, bottom], axis=0))
+    return np.stack(stitched, axis=0)
+
+
+def _run_generation(
+    model: str, deploy_config_path: Path, observations: list[dict]
+) -> tuple[Omni, list[OmniRequestOutput]]:
+    omni = Omni(
+        model=model,
+        deploy_config=str(deploy_config_path),
+        enforce_eager=True,
+        worker_extension_cls=WORKER_EXTENSION,
+    )
+
+    outputs: list[OmniRequestOutput] = []
+    for index, obs in enumerate(observations):
+        sampling_params = OmniDiffusionSamplingParams(
+            extra_args={
+                "reset": index == 0,
+                "session_id": obs["session_id"],
+                "robot_obs": obs,
+            }
+        )
+        result = omni.generate(obs["prompt"], sampling_params_list=[sampling_params])
+        if not result:
+            raise RuntimeError(f"No output returned for DreamZero request {index}")
+        outputs.append(result[0])
+    return omni, outputs
+
+
+def _decode_with_worker(omni: Omni, full_latents: torch.Tensor) -> np.ndarray:
+    stage_client = omni.engine.stage_clients[0]
+    engine = getattr(stage_client, "_engine", None)
+    if engine is None:
+        raise RuntimeError("DreamZero export requires inline diffusion stage access.")
+
+    decoded = engine.executor.collective_rpc(
+        "decode_video_latents_to_uint8",
+        args=(full_latents,),
+        unique_reply_rank=0,
+        exec_all_ranks=True,
+    )
+    if isinstance(decoded, torch.Tensor):
+        decoded = decoded.numpy()
+    if not isinstance(decoded, np.ndarray):
+        raise TypeError(f"Unexpected decoded output type: {type(decoded)!r}")
+    return decoded
+
+
+def main() -> None:
+    args = _parse_args()
+    session_id = args.session_id or f"{DEFAULT_SESSION_PREFIX}-{uuid.uuid4()}"
+
+    camera_frames, observations = _build_observations(
+        video_dir=args.video_dir,
+        prompt=args.prompt,
+        session_id=session_id,
+    )
+
+    args.output_dir.mkdir(parents=True, exist_ok=True)
+
+    if args.save_input_video:
+        input_frames = _stitch_input_frames(camera_frames)
+        _write_mp4(args.output_dir / f"{args.output_stem}_input.mp4", input_frames, fps=15)
+        if args.save_gif:
+            _write_gif(args.output_dir / f"{args.output_stem}_input.gif", input_frames[::3], fps=5)
+
+    omni = None
+    try:
+        omni, outputs = _run_generation(
+            model=args.model,
+            deploy_config_path=args.deploy_config,
+            observations=observations,
+        )
+        latent_steps = [_extract_latents(output) for output in outputs]
+        full_latents = torch.cat(latent_steps, dim=2)
+        frames = _decode_with_worker(omni, full_latents)
+    finally:
+        if omni is not None:
+            omni.close()
+
+    mp4_path = args.output_dir / f"{args.output_stem}.mp4"
+
+    _write_mp4(mp4_path, frames, fps=args.fps)
+    print(f"SAVED_MP4={mp4_path}")
+
+    if args.save_gif:
+        gif_path = args.output_dir / f"{args.output_stem}.gif"
+        _write_gif(gif_path, frames, fps=args.fps)
+        print(f"SAVED_GIF={gif_path}")
+
+    if args.save_actions:
+        npz_path = args.output_dir / f"{args.output_stem}_actions.npz"
+        np.savez(
+            npz_path,
+            step0=np.asarray(outputs[0].multimodal_output.get("actions")),
+            step1=np.asarray(outputs[1].multimodal_output.get("actions")),
+        )
+        print(f"SAVED_ACTIONS={npz_path}")
+
+
+if __name__ == "__main__":
+    main()

examples/offline_inference/dynin_omni/README.md

@@ -42,7 +42,7 @@ pip install \
 ## 3. Hardware and VRAM Requirements
 
 This example uses a 3-stage pipeline on one GPU by default
-([`dynin_omni.yaml`](../../../vllm_omni/model_executor/stage_configs/dynin_omni.yaml)):
+([`dynin_omni.yaml`](../../../vllm_omni/deploy/dynin_omni.yaml)):
 
 - Stage-0 (`token2text`): `gpu_memory_utilization: 0.5`
 - Stage-1 (`token2image`): `gpu_memory_utilization: 0.1`

examples/offline_inference/dynin_omni/end2end.py

@@ -18,8 +18,6 @@ import numpy as np
 import torch
 from PIL import Image
 
-from vllm_omni.engine.arg_utils import nullify_stage_engine_defaults
-
 TASK_CHOICES = ("t2t", "t2i", "t2s", "i2i", "i2t", "s2t", "v2t")
 
