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

Serialize full-repo Hugging Face snapshot materialization with a repo-wide
node lock and retry incomplete snapshots so CI does not race on shared caches.

Co-authored-by: Cursor <cursoragent@cursor.com>

.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: "v0.24.0"
+          VLLM_VERSION: "v0.22.0"
         priority: 100
         timeout_in_minutes: 60
         soft_fail: false

.buildkite/pipeline.yml

@@ -1,12 +1,11 @@
-# Document 1: Buildkite loads only this block on first parse. The next step resolves docs-only,
-# pytest skip-mark-only, or combined skip-ci from git diff, then uploads document 2. When skip-ci
-# applies, image-build still runs for the 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 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.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.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 (docs / skip marks) & upload pipeline"
+  - label: ":github: Resolve skip-ci & upload pipeline"
     key: upload-ci-pipeline
     commands:
       - "python3 .buildkite/scripts/upload_pipeline.py --upload .buildkite/pipeline.yml"
@@ -26,33 +25,23 @@ steps:
     agents:
       queue: "cpu_queue_premerge"
 
-  # L2 Test — PR ready label (diff-aware) or main+NIGHTLY=1 (--e2e, full E2E group)
+  # L2 Test
   - label: "Upload Ready Pipeline"
     depends_on: image-build
     key: upload-ready-pipeline
     if: __UPLOAD_READY_IF__
     commands:
-      - |
-        EXTRA=""
-        if [ "${NIGHTLY:-}" = "1" ] && [ "${BUILDKITE_BRANCH}" = "main" ]; then
-          EXTRA="--e2e"
-        fi
-        python3 .buildkite/scripts/upload_pipeline.py --upload $$EXTRA .buildkite/test-ready.yml
+      - python3 .buildkite/scripts/upload_pipeline.py --upload .buildkite/test-ready.yml
     agents:
       queue: "cpu_queue_premerge"
 
-  # L3 Test — PR merge-test label (diff-aware) or main+NIGHTLY=1 (--e2e, full E2E group)
+  # L3 Test
   - label: "Upload Merge Pipeline"
     depends_on: image-build
     key: upload-merge-pipeline
     if: __UPLOAD_MERGE_IF__
     commands:
-      - |
-        EXTRA=""
-        if [ "${NIGHTLY:-}" = "1" ] && [ "${BUILDKITE_BRANCH}" = "main" ]; then
-          EXTRA="--e2e"
-        fi
-        python3 .buildkite/scripts/upload_pipeline.py --upload $$EXTRA .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:
-      - python3 .buildkite/scripts/upload_pipeline.py --upload --all .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:
-      - python3 .buildkite/scripts/upload_pipeline.py --upload --all .buildkite/test-merge.yml
+      - python3 .buildkite/scripts/upload_pipeline.py --upload .buildkite/test-merge.yml
     agents:
       queue: "cpu_queue_premerge"
 
@@ -33,6 +33,6 @@ steps:
     key: upload-nightly-pipeline
     if: build.env("NIGHTLY") == "1"
     commands:
-      - python3 .buildkite/scripts/upload_pipeline.py --upload --all .buildkite/test-nightly.yml
+      - buildkite-agent pipeline upload .buildkite/test-nightly.yml
     agents:
       queue: "cpu_queue_premerge"

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

@@ -64,8 +64,7 @@ container_name="rocm_${BUILDKITE_COMMIT}_$(tr -dc A-Za-z0-9 < /dev/urandom | hea
 docker pull "${image_name}"
 
 remove_docker_container() {
-   docker ps -aq --filter "name=${container_name}" | xargs -r docker rm -f || true
-   docker image rm -f "${image_name}" || true
+   docker rm -f "${container_name}" || docker image rm -f "${image_name}" || true
 }
 trap remove_docker_container EXIT
 
@@ -108,6 +107,7 @@ if [[ $commands == *"--shard-id="* ]]; then
         --network=host \
         --shm-size=16gb \
         --group-add "$render_gid" \
+        --rm \
         -e MIOPEN_DEBUG_CONV_DIRECT=0 \
         -e MIOPEN_DEBUG_CONV_GEMM=0 \
         -e VLLM_ROCM_USE_AITER=0 \
@@ -140,6 +140,7 @@ else
           --network=host \
           --shm-size=16gb \
           --group-add "$render_gid" \
+          --rm \
           -e MIOPEN_DEBUG_CONV_DIRECT=0 \
           -e MIOPEN_DEBUG_CONV_GEMM=0 \
           -e VLLM_ROCM_USE_AITER=0 \

.buildkite/scripts/upload_pipeline.py

@@ -3,8 +3,7 @@
 """Render and optionally upload Buildkite pipeline YAML with diff-aware logic.
 
 Bootstrap mode (pipeline.yml with __IMAGE_BUILD_IF__ placeholders):
-  - Detect docs-only, pytest skip-mark-only, or combined docs+skip-mark PR/main changes and
-    substitute skip-ci ``if`` expressions.
+  - 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.
@@ -13,26 +12,19 @@ Test pipeline mode (e.g. test-merge.yml):
     recognize this uploader-only key).
 
 Usage:
-  python3 upload_pipeline.py [--upload] [--all | --e2e] <pipeline.yml>
+  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
-
-  # Full suite (rebase pipeline): keep all steps, still strip the uploader-only key:
-  python3 upload_pipeline.py --upload --all .buildkite/test-merge.yml
-
-  # Nightly L2 E2E only: keep the E2E Test group, ignore source_file_dependencies:
-  python3 upload_pipeline.py --upload --e2e .buildkite/test-ready.yml
 """
 
 from __future__ import annotations
 
 import argparse
 import os
-import re
 import subprocess
 import sys
 from pathlib import Path
@@ -50,7 +42,6 @@ LOG = "upload_pipeline"
 ROOT = Path(__file__).resolve().parent.parent.parent
 DOC_SEP = "\n---\n"
 BOOTSTRAP_MARKER = "__IMAGE_BUILD_IF__"
-E2E_GROUP_MARKER = "E2E Test"
 
 
 def _log(message: str) -> None:
@@ -66,8 +57,8 @@ def _git(*args: str) -> subprocess.CompletedProcess[str]:
     )
 
 
-def resolve_diff_range() -> str | None:
-    """Return a git diff range for PR or main builds, or None when unavailable."""
+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", ""
     )
@@ -85,22 +76,14 @@ def resolve_diff_range() -> str | None:
             else:
                 _log(f"cannot resolve PR base {base_branch}; using safe defaults")
                 return None
-        return f"{base_ref}...{commit}"
-
-    if os.environ.get("BUILDKITE_BRANCH", "") == "main":
+        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
-        return f"{commit}^..{commit}"
-
-    _log("not PR/main build; using safe defaults")
-    return None
-
-
-def resolve_changed_files() -> list[str] | None:
-    """Return changed file paths, or None when diff cannot be resolved."""
-    diff_range = resolve_diff_range()
-    if diff_range is None:
+        diff_range = f"{commit}^..{commit}"
+    else:
+        _log("not PR/main build; using safe defaults")
         return None
 
     result = _git("diff", "--name-only", diff_range)
@@ -113,207 +96,30 @@ def resolve_changed_files() -> list[str] | None:
     return files
 
 
-def _is_doc_file(file_path: str) -> bool:
-    if not file_path:
-        return False
-    if file_path.startswith("docs/"):
-        return True
-    if file_path.endswith(".md"):
-        return True
-    return file_path == "mkdocs.yml"
-
-
 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 not _is_doc_file(file_path):
-            return False
-    return has_any
-
-
-def _is_test_python_file(file_path: str) -> bool:
-    path = Path(file_path)
-    return path.suffix == ".py" and bool(path.parts) and path.parts[0] == "tests"
-
-
-_SKIP_MARK_RE = re.compile(r"pytest\.mark\.skip(?:if)?\b|pytest\.skip\s*\(")
-_PYTESTMARK_SKIP_RE = re.compile(r"pytest\.mark\.skip\b")
-_PYTEST_MARK_ONLY_RE = re.compile(r"pytest\.mark\.\w+")
-
-
-def _paren_balance(line: str) -> int:
-    return line.count("(") - line.count(")")
-
-
-def _is_skip_mark_related_content(line: str) -> bool:
-    stripped = line.strip()
-    if not stripped:
-        return True
-    if stripped.startswith("#"):
-        return True
-    return _SKIP_MARK_RE.search(stripped) is not None
-
-
-def _is_pytestmark_adjacent_content(line: str) -> bool:
-    """Allow pytestmark list refactors that only add skip/skipif alongside existing marks."""
-    stripped = line.strip().rstrip(",")
-    if not stripped:
-        return True
-    if stripped in {"[", "]"}:
-        return True
-    if stripped.startswith("#"):
-        return True
-    if stripped.startswith("pytestmark"):
-        return True
-    return _PYTEST_MARK_ONLY_RE.search(stripped) is not None
-
-
-def diff_only_contains_skip_mark_changes(diff_text: str) -> bool:
-    """True when diff only edits skip marks or reformats pytestmark to add them."""
-    pending_depth = 0
-    saw_change = False
-    has_skip_mark_edit = False
-    for raw_line in diff_text.splitlines():
-        if raw_line.startswith("@@"):
-            pending_depth = 0
+        if file_path.startswith("docs/"):
             continue
-        if not (raw_line.startswith("+") or raw_line.startswith("-")):
+        if file_path.endswith(".md"):
             continue
-        if raw_line.startswith("+++") or raw_line.startswith("---"):
+        if file_path == "mkdocs.yml":
             continue
-
-        saw_change = True
-        content = raw_line[1:]
-
-        if pending_depth > 0:
-            pending_depth += _paren_balance(content)
-            if pending_depth < 0:
-                pending_depth = 0
-            continue
-
-        if _is_skip_mark_related_content(content):
-            has_skip_mark_edit = True
-            pending_depth = max(0, _paren_balance(content))
-            continue
-
-        if _is_pytestmark_adjacent_content(content):
-            continue
-
-        return False
-
-    return saw_change and has_skip_mark_edit
-
-
-def _git_file_diff(file_path: str, diff_range: str) -> str | None:
-    result = _git("diff", diff_range, "--", file_path)
-    if result.returncode != 0:
-        _log(f"skip-mark-only: git diff failed for {file_path}")
-        return None
-    return result.stdout
-
-
-def _is_new_file_diff(diff_text: str) -> bool:
-    return "new file mode" in diff_text or "--- /dev/null" in diff_text
-
-
-def _file_has_module_level_pytest_skip(content: str) -> bool:
-    """True when ``pytestmark`` applies unconditional ``pytest.mark.skip`` to the module."""
-    match = re.search(r"^pytestmark\s*=\s*\[(.*?)^\s*\]", content, re.MULTILINE | re.DOTALL)
-    if match is not None:
-        return _PYTESTMARK_SKIP_RE.search(match.group(1)) is not None
-    match = re.search(r"^pytestmark\s*=\s*pytest\.mark\.skip\b", content, re.MULTILINE)
-    return match is not None
-
-
-def _file_content_at_commit(file_path: str, commit: str) -> str | None:
-    result = _git("show", f"{commit}:{file_path}")
-    if result.returncode != 0:
-        _log(f"skip-mark-only: cannot read {file_path} at {commit}")
-        return None
-    return result.stdout
-
-
-def _qualifies_as_skip_mark_test_change(file_path: str, diff_range: str) -> bool:
-    diff_text = _git_file_diff(file_path, diff_range)
-    if diff_text is None:
-        return False
-    if not diff_text.strip():
-        _log(f"skip-mark-only: empty diff for {file_path}")
-        return False
-
-    if diff_only_contains_skip_mark_changes(diff_text):
-        return True
-
-    if not _is_new_file_diff(diff_text):
-        _log(f"skip-mark-only: non-skip changes in {file_path}")
-        return False
-
-    commit = os.environ.get("BUILDKITE_COMMIT", "")
-    if not commit:
-        return False
-    content = _file_content_at_commit(file_path, commit)
-    if content is None:
-        return False
-    if _file_has_module_level_pytest_skip(content):
-        _log(f"skip-mark-only: new test file with module-level pytest.mark.skip: {file_path}")
-        return True
-
-    _log(f"skip-mark-only: new test file without module-level pytest.mark.skip: {file_path}")
-    return False
-
-
-def is_skip_mark_only_change(changed_files: list[str], *, diff_range: str) -> bool:
-    """True when every changed file is a tests/ module with only skip-mark edits."""
-    has_any = False
-    for file_path in changed_files:
-        if not file_path:
-            continue
-        has_any = True
-        if not _is_test_python_file(file_path):
-            return False
-        if not _qualifies_as_skip_mark_test_change(file_path, diff_range):
-            return False
-    return has_any
-
-
-def is_docs_or_skip_mark_only_change(changed_files: list[str], *, diff_range: str) -> bool:
-    """True when each changed file is a doc path or a qualifying skip-mark test change."""
-    has_any = False
-    for file_path in changed_files:
-        if not file_path:
-            continue
-        has_any = True
-        if _is_doc_file(file_path):
-            continue
-        if _is_test_python_file(file_path) and _qualifies_as_skip_mark_test_change(file_path, diff_range):
-            continue
-        _log(f"docs/skip-mark-only: rejecting {file_path}")
         return False
     return has_any
 
 
-def resolve_skip_ci(changed_files: list[str] | None, *, diff_range: str | None = None) -> bool:
+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 diff_range is not None and is_docs_or_skip_mark_only_change(changed_files, diff_range=diff_range):
-        if is_docs_only_change(changed_files):
-            _log("docs-only change detected; skip-ci=1")
-        elif is_skip_mark_only_change(changed_files, diff_range=diff_range):
-            _log("pytest skip-mark-only change detected; skip-ci=1")
-        else:
-            _log("docs + pytest skip-mark-only change detected; skip-ci=1")
-        return True
-
-    if diff_range is None and is_docs_only_change(changed_files):
+    if is_docs_only_change(changed_files):
         _log("docs-only change detected; skip-ci=1")
         return True
-
-    _log("non-doc/non-skip-mark changes detected; skip-ci=0")
+    _log("non-doc changes detected; skip-ci=0")
     return False
 
 
@@ -326,18 +132,18 @@ def render_bootstrap_pipeline(text: str, *, skip_ci: bool) -> str:
     nightly_only = (
         '(build.pull_request.labels includes "nightly-test") || (build.branch == "main" && build.env("NIGHTLY") == "1")'
     )
-    nightly_main = 'build.branch == "main" && build.env("NIGHTLY") == "1"'
-    ready_pr = 'build.branch != "main" && build.pull_request.labels includes "ready"'
-    merge_main = 'build.branch == "main" && build.env("NIGHTLY") != "1" && build.env("WEEKLY") != "1"'
-    merge_pr = 'build.branch != "main" && build.pull_request.labels includes "merge-test"'
     if skip_ci:
         image_if = f"'{nightly_only}'"
-        ready_if = f"'({nightly_main})'"
-        merge_if = f"'({nightly_main})'"
+        ready_if = "'false'"
+        merge_if = "'false'"
     else:
         image_if = "'true'"
-        ready_if = f"'(({nightly_main}) || ({ready_pr}))'"
-        merge_if = f"'(({nightly_main}) || (({merge_main}) || ({merge_pr})))'"
+        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)
@@ -400,20 +206,6 @@ def _filter_steps(steps: list[Any], changed_files: list[str]) -> list[Any]:
     return filtered
 
 
-def _is_e2e_group(step: dict[str, Any]) -> bool:
-    group = step.get("group")
-    return isinstance(group, str) and E2E_GROUP_MARKER in group
-
-
-def _select_e2e_group_steps(steps: list[Any]) -> list[Any]:
-    selected = [step for step in steps if isinstance(step, dict) and _is_e2e_group(step)]
-    if not selected:
-        _log(f"no group matching {E2E_GROUP_MARKER!r} found")
-    else:
-        _log(f"keep {len(selected)} group(s) matching {E2E_GROUP_MARKER!r}")
-    return selected
-
-
 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] = []
@@ -440,16 +232,11 @@ def _strip_uploader_metadata_from_steps(steps: list[Any]) -> list[Any]:
 def render_test_pipeline(
     doc: dict[str, Any],
     changed_files: list[str] | None,
-    *,
-    e2e_only: bool = False,
 ) -> dict[str, Any]:
     steps = doc.get("steps")
     if not isinstance(steps, list):
         return doc
-    if e2e_only:
-        steps = _select_e2e_group_steps(steps)
-        steps = _strip_uploader_metadata_from_steps(steps)
-    elif changed_files is not None:
+    if changed_files is not None:
         steps = _filter_steps(steps, changed_files)
     else:
         steps = _strip_uploader_metadata_from_steps(steps)
@@ -463,34 +250,19 @@ def resolve_pipeline_path(arg: str) -> Path:
     return ROOT / path
 
 
-def render_pipeline(
-    path: Path,
-    *,
-    force_all: bool = False,
-    e2e_only: bool = False,
-) -> str:
+def render_pipeline(path: Path) -> str:
     text = path.read_text(encoding="utf-8")
-    diff_range = resolve_diff_range()
     changed_files = resolve_changed_files()
 
-    # ``--all`` forces the keep-all-steps path (no diff-aware skipping) while still
-    # stripping ``source_file_dependencies``. Used by the rebase pipeline so main builds
-    # run the full e2e suite (see .buildkite/rebase-pipeline.yaml).
-    if force_all or e2e_only:
-        changed_files = None
-
     if BOOTSTRAP_MARKER in text:
-        rendered = render_bootstrap_pipeline(
-            text,
-            skip_ci=resolve_skip_ci(changed_files, diff_range=diff_range),
-        )
+        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, e2e_only=e2e_only)
+    doc = render_test_pipeline(doc, changed_files)
 
     return yaml.safe_dump(doc, sort_keys=False)
 
@@ -517,17 +289,6 @@ def main() -> int:
         action="store_true",
         help="Pipe rendered YAML to buildkite-agent pipeline upload",
     )
-    mode = parser.add_mutually_exclusive_group()
-    mode.add_argument(
-        "--all",
-        action="store_true",
-        help="Keep all steps (disable diff-aware skipping); still strips source_file_dependencies",
-    )
-    mode.add_argument(
-        "--e2e",
-        action="store_true",
-        help="Keep only the E2E Test group; disable diff-aware skipping for those steps",
-    )
     args = parser.parse_args()
 
     path = resolve_pipeline_path(args.pipeline)
@@ -535,7 +296,7 @@ def main() -> int:
         _log(f"missing pipeline file: {path}")
         return 1
 
-    rendered = render_pipeline(path, force_all=args.all, e2e_only=args.e2e)
+    rendered = render_pipeline(path)
     if args.upload:
         upload_to_buildkite(rendered)
     else:

.buildkite/test-amd-merge.yml

@@ -7,29 +7,17 @@ steps:
 
 - group: ":card_index_dividers: Simple Test"
   steps:
-    - label: "Simple · Diffusion & Model Executor Test (1/2)"
+    - label: "Simple · Diffusion & Model Executor Test"
       agent_pool: mi325_1
       depends_on: amd-build
-      timeout_in_minutes: 70
+      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
-        # CPU tests on ROCm gpu machines are very slow, so we give them more time.
-        - "timeout 60m pytest -sv tests/diffusion -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: "Simple · Diffusion & Model Executor Test (2/2)"
-      agent_pool: mi325_1
-      depends_on: amd-build
-      timeout_in_minutes: 70
-      mirror_hardwares: [amdproduction]
-      grade: Blocking
-      commands:
-        - export VLLM_ROCM_USE_AITER=0
-        # CPU tests on ROCm gpu machines are very slow, so we give them more time.
-        - "timeout 60m pytest -sv tests/model_executor -m 'core_model and cpu' --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
+        - "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: "Simple · Other Test"
       agent_pool: mi325_1
@@ -170,7 +158,7 @@ steps:
       grade: Blocking
       commands:
         - |
-          timeout 50m bash -c '
+          timeout 20m bash -c '
             export VLLM_LOGGING_LEVEL=DEBUG
             export VLLM_WORKER_MULTIPROC_METHOD=spawn
             export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
@@ -182,14 +170,26 @@ steps:
       depends_on: amd-build
       mirror_hardwares: [amdproduction]
       grade: Blocking
-      timeout_in_minutes: 50
       commands:
         - |
-          timeout 40m bash -c '
+          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 and cuda' --run-level 'advanced_model'
+            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

.buildkite/test-amd-ready.yaml

@@ -161,6 +161,19 @@ steps:
 
 - 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
@@ -172,6 +185,20 @@ steps:
         - 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: "CosyVoice3-TTS E2E Test"
       agent_pool: mi325_1
       depends_on: amd-build
@@ -180,7 +207,7 @@ steps:
       commands:
         - |
           timeout 20m bash -c '
-            pytest -s -v tests/e2e/online_serving/test_cosyvoice3_tts_expansion.py -m "slow" --run-level "core_model"
+            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

.buildkite/test-merge.yml

@@ -61,14 +61,13 @@ steps:
                 - "/fsx/hf_cache:/fsx/hf_cache"
 
   - label: "Distributed Test"
-    depends_on: upload-merge-pipeline
     timeout_in_minutes: 60
     commands:
       - |
         timeout 55m bash -c "
           set -e
           export VLLM_IMAGE_FETCH_TIMEOUT=60
-          pytest -s -v tests/distributed -m 'advanced_model and cuda and H100' --run-level 'advanced_model'
+          pytest -s -v tests/distributed -m 'advanced_model and cuda' --run-level 'advanced_model'
         "
     agents:
       queue: "mithril-h100-pool"
@@ -150,7 +149,7 @@ steps:
         commands:
           - pytest -s -v tests/entrypoints/ -m "advanced_model and cuda and L4" --run-level "advanced_model"
         agents:
-          queue: "gpu_4_queue" # g6.12xlarge instance on AWS, has 4 L4 GPU
+          queue: "gpu_4_queue" # g6.4xlarge instance on AWS, has 1 L4 GPU
         plugins:
           - docker#v5.2.0:
               image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
@@ -166,21 +165,15 @@ steps:
     depends_on: upload-merge-pipeline
     steps:
       - label: "TTS · Qwen3-TTS CustomVoice Test"
-        source_file_dependencies:
-          - tests/e2e/online_serving/test_qwen3_tts_customvoice.py
-          - tests/e2e/offline_inference/test_qwen3_tts_customvoice.py
-          - vllm_omni/model_executor/models/qwen3_tts/
-          - vllm_omni/model_executor/stage_input_processors/qwen3_tts.py
-          - vllm_omni/deploy/qwen3_tts.yaml
         commands:
           - |
-            timeout 40m bash -c "
+            timeout 20m bash -c "
               export VLLM_LOGGING_LEVEL=DEBUG
               export VLLM_ALLOW_LONG_MAX_MODEL_LEN=1
               pytest -s -v tests/e2e/online_serving/test_qwen3_tts_customvoice.py tests/e2e/offline_inference/test_qwen3_tts_customvoice.py -m 'advanced_model and cuda' --run-level 'advanced_model'
             "
         agents:
-          queue: "gpu_1_queue" # g6.4xlarge instance on AWS, has 1 L4 GPU
+          queue: "gpu_4_queue"
         plugins:
           - docker#v5.2.0:
               image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
@@ -193,21 +186,15 @@ steps:
                 - "/fsx/hf_cache:/fsx/hf_cache"
 
       - label: "TTS · Qwen3-TTS Base Test"
-        source_file_dependencies:
-          - tests/e2e/online_serving/test_qwen3_tts_base.py
-          - tests/e2e/offline_inference/test_qwen3_tts_base.py
-          - vllm_omni/model_executor/models/qwen3_tts/
-          - vllm_omni/model_executor/stage_input_processors/qwen3_tts.py
-          - vllm_omni/deploy/qwen3_tts.yaml
         commands:
           - |
-            timeout 40m bash -c "
+            timeout 20m bash -c "
               export VLLM_LOGGING_LEVEL=DEBUG
               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 and cuda' --run-level 'advanced_model'
             "
         agents:
-          queue: "gpu_1_queue" # g6.4xlarge instance on AWS, has 1 L4 GPU
+          queue: "gpu_4_queue"
         plugins:
           - docker#v5.2.0:
               image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
@@ -219,19 +206,13 @@ steps:
               volumes:
                 - "/fsx/hf_cache:/fsx/hf_cache"
 
-      - label: "TTS · Higgs-Audio-V3 Test"
-        source_file_dependencies:
-          - tests/e2e/online_serving/test_higgs_audio_v3.py
-          - vllm_omni/model_executor/models/higgs_audio_v3/
-          - vllm_omni/model_executor/stage_input_processors/higgs_audio_v3.py
-          - vllm_omni/deploy/higgs_multimodal_qwen3.yaml
+      - label: "TTS · Ming-Omni-TTS Test"
+        timeout_in_minutes: 20
         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 'advanced_model' --run-level 'advanced_model'
+              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"
@@ -243,45 +224,104 @@ steps:
               shm-size: "8gb"
               environment:
                 - "HF_HOME=/fsx/hf_cache"
-                - "HF_TOKEN"
               volumes:
                 - "/fsx/hf_cache:/fsx/hf_cache"
 
-      - label: "TTS · IndexTTS2 Test"
+      - label: "TTS · Voxtral-TTS Test"
         source_file_dependencies:
-          - tests/e2e/online_serving/test_indextts2.py
-          - tests/e2e/offline_inference/test_indextts2.py
-          - tests/model_executor/models/indextts2/
-          - tests/model_executor/stage_input_processors/test_indextts2.py
-          - tests/assets/indextts2/
-          - vllm_omni/model_executor/models/indextts2/
-          - vllm_omni/model_executor/stage_input_processors/indextts2.py
-          - vllm_omni/deploy/indextts2.yaml
-          - vllm_omni/deploy/indextts2_low_latency.yaml
-          - vllm_omni/entrypoints/openai/serving_speech.py
-          - vllm_omni/entrypoints/openai/tts_adapters/indextts2.py
-          - vllm_omni/config/pipeline_registry.py
-          - vllm_omni/model_executor/models/registry.py
-        timeout_in_minutes: 60
+          - 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:
           - |
-            timeout 45m bash -c "
+            timeout 20m bash -c "
               export VLLM_LOGGING_LEVEL=DEBUG
-              pytest -s -v tests/e2e/online_serving/test_indextts2.py tests/e2e/offline_inference/test_indextts2.py -m 'advanced_model and cuda' --run-level 'advanced_model'
+              pytest -s -v tests/e2e/offline_inference/test_voxtral_tts.py tests/e2e/online_serving/test_voxtral_tts.py -m 'advanced_model and cuda' --run-level 'advanced_model'
             "
         agents:
-          queue: "gpu_4_queue"
+          queue: "mithril-h100-pool"
         plugins:
-          - docker#v5.2.0:
-              image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-              always-pull: true
-              propagate-environment: true
-              environment:
-                - "HF_HOME=/fsx/hf_cache"
-                - "HF_TOKEN"
-              gpus: all
-              volumes:
-                - "/fsx/hf_cache:/fsx/hf_cache"
+          - kubernetes:
+              podSpec:
+                containers:
+                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$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: "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:
+          - |
+            timeout 20m bash -c "
+              export VLLM_LOGGING_LEVEL=DEBUG
+              pytest -s -v tests/e2e/online_serving/test_glm_tts.py tests/e2e/offline_inference/test_glm_tts.py -m 'advanced_model and cuda' --run-level 'advanced_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: 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: "Omni · Qwen3-Omni Test"
         source_file_dependencies:
@@ -290,7 +330,7 @@ steps:
           - 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: 50
+        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'
         agents:
@@ -327,12 +367,50 @@ steps:
                       path: /mnt/hf-cache
                       type: DirectoryOrCreate
 
-      - label: "TTS · VoxCPM2 Test"
+      - label: "Omni · Mimo Audio Test"
         source_file_dependencies:
-          - tests/e2e/offline_inference/test_voxcpm2_tts.py
-          - tests/e2e/online_serving/test_voxcpm2_tts.py
-          - vllm_omni/model_executor/models/voxcpm2/
-          - vllm_omni/deploy/voxcpm2.yaml
+          - 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'
+        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: "TTS · VoxCPM2 Test"
         timeout_in_minutes: 30
         commands:
           - |
@@ -355,19 +433,16 @@ steps:
               volumes:
                 - "/fsx/hf_cache:/fsx/hf_cache"
 
-      - label: "TTS · SoulX-Singer Test"
-        source_file_dependencies:
-          - tests/e2e/offline_inference/test_soulxsinger.py
-          - vllm_omni/diffusion/models/soulx_singer/
-          - vllm_omni/deploy/soulxsinger_svc.yaml
-          - vllm_omni/deploy/soulxsinger_svs.yaml
+      - 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
-              pytest -s -v tests/e2e/offline_inference/test_soulxsinger.py -k 'svc or precomputed' -m 'advanced_model and cuda' --run-level 'advanced_model'
+              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"
@@ -424,6 +499,88 @@ steps:
                       path: /mnt/hf-cache
                       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'
+        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:$BUILDKITE_COMMIT
+                    resources:
+                      limits:
+                        nvidia.com/gpu: 1
+                    volumeMounts:
+                      - name: devshm
+                        mountPath: /dev/shm
+                      - name: hf-cache
+                        mountPath: /root/.cache/huggingface
+                    env:
+                      - name: HF_HOME
+                        value: /root/.cache/huggingface
+                      - name: HF_TOKEN
+                        valueFrom:
+                          secretKeyRef:
+                            name: hf-token-secret
+                            key: token
+                nodeSelector:
+                  node.kubernetes.io/instance-type: gpu-h100-sxm
+                volumes:
+                  - name: devshm
+                    emptyDir:
+                      medium: Memory
+                  - name: hf-cache
+                    hostPath:
+                      path: /mnt/hf-cache
+                      type: DirectoryOrCreate
+
+      - label: "Diffusion · Longcat Image Test"
+        source_file_dependencies:
+          - tests/e2e/online_serving/test_longcat_image.py
+          - vllm_omni/diffusion/models/longcat_image/
+        timeout_in_minutes: 20
+        commands:
+          - pytest -s -v tests/e2e/online_serving/test_longcat_image.py -m 'advanced_model and cuda' --run-level 'advanced_model'
+        agents:
+          queue: "mithril-h100-pool"
+        plugins:
+          - kubernetes:
+              podSpec:
+                containers:
+                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$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: "Diffusion · Bagel Test"
         source_file_dependencies:
           - tests/e2e/offline_inference/test_bagel.py
@@ -603,11 +760,51 @@ steps:
                       path: /mnt/hf-cache
                       type: DirectoryOrCreate
 