 TASK_DEFAULT_RUNTIME = {
@@ -880,8 +878,8 @@ def parse_args(repo_root: Path) -> argparse.Namespace:
     parser.add_argument(
         "--stage-config-path",
         type=str,
-        default=str(repo_root / "vllm_omni/model_executor/stage_configs/dynin_omni.yaml"),
-        help="Path to stage config yaml.",
+        default=str(repo_root / "vllm_omni/deploy/dynin_omni.yaml"),
+        help="Path to deploy config yaml.",
     )
     parser.add_argument(
         "--dynin-config-path",
@@ -972,8 +970,6 @@ def parse_args(repo_root: Path) -> argparse.Namespace:
     parser.add_argument("--vq-model-audio-local-files-only", action=argparse.BooleanOptionalAction, default=None)
 
     parser.add_argument("--disable-hf-xet", action=argparse.BooleanOptionalAction, default=True)
-
-    nullify_stage_engine_defaults(parser)
     return parser.parse_args()
 
 

examples/offline_inference/hunyuan_image3/end2end.py

@@ -17,7 +17,7 @@ from vllm_omni.entrypoints.omni import Omni
 from vllm_omni.inputs.data import OmniPromptType
 
 _REPO_ROOT = Path(__file__).resolve().parents[3]
-_DEFAULT_DEPLOY_CONFIG = str(_REPO_ROOT / "vllm_omni" / "deploy" / "hunyuan_image3.yaml")
+_DEFAULT_DEPLOY_CONFIG = str(_REPO_ROOT / "vllm_omni" / "deploy" / "hunyuan_image_3_moe.yaml")
 _DEFAULT_AR_DEPLOY_CONFIG = str(_REPO_ROOT / "vllm_omni" / "deploy" / "hunyuan_image3_ar.yaml")
 
 _MODALITY_TASK_MAP: dict[str, tuple[str, str | None]] = {
@@ -61,8 +61,8 @@ def parse_args():
     parser.add_argument("--steps", type=int, default=50, help="Number of inference steps.")
     parser.add_argument("--guidance-scale", type=float, default=5.0, help="Classifier-free guidance scale.")
     parser.add_argument("--seed", type=int, default=42, help="Random seed.")
-    parser.add_argument("--height", type=int, default=1024, help="Output image height.")
-    parser.add_argument("--width", type=int, default=1024, help="Output image width.")
+    parser.add_argument("--height", type=int, default=None, help="Output image height.")
+    parser.add_argument("--width", type=int, default=None, help="Output image width.")
     parser.add_argument("--vae-use-tiling", action="store_true", help="Enable VAE tiling.")
     parser.add_argument(
         "--bot-task",
@@ -107,9 +107,6 @@ def parse_args():
         ),
     )
 
-    from vllm_omni.engine.arg_utils import nullify_stage_engine_defaults
-
-    nullify_stage_engine_defaults(parser)
     return parser.parse_args()
 
 
@@ -229,7 +226,21 @@ def main():
 
     from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 
-    ar_stop_token_ids = resolve_stop_token_ids(task=task, bot_task=bot_task, tokenizer=tokenizer)
+    if (args.height is None) != (args.width is None):
+        raise ValueError("--height and --width must both be specified or both omitted.")
+    user_specified_size = args.height is not None and args.width is not None
+    if args.modality in ("img2text", "text2text"):
+        ar_image_size = "auto"
+    elif user_specified_size:
+        ar_image_size = f"{args.width}x{args.height}"
+    else:
+        ar_image_size = None
+    ar_stop_token_ids = resolve_stop_token_ids(
+        task=task, bot_task=bot_task, tokenizer=tokenizer, image_size=ar_image_size
+    )
+    print(
+        f"[AR Config] task={task}, bot_task={bot_task}, image_size={ar_image_size}, stop_token_ids={ar_stop_token_ids}"
+    )
     for sp in params_list:
         if isinstance(sp, OmniDiffusionSamplingParams):
             sp.num_inference_steps = args.steps

examples/offline_inference/image_to_image/image_edit.py

@@ -143,6 +143,20 @@ def parse_args() -> argparse.Namespace:
         default=None,
         required=False,
     )
+    parser.add_argument(
+        "--width",
+        type=int,
+        default=None,
+        metavar="W",
+        help="Output image width in pixels. Default: None (pipeline's default).",
+    )
+    parser.add_argument(
+        "--height",
+        type=int,
+        default=None,
+        metavar="H",
+        help="Output image height in pixels. Default: None (pipeline's default).",
+    )
     parser.add_argument(
         "--seed",
         type=int,
@@ -236,8 +250,8 @@ def parse_args() -> argparse.Namespace:
     parser.add_argument(
         "--resolution",
         type=int,
-        default=640,
-        help="Bucket in (640, 1024) to determine the condition and output resolution",
+        default=None,
+        help="Bucket in (640, 1024) to determine the condition and output resolution. If width and height are not provided, this will be set to default 640.",
     )
 
     parser.add_argument(
@@ -384,6 +398,15 @@ def parse_args() -> argparse.Namespace:
 def main():
     args = parse_args()
 