-      - label: "Diffusion · Z Image Test"
+      - label: "Diffusion · HunyuanVideo 1.5 Test"
         source_file_dependencies:
-          - tests/e2e/offline_inference/test_z_image.py
-          - tests/e2e/online_serving/test_z_image.py
-          - vllm_omni/diffusion/models/z_image/
+          - tests/e2e/online_serving/test_hunyuan_video_15.py
+          - vllm_omni/diffusion/models/hunyuan_video/
+        timeout_in_minutes: 40
+        commands:
+          - |
+            timeout 40m bash -c "
+              pytest -s -v tests/e2e/online_serving/test_hunyuan_video_15.py -m 'advanced_model and cuda and diffusion' --run-level 'advanced_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: 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: "Diffusion · Z Image Test"
         timeout_in_minutes: 30
         commands:
           - pytest -s -v tests/e2e/offline_inference/test_z_image.py tests/e2e/online_serving/test_z_image.py -m 'advanced_model and cuda and diffusion' --run-level 'advanced_model'
@@ -625,12 +822,9 @@ steps:
                 - "/fsx/hf_cache:/fsx/hf_cache"
 
       - label: "Diffusion · FLUX.2 Klein Test"
-        source_file_dependencies:
-          - tests/e2e/offline_inference/test_flux2_klein.py
-          - vllm_omni/diffusion/models/flux2_klein/
         timeout_in_minutes: 30
         commands:
-          - pytest -s -v tests/e2e/offline_inference/test_flux2_klein.py -m 'advanced_model and cuda and diffusion' --run-level 'advanced_model'
+          - pytest -s -v tests/e2e/offline_inference/test_flux2_klein.py tests/e2e/online_serving/test_flux2_klein.py -m 'advanced_model and cuda and diffusion' --run-level 'advanced_model'
         agents:
           queue: "gpu_1_queue" # g6.4xlarge instance on AWS, has 1 L4 GPU
         plugins:
@@ -643,3 +837,48 @@ steps:
                 - "HF_TOKEN"
               volumes:
                 - "/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:
+          - |
+            timeout 45m bash -c "
+              export VLLM_IMAGE_FETCH_TIMEOUT=60
+              pytest -s -v tests/e2e/online_serving/test_sd3.py -m 'advanced_model and cuda and diffusion' --run-level 'advanced_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

.buildkite/test-nightly.yml

@@ -12,7 +12,7 @@ steps:
     if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test" || build.pull_request.labels includes "omni-test"
     steps:
       - label: ":full_moon: Omni · Function Test with H100"
-        timeout_in_minutes: 120
+        timeout_in_minutes: 90
         commands:
           - pytest -sv tests/e2e --run-level "full_model" -m "full_model and H100 and omni" --ignore=tests/e2e/accuracy --ignore=tests/e2e/online_serving/test_qwen3_omni_multi_replicas.py
         agents:
@@ -49,6 +49,25 @@ steps:
                       path: /mnt/hf-cache
                       type: DirectoryOrCreate
 
+      - label: ":full_moon: Omni · Function Test with L4"
+        timeout_in_minutes: 90
+        commands:
+          - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
+          - pytest -s -v tests/e2e/*/test_qwen2_5_omni_expansion.py --run-level "core_model"
+        agents:
+          queue: "gpu_4_queue" # g6.12xlarge instance on AWS, has 4 L4 GPU
+        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: Omni · Doc Test with L4"
         timeout_in_minutes: 90
         commands:
@@ -153,7 +172,7 @@ steps:
 
       - label: ":full_moon: Omni · Perf Test · No Async Chunk"
         key: nightly-omni-performance-no-async-chunk
-        timeout_in_minutes: 300
+        timeout_in_minutes: 180
         commands:
           - export BENCHMARK_DIR=tests/dfx/perf/results
           - |
@@ -376,11 +395,9 @@ steps:
     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 with L4"
-        timeout_in_minutes: 120
+        timeout_in_minutes: 90
         commands:
           - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
-          - export VLLM_USE_DEEP_GEMM="0"
-          - export VLLM_MOE_USE_DEEP_GEMM="0"
           - 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
@@ -551,84 +568,54 @@ steps:
                       path: /mnt/hf-cache
                       type: DirectoryOrCreate
 
-      - label: ":full_moon: TTS · Higgs-Audio-V3 · Perf Test"
-        key: nightly-tts-performance-higgs-audio-v3
-        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
-
-  - group: ":card_index_dividers: Tiny Model Tests [Multi-GPU]"
-    key: nightly-tiny-model-test-group
+  - 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"
-    steps:
-      - label: ":full_moon: Tiny Model Tests · Diffusion (Multi-GPU accelerations)"
-        timeout_in_minutes: 30
-        # NOTE: Single-GPU (core_model) tiny model tests run on the ready label,
-        # so nightly only adds the multi-GPU parallelism configurations.
-        #
-        # This is a bit confusing, but `full_model` is used to mark the multigpu tests, and
-        # --run-level `core_model` is used to indicate that we should use tiny model weights
-        # for the CI, since these tests should pass irrespective of whether or not we use
-        # the full weights or not.
-        commands:
-          - timeout 25m pytest -sv tests/model_tests/diffusion -m 'full_model and cuda' --run-level "core_model"
-        agents:
-          queue: "gpu_4_queue" # g6.12xlarge instance on AWS, has 4 L4 GPU
-        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"
+    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"
@@ -649,9 +636,11 @@ steps:
             tests/e2e/online_serving/test_qwen_image_expansion.py
             tests/e2e/online_serving/test_qwen_image_edit_expansion.py
             tests/e2e/online_serving/test_qwen_image_layered_expansion.py
+            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/online_serving/test_bagel_expansion.py
-            tests/e2e/online_serving/test_krea2_expansion.py
-            tests/e2e/offline_inference/test_krea2_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:
@@ -695,7 +684,6 @@ steps:
             pytest -sv
             tests/e2e/online_serving/test_qwen_image_expansion.py
             tests/e2e/online_serving/test_qwen_image_edit_expansion.py
-            tests/e2e/offline_inference/test_krea2_expansion.py
             -m "full_model and diffusion and H100 and distributed_cuda"
             --run-level "full_model"
         agents:
@@ -738,6 +726,10 @@ steps:
           - >-
             pytest -sv
             tests/e2e/online_serving/test_qwen_image_layered_expansion.py
+            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:
@@ -816,14 +808,13 @@ steps:
                       path: /mnt/hf-cache
                       type: DirectoryOrCreate
 
-      - label: ":full_moon: Diffusion X2I(&A&T) · GLM-Image Function Test with H100"
+      - 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_glm_image_expansion.py
-            tests/e2e/offline_inference/test_glm_image_autoround_w4a16_expansion.py
-            -m "full_model and diffusion and H100"
+            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"
@@ -860,8 +851,47 @@ steps:
                       type: DirectoryOrCreate
 
 
-      - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with L4"
+      - 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
+                      - 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 L4"
+        timeout_in_minutes: 60
         commands:
           - pytest -sv tests/e2e/ -k "not test_wan and not test_bagel_expansion and not hunyuan" -m "full_model and diffusion and L4" --run-level "full_model" --ignore=tests/e2e/accuracy
         agents:
@@ -1246,10 +1276,10 @@ steps:
       build.pull_request.labels includes "nightly-test" ||
       build.pull_request.labels includes "diffusion-x2v-test"
     steps:
-      - label: ":full_moon: Diffusion X2V · Wan2.2 T2V Function Test"
+      - label: ":full_moon: Diffusion X2V · Function Test"
         timeout_in_minutes: 90
         commands:
-          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "t2v" -m "full_model and cuda" --run-level "full_model"
+          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py tests/e2e/online_serving/test_wan_2_1_vace_expansion.py tests/e2e/online_serving/test_hunyuan_video_15_expansion.py tests/e2e/offline_inference/test_wan22_autoround_w4a16_expansion.py -m "full_model and cuda" --run-level "full_model"
         agents:
           queue: "mithril-h100-pool"
         plugins:
@@ -1284,167 +1314,14 @@ steps:
                       path: /mnt/hf-cache
                       type: DirectoryOrCreate
 
-      - label: ":full_moon: Diffusion X2V · Wan2.2 I2V/TI2V Function Test"
-        timeout_in_minutes: 90
-        commands:
-          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "i2v" -m "full_model and 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:$BUILDKITE_COMMIT
-                    resources:
-                      limits:
-                        nvidia.com/gpu: 2
-                    volumeMounts:
-                      - name: devshm
-                        mountPath: /dev/shm
-                      - name: hf-cache
-                        mountPath: /root/.cache/huggingface
-                    env:
-                      - name: HF_HOME
-                        value: /root/.cache/huggingface
-                      - name: HF_TOKEN
-                        valueFrom:
-                          secretKeyRef:
-                            name: hf-token-secret
-                            key: token
-                nodeSelector:
-                  node.kubernetes.io/instance-type: gpu-h100-sxm
-                volumes:
-                  - name: devshm
-                    emptyDir:
-                      medium: Memory
-                  - name: hf-cache
-                    hostPath:
-                      path: /mnt/hf-cache
-                      type: DirectoryOrCreate
-
-      - label: ":full_moon: Diffusion X2V · Doc Test"
-        timeout_in_minutes: 60
-        commands:
-          - pytest -s -v tests/examples/offline_inference/test_image_to_video.py -m "full_model and example and H100" --run-level "full_model"
-        agents:
-          queue: "mithril-h100-pool"
-        plugins:
-          - kubernetes:
-              podSpec:
-                containers:
-                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                    resources:
-                      limits:
-                        nvidia.com/gpu: 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: Diffusion X2V · Other Function Test"
-        timeout_in_minutes: 90
-        commands:
-          - pytest -s -v tests/e2e/offline_inference/test_wan22_autoround_w4a16_expansion.py -m "full_model and cuda" --run-level "full_model"
-          - pytest -s -v tests/e2e/online_serving/test_wan_2_1_vace_expansion.py tests/e2e/online_serving/test_ltx2_expansion.py -m "full_model and 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:$BUILDKITE_COMMIT
-                    resources:
-                      limits:
-                        nvidia.com/gpu: 2
-                    volumeMounts:
-                      - name: devshm
-                        mountPath: /dev/shm
-                      - name: hf-cache
-                        mountPath: /root/.cache/huggingface
-                    env:
-                      - name: HF_HOME
-                        value: /root/.cache/huggingface
-                      - name: HF_TOKEN
-                        valueFrom:
-                          secretKeyRef:
-                            name: hf-token-secret
-                            key: token
-                nodeSelector:
-                  node.kubernetes.io/instance-type: gpu-h100-sxm
-                volumes:
-                  - name: devshm
-                    emptyDir:
-                      medium: Memory
-                  - name: hf-cache
-                    hostPath:
-                      path: /mnt/hf-cache
-                      type: DirectoryOrCreate
-
-      - label: ":full_moon: Diffusion X2V · Wan2.2 Accuracy Test"
+      - label: ":full_moon: Diffusion X2V · Accuracy Test"
         timeout_in_minutes: 180
         commands:
           - pytest -s -v tests/e2e/accuracy/wan22_i2v/test_wan22_i2v_video_similarity.py -m full_model --run-level full_model
-          - pytest -s -v tests/e2e/accuracy/test_diffusers_backend_similarity.py -k '2v_matches_diffusers' -m full_model --run-level full_model
-        agents:
-          queue: "mithril-h100-pool"
-        plugins:
-          - kubernetes:
-              podSpec:
-                containers:
-                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                    resources:
-                      limits:
-                        nvidia.com/gpu: 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 X2V · Other Accuracy Test"
-        timeout_in_minutes: 180
-        commands:
           - 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_video_streaming_output_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:
           queue: "mithril-h100-pool"
         plugins:
@@ -1588,7 +1465,7 @@ steps:
     key: nightly-testcase-statistics
     timeout_in_minutes: 120
     depends_on: upload-nightly-pipeline
-    if: build.env("NIGHTLY") == "1"
+    if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test"
     commands:
       - python tools/nightly/buildkite_testcase_statistics.py -o tests/dfx/perf/results/buildkite_testcase_statistics.html
       - buildkite-agent artifact upload "tests/dfx/perf/results/*.html"

.buildkite/test-ready.yml

@@ -61,7 +61,7 @@ steps:
     steps:
       - label: "Diffusion · Offloader Test"
         commands:
-          - timeout 40m pytest -s -v tests/diffusion/offloader -m "core_model and cuda" --run-level "core_model"
+          - timeout 10m pytest -s -v tests/diffusion/offloader -m "core_model and cuda" --run-level "core_model"
         agents:
           queue: "gpu_1_queue" # g6.4xlarge instance on AWS, has 1 L4 GPU
         plugins:
@@ -157,27 +157,6 @@ steps:
                 - "/fsx/hf_cache:/fsx/hf_cache"
 
 
-  - group: ":card_index_dividers: Tiny Model Tests [Single GPU]"
-    depends_on: upload-ready-pipeline
-    steps:
-      - label: "Tiny Model Tests · Diffusion (base)"
-        commands:
-          - timeout 15m pytest -sv tests/model_tests/diffusion -m 'core_model and cuda' --run-level "core_model"
-        agents:
-          # Ready label only runs the cases that don't need parallelism for tiny
-          # model tests, which is usually just a smoke test for TTI and I2I
-          queue: "gpu_1_queue" # g6.4xlarge instance on AWS, has 1 L4 GPU
-        plugins:
-          - docker#v5.2.0:
-              image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-              always-pull: true
-              propagate-environment: true
-              environment:
-                - "HF_HOME=/fsx/hf_cache"
-                - "HF_TOKEN"
-              volumes:
-                - "/fsx/hf_cache:/fsx/hf_cache"
-
   - label: "Engine&Model Executor Test"
     depends_on: upload-ready-pipeline
     commands:
@@ -205,21 +184,36 @@ steps:
     commands:
       - |
         timeout 20m bash -c "
-          pytest -sv tests/distributed/ -m 'core_model and cuda and L4' --run-level 'core_model'
+                  pytest -sv tests/distributed/ -m 'core_model and cuda' --run-level 'core_model'
         "
     agents:
-      queue: "gpu_1_queue" # g6.4xlarge instance on AWS, has 1 L4 GPU
+      queue: "mithril-h100-pool"
     plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$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"
+      - 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
+            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: "Entrypoints Test"
     depends_on: upload-ready-pipeline
@@ -230,31 +224,38 @@ steps:
           pytest -sv tests/entrypoints/ -m 'core_model and cuda' --run-level 'core_model'
         "
     agents:
-      queue: "gpu_1_queue"
+      queue: "mithril-h100-pool"
     plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          shm-size: "8gb"
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-            - "HF_TOKEN"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
+      - kubernetes:
+          podSpec:
+            containers:
+              - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$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:
       - label: "RLHF · VeRL-Omni E2E Test"
-        source_file_dependencies:
-          - tests/e2e/offline_inference/rlhf_test/test_verl_omni_e2e.py
-          - tests/e2e/offline_inference/custom_pipeline/
-          - vllm_omni/diffusion/
-          - vllm_omni/engine/
-          - vllm_omni/entrypoints/
-          - vllm_omni/inputs/
-          - vllm_omni/outputs/
         commands:
           - |
             timeout 20m bash -c "
@@ -315,17 +316,13 @@ steps:
                       type: DirectoryOrCreate
 
       - label: "TTS · VoxCPM2 Test"
-        source_file_dependencies:
-          - tests/e2e/online_serving/test_voxcpm2_tts.py
-          - vllm_omni/model_executor/models/voxcpm2/
-          - vllm_omni/deploy/voxcpm2.yaml
         timeout_in_minutes: 30
         commands:
           - |
             timeout 30m bash -c "
               export VLLM_LOGGING_LEVEL=DEBUG
               export VLLM_WORKER_MULTIPROC_METHOD=spawn
-              pytest -s -v tests/e2e/online_serving/test_voxcpm2_tts.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"
@@ -342,11 +339,6 @@ steps:
                 - "/fsx/hf_cache:/fsx/hf_cache"
 
       - label: "TTS · Qwen3-TTS CustomVoice Test"
-        source_file_dependencies:
-          - tests/e2e/online_serving/test_qwen3_tts_customvoice.py
-          - vllm_omni/model_executor/models/qwen3_tts/
-          - vllm_omni/model_executor/stage_input_processors/qwen3_tts.py
-          - vllm_omni/deploy/qwen3_tts.yaml
         commands:
           - |
             timeout 20m bash -c "
@@ -354,7 +346,7 @@ steps:
               pytest -s -v tests/e2e/online_serving/test_qwen3_tts_customvoice.py -m 'core_model' --run-level 'core_model'
             "
         agents:
-          queue: "gpu_1_queue"
+          queue: "gpu_4_queue"
         plugins:
           - docker#v5.2.0:
               image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
@@ -368,11 +360,6 @@ steps:
                 - "/fsx/hf_cache:/fsx/hf_cache"
 
       - label: "TTS · Qwen3-TTS Base Test"
-        source_file_dependencies:
-          - tests/e2e/online_serving/test_qwen3_tts_base.py
-          - vllm_omni/model_executor/models/qwen3_tts/
-          - vllm_omni/model_executor/stage_input_processors/qwen3_tts.py
-          - vllm_omni/deploy/qwen3_tts.yaml
         commands:
           - |
             timeout 20m bash -c "
@@ -382,7 +369,7 @@ steps:
               pytest -s -v tests/e2e/online_serving/test_qwen3_tts_base.py -m 'core_model' --run-level 'core_model'
             "
         agents:
-          queue: "gpu_1_queue"
+          queue: "gpu_4_queue"
         plugins:
           - docker#v5.2.0:
               image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
@@ -396,11 +383,6 @@ steps:
                 - "/fsx/hf_cache:/fsx/hf_cache"
 
       - label: "TTS · Higgs-Audio-V3 Test"
-        source_file_dependencies:
-          - tests/e2e/online_serving/test_higgs_audio_v3.py
-          - vllm_omni/model_executor/models/higgs_audio_v3/
-          - vllm_omni/model_executor/stage_input_processors/higgs_audio_v3.py
-          - vllm_omni/deploy/higgs_multimodal_qwen3.yaml
         commands:
           - |
             timeout 20m bash -c "
@@ -410,7 +392,7 @@ steps:
               pytest -s -v tests/e2e/online_serving/test_higgs_audio_v3.py -m 'core_model' --run-level 'core_model'
             "
         agents:
-          queue: "gpu_1_queue"
+          queue: "gpu_4_queue"
         plugins:
           - docker#v5.2.0:
               image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
@@ -552,50 +534,6 @@ steps:
                       path: /mnt/hf-cache
                       type: DirectoryOrCreate
 
-      - label: "Diffusion · Cosmos3 Test"
-        source_file_dependencies:
-          - tests/e2e/online_serving/test_cosmos3.py
-          - vllm_omni/diffusion/models/cosmos3/
-        commands:
-          - |
-            timeout 40m bash -c "
-              export VLLM_IMAGE_FETCH_TIMEOUT=60
-              pytest -s -v tests/e2e/online_serving/test_cosmos3.py -m 'core_model' --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: 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: "Diffusion · Qwen Image Test"
         source_file_dependencies:
@@ -684,24 +622,3 @@ steps:
                     hostPath:
                       path: /mnt/hf-cache
                       type: DirectoryOrCreate
-
-      - label: "Diffusion · GGUF Plugin Test"
-        soft_fail: true
-        source_file_dependencies:
-          - tests/plugin/test_gguf_diffusion.py
-          - vllm_omni/diffusion/model_loader
-        commands:
-          - pip install "vllm-gguf-plugin>=0.0.3"
-          - timeout 30m pytest -sv tests/plugin/test_gguf_diffusion.py
-        agents:
-          queue: "gpu_1_queue" # g6.4xlarge instance on AWS, has 1 L4 GPU
-        plugins:
-          - docker#v5.2.0:
-              image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-              always-pull: true
-              propagate-environment: true
-              environment:
-                - "HF_HOME=/fsx/hf_cache"
-                - "HF_TOKEN"
-              volumes:
-                - "/fsx/hf_cache:/fsx/hf_cache"

.buildkite/test-weekly.yml

@@ -85,66 +85,6 @@ steps:
                   path: /mnt/hf-cache
                   type: DirectoryOrCreate
 
-  - label: "Reliability Test - hunyuan-image"
-    timeout_in_minutes: 180
-    depends_on: upload-weekly-pipeline
-    if: build.env("WEEKLY") == "1" || build.pull_request.labels includes "weekly-test"
-    commands:
-      - pytest -s -v tests/dfx/reliability/test_reliability_hunyuan_image.py -m "slow"
-    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:$BUILDKITE_COMMIT
-                resources:
-                  limits:
-                    nvidia.com/gpu: 4
-                volumeMounts:
-                  - name: devshm
-                    mountPath: /dev/shm
-                  - name: hf-cache
-                    mountPath: /root/.cache/huggingface
-                env:
-                  - name: HF_HOME
-                    value: /root/.cache/huggingface
-                  - name: HF_TOKEN
-                    valueFrom:
-                      secretKeyRef:
-                        name: hf-token-secret
-                        key: token
-            nodeSelector:
-              node.kubernetes.io/instance-type: gpu-h100-sxm
-            volumes:
-              - name: devshm
-                emptyDir:
-                  medium: Memory
-              - name: hf-cache
-                hostPath:
-                  path: /mnt/hf-cache
-                  type: DirectoryOrCreate
-
-  - label: "Reliability Test - voxcpm2"
-    timeout_in_minutes: 180
-    depends_on: upload-weekly-pipeline
-    if: build.env("WEEKLY") == "1" || build.pull_request.labels includes "weekly-test"
-    commands:
-      - pytest -s -v tests/dfx/reliability/test_reliability_voxcpm2.py -m "slow"
-    agents:
-      queue: "gpu_1_queue"
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$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"
-
   # Invalid HTTP / WS payloads vs live Omni servers (Issue #3649 tracks known skips).
   - group: "Reliability Test - Invalid parameters Test"
     depends_on: upload-weekly-pipeline
@@ -206,246 +146,3 @@ steps:
                 - "HF_TOKEN"
               volumes:
                 - "/fsx/hf_cache:/fsx/hf_cache"
-
-  - group: ":card_index_dividers: E2E Tests"
-    depends_on: upload-weekly-pipeline
-    if: build.env("WEEKLY") == "1" || build.pull_request.labels includes "weekly-test"
-    steps:
-      - label: "Omni · L4"
-        timeout_in_minutes: 90
-        commands:
-          - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
-          - pytest -s -v tests/e2e/*/test_qwen2_5_omni_expansion.py --run-level "core_model"
-          - >-
-            pytest -s -v tests/e2e/
-            --ignore=tests/e2e/online_serving/test_qwen2_5_omni_expansion.py
-            --ignore=tests/e2e/offline_inference/test_qwen2_5_omni_expansion.py
-            -m "slow and L4 and omni"
-            --run-level "full_model"
-        agents:
-          queue: "gpu_4_queue"
-        plugins:
-          - docker#v5.2.0:
-              image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-              always-pull: true
-              propagate-environment: true
-              shm-size: "8gb"
-              environment:
-                - "HF_HOME=/fsx/hf_cache"
-                - "HF_TOKEN"
-              volumes:
-                - "/fsx/hf_cache:/fsx/hf_cache"
-
-      - label: "Omni · H100"
-        timeout_in_minutes: 90
-        commands:
-          - >-
-            pytest -sv
-            tests/e2e/ -m "slow and H100 and omni"
-            --run-level "full_model"
-        agents:
-          queue: "mithril-h100-pool"
-        plugins:
-          - kubernetes:
-              podSpec:
-                containers:
-                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$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: "TTS · L4"
-        timeout_in_minutes: 90
-        commands:
-          - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
-          - export VLLM_USE_DEEP_GEMM="0"
-          - export VLLM_MOE_USE_DEEP_GEMM="0"
-          - |
-            failed=0
-            for f in $(pytest --collect-only -q tests/e2e/ -m "slow and L4 and tts" --run-level "full_model" | grep '\.py::' | sed -E 's|::.*||' | sort -u); do
-              pytest -s -v "$$f" -m "slow and L4 and tts" --run-level "full_model" || failed=1
-            done
-            exit $$failed
-        agents:
-          queue: "gpu_1_queue"
-        plugins:
-          - docker#v5.2.0:
-              image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-              always-pull: true
-              propagate-environment: true
-              shm-size: "8gb"
-              environment:
-                - "HF_HOME=/fsx/hf_cache"
-                - "HF_TOKEN"
-              volumes:
-                - "/fsx/hf_cache:/fsx/hf_cache"
-
-      - label: "TTS · H100"
-        timeout_in_minutes: 90
-        commands:
-          - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
-          - export VLLM_USE_DEEP_GEMM="0"
-          - export VLLM_MOE_USE_DEEP_GEMM="0"
-          - >-
-            pytest -sv
-            tests/e2e/ -m "slow and H100 and tts"
-            --run-level "full_model"
-        agents:
-          queue: "mithril-h100-pool"
-        plugins:
-          - kubernetes:
-              podSpec:
-                containers:
-                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                    resources:
-                      limits:
-                        nvidia.com/gpu: 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: "Diffusion · L4"
-        timeout_in_minutes: 60
-        commands:
-          - >-
-            pytest -sv
-            tests/e2e/ -m "slow and diffusion and L4"
-            --run-level "full_model"
-        agents:
-          queue: "gpu_4_queue"
-        plugins:
-          - docker#v5.2.0:
-              image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-              always-pull: true
-              propagate-environment: true
-              shm-size: "8gb"
-              environment:
-                - "HF_HOME=/fsx/hf_cache"
-                - "HF_TOKEN"
-              volumes:
-                - "/fsx/hf_cache:/fsx/hf_cache"
-
-      - label: "Diffusion · H100 · Single-GPU"
-        timeout_in_minutes: 120
-        commands:
-          - >-
-            pytest -sv
-            tests/e2e/ -m "slow and diffusion and H100 and not distributed_cuda"
-            --run-level "full_model"
-        agents:
-          queue: "mithril-h100-pool"
-        plugins:
-          - kubernetes:
-              podSpec:
-                containers:
-                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                    resources:
-                      limits:
-                        nvidia.com/gpu: 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: "Diffusion · H100 · Multi-GPU"
-        timeout_in_minutes: 90
-        commands:
-          - >-
-            pytest -sv
-            tests/e2e/ -m "slow 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

.claude/skills/add-diffusion-model/SKILL.md

@@ -24,7 +24,7 @@ Check the model's HF repo for `model_index.json`. This determines your path:
 
 | Scenario | How to identify | Migration path |
 |----------|----------------|----------------|
-| **Already supported** | `_class_name` in `model_index.json` matches a key in `_DIFFUSION_MODELS` in `registry.py` | Skip implementation, then validate model-specific examples, tests, and docs as needed |
+| **Already supported** | `_class_name` in `model_index.json` matches a key in `_DIFFUSION_MODELS` in `registry.py` | Skip to Step 5 (test) and Step 7 (docs) |
 | **Diffusers-based** | Has standard `model_index.json` with `_diffusers_version`, subfolders for `transformer/`, `vae/`, etc. | Follow **Path A** below |
 | **Custom/private repo** | No diffusers `model_index.json`, weights in non-standard format, custom model code in a separate git repo | Follow **Path B** below |
 | **Hybrid** | Has some diffusers components (VAE) but custom transformer/fusion | Mix of Path A and Path B |
@@ -52,7 +52,7 @@ vllm_omni/diffusion/models/your_model_name/
 2. Replace attention with `vllm_omni.diffusion.attention.layer.Attention` (QKV shape: `[B, seq, heads, head_dim]`).
 3. Add `od_config: OmniDiffusionConfig | None = None` to `__init__`.
 4. Add `load_weights()` method mapping diffusers weight names to vllm-omni names.
-5. Add class attributes for acceleration features such as `_repeated_blocks` and `_layerwise_offload_blocks_attrs` (see [references/transformer-adaptation.md](references/transformer-adaptation.md) for examples).
+5. Add class attributes: `_repeated_blocks`, `_layerwise_offload_blocks_attr`.
 
 ### A4. Adapt pipeline
 
@@ -78,34 +78,13 @@ class YourPipeline(nn.Module):
 
 Add post/pre-process functions in the same pipeline file. Register them in `registry.py`.
 
-### A4.1 Add progress bar support (recommended)
-
-For pipelines with a standard denoising loop, prefer the existing progress bar pattern instead of hand-rolled logging.
-
-```python
-from vllm_omni.diffusion.models.progress_bar import ProgressBarMixin
-
-class YourPipeline(nn.Module, ProgressBarMixin):
-    def forward(self, req: OmniDiffusionRequest) -> DiffusionOutput:
-        # ... prepare timesteps / latents ...
-        with self.progress_bar(total=len(timesteps)) as progress_bar:
-            for i, t in enumerate(timesteps):
-                # predict noise / scheduler step
-                latents = ...
-                progress_bar.update()
-
-        return DiffusionOutput(output=output)
-```
-
-For custom loop structures, follow `vllm_omni/diffusion/models/progress_bar.py` and existing pipelines using `ProgressBarMixin`.
-
 ### A5. Register, test, docs → continue at Step 4 below.
 