+    if args.resolution and (args.width or args.height):
+        raise ValueError("--resolution and --width/--height cannot be specified together")
+    if args.width is not None and args.width <= 0:
+        raise ValueError("--width must be a positive integer")
+    if args.height is not None and args.height <= 0:
+        raise ValueError("--height must be a positive integer")
+    if not args.width and not args.height and not args.resolution:
+        args.resolution = 640
+
     # Validate input images exist and load them
     input_images = []
     for image_path in args.image:
@@ -464,6 +487,7 @@ def main():
     print(
         f"  Parallel configuration: ulysses_degree={args.ulysses_degree}, ring_degree={args.ring_degree}, cfg_parallel_size={args.cfg_parallel_size}, tensor_parallel_size={args.tensor_parallel_size}, enable_expert_parallel: {args.enable_expert_parallel}"
     )
+    print(f"  Output image size: {args.width or 'default'}x{args.height or 'default'}")
     print(f"{'=' * 60}\n")
 
     generation_start = time.perf_counter()
@@ -473,22 +497,26 @@ def main():
         omni.start_profile()
 
     # Generate edited image
+    sampling_params = OmniDiffusionSamplingParams(
+        width=args.width,
+        height=args.height,
+        generator=generator,
+        true_cfg_scale=args.cfg_scale,
+        guidance_scale=args.guidance_scale,
+        guidance_scale_2=args.guidance_scale_2,
+        num_inference_steps=args.num_inference_steps,
+        num_outputs_per_prompt=args.num_outputs_per_prompt,
+        layers=args.layers,
+    )
+    if args.resolution:
+        sampling_params.resolution = args.resolution
     outputs = omni.generate(
         {
             "prompt": args.prompt,
             "negative_prompt": args.negative_prompt,
             "multi_modal_data": {"image": input_image},
         },
-        OmniDiffusionSamplingParams(
-            generator=generator,
-            true_cfg_scale=args.cfg_scale,
-            guidance_scale=args.guidance_scale,
-            guidance_scale_2=args.guidance_scale_2,
-            num_inference_steps=args.num_inference_steps,
-            num_outputs_per_prompt=args.num_outputs_per_prompt,
-            layers=args.layers,
-            resolution=args.resolution,
-        ),
+        sampling_params,
     )
     generation_end = time.perf_counter()
     generation_time = generation_end - generation_start

examples/offline_inference/internvla_a1/end2end.py

@@ -127,7 +127,12 @@ def run_one_path(
             ),
             args.device,
         )
-        pred = run_pipeline_forward(pipeline, batch_inputs, noise)
+        pred = run_pipeline_forward(
+            pipeline,
+            batch_inputs,
+            noise,
+            request_id=f"internvla-a1-sample-{index}",
+        )
         pred = pred[:, :, : dataset.physical_action_dim].to(torch.float32).cpu()
         results.append(
             {
@@ -150,6 +155,7 @@ def run_pipeline_forward(
     pipeline,
     batch_inputs: dict[str, torch.Tensor],
     noise: torch.Tensor,
+    request_id: str,
 ) -> torch.Tensor:
     output = pipeline.forward(
         OmniDiffusionRequest(
@@ -161,6 +167,7 @@ def run_pipeline_forward(
                     "decode_image": False,
                 }
             ),
+            request_id=request_id,
         )
     )
     if output.error:
@@ -212,23 +219,38 @@ def benchmark_forward(
 
     _synchronize(args.device)
     cold_start_begin = time.perf_counter()
-    pred = run_pipeline_forward(pipeline, batch_inputs, noise)
+    pred = run_pipeline_forward(
+        pipeline,
+        batch_inputs,
+        noise,
+        request_id=f"internvla-a1-benchmark-{index}-cold",
+    )
     _synchronize(args.device)
     cold_start_ms = (time.perf_counter() - cold_start_begin) * 1000.0
 
     warmup_ms: list[float] = []
-    for _ in range(args.warmup_iters):
+    for iter_idx in range(args.warmup_iters):
         _synchronize(args.device)
         begin = time.perf_counter()
-        _ = run_pipeline_forward(pipeline, batch_inputs, noise)
+        _ = run_pipeline_forward(
+            pipeline,
+            batch_inputs,
+            noise,
+            request_id=f"internvla-a1-benchmark-{index}-warmup-{iter_idx}",
+        )
         _synchronize(args.device)
         warmup_ms.append((time.perf_counter() - begin) * 1000.0)
 
     benchmark_ms: list[float] = []
-    for _ in range(args.benchmark_iters):
+    for iter_idx in range(args.benchmark_iters):
         _synchronize(args.device)
         begin = time.perf_counter()
-        _ = run_pipeline_forward(pipeline, batch_inputs, noise)
+        _ = run_pipeline_forward(
+            pipeline,
+            batch_inputs,
+            noise,
+            request_id=f"internvla-a1-benchmark-{index}-iter-{iter_idx}",
+        )
         _synchronize(args.device)
         benchmark_ms.append((time.perf_counter() - begin) * 1000.0)
 