 ---
 
 ## Path B: Custom/Private Repo Model
 
-For models without a diffusers pipeline — weights in custom format, model code in a private repo. Real examples: DreamID-Omni, BAGEL, HunyuanImage3. See [references/custom-model-patterns.md](references/custom-model-patterns.md) for concrete custom-model integration patterns.
+For models without a diffusers pipeline — weights in custom format, model code in a private repo. Real examples: DreamID-Omni, BAGEL, HunyuanImage3.
 
 ### B1. Understand the reference repo
 
@@ -155,7 +134,7 @@ Copy the essential model files into `vllm_omni/diffusion/models/<name>/` and ada
 
 ### B4. Handle custom weight loading
 
-Custom models have two common patterns for weight loading:
+Custom models have two patterns for weight loading:
 
 **Pattern 1: Bypass standard loader** (DreamID-Omni style)
 
@@ -170,10 +149,8 @@ class CustomPipeline(nn.Module):
         self.vae = custom_init_vae(model, device=self.device)
         self.text_encoder = custom_init_text_encoder(model, device=self.device)
         self.transformer = CustomFusionModel(CONFIG)
-        load_custom_checkpoint(
-            self.transformer,
-            checkpoint_path=os.path.join(model, "model.safetensors"),
-        )
+        load_custom_checkpoint(self.transformer,
+            checkpoint_path=os.path.join(model, "model.safetensors"))
         # NO weights_sources defined — bypasses standard loader
 
     def load_weights(self, weights):
@@ -185,8 +162,6 @@ class CustomPipeline(nn.Module):
 When weights are in safetensors format but need name remapping:
 
 ```python
-from vllm.model_executor.model_loader.weight_utils import default_weight_loader
-
 class CustomPipeline(nn.Module):
     def __init__(self, *, od_config, prefix=""):
         super().__init__()
@@ -212,9 +187,7 @@ class CustomPipeline(nn.Module):
             # Remap original weight names to vllm-omni module names
             name = self._remap_weight_name(name)
             if name in params:
-                param = params[name]
-                weight_loader = getattr(param, "weight_loader", default_weight_loader)
-                weight_loader(param, tensor)
+                default_weight_loader(params[name], tensor)
                 loaded.add(name)
         return loaded
 ```
@@ -308,43 +281,16 @@ For Omni or custom models, create:
 ### Step 7: Update Documentation
 
 Required updates:
-1. `docs/user_guide/diffusion/parallelism/overview.md` — parallelism support overview/table
+1. `docs/user_guide/diffusion/parallelism_acceleration.md` — parallelism support table
 2. `docs/user_guide/diffusion/cpu_offload_diffusion.md` — if CPU offload supported (add to supported models table)
-3. `docs/user_guide/diffusion/cache_acceleration/teacache.md` — if TeaCache supported
-4. `docs/user_guide/diffusion/cache_acceleration/cache_dit.md` — if Cache-DiT supported
-5. Offline example docs under `examples/offline_inference/<name>/` (`README.md` or category-specific `.md`)
-6. `examples/online_serving/<name>/README.md` — online serving docs
+3. `docs/user_guide/diffusion/teacache.md` — if TeaCache supported
+4. `docs/user_guide/diffusion/cache_dit_acceleration.md` — if Cache-DiT supported
+5. `examples/offline_inference/xxx/README.md` — offline example docs
+6. `examples/online_serve/xxx/README.md` — online serve docs
 
-### Step 8: Add E2E Tests
+### Step 8: Add E2E Tests (Recommended)
 
-**Follow the [vllm-omni-test skill](../vllm-omni-test/SKILL.md)** for markers, file naming, Buildkite wiring, and run commands. Also read [l4_functionality_tests.inc.md](https://github.com/vllm-project/vllm-omni/blob/main/docs/contributing/ci/test_examples/l4_functionality_tests.inc.md) and [CI_5levels.md](https://github.com/vllm-project/vllm-omni/blob/main/docs/contributing/ci/CI_5levels.md).
-
-Classify the model's **CI priority** first:
-
-| Priority | Required test levels | Files & markers |
-|----------|---------------------|-----------------|
-| **High** (listed in [#1832](https://github.com/vllm-project/vllm-omni/issues/1832) or on the diffusion hot path) | **L1** · **L2** online · **L3** online + offline · **L4** feature + performance | See table below |
-| **Medium** (*normal priority* in L4 docs) | **L3** online + offline · **L4** feature only | Fewer L4 parametrized rows |
-| **Low** | **L4** feature only | One or two `*_expansion.py` cases |
-
-**Per-level deliverables (diffusion / `pytest.mark.diffusion`):**
-
-| Level | Location | Marker | CI pipeline | Notes |
-|-------|----------|--------|-------------|-------|
-| **L1** | `tests/diffusion/models/{slug}/`, `tests/diffusion/cache/`, transformer unit tests | `core_model` + `cpu` | `test-ready.yml` | Weight remap, `_sp_plan`, cache enabler registration, shape contracts |
-| **L2** | `tests/e2e/online_serving/test_{slug}.py` (and offline if the category is offline-first) | **`core_model` + `advanced_model`** (both on baseline smoke) + `diffusion` + `@hardware_test` / `hardware_marks` | `test-ready.yml` | Default deploy smoke — minimal `num_inference_steps`, single prompt |
-| **L3** | `tests/e2e/online_serving/test_{slug}.py` **and** `tests/e2e/offline_inference/test_{slug}.py` when offline matters | Baseline smoke: **`core_model` + `advanced_model`**; heavier cases: `advanced_model` only (+ `diffusion`) | `test-merge.yml` **or** merged into nightly diffusion function job | Real weights, streaming/API paths, LoRA/offload smoke |
-| **L4** | `tests/e2e/online_serving/test_{slug}_expansion.py` (+ offline expansion if needed) | `full_model` + `diffusion` | `test-nightly.yml` (X2I / X2V / X2A function groups) | Feature combos per [#1832](https://github.com/vllm-project/vllm-omni/issues/1832); perf → `tests/dfx/perf/tests/test_{model}_vllm_omni.json` |
-
-**L2 & L3 online — same file, dual marks on the baseline smoke:** The **first / simplest** case in `test_{slug}.py` (default deploy, minimal steps, single prompt) should carry **both** `@pytest.mark.core_model` and `@pytest.mark.advanced_model` on the **same** function so L2 (`test-ready.yml`) and L3 (`test-merge.yml`) share one smoke test. Heavier deploy variants or API paths in the same file use **`advanced_model` only**. When L3 moves to nightly, migrate those heavier cases into `test_{slug}_expansion.py` with `full_model` and remove the dedicated `test-merge.yml` job (see `test_longcat_image_expansion.py`, `test_qwen_image_expansion.py`).
-
-**L4 design (high priority):** Combine multiple supported features (Cache-DiT, TP, USP, CFG, HSDP, CPU offload, quantization) into **few parametrized** `OmniServerParams` rows so each feature appears in at least one case without exploding GPU jobs. Shard single-GPU vs multi-GPU cases across the nightly X2I/X2V function steps (1-GPU vs `distributed_cuda`).
-
-**L4 design (medium / low):** One or two parametrized rows covering the best quality/perf trade-off; skip perf JSON unless the model is high priority.
-
-**Reference implementations:** `tests/e2e/online_serving/test_qwen_image_edit_expansion.py`, `tests/e2e/online_serving/test_longcat_image_expansion.py`, `tests/e2e/online_serving/test_hunyuan_video_15_expansion.py`.
-
-**Keep model-specific code inside test modules — not `tests/helpers/{slug}.py`:** deploy constants, prompts, sampling dicts, and inline `request_config` / `form_data` belong in each `test_{slug}.py` and `test_{slug}_expansion.py`. Do not add per-model files under `tests/helpers/`; reuse only repo-wide harness (`mark`, `media`, `runtime`, `stage_config`, `assertions`). **L2+ online/offline e2e:** reuse or add `send_*_request` in `tests/helpers/runtime.py` — tests call the handler, not raw `omni.generate` / HTTP. See [vllm-omni-test skill](../vllm-omni-test/SKILL.md) § **Runtime send helpers**.
+Create `tests/e2e/online_serving/test_your_model_expansion.py`.
 
 ### Step 9: Add Cache-DiT Acceleration
 
@@ -564,7 +510,7 @@ class YourTransformer(nn.Module):
 #### 10e. Update parallelism documentation
 
 After adding parallelism support, update:
-1. `docs/user_guide/diffusion/parallelism/overview.md` — add your model to the support overview/table
+1. `docs/user_guide/diffusion/parallelism_acceleration.md` — add your model to the support table
 2. Record which parallelism methods are supported (USP, Ring, CFG, TP, HSDP, VAE-Patch)
 
 ### Step 11: Add CPU Offload Support
@@ -622,7 +568,6 @@ See the [Profiling Single-Stage Diffusion](../../../docs/contributing/profiling.
 
 ## Reference Files
 
-- [vllm-omni-test skill](../vllm-omni-test/SKILL.md) — L1–L4 markers, naming, Buildkite wiring, run commands
 - [Transformer Adaptation](references/transformer-adaptation.md) — porting transformers from diffusers
 - [Custom Model Patterns](references/custom-model-patterns.md) — patterns for non-diffusers models
 - [Parallelism Patterns](references/parallelism-patterns.md) — TP, SP/USP, CFG parallel, HSDP implementation details

.claude/skills/add-diffusion-model/references/parallelism-patterns.md

@@ -569,4 +569,4 @@ Check which parallelism methods a model supports:
 | **TP** | Uses `ColumnParallelLinear` / `QKVParallelLinear`. Search: `grep -r 'ParallelLinear\|QKVParallel' vllm_omni/diffusion/models/<model>/` |
 | **HSDP** | Transformer defines `_hsdp_shard_conditions`. Search: `grep -r '_hsdp_shard_conditions' vllm_omni/diffusion/models/` |
 
-The canonical per-model support overview/table is in `docs/user_guide/diffusion/parallelism/overview.md`.
+The canonical per-model support table is in `docs/user_guide/diffusion/parallelism_acceleration.md`.

.claude/skills/add-diffusion-model/references/transformer-adaptation.md

@@ -219,14 +219,13 @@ def load_weights(self, weights):
 
 ### Class-Level Attributes for Features
 
-
 ```python
 class MyTransformer(nn.Module):
     # torch.compile: list block class names that repeat and can be compiled
     _repeated_blocks = ["MyTransformerBlock"]
 
     # CPU offload: attribute name of the nn.ModuleList containing blocks
-    _layerwise_offload_blocks_attrs = ["blocks"]
+    _layerwise_offload_blocks_attr = "blocks"
 
     # LoRA: mapping of fused param names to original param names
     packed_modules_mapping = {"to_qkv": ["to_q", "to_k", "to_v"]}

.claude/skills/add-tts-model/SKILL.md

@@ -305,72 +305,13 @@ def build_voice_clone_prompt(ref_audio_path: str, text: str, codec) -> list:
     ]
 ```
 
-### Test Case Writing (CI Levels)
-
-**Follow the [vllm-omni-test skill](../vllm-omni-test/SKILL.md)** for markers, file naming (`test_{slug}.py` / `test_{slug}_expansion.py`), Buildkite wiring, and copy-paste run commands. Also read [CI_5levels.md](https://github.com/vllm-project/vllm-omni/blob/main/docs/contributing/ci/CI_5levels.md) and [tests_style.md](https://github.com/vllm-project/vllm-omni/blob/main/docs/contributing/ci/tests_style.md).
-
-Classify the model's **CI priority** first (high / medium / low). High-priority TTS models are typically those on the integration hot path or listed in tracking issues such as [#1832](https://github.com/vllm-project/vllm-omni/issues/1832); medium and low tiers cover the long tail. When unsure, ask the reviewer which tier applies.
-
-| Priority | Required test levels | Files & markers |
-|----------|---------------------|-----------------|
-| **High** | **L1** unit/logic · **L2** online smoke · **L3** online + offline integration · **L4** feature + performance | See table below |
-| **Medium** | **L3** online + offline · **L4** feature only | Skip dedicated L1/L2 unless fixing a logic bug |
-| **Low** | **L4** feature only | One or two `*_expansion.py` parametrized cases |
-
-**Per-level deliverables (TTS / `pytest.mark.tts`):**
-
-| Level | Location | Marker | CI pipeline | Notes |
-|-------|----------|--------|-------------|-------|
-| **L1** | `tests/model_executor/…`, `tests/entrypoints/openai_api/…`, stage-processor tests | `core_model` + `cpu` | `test-ready.yml` | Prompt assembly, async_chunk helpers, `serving_speech` validation — no GPU |
-| **L2** | `tests/e2e/online_serving/test_{slug}.py` | **`core_model` + `advanced_model`** (both on baseline smoke) + `tts` + `@hardware_test(...)` | `test-ready.yml` (`ready` label) | Default deploy smoke: single `/v1/audio/speech` or offline `OmniRunner` path |
-| **L3** | `tests/e2e/online_serving/test_{slug}.py` **and** `tests/e2e/offline_inference/test_{slug}.py` | Baseline smoke: **`core_model` + `advanced_model`**; heavier cases: `advanced_model` only (+ `tts`) | `test-merge.yml` **or** merged into nightly TTS function job | Streaming, voice clone, batch/queue, async_chunk |
-| **L4** | `tests/e2e/online_serving/test_{slug}_expansion.py`, optional offline expansion | `full_model` + `tts` | `test-nightly.yml` (`:full_moon: TTS · Function Test with L4`) | Feature matrix; perf → `tests/dfx/perf/tests/test_tts.json` |
-
-**L2 & L3 online — same file, dual marks on the baseline smoke:** The **first / simplest** case in `test_{slug}.py` (default deploy, single non-streaming `/v1/audio/speech` or equivalent offline path) should carry **both** `@pytest.mark.core_model` **and** `@pytest.mark.advanced_model` on the **same** function so it runs in L2 (`test-ready.yml`, `--run-level core_model`, basic validation) and L3 (`test-merge.yml`, `--run-level advanced_model`, deeper validation) without duplicating the test. In-tree examples: `test_voxcpm2_tts.py::test_text_to_audio_001`, `test_qwen3_tts_customvoice.py::test_text_to_audio_001`.
-
-Heavier scenarios in the same file use **`advanced_model` only** (streaming, extra languages, concurrency, async_chunk, batch). Example: `test_voice_clone_en_streaming_001` → `advanced_model` only. When migrating L3 to nightly, move those heavier cases into `test_{slug}_expansion.py` with `full_model` and drop the dedicated merge job (see `test_ming_tts_expansion.py`, `test_glm_tts_expansion.py`).
-
-```python
-@pytest.mark.core_model
-@pytest.mark.advanced_model
-@pytest.mark.tts
-@hardware_test(res={"cuda": "L4"}, num_cards=1)
-@pytest.mark.parametrize("omni_server", tts_server_params, indirect=True)
-def test_voice_clone_en_non_streaming_001(omni_server, openai_client) -> None:
-    openai_client.send_audio_speech_request({...})
-```
-
-**L4 consolidation:** Prefer parametrized `OmniServerParams` rows (`default`, `async_chunk`, feature flags) in one expansion module rather than many merge-only files ([#1832](https://github.com/vllm-project/vllm-omni/issues/1832)).
-
-**L4 performance (high-priority models):** Add latency / throughput / stress rows in `tests/dfx/perf/tests/test_tts.json`, or a dedicated `tests/dfx/perf/tests/test_{slug}.json` when the model must not join the shared nightly server matrix before integration lands (see VoxCPM2 / Coqui XTTS pattern). Register the model in `benchmarks/tts/model_configs.yaml` for local `bench_tts.py`. Wire a **separate** `test-nightly.yml` Perf Test step when the JSON is not merged into `test_tts.json` yet.
-
-**Keep model-specific code inside test modules — not `tests/helpers/{slug}.py`:**
-
-- Put `MODEL`, deploy path, vendored `REF_AUDIO_URL`, `get_prompt()`, and inline `request_config` dicts **in each** `test_{slug}.py`, `test_{slug}_expansion.py`, offline `test_{slug}.py`, and L1 `test_{slug}_*.py` as needed.
-- **Do not** add `tests/helpers/{slug}.py` (or `tests/helpers/{model_name}.py`) to deduplicate constants or request builders across those files. A little duplication is intentional; follow in-tree references such as `tests/e2e/online_serving/test_glm_tts.py` and `tests/e2e/online_serving/test_cosyvoice3_tts_expansion.py`.
-- `tests/helpers/` is for **repo-wide** harness code only (`mark.py`, `media.py`, `runtime.py`, `stage_config.py`, `assertions.py`, `fixtures/`). Import those; do not extend the tree with per-model modules.
-
-**Runtime send helpers (`tests/helpers/runtime.py`) — online and offline e2e:**
-
-| Path | Fixture | Call |
-|------|---------|------|
-| Online `/v1/*` | `openai_client` | `openai_client.send_*_request(request_config)` |
-| Offline inference | `omni_runner_handler` | `omni_runner_handler.send_*_request(request_config)` |
-
-1. **Grep `runtime.py` first** — reuse `send_omni_request`, `send_diffusion_request`, `send_audio_speech_request` (online + offline Qwen-style TTS), `send_single_stage_tts_request` (Coqui XTTS / MOSS-TTS-Nano offline), etc.
-2. **No matching helper** → add `send_<feature>_request` (or `send_<route>_http_request` for negative/dfx) in **`runtime.py`** with general `assert_*` bundled inside, **then** call it from the test.
-3. **Test file** holds `request_config` dicts only — not `omni.generate`, not `_collect_audio()`, not raw HTTP/SDK.
-4. Wire `tests/helpers/runtime.py` into Buildkite `source_file_dependencies` when you add helpers.
-
-See [vllm-omni-test skill](../vllm-omni-test/SKILL.md) § **Runtime send helpers** for full tables and exceptions.
-
 ### Deliverables
 