@@ -295,7 +317,12 @@ def main() -> None:
             dataset=dataset,
             train_meta=train_meta,
             collate_samples=collate_open_loop_samples,
-            run_sample_actions=lambda policy, batch_inputs, noise: run_pipeline_forward(policy, batch_inputs, noise),
+            run_sample_actions=lambda policy, batch_inputs, noise: run_pipeline_forward(
+                policy,
+                batch_inputs,
+                noise,
+                request_id="internvla-a1-open-loop",
+            ),
             num_episodes=args.num_episodes,
             seed=args.seed,
             device=args.device,

examples/offline_inference/lance/README.md

@@ -0,0 +1,52 @@
+# Lance (ByteDance) — offline inference
+
+[Lance](https://huggingface.co/bytedance-research/Lance) is a 3B unified
+autoregressive + diffusion multimodal model on a Qwen2.5-VL backbone. It is
+**BAGEL-lineage** (ByteDance Mixture-of-Transformers): the released `Lance_3B`
+checkpoint uses the same `*_moe_gen` MoT weight layout as BAGEL, so vLLM-Omni
+implements it by reusing the BAGEL transformer core and specializing only the
+ViT (Qwen2.5-VL vision), the VAE (Wan2.2) and the checkpoint layout.
+
+This example covers all six Lance modalities from the upstream HF model card:
+`t2i`, `t2v`, `image_edit`, `video_edit`, `x2t_image` (image understanding) and
+`x2t_video` (video understanding).
+
+## Hardware
+
+Single NVIDIA GPU with 16 GB+ VRAM in BF16 (we test on B300 / A100). CUDA ≥ 12.4.
+
+## Run
+
+```bash
+# Text-to-image
+python examples/offline_inference/lance/end2end.py \
+    --model bytedance-research/Lance \
+    --prompts "a corgi astronaut on the moon, cinematic" \
+    --steps 30 --cfg-text-scale 4.0 --timestep-shift 3.5 \
+    --height 1024 --width 1024 \
+    --output ./out
+
+# Text-to-video (uses the Lance_3B_Video subfolder; see ``--modality``
+# choices for all six task variants)
+python examples/offline_inference/lance/end2end.py \
+    --model bytedance-research/Lance/Lance_3B_Video --modality text2video \
+    --num-frames 25 --video-height 480 --video-width 768 \
+    --prompts "a cat playing piano, cinematic" \
+    --steps 30 --fps 8 --output ./out
+```
+
+`video_edit` requires `--model bytedance-research/Lance/Lance_3B_Video` so
+the 3-D `latent_pos_embed` table is loaded; the other paths can point at
+the top-level `bytedance-research/Lance` repo and resolve the right
+sub-checkpoint automatically.
+
+The HF repo bundles everything (`Lance_3B/`, `Lance_3B_Video/`,
+`Qwen2.5-VL-ViT/`, `Wan2.2_VAE.pth`); no separate downloads are required.
+
+## Defaults
+
+Matches upstream `inference_lance.sh`: 30 denoising steps, timestep-shift 3.5,
+text CFG 4.0, seed 42, 1024×1024 (override with `--height` / `--width`).  For
+the understanding paths (`img2text` / `video2text`), sampling is enabled by
+default at `--text-temperature 0.8` because Lance's greedy decoder emits an
+immediate EOS for many prompts.