 - Updated `serving_speech.py` with all 5 integration points (single commit)
 - Client scripts and server launcher under `examples/online_serving/text_to_speech/<model>/`
 - Gradio demo with streaming and voice cloning UI in the same dir
-- E2E tests per **Test Case Writing (CI Levels)** above (priority tier determines L1–L4 scope)
-- Buildkite wired per level: `test-ready.yml` (L1/L2), `test-merge.yml` or nightly function job (L3), `test-nightly.yml` (L4) — see [vllm-omni-test skill](../vllm-omni-test/SKILL.md)
+- E2E online serving test (`tests/e2e/online_serving/test_<model>.py`)
+- Buildkite CI entry in `.buildkite/test-merge.yml`
 - New section in `examples/online_serving/text_to_speech/README.md` (table row + per-model section). Do **not** create a top-level `examples/online_serving/<model>/` dir or a per-model `README.md` inside `text_to_speech/<model>/`.
 
 ### E2E test pitfalls to avoid
@@ -385,9 +326,9 @@ See [vllm-omni-test skill](../vllm-omni-test/SKILL.md) § **Runtime send helpers
 - **Use the harness readiness gate.** The fixture waits for HTTP 200 on
   `/health`; don't add `time.sleep` in tests. If warmup is incomplete, make
   `/health` return non-200 until you're actually ready.
-- **Mark tests per the CI Levels table** — baseline smoke: `core_model` + `advanced_model`; heavier cases: `advanced_model` only; L4 expansion: `full_model`
-- **No per-model helper modules** — do not create `tests/helpers/{slug}.py`; keep constants and `request_config` payloads in the test file
-- **Online and offline e2e go through `runtime.py`** — `openai_client.send_audio_speech_request` (online); `omni_runner_handler.send_audio_speech_request` (Qwen-style offline) or `send_single_stage_tts_request` (single-stage offline). Add a new `send_*_request` in `runtime.py` when none fits; do not embed `omni.generate` or HTTP in tests
+- **Mark with `@pytest.mark.core_model` + `hardware_test(res={"cuda": "H100"})`**
+  so the test lands in `test-ready.yml` (triggered by the `ready` label) rather
+  than only nightly.
 
 ## Phase 4: Async Chunk (Streaming)
 
@@ -520,8 +461,8 @@ Use this checklist when integrating a new TTS model:
 - [ ] Voice cloning works (if supported)
 - [ ] All response formats work (wav, mp3, flac, pcm)
 - [ ] Client scripts and server launcher created
-- [ ] E2E tests added per model priority tier (see **Test Case Writing (CI Levels)**)
-- [ ] Buildkite entries match level: `test-ready.yml` / `test-merge.yml` or nightly TTS job / `test-nightly.yml`
+- [ ] E2E online serving test written (`tests/e2e/online_serving/test_<model>.py`)
+- [ ] Buildkite CI entry added to `.buildkite/test-merge.yml`
 - [ ] Gradio demo working
 - [ ] Documentation added (offline + online docs, nav, supported models)
 
@@ -559,6 +500,5 @@ Project docs and adjacent skills:
 - [TTS audio skill](../vllm-omni-audio-tts/SKILL.md) — supported models and usage
 - [Fish Speech integration](../vllm-omni-audio-tts/references/fish-speech.md) — complete example of Phases 1–3
 - [Qwen3-TTS reference](../vllm-omni-audio-tts/references/qwen-tts.md) — complete example of all 5 phases
-- [vllm-omni-test skill](../vllm-omni-test/SKILL.md) — L1–L4 markers, naming, Buildkite wiring, run commands
 - [Adding a TTS model (developer guide)](https://github.com/vllm-project/vllm-omni/blob/main/docs/contributing/model/adding_tts_model.md)
 - `plan/voxcpm2_native_ar_design.md` — VoxCPM2's vLLM-native AR + side-computation pattern (distinct from the generator-based single-stage described above)

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

@@ -63,8 +63,6 @@ If multiple rows apply (e.g., a diffusion model is also a new model), union the
 
 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 ⚠.
 