examples/offline_inference/lance/end2end.py

@@ -0,0 +1,339 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Lance-3B offline inference (single-stage).
+
+Lance is BAGEL-lineage; this example covers the single-stage paths wired in
+this PR:
+
+    text2img  — image generation from text
+    img2text  — image understanding (caption / VQA)
+
+Examples:
+
+    python examples/offline_inference/lance/end2end.py \
+        --model bytedance-research/Lance \
+        --prompts "a corgi astronaut on the moon, cinematic" \
+        --output ./out
+
+    python examples/offline_inference/lance/end2end.py \
+        --model bytedance-research/Lance --modality img2text \
+        --image-path /path/to/photo.jpg \
+        --prompts "Describe this image in detail."
+"""
+
+import argparse
+import os
+
+
+def parse_args():
+    p = argparse.ArgumentParser()
+    p.add_argument("--model", default="bytedance-research/Lance", help="HF repo or local path.")
+    p.add_argument("--prompts", nargs="+", default=None, help="Input text prompts.")
+    p.add_argument("--txt-prompts", type=str, default=None, help="File with one prompt per line.")
+    p.add_argument(
+        "--modality",
+        default="text2img",
+        choices=["text2img", "img2text", "text2video", "video2text", "img2img", "video2video", "image2video"],
+        help="Lance single-stage modality.",
+    )
+    p.add_argument("--image-path", type=str, default=None, help="Input image for img2text.")
+    p.add_argument("--video-path", type=str, default=None, help="Input video file for video2text.")
+    p.add_argument("--num-frames", type=int, default=25, help="Number of RGB frames for text2video (max 121).")
+    p.add_argument("--video-height", type=int, default=480, help="Video frame height.")
+    p.add_argument("--video-width", type=int, default=768, help="Video frame width.")
+    p.add_argument("--height", type=int, default=None, help="Image height (t2i). Default = max_hw (1024).")
+    p.add_argument("--width", type=int, default=None, help="Image width (t2i). Default = max_hw (1024).")
+    p.add_argument(
+        "--fps", type=int, default=12, help="Output video FPS when saving MP4 (matches upstream Lance's save_fps=12)."
+    )
+    p.add_argument("--output", type=str, default=".", help="Output directory.")
+    p.add_argument("--steps", type=int, default=30, help="Denoising steps (Lance default 30).")
+    p.add_argument("--cfg-text-scale", type=float, default=4.0, help="Text CFG scale (Lance default 4.0).")
+    p.add_argument("--timestep-shift", type=float, default=3.5, help="Flow-match timestep shift (Lance default 3.5).")
+    p.add_argument("--negative-prompt", type=str, default=None)
+    p.add_argument("--seed", type=int, default=42)
+    p.add_argument("--max-text-tokens", type=int, default=512, help="Max tokens for img2text generation.")
+    p.add_argument(
+        "--do-sample",
+        action="store_true",
+        default=True,
+        help="Sample (vs greedy) for x2t generation; greedy frequently emits an immediate EOS for Lance.",
+    )
+    p.add_argument("--no-sample", dest="do_sample", action="store_false", help="Disable sampling (greedy decoding).")
+    p.add_argument(
+        "--text-temperature",
+        type=float,
+        default=0.8,
+        help="Sampling temperature for x2t generation. Lance frequently emits an immediate EOS below ~0.7; 0.8 is a good default.",
+    )
+    p.add_argument(
+        "--system-prompt",
+        type=str,
+        default=None,
+        help=(
+            "Override the task system prompt (used by x2t_image / x2t_video for "
+            'per-example QA instructions like "Look at the image carefully and '
+            'answer the question."; without it x2t falls back to a '
+            "caption-style default and the model describes instead of answering)."
+        ),
+    )
+    # Lance is single-stage diffusion — no deploy YAML needed.  Required
+    # engine knobs (``pipeline``, ``enforce_eager``, ``trust_remote_code``,
+    # ``max_num_seqs=1`` …) are passed as flat kwargs to ``Omni`` below
+    # and ``create_default_diffusion`` materializes the stage config.
+
+    from vllm_omni.engine.arg_utils import nullify_stage_engine_defaults
+
+    nullify_stage_engine_defaults(p)
+    return p.parse_args()
+
+
+from vllm_omni.diffusion.models.lance.prompts import (  # noqa: E402
+    VIDEO_PAD,
+    VISION_END,
+    VISION_START,
+    render_lance_prompt,
+)
+
+# Visual block matching upstream Lance: <|vision_start|><|video_pad|><|vision_end|>
+# (Lance's renderer uses ``<|video_pad|>`` even for image inputs by default.)
+_VISION_BLOCK = f"{VISION_START}{VIDEO_PAD}{VISION_END}"
+
+
+def _format_text2img_prompts(prompts):
+    return [{"prompt": render_lance_prompt("t2i", p), "modalities": ["image"]} for p in prompts]
+
+
+def _format_text2video_prompts(prompts, num_frames, video_h, video_w):
+    extra = {"num_frames": num_frames, "video_height": video_h, "video_width": video_w}
+    return [{"prompt": render_lance_prompt("t2v", p), "modalities": ["video"], "extra_args": extra} for p in prompts]
+
+
+def _format_image2video_prompts(prompts, image_path, num_frames, video_h, video_w):
+    """Image-to-Video (no first-frame pin): image + text → long video.
+
+    Passes the input image via ``multi_modal_data.first_frame``.  The pipeline
+    treats it as a 1-frame reference (VAE+ViT prefill) and generates a fresh
+    multi-frame video at the requested ``num_frames`` × ``video_h`` ×
+    ``video_w`` shape.
+    """
+    import os as _os
+
+    if not image_path or not _os.path.exists(image_path):
+        raise ValueError(f"image2video requires --image-path pointing to an existing file, got: {image_path}")
+    from PIL import Image as _Image
+
+    img = _Image.open(image_path).convert("RGB")
+    extra = {"num_frames": num_frames, "video_height": video_h, "video_width": video_w}
+    return [
+        {
+            "prompt": render_lance_prompt("i2v", p, vision_token=_VISION_BLOCK),
+            "multi_modal_data": {"first_frame": img},
+            "modalities": ["video"],
+            "extra_args": extra,
+        }
+        for p in prompts
+    ]
+
+
+def _format_img2img_prompts(prompts, image_path):
+    import os as _os
+
+    if not image_path or not _os.path.exists(image_path):
+        raise ValueError(f"img2img requires --image-path pointing to an existing file, got: {image_path}")
+    from PIL import Image as _Image
+
+    img = _Image.open(image_path).convert("RGB")
+    return [
+        {
+            "prompt": render_lance_prompt("image_edit", p, vision_token=_VISION_BLOCK),
+            "multi_modal_data": {"img2img": img},
+            "modalities": ["image"],
+        }
+        for p in prompts
+    ]
+
+
+def _format_video2video_prompts(prompts, video_path, num_frames, video_h, video_w):
+    import os as _os
+
+    if not video_path or not _os.path.exists(video_path):
+        raise ValueError(f"video2video requires --video-path pointing to an existing file, got: {video_path}")
+
+    extra = {"num_frames": num_frames, "video_height": video_h, "video_width": video_w}
+    return [
+        {
+            "prompt": render_lance_prompt("video_edit", p, vision_token=_VISION_BLOCK),
+            "multi_modal_data": {"video": video_path},
+            "modalities": ["video"],
+            "extra_args": extra,
+        }
+        for p in prompts
+    ]
+
+
+def _format_video2text_prompts(prompts, video_path, system_prompt=None):
+    import os as _os
+
+    if not video_path or not _os.path.exists(video_path):
+        raise ValueError(f"video2text requires --video-path pointing to an existing file, got: {video_path}")
+    import imageio.v3 as iio
+
+    video = iio.imread(video_path)
+    return [
+        {
+            "prompt": render_lance_prompt("x2t_video", p, vision_token=_VISION_BLOCK, system_prompt=system_prompt),
+            "multi_modal_data": {"video": video},
+            "modalities": ["text"],
+        }
+        for p in prompts
+    ]
+
+
+def _format_img2text_prompts(prompts, image_path, system_prompt=None):
+    from PIL import Image
+
+    if not image_path or not os.path.exists(image_path):
+        raise ValueError(f"img2text requires --image-path pointing to an existing file, got: {image_path}")
+    img = Image.open(image_path).convert("RGB")
+    return [
+        {
+            "prompt": render_lance_prompt("x2t_image", p, vision_token=_VISION_BLOCK, system_prompt=system_prompt),
+            "multi_modal_data": {"image": img},
+            "modalities": ["text"],
+        }
+        for p in prompts
+    ]
+
+
+def main():
+    args = parse_args()
+    os.makedirs(args.output, exist_ok=True)
+
+    # Lance aligns more naturally with upstream when using FLASH_ATTN
+    # (upstream uses flash_attn_varlen_func; SDPA accumulates ~5x more
+    # numerical drift through the 36-layer Qwen2 stack on B300).  Default
+    # to flash_attn when the user hasn't explicitly set a backend.
+    os.environ.setdefault("DIFFUSION_ATTENTION_BACKEND", "FLASH_ATTN")
+
+    if args.txt_prompts:
+        with open(args.txt_prompts, encoding="utf-8") as f:
+            prompts = [ln.strip() for ln in f if ln.strip()]
+    elif args.modality == "img2text":
+        prompts = args.prompts or ["Describe this image in detail."]
+    else:
+        prompts = args.prompts or ["A cute cat, studio lighting, 4k"]
+
+    from vllm_omni.entrypoints.omni import Omni
+
+    # Video modalities require the Lance_3B_Video checkpoint (3D
+    # latent_pos_embed shaped (126976, 2048)).  Without it, vllm-omni silently
+    # loads Lance_3B (4096, 2048) — the image-only table — and any t_lat >= 1
+    # immediately indexes out of bounds.  Rewrite the model path so
+    # LancePipeline._select_video_variant picks the video subfolder.
+    resolved_model = args.model
+    if args.modality in ("text2video", "video2video", "video2text", "image2video"):
+        if not str(resolved_model).rstrip("/").endswith("Lance_3B_Video"):
+            candidate = os.path.join(resolved_model, "Lance_3B_Video")
+            if os.path.isdir(candidate):
+                resolved_model = candidate
+
+    omni_kwargs = vars(args).copy()
+    omni_kwargs["model"] = resolved_model
+    # Lance single-stage defaults (formerly in vllm_omni/deploy/lance.yaml):
+    omni_kwargs.setdefault("pipeline", "lance")
+    omni_kwargs.setdefault("max_num_batched_tokens", 32768)
+    omni_kwargs.setdefault("max_num_seqs", 1)
+    omni_kwargs.setdefault("enforce_eager", True)
+    omni_kwargs.setdefault("trust_remote_code", True)
+    omni_kwargs.setdefault("enable_prefix_caching", False)
+    omni_kwargs.setdefault("async_chunk", False)
+    omni = Omni(**omni_kwargs)
+
+    if args.modality == "img2text":
+        formatted = _format_img2text_prompts(prompts, args.image_path, system_prompt=args.system_prompt)
+    elif args.modality == "text2video":
+        formatted = _format_text2video_prompts(prompts, args.num_frames, args.video_height, args.video_width)
+    elif args.modality == "video2text":
+        formatted = _format_video2text_prompts(prompts, args.video_path, system_prompt=args.system_prompt)
+    elif args.modality == "img2img":
+        formatted = _format_img2img_prompts(prompts, args.image_path)
+    elif args.modality == "video2video":
+        formatted = _format_video2video_prompts(
+            prompts, args.video_path, args.num_frames, args.video_height, args.video_width
+        )
+    elif args.modality == "image2video":
+        formatted = _format_image2video_prompts(
+            prompts, args.image_path, args.num_frames, args.video_height, args.video_width
+        )
+    else:
+        formatted = _format_text2img_prompts(prompts)
+
+    params_list = omni.default_sampling_params_list
+    diffusion_params = params_list[0]  # single-stage: one param set
+    diffusion_params.num_inference_steps = args.steps  # type: ignore
+    if args.seed is not None:
+        diffusion_params.seed = args.seed  # type: ignore
+    if args.height is not None:
+        diffusion_params.height = args.height  # type: ignore
+    if args.width is not None:
+        diffusion_params.width = args.width  # type: ignore
+    extra = getattr(diffusion_params, "extra_args", {}) or {}
+    extra["cfg_text_scale"] = args.cfg_text_scale
+    extra["timestep_shift"] = args.timestep_shift
+    if args.modality in ("img2text", "video2text"):
+        extra["max_think_tokens"] = args.max_text_tokens
+        extra["do_sample"] = args.do_sample
+        extra["text_temperature"] = args.text_temperature
+    if args.negative_prompt is not None:
+        extra["negative_prompt"] = args.negative_prompt
+    diffusion_params.extra_args = extra  # type: ignore
+
+    outputs = list(omni.generate(prompts=formatted, sampling_params_list=params_list))
+
+    if args.modality in ("img2text", "video2text"):
+        for i, req_output in enumerate(outputs):
+            text = getattr(req_output, "output", None) or getattr(req_output, "text", None)
+            if text is None and hasattr(req_output, "custom_output"):
+                text = (req_output.custom_output or {}).get("text_output")
+            print(f"[Output {i}] {text}")
+        return
+
+    if args.modality in ("text2video", "video2video", "image2video"):
+        for i, req_output in enumerate(outputs):
+            custom = getattr(req_output, "custom_output", None) or {}
+            frames = custom.get("video_frames")
+            if not frames:
+                print(f"[Output {i}] no video frames returned")
+                continue
+            mp4_path = os.path.join(args.output, f"lance_{i}.mp4")
+            try:
+                import imageio.v3 as iio
+                import numpy as np
+
+                arr = np.stack([np.asarray(f) for f in frames], axis=0)
+                iio.imwrite(mp4_path, arr, fps=args.fps, codec="libx264", quality=8)
+                print(f"[Output {i}] Saved MP4 ({len(frames)} frames, {arr.shape[1]}x{arr.shape[2]}) to {mp4_path}")
+            except Exception as exc:
+                print(f"[Output {i}] MP4 encode failed ({exc!r}); writing per-frame ONGs instead")
+                frame_dir = os.path.join(args.output, f"lance_{i}_frames")
+                os.makedirs(frame_dir, exist_ok=True)
+                for j, frame in enumerate(frames):
+                    frame.save(os.path.join(frame_dir, f"{j:04d}.png"))
+                print(f"  → {frame_dir} ({len(frames)} frames)")
+        return
+
+    idx = 0
+    for req_output in outputs:
+        images = getattr(req_output, "images", None)
+        if not images:
+            continue
+        for j, img in enumerate(images):
+            path = os.path.join(args.output, f"lance_{idx}_{j}.png")
+            img.save(path)
+            print(f"[Output] Saved image to {path}")
+        idx += 1
+
+
+if __name__ == "__main__":
+    main()