-**Also run the Code-Quality sweep on every PR**, regardless of type or mode: the five diff-scoped checks in [references/code-quality.md](references/code-quality.md) — kwargs fragility, broad-except swallow, `Any`/wrong type hints, hot-path `.clone()`/`deepcopy`, and event-loop blocking — plus the advisory conventions (log level, structured logging, synchronization, cleanup, dependencies, naming) in [checklists.md](references/checklists.md). These count only lines the PR *adds* — the pre-existing backlog across the repo is out of scope.
-
 ### Step 5: Print Report
 
 ```
@@ -76,7 +74,6 @@ Pre-check report for <branch>
   Dimension          Result
   ─────────────────  ──────
   PR title format    ✓
-  Code quality       ⚠ 1 broad except, 2 Any hints
   PR desc integrity  ✓
   Registry/config    ✓
   Dead code          ⚠ 2 warnings

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

@@ -35,31 +35,6 @@ PR title must follow the project convention documented in `docs/contributing/REA
 
 ---
 
-## All PRs: Code Quality (run on every PR, quick & full)
-
-Diff-scoped sweep for five fragility patterns that are pervasive in the existing codebase — **only lines the PR adds count**, not the pre-existing backlog. Detection commands, codebase examples, and full severity rules live in [code-quality.md](code-quality.md).
-
-- [ ] **No new `**kwargs` string-lookup plumbing:** the diff adds no `**kwargs` + `kwargs["..."]` / `kwargs.get("...")` / `"..." in kwargs`. ⚠ any new instance; ✗ if a new string key is duplicated across ≥2 files without a shared constant, or unknown keys are silently dropped on a fail-fast path.
-- [ ] **No new broad exception swallow:** the diff adds no `except Exception:` / bare `except:`. ⚠ any new instance; ✗ for `except: pass` / `except Exception: return None` / `continue` on a fail-fast path (init, config validation, weight loading, request handling).
-- [ ] **No new `Any` / wrong type hints:** the diff adds no `: Any` / `-> Any` / untyped production signature, and no new `SimpleNamespace` in tests faking an object that already has a real typed stub. ⚠ any new instance; ✗ for a *wrong* (actively misleading) annotation or a `SimpleNamespace` test fake of a typed object.
-- [ ] **No new hot-path copy:** the diff adds no `.clone()` / `.copy_()` / `copy.deepcopy(...)` inside a per-step loop or per-request path. ⚠ any new instance; ✗ for `.clone()` of a full latent/activations tensor inside an AR or diffusion step loop without a comment, or `deepcopy` of a scheduler / sampling-config on the request path.
-- [ ] **No new event-loop blocking:** the diff adds no `time.sleep` / blocking HTTP (`requests`, `urllib`) / `await` inside a held lock in `async def` code. ⚠ any new instance; ✗ for blocking sleep or HTTP on the serving path (`engine/`, `entrypoints/`, async `connectors/`), or a lock held across an `await`. Blocking inside a dedicated worker thread is fine.
-
-Roll the ⚠/✗ counts into the report as a single **Code quality** dimension row.
-
-### Conventions (eyeball — no grep)
-
-Judgment calls that don't grep cleanly — apply them while reading the diff, not as a mechanical check:
-
-- **Right log level** — `info` is for end-user-visible events; routine internal events are `debug`; hot paths are `trace` or removed. Logging is not free.
-- **Structured fields over f-strings** — pass dynamic values as structured fields (`logger.info("msg", extra={...})`), not interpolated strings, so they stay greppable and parseable.
-- **No reflexive synchronization** — don't add `threading.Lock` / `asyncio.Lock` / `deepcopy` / re-wrap shared state unless there is real concurrent mutation; the GIL covers most Python code. Owners decide their own synchronization — don't pre-lock in a constructor.
-- **Context managers for cleanup** — use `with` / `contextlib` for files, locks, CUDA streams, and profilers; not raw `open()` + manual `.close()` or ad-hoc `finally`.
-- **No new dependencies without justification** — prefer the stdlib and existing vllm-omni deps; a new top-level dep needs a reason (check `pyproject.toml` / `requirements*.txt`).
-- **Naming / interface discipline** — don't rename or change interfaces just for taste in this PR, but do fix names that actively mislead (e.g. `serve` implying a long-running server, `Instance` where multiple instance kinds coexist).
-
----
-
 ## Bug Fix PRs
 
 ### Quick
@@ -74,7 +49,7 @@ Judgment calls that don't grep cleanly — apply them while reading the diff, no
 - [ ] **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. See the broad-except pattern in [code-quality.md](code-quality.md#2-broad-exception-swallow) for the full severity rule (⚠ any new broad catch; ✗ for a swallow on a fail-fast path).
+- [ ] **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
 

.claude/skills/precheck-pr/references/code-quality.md

@@ -1,179 +0,0 @@
-# Code-Quality Patterns
-
-Five fragility patterns to sweep on **every** PR, in both quick and full mode. These are pervasive in the existing codebase (hundreds of pre-existing sites) — so the checks are **diff-scoped**: only lines the PR *adds* count. Whole-repo greps light up every PR with the backlog and are useless.
-
-All commands assume:
-```bash
-BASE="$(git merge-base HEAD origin/main)"
-```
-
-**Severity:** ⚠ for any *new* instance introduced by the diff; ✗ for the acute sub-cases defined per pattern. Acute ✗ findings belong in the report's blocking count.
-
----
-
-## 1. `**kwargs` string-lookup passthrough
-
-### Anti-pattern
-```python
-def forward(self, **kwargs):
-    # BLOCKER-prone: raw dict + string keys duplicated across files
-    if "runtime_additional_information" in kwargs and \
-       "model_intermediate_buffer" not in kwargs:
-        ...
-```
-Real instances of this exact guard are copy-pasted across `cosyvoice3.py`, `indextts2_talker.py` (×2), `indextts2_s2mel_decoder.py`, `qwen3_tts_talker.py`, `qwen3_omni.py`. A typo in any string key silently no-ops; there is no type check, no IDE completion, and unknown keys are silently dropped.
-
-### Why dangerous
-Raw `dict` + `**kwargs` + lists of param-name strings is **fragile by construction**: the compiler can't see a missing or misspelled key, so failures are silent. Each duplicated copy is a new place for the strings to drift.
-
-### Severity
-- ⚠ any *new* `**kwargs` signature whose body does `kwargs["..."]` / `kwargs.get("...")` / `"..." in kwargs`.
-- ✗ when the diff introduces a **new** string key that is duplicated across ≥2 files without a shared module constant, or when `**kwargs` plumbing **silently drops unknown keys** on a fail-fast path (init, config validation, weight loading).
-
-### Detect (diff-scoped)
-```bash
-git diff ${BASE}...HEAD -- "*.py" | grep "^+[^+]" \
-  | grep -E '\*\*kwargs|kwargs(\.get|\[)|"[a-z_]+"\s+in\s+kwargs'
-```
-
-### Fix
-Prefer explicit typed params. The repo already has the right primitive: `msgspec.Struct` with `forbid_unknown_fields=True` rejects unknown keys instead of silently dropping them — see `_StructBase` in `vllm_omni/data_entry_keys.py:105` and `MoriPullRequest` in `vllm_omni/distributed/omni_connectors/connectors/mori_transfer_engine_connector.py:81`. A `@dataclass` or `TypedDict` works too.
-
-If `**kwargs` is unavoidable (vLLM base-class compat), centralize the key strings as module-level constants behind **one** typed accessor, instead of repeating the literal across files.
-
----
-
-## 2. Broad exception swallow
-
-### Anti-pattern
-```python
-# BLOCKER-prone: catches everything, returns None on any failure
-try:
-    evt = build_event(...)
-    return evt
-except Exception:
-    return None
-```
-Real clusters: `vllm_omni/metrics/stats.py:240,274,367,450` and `vllm_omni/metrics/{modality,utils}.py` — all `except Exception: return None`.
-
-### Why dangerous
-A generic `except Exception:` is **too flexible**. If we catch an exception type we don't expect, it is almost always an unhandled edge case that needs to be *fixed* — not swallowed into `None`/`pass`/`continue`. Broad catches turn fixable bugs into silent wrong behavior and make debugging impossible.
-
-The repo backs this with ruff `BLE001` (broad-exception catch); it is currently suppressed via `# noqa: BLE001` in `vllm_omni/patch.py:413`. Prefer fixing over suppressing.
-
-### Severity
-- ⚠ any *new* `except Exception:` / `except BaseException:` / bare `except:`.
-- ✗ for `except:` / `except Exception: pass|return None|continue` on a **fail-fast path** (init, config validation, weight loading, request handling, connector setup). This extends the Bug-Fix "No silent failure risk" ✗ in [checklists.md](checklists.md).
-
-### Detect (diff-scoped)
-```bash
-# Any new broad catch (⚠ baseline):
-git diff ${BASE}...HEAD -- "*.py" | grep "^+[^+]" \
-  | grep -E 'except\s*(Exception|BaseException)?\s*:'
-# Note: this matches only the immediate-colon swallow forms (`except :`,
-# `except Exception:`) — it deliberately excludes specific catches like
-# `except ValueError:` and the `except Exception as e:` variant. Eyeball
-# the latter; `as e` usually means it is logged, but still verify.
-```
-
-### Fix
-Catch the **specific** types you actually expect (`ValueError`, `KeyError`, `AttributeError`, `OSError`, `torch.cuda.OutOfMemoryError`, `msgspec.ValidationError`, …). At a genuine top-level / best-effort boundary (metrics, signal handlers), at minimum **log** the exception; never swallow into a bare `pass`/`return None` on a path that should fail loudly.
-
----
-
-## 3. `Any` / wrong type annotations
-
-### Anti-pattern
-```python
-# ⚠ Any param + Any return — no contract, no narrowing
-def _extract_mm_output(engine_outputs: Any) -> dict[str, Any]:
-    ...
-```
-
-### Why dangerous (root cause)
-`Any` disables the type checker for that position, so typos and wrong shapes pass silently. The repo has 636 `: Any` params, 244 `-> Any` returns, and **1,173 `SimpleNamespace` in `tests/`**. The `Any` leak is largely test-driven: vibe-coded unit tests fake objects with `SimpleNamespace`, which forces the production code consuming them to be typed `Any`, and the `Any` then propagates outward into public signatures.
-
-### Severity
-- ⚠ any *new* `: Any` / `-> Any` / fully-untyped signature in production code.
-- ✗ for a **wrong** annotation (actively misleading — e.g. `-> bool` that returns `Optional[bool]`), or a *new* `SimpleNamespace` in a test that mimics an object which already has a real typed stub / `@dataclass` / `TypedDict` / `Protocol`.
-
-### Detect (diff-scoped)
-```bash
-# Any leaking into new signatures:
-git diff ${BASE}...HEAD -- "*.py" | grep "^+[^+]" \
-  | grep -E ':\s*Any\b|->\s*Any\b'
-# SimpleNamespace newly added in tests (the Any-leak source):
-git diff ${BASE}...HEAD -- "tests/" | grep "^+[^+]" | grep "SimpleNamespace"
-```
-
-### Fix
-Replace `Any` with the concrete type, a `Protocol`, or a `Union`. If a type is genuinely dynamic, prefer `object` + `isinstance` narrowing over `Any` — `object` keeps the checker engaged. In tests, replace `SimpleNamespace` with the real class, a small `@dataclass`/`TypedDict`, or a `Protocol` + fake, so the system under test stays fully typed.
-
----
-
-## 4. Unnecessary copy — `.clone()` / `copy.deepcopy` in hot paths
-
-### Anti-pattern
-```python
-# Per-step in a diffusion/AR loop: clones the full latent every iteration
-for t in timesteps:
-    trajectory_latents.append(x_t.clone())   # O(N) GPU alloc + memcpy each step
-```
-Real clusters: `vllm_omni/diffusion/models/bagel/bagel_transformer.py` calls `x_t.clone()` ~9× across a trajectory loop (lines 1820–2186); the cache hooks `vllm_omni/diffusion/cache/teacache/hook.py:155` and `magcache/hook.py:182` clone `hidden_states` per step. Separately, `copy.deepcopy(self.scheduler)` / `copy.deepcopy(req.sampling_params)` is copy-pasted across diffusion pipelines — `pipeline_qwen_image.py:786`, `pipeline_hunyuan_image3.py:1904`, `pipeline_ltx2*.py` (×3), `pipeline_dreamzero.py:1085`, `diffusion_model_runner.py:398` — a full object-graph deepcopy on the request path. (Contrast `dreamzero/causal_wan_model.py:203`, which carries a `# No .clone() needed.` comment — the aware pattern.)
-
-### Why dangerous
-A `.clone()` of a GPU tensor or a `copy.deepcopy` of a config object inside a per-step / per-request path is a hidden allocator: it reads as one Python line but triggers a GPU memcpy or a deep object-graph walk every iteration. Across 50 diffusion steps or hundreds of AR steps it dominates latency and VRAM bandwidth. `deepcopy` additionally ignores any custom device / `__copy__` logic and can drag tensors through CPU.
-
-### Severity
-- ⚠ any *new* `.clone()` / `.copy_()` / `copy.deepcopy(...)` inside a per-step loop or per-request path.
-- ✗ for `.clone()` of a full latent/activations tensor inside an AR or diffusion step loop without an explanatory comment, or `copy.deepcopy` of a scheduler / sampling-config on the request hot path.
-
-### Detect (diff-scoped)
-```bash
-git diff ${BASE}...HEAD -- "*.py" | grep "^+[^+]" \
-  | grep -E '\.clone\(\)|\.copy_\(|copy\.deepcopy|deepcopy\('
-```
-
-### Fix
-Tensors: prefer a view (`x[..., :k]`, `torch.narrow`, `as_strided`) or write into a pre-allocated buffer with `dst.copy_(src)` instead of `.clone()`; if the caller no longer needs the original, `move`/`del` it rather than copy. Schedulers / configs: build a fresh lightweight instance per request, or split out the mutable per-request bits so the shared immutable part isn't deepcopied. If a copy is genuinely required (e.g. the source is mutated in place later), keep it and add a one-line comment saying why.
-
----
-
-## 5. Blocking the asyncio event loop / lock held across `await`
-
-### Anti-pattern
-```python
-# BLOCKER-prone: blocking sleep / blocking HTTP / lock held across an await,
-# all inside an async (event-loop) function
-async def handle(self, req):
-    with self._lock:
-        resp = await self._client.call(req)   # lock held across await -> serializes all requests
-    time.sleep(0.1)                           # stalls the single loop for every concurrent request
-```
-
-### Why dangerous
-vLLM-Omni serves over a single asyncio event loop. Any synchronous blocking work on an `async def` path — `time.sleep`, blocking HTTP (`requests`/`urllib`), heavy CPU work, or a lock held across an `await` — stalls the loop for *every* concurrent request, not just the blocking one. A lock held across `await` silently serializes the whole pipeline; blocking I/O tanks throughput and inflates tail latency.
-
-### Severity
-- ⚠ any *new* `time.sleep(`, blocking HTTP call (`requests.get/post`, `urllib.request`, `urlopen`), or `await` inside a `with Lock` / `async with` critical section, in `async def` code.
-- ✗ for `time.sleep` or blocking HTTP on the serving path (`engine/`, `entrypoints/`, async `connectors/`), or a lock acquired and held across an `await` on the request path.
-
-### Detect (diff-scoped)
-```bash
-# New blocking primitives introduced by the diff — then eyeball whether each
-# sits in an async function on the loop vs a dedicated worker thread:
-git diff ${BASE}...HEAD -- "*.py" | grep "^+[^+]" \
-  | grep -E 'time\.sleep|requests\.(get|post|put|delete)|urllib\.request|urlopen'
-# Lock acquired with an await potentially inside the critical section (manual eyeball):
-git diff ${BASE}...HEAD -- "*.py" | grep "^+[^+]" | grep -E 'async with|\.acquire\(\)'
-```
-Note: blocking inside a **dedicated worker thread is correct** — the repo already does this (`diffusion/model_loader/hub_prefetch.py`, `distributed/omni_connectors/kv_transfer_manager.py`, the `mooncake`/`mori` connectors poll with `time.sleep` on their own threads). The check targets the *event-loop* path only.
-
-### Fix
-Move blocking work off the loop with `asyncio.to_thread(...)` / `run_in_executor`, or use a native async client (`httpx.AsyncClient` / `aiohttp`). Never `await` while holding a lock — drop the lock before the `await` and re-acquire after, or restructure so the critical section contains no `await`.
-
----
-
-## Running the sweep
-
-Run all five detections against `${BASE}...HEAD`, then for each hit decide ⚠ vs ✗ using the severity rules above. Roll the results into the report as a single **Code quality** dimension row (count of ⚠ and ✗), alongside the type-specific checklist from [checklists.md](checklists.md).

.claude/skills/readme.md

@@ -30,12 +30,6 @@ include:
 - `precheck-pr`: self-check a branch before creating a PR — validates PR title
   format, catches dead code, verifies accuracy/perf claims, and confirms merge
   readiness
-- `vllm-omni-test`: guides generation and execution of CI-aligned tests (L1–L4),
-  pytest marker selection (`core_model` / `advanced_model` / `full_model`,
-  `omni` / `tts` / `diffusion`), Buildkite wiring (`test-ready.yml`,
-  `test-merge.yml`, `test-nightly.yml`, `test-weekly.yml`), and copy-paste
-  local plus CI-like `pytest` commands; see `references/test-routing.md` for
-  level-to-command mapping
 - `review-pr`: provides a structured workflow for reviewing pull requests
 - `vllm-omni-npu-model-runner-upgrade`: upgrades NPU model runners to align with the
   latest vllm-ascend NPUModelRunner

.claude/skills/vllm-omni-test/SKILL.md

@@ -1,890 +0,0 @@
----
-name: vllm-omni-test
-description: Generate and run tests for vllm-project/vllm-omni with CI-aligned levels and markers; wire new tests into Buildkite (test-ready.yml for L1/L2, test-merge.yml for L3, test-nightly.yml for L4). On completion, always provide copy-paste local and CI-like pytest commands plus prerequisites. Use when creating regression tests, adding L1-L4 coverage, selecting pytest markers, or validating fixes from issues/PRs.
----
-
-# vLLM-Omni Test Generator & Runner
-
-## Purpose
-
-Use this skill to generate minimal, stable test cases and run them with the correct marker/level strategy for [vllm-project/vllm-omni](https://github.com/vllm-project/vllm-omni).
-
-**Link convention:** Paths such as `.buildkite/` and `docs/contributing/` live at the **vllm-omni repo root**. Markdown links use repo-relative paths from this skill file (e.g. `../../../.buildkite/test-ready.yml`, `../../../docs/contributing/ci/CI_5levels.md`).
-
-Default priorities:
-