examples/offline_inference/mammothmodal2_preview/run_mammothmoda2_image_summarize.py

@@ -19,7 +19,6 @@ from vllm import SamplingParams
 from vllm.multimodal.image import convert_image_mode
 
 from vllm_omni import Omni
-from vllm_omni.engine.arg_utils import nullify_stage_engine_defaults
 
 DEFAULT_SYSTEM = "You are a helpful assistant."
 DEFAULT_QUESTION = "Please summarize the content of this image."
@@ -49,7 +48,6 @@ def parse_args() -> argparse.Namespace:
         action="store_true",
         help="Enable diffusion pipeline profiler to display stage durations.",
     )
-    nullify_stage_engine_defaults(parser)
     return parser.parse_args()
 
 

examples/offline_inference/mammothmodal2_preview/run_mammothmoda2_t2i.py

@@ -29,7 +29,6 @@ from PIL import Image
 from vllm.sampling_params import SamplingParams
 
 from vllm_omni import Omni
-from vllm_omni.engine.arg_utils import nullify_stage_engine_defaults
 
 logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
 logger = logging.getLogger(__name__)
@@ -118,7 +117,6 @@ def parse_args() -> argparse.Namespace:
     )
     p.add_argument("--out", type=str, default="output.png", help="Path to save the generated image.")
     p.add_argument("--trust-remote-code", action="store_true", help="Trust remote code when loading the model.")
-    nullify_stage_engine_defaults(p)
     args = p.parse_args()
     if not args.prompt:
         args.prompt = ["A stylish woman with sunglasses riding a motorcycle in NYC."]

examples/offline_inference/mimo_audio/end2end.py

@@ -21,11 +21,10 @@ from message_convert import (
     to_prompt,
 )
 from vllm import SamplingParams
-from vllm.utils.argparse_utils import FlexibleArgumentParser
 
-from vllm_omni.engine.arg_utils import nullify_stage_engine_defaults
 from vllm_omni.entrypoints.omni import Omni
 from vllm_omni.inputs.data import OmniTokensPrompt
+from vllm_omni.utils.tracking_parser import TrackingArgumentParser
 
 SEED = 42
 
@@ -343,7 +342,7 @@ def main(args):
 
 
 def parse_args():
-    parser = FlexibleArgumentParser(description="Demo on using vLLM for offline inference with audio language models")
+    parser = TrackingArgumentParser(description="Demo on using vLLM for offline inference with audio language models")
     parser.add_argument(
         "--model-name",
         "-m",
@@ -439,7 +438,6 @@ def parse_args():
         "vllm_omni/deploy/mimo_audio.yaml based on the HF model_type.",
     )
 
-    nullify_stage_engine_defaults(parser)
     return parser.parse_args()
 
 

examples/offline_inference/ming_flash_omni/README.md

@@ -2,12 +2,13 @@
 
 [Ming-flash-omni-2.0](https://github.com/inclusionAI/Ming) is an omni-modal model supporting text, image, video, and audio understanding, with text and speech outputs.
 
-vLLM-Omni supports two deployment modes:
+vLLM-Omni supports three deployment modes:
 
 | Mode | Deploy config | Output |
 |------|--------------|--------|
 | Thinker + Talker (omni-speech, default) | `vllm_omni/deploy/ming_flash_omni.yaml` | Text + Audio |
 | Thinker only (multimodal understanding) | `vllm_omni/deploy/ming_flash_omni_thinker_only.yaml` | Text |
+| Thinker + Imagegen (text-to-image / img2img) | `vllm_omni/deploy/ming_flash_omni_image.yaml` | Image (online-serving only at the moment) |
 
 For standalone TTS (talker only), see the [Ming-flash-omni-TTS section in the Text-To-Speech hub](../text_to_speech/README.md#ming-flash-omni-tts).
 
@@ -52,7 +53,7 @@ python examples/offline_inference/ming_flash_omni/end2end.py \
     --image-path ./3_0.png
 ```
 
-### Omni-Speech (Thinker + Talker)
+### Omni-Speech (Thinker + Talker) {#omni-speech-thinker--talker}
 
 The default deploy YAML already runs thinker+talker, so spoken output only requires requesting `audio` (or `text,audio`) modalities.
 The thinker processes your multimodal input, generates text, then the talker synthesises the response as speech.
@@ -87,6 +88,17 @@ The default deploy YAML allocates thinker on GPUs 0–3 and talker on GPU 3 for
 
 Pass `--deploy-config /path/to/your_deploy.yaml` to any of the commands above to override the bundled deploy config.
 
+### Image generation (text-to-image / img2img)
+
+The diffusion-stage image-generation path is currently only wired through
+the online OpenAI-compatible chat endpoint (`/v1/chat/completions` with
+`"modalities": ["image"]`). For payloads, optional knobs
+(`steps`/`cfg`/`seed`/`byte5_text`/`negative_prompt`), and the img2img
+reference-image flow, see the
+[image-gen section in the online-serving README](../../online_serving/ming_flash_omni/README.md#image-generation-text-to-image).
+`end2end.py` does not yet exercise the imagegen stage; that is tracked as
+follow-up work.
+
 ## Online serving
 
 For online serving via the OpenAI-compatible API, see [examples/online_serving/ming_flash_omni/README.md](../../online_serving/ming_flash_omni/README.md).

examples/offline_inference/ming_flash_omni/end2end.py

@@ -17,14 +17,13 @@ from vllm.assets.image import ImageAsset
 from vllm.assets.video import VideoAsset, video_to_ndarrays
 from vllm.multimodal.image import convert_image_mode
 from vllm.multimodal.media.audio import load_audio
-from vllm.utils.argparse_utils import FlexibleArgumentParser
 
 import vllm_omni
-from vllm_omni.engine.arg_utils import nullify_stage_engine_defaults
 from vllm_omni.entrypoints.omni import Omni
 
 # Imports the processor also registers itself
 from vllm_omni.transformers_utils.processors.ming import MingFlashOmniProcessor  # noqa: F401
+from vllm_omni.utils.tracking_parser import TrackingArgumentParser
 
 SEED = 42
 MODEL_NAME = "Jonathan1909/Ming-flash-omni-2.0"
@@ -397,7 +396,7 @@ def main(args):
 
 
 def parse_args():
-    parser = FlexibleArgumentParser(description="Ming-flash-omni 2.0 offline inference example")
+    parser = TrackingArgumentParser(description="Ming-flash-omni 2.0 offline inference example")
     parser.add_argument(
         "--query-type",
         "-q",
@@ -496,7 +495,6 @@ def parse_args():
         help="Output directory for results.",
     )
 
-    nullify_stage_engine_defaults(parser)
     return parser.parse_args()
 
 

examples/offline_inference/qwen2_5_omni/end2end.py

@@ -19,10 +19,9 @@ from vllm.assets.video import VideoAsset, video_to_ndarrays
 from vllm.multimodal.image import convert_image_mode
 from vllm.multimodal.media.audio import load_audio
 from vllm.sampling_params import SamplingParams
-from vllm.utils.argparse_utils import FlexibleArgumentParser
 
-from vllm_omni.engine.arg_utils import nullify_stage_engine_defaults
 from vllm_omni.entrypoints.omni import Omni
+from vllm_omni.utils.tracking_parser import TrackingArgumentParser
 
 SEED = 42
 
@@ -432,7 +431,7 @@ def main(args):
 
 
 def parse_args():
-    parser = FlexibleArgumentParser(description="Demo on using vLLM for offline inference with audio language models")
+    parser = TrackingArgumentParser(description="Demo on using vLLM for offline inference with audio language models")
     parser.add_argument(
         "--model",
         type=str,
@@ -554,7 +553,6 @@ def parse_args():
         default=False,
         help="Use py_generator mode. The returned type of Omni.generate() is a Python Generator object.",
     )
-    nullify_stage_engine_defaults(parser)
     return parser.parse_args()