-1. Reproducible regression coverage for bug fixes
-2. Correct test level and marker selection
-3. Low flake, low dependency tests first
-4. CI-compatible run commands
-5. **Actionable run commands for the human**: whenever you add or change tests, always finish with **copy-paste-ready** `pytest` lines (local: single file and/or single test; CI-like: markers + `--run-level`), plus short **prerequisites** (GPU tier, HF cache, optional `model_prefix`). Do not assume the reader will infer commands from `test-routing.md` alone.
-
-## Inputs
-
-- Issue/PR link and summary
-- Changed files or suspected code path
-- Whether the user wants local quick validation or CI-equivalent validation
-- Hardware constraints (CPU only / CUDA / ROCm / NPU)
-
-## Workflow
-
-### Step 1: Classify Test Goal
-
-- **Bugfix regression**: start from a minimal failing scenario and add assertions that prevent recurrence. Before writing tests, output **`required` / `recommended` / `not_needed`**: **`required`** — stable logic/contract bug that should have been caught; **`recommended`** — environment-sensitive but a small regression still helps; **`not_needed`** — one-off external/config failure or existing tests already cover the path. Prefer the narrowest stable L1 (CPU) case; escalate to L2/L3 only when the bug needs real weights or serving.
-- **Feature coverage**: verify new behavior and one negative/boundary case.
-- **Perf/benchmark claim**: require benchmark-oriented tests and explicit metrics.
-
-### Step 2: Select Test Level
-
-- **L1**: unit/logic, deterministic, CPU-friendly, fastest feedback.
-- **L2**: basic e2e/integration and platform-dependent checks.
-- **L3/L4**: advanced model/integration/perf validation.
-
-Use [references/test-routing.md](references/test-routing.md) for level-to-marker and command mapping.
-
-### Step 3: Pick Markers
-
-Always attach markers deliberately:
-
-- **Level**: `core_model` (L1/L2) and/or `advanced_model` (L3) and/or `full_model` (L4 nightly)
-- **Model type** (required on model-centric e2e — pick exactly one):
-  - `omni` — end-to-end multimodal LLM pipelines (thinker/talker/stages; Qwen-Omni family)
-  - `tts` — speech synthesis / TTS-only models (`/v1/audio/speech`, voice clone, etc.)
-  - `diffusion` — generative diffusion models (image / audio / text / video from noise)
-- **Cross-cutting area** (when relevant): `parallel`, `cache`, `example`, `benchmark`
-- **Hardware**: `cpu`, `gpu`, `cuda`, `rocm`, `npu`, `L4`, `H100`, `distributed_cuda`, …
-- Optional: `slow`, distributed markers when multi-card is required
-
-**Baseline smoke (L2 + L3):** The simplest e2e case per model — default deploy, minimal request — should usually carry **both** `@pytest.mark.core_model` and `@pytest.mark.advanced_model` on the **same** test function so `test-ready.yml` and `test-merge.yml` share one test. `send_*_request` picks validation depth from `--run-level`. References: `test_voxcpm2_tts.py::test_text_to_audio_001`, `test_qwen3_tts_customvoice.py::test_text_to_audio_001`. Heavier scenarios use **`advanced_model` only**; L4 expansion uses **`full_model`**.
-
-For hardware-aware tests, prefer `@hardware_test(...)` or `hardware_marks(...)` in `tests/helpers/mark.py`.
-
-**Diffusion nightly split** (`test-nightly.yml`): all diffusion tests use `pytest.mark.diffusion`, but CI groups them by **output modality**, not by separate pytest markers:
-
-| Nightly group | Scope | Typical models / paths |
-|---------------|--------|-------------------------|
-| **Diffusion X2I(&A&T) Model Test** | x2**i** (image), x2**a** (audio), x2**t** (text) and other **non-video** diffusion | Qwen-Image*, BAGEL, FLUX, SD3, Z-Image, LongCat, DreamZero, … — `test_*_expansion.py` under X2I shards; L4 sweep uses `-k "not test_wan and not test_bagel_expansion and not hunyuan"` for L4 |
-| **Diffusion X2V Model Test** | x2**v** (video) only | Wan2.2, HunyuanVideo 1.5, Wan VACE, … — e.g. `test_wan22_expansion.py`, `test_hunyuan_video_15_expansion.py` |
-
-Wire new **video** diffusion expansion tests into the **X2V** group; wire **image/audio/text** diffusion expansion tests into **X2I(&A&T)**. Do not place x2v modules in X2I shards (see comment in `test-nightly.yml` above the X2I group).
-
-### Naming: generated test module files (L2–L4, model-centric e2e)
-
-When adding **new** pytest modules whose primary scope is a **specific model** (typical under `tests/e2e/offline_inference/` or `tests/e2e/online_serving/`), use this filename pattern:
-
-| Level | Filename pattern | Example (model `Qwen/Qwen2.5-Omni-7B`) |
-|-------|------------------|----------------------------------------|
-| **L2**, **L3** | `test_{lowercase_model_slug}.py` | `test_qwen2_5_omni.py` |
-| **L4** | `test_{lowercase_model_slug}_expansion.py` | `test_qwen2_5_omni_expansion.py` |
-
-**Slug rules for `{lowercase_model_slug}`:**
-
-1. Start from the HuggingFace-style id (e.g. `Qwen/Qwen2.5-Omni-7B`), but **do not** put the org into the filename: use the **repo segment only** (`Qwen2.5-Omni-7B`), not `Qwen_...` / `qwen_qwen2_5_...`.
-2. **Lowercase**; replace `.`, `-`, and whitespace with a single `_` (e.g. `Qwen2.5-Omni-7B` → `qwen2_5_omni`). **Omit** trailing size tokens such as `7b` / `30b` in the basename when a single file covers that model line in the directory (matches `test_qwen2_5_omni.py` in-tree).
-3. If two checkpoints in the same folder need separate modules, add a **minimal** disambiguator (e.g. `_7b` vs `_3b`) only then.
-4. **L1** unit tests are **not** bound to this pattern; use `tests/<area>/test_<feature>.py` as today.
-
-Routing tables and commands: [references/test-routing.md](references/test-routing.md) § *Model-centric e2e filename convention*.
-
-Existing references: `tests/e2e/offline_inference/test_qwen2_5_omni.py` (L2-style omni), `tests/e2e/offline_inference/test_qwen3_5_9b.py` (L2-style omni, single-stage VL), `tests/e2e/online_serving/test_qwen3_omni_expansion.py` (L4-style omni), `tests/e2e/online_serving/test_qwen_image_edit_expansion.py` / `test_qwen_image_expansion.py` (L4-style diffusion).
-
-### Step 4: Generate Test Case Skeleton
-
-**1. Pick the functional scenario** (then choose directory, fixtures, and markers):
-
-| Scenario | Typical location | Fixtures / runner pattern | Baseline markers & level |
-|----------|------------------|---------------------------|---------------------------|
-| **Offline inference e2e** | `tests/e2e/offline_inference/` | **Module (default):** `omni_runner` + `omni_runner_handler`. **Function (isolation only):** `omni_runner_function` + `omni_runner_handler_function`. Diffusion/TTS may use `Omni(...).generate` directly | L2: `core_model` + **one of** `omni` / `tts` / `diffusion`; `@hardware_test(...)` when GPU/NPU is required |
-| **Online serving e2e** | `tests/e2e/online_serving/` | **Module (default):** `omni_server` + `openai_client`. **Function (isolation only):** `omni_server_function` + `openai_client_function`. Clients: `send_omni_request` (omni), `send_audio_speech_request` (tts), `send_diffusion_request` / `send_video_diffusion_request` / `send_images_generations_request` (diffusion) | Baseline smoke: **`core_model` + `advanced_model`**; heavier paths: `advanced_model` only; L4 expansion: `full_model` |
-| **Documentation / runnable examples** | `tests/examples/offline_inference/`, `tests/examples/online_serving/` | **Offline docs (preferred):** extract Python/Bash blocks from the doc README (e.g. `ReadmeSnippet.extract_readme_snippets`), `pytest.mark.parametrize` each snippet, run via `example_runner.run` with a stable `output_subfolder`. **Online docs:** copy client/request scripts into dedicated tests and keep them in sync with the doc page. | Usually **L4**: `advanced_model`, often `example` plus hardware marks matching the nightly docs-example job (see `.buildkite/test-nightly.yml`). Full conventions: [docs/contributing/ci/test_examples/l4_doc_example_tests.inc.md](../../../docs/contributing/ci/test_examples/l4_doc_example_tests.inc.md) (introduced in [PR #1910](https://github.com/vllm-project/vllm-omni/pull/1910): naming, output directory layout, skip rules, avoid trimming `num_inference_steps` without a strong CI reason). |
-| **Performance / benchmark** | `tests/dfx/perf/tests/*.json` + `run_*_benchmark.py` | JSON or script-driven server + load config; assert explicit metrics / baselines | L4 Perf: `full_model` + `benchmark`; wire `test-nightly.yml` Perf steps |
-| **Invalid parameter / negative HTTP validation** | `tests/dfx/reliability/invalid_param_test/` | Live `omni_server` + low-level `send_*_http_request` with `err_code` / `err_message` | `pytest.mark.slow` + `omni` / `tts` / `diffusion` + `@hardware_marks` (`H100` or `L4`); CI in **`test-weekly.yml`** (not ready/merge/nightly) |
-
-**If the user’s test plan includes invalid parameter validation / invalid params / negative HTTP / 400 validation:** do **not** add those `test_*` functions to `tests/e2e/online_serving/test_*.py` or `*_expansion.py`. **Move or author them** under `tests/dfx/reliability/invalid_param_test/` in the **endpoint-matching script** (see **Invalid parameter validation** below). Success-path e2e and invalid-param dfx tests must stay in separate modules.
-
-**1b. Model type — `omni` vs `tts` vs `diffusion`**
-
-After choosing offline/online/docs/perf, classify the **product under test** and attach **exactly one** model-type marker. All three can live under the same `tests/e2e/` trees; conventions diverge:
-
-| Dimension | **Omni** (`pytest.mark.omni`) | **TTS** (`pytest.mark.tts`) | **Diffusion** (`pytest.mark.diffusion`) |
-|-----------|------------------------------|----------------------------|----------------------------------------|
-| **What it is** | Multimodal LLM pipeline (thinker/talker/stages; text + vision + audio I/O) | Speech synthesis / voice models | Generative diffusion (noise → image, audio, text, or video) |
-| **Examples** | Qwen2.5-Omni, Qwen3-Omni | Qwen3-TTS, VoxCPM2, Higgs-Audio, Step-Audio2 | Qwen-Image, BAGEL, Wan2.2, HunyuanVideo |
-| **Offline runner** | `omni_runner` + `omni_runner_handler`, `generate_multimodal` | `Omni(...).generate` or stage YAML + TTS params | `Omni(...).generate` + `OmniDiffusionSamplingParams` |
-| **Online client** | `openai_client.send_omni_request` (chat completions, modalities) | `openai_client.send_audio_speech_request` (`/v1/audio/speech`) | `send_diffusion_request` (chat/T2I), `send_video_diffusion_request` (`/v1/videos`, X2V), or `send_images_generations_http_request` / `send_images_edits_http_request` (DALL-E routes) |
-| **Typical assertions** | Stage outputs, text/audio keywords via `OmniRunnerHandler` / response handler | WAV bytes, stream chunks, speech endpoint contract | Image/video dimensions, `final_output_type`, `assert_diffusion_response` |
-| **Stage / deploy YAML** | Per-model omni stage configs (`ci/qwen3_omni_moe.yaml`, …) | `qwen3_tts.yaml`, `voxcpm2.yaml`, … | Often default serve; parallel/offload YAML for heavy DiT |
-| **Nightly group** (`test-nightly.yml`) | **Omni Model Test** — `-m "full_model and omni"` | **TTS Model Test** — `-m "full_model and tts"` | **Diffusion X2I(&A&T)** *or* **Diffusion X2V** (see Step 3 table; same `diffusion` marker, different YAML group / file shard) |
-| **L4 pressure** | Expansion per modality/model as needed | Expansion + accuracy/perf in TTS group | X2I: merge feature combos per [#1832](https://github.com/vllm-project/vllm-omni/issues/1832); X2V: separate nightly group |
-
-Do **not** mix fixtures across types (e.g. do not use `omni_runner` layout for a pure diffusion or TTS model without mirroring an in-tree test in that family).
-
-**Diffusion only — X2I(&A&T) vs X2V (nightly routing, not extra markers):**
-
-- **X2I(&A&T)**: image / audio / text generation — Qwen-Image*, FLUX, SD3, Z-Image, BAGEL (expansion), LongCat, audio diffusion, etc.
-- **X2V**: video generation only — Wan2.2, HunyuanVideo 1.5, Wan VACE, LTX video similarity paths.
-
-When adding a new `test_*_expansion.py`, place it in the matching **nightly group** step (explicit file list in `test-nightly.yml`), not only by marker expression.
-
-**2. Use the narrowest deterministic skeleton for the scenario**
-
-*L1 unit / logic (CPU-first):*
-
-**Mocking rule (L1 only):** use **pytest** integration — **`mocker`** (`pytest-mock`) or **`monkeypatch`** (built-in). **Do not** import or call **`unittest.mock`** (`patch`, `MagicMock`, `@patch`, `with patch(...)`, etc.) in L1 tests; patches must auto-revert with the test lifecycle.
-
-```python
-import pytest
-
-pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
-
-
-def test_<scenario_name>(mocker):
-    # Prefer mocker.patch / mocker.spy / mocker.Mock — not unittest.mock.patch
-    fake_fn = mocker.patch("vllm_omni.some.module.expensive_call", return_value=...)
-    # Act
-    # Assert
-    fake_fn.assert_called_once()
-
-
-def test_<env_or_attr>(monkeypatch):
-    # For simple env / attribute substitution without a Mock object
-    monkeypatch.setenv("SOME_FLAG", "1")
-    monkeypatch.setattr("vllm_omni.some.module.CONST", 42)
-    ...
-```
-
-**Avoid in L1:**
-
-```python
-# BAD — do not use in L1 unit tests
-from unittest.mock import patch, MagicMock
-
-@patch("vllm_omni.some.module.fn")
-def test_bad(mock_fn): ...
-
-def test_also_bad():
-    with patch("...") as m: ...
-```
-
-See **L1 unit test constraints (mocking)** below for the full do/don't list.
-
-*Offline multimodal e2e — **Omni** (representative):*
-
-```python
-@pytest.mark.core_model
-@pytest.mark.omni
-@hardware_test(...)
-@pytest.mark.parametrize("omni_runner", test_params, indirect=True)
-def test_<scenario>(omni_runner, omni_runner_handler) -> None:
-    request_config = {"prompts": ..., "modalities": [...]}  # optional: images, videos, audios
-    omni_runner_handler.send_omni_request(request_config)
-```
-
-*Offline generative e2e — **Diffusion** (representative):*
-
-```python
-@pytest.mark.core_model
-@pytest.mark.diffusion
-@hardware_test(...)
-@pytest.mark.parametrize("omni_runner", test_params, indirect=True)
-def test_text_to_image_001(omni_runner_handler) -> None:
-    omni_runner_handler.send_diffusion_request({"prompt": "...", "extra_body": {"num_inference_steps": 4, ...}})
-```
-
-*Offline TTS e2e — **Qwen3-TTS** (two-stage; representative):*
-
-```python
-@pytest.mark.advanced_model
-@pytest.mark.tts
-@hardware_test(...)
-@pytest.mark.parametrize("omni_runner", tts_server_params, indirect=True)
-def test_text_to_audio_001(omni_runner, omni_runner_handler) -> None:
-    omni_runner_handler.send_audio_speech_request({
-        "input": "...",
-        "task_type": "Base",
-        "ref_audio": REF_AUDIO_URL,
-        "ref_text": REF_TEXT,
-    })
-```
-
-*Offline TTS e2e — **single-stage** (Coqui XTTS, MOSS-TTS-Nano; representative):*
-
-```python
-@pytest.mark.advanced_model
-@pytest.mark.tts
-@hardware_test(...)
-@pytest.mark.parametrize("omni_runner", tts_server_params, indirect=True)
-def test_voice_clone_001(omni_runner_handler) -> None:
-    omni_runner_handler.send_single_stage_tts_request({
-        "input": "...",
-        "language": "en",
-        "prompt_audio_path": REF_AUDIO_PATH,
-        "response_format": "wav",
-        "run_level": "advanced_model",
-    })
-```
-
-*Online serving e2e — **Omni** (representative):*
-
-```python
-@pytest.mark.core_model
-@pytest.mark.omni
-@hardware_test(...)
-@pytest.mark.parametrize("omni_server", test_params, indirect=True)
-def test_text_to_text_001(omni_server, openai_client) -> None:
-    request_config = {"model": omni_server.model, "messages": ..., "modalities": ["text"]}
-    openai_client.send_omni_request(request_config)
-```
-
-*Online serving e2e — **TTS** (representative):*
-
-```python
-@pytest.mark.core_model
-@pytest.mark.advanced_model
-@pytest.mark.tts
-@hardware_test(...)
-@pytest.mark.parametrize("omni_server", tts_server_params, indirect=True)
-def test_text_to_audio_001(omni_server, openai_client) -> None:
-    request_config = {"model": omni_server.model, "input": "...", "response_format": "wav", ...}
-    openai_client.send_audio_speech_request(request_config)
-```
-
-*Online serving e2e — **Diffusion** X2I (representative):*
-
-```python
-@pytest.mark.core_model
-@pytest.mark.diffusion
-@pytest.mark.parametrize("omni_server", _get_default_case(MODEL), indirect=True)
-def test_text_to_image_001(omni_server, openai_client) -> None:
-    openai_client.send_diffusion_request({...})  # chat completions + extra_body
-```
-
-*Online serving e2e — **Diffusion** X2V (representative):*
-
-```python
-@pytest.mark.core_model
-@pytest.mark.diffusion
-def test_text_to_video_001(omni_server, openai_client) -> None:
-    openai_client.send_video_diffusion_request({"model": ..., "form_data": {...}})  # /v1/videos
-```
-
-*Documentation example tests:* follow the **Preferred Test Strategy** in [l4_doc_example_tests.inc.md](../../../docs/contributing/ci/test_examples/l4_doc_example_tests.inc.md): dynamic extraction for offline READMEs; explicit copied client code for online pages until extraction is justified; use the documented **naming**, **output directory** (page folder + case id), and **skipping** rules (e.g. Gradio-only scripts).
-
-*Performance tests:* add or extend entries under `tests/perf/` (and JSON configs where the project uses them), with **explicit baselines** and the same marker/run-level pairing as the CI step that will execute them.
-
-**3. Cross-cutting rules**
-
-- Reuse existing fixtures for the chosen scenario; do not mix “online client” assumptions into offline `OmniRunner` tests without a clear reason.
-- Avoid external network dependency in assertions unless the scenario is explicitly “online serving” or doc examples that require a model hub (then align with CI secrets/cache).
-- Keep **one test function = one intent** (one modality combo, one endpoint contract, or one acceleration combo).
-- **E2E test function layout**: **one case → one `test_<scenario>` function** with a name that states what is validated (endpoint, size/`n`, server flag, or route). **Do not** merge multiple cases into a single test that branches on `request.node.callspec.id`, `param.id`, or `if case_id == ...`. Use `@pytest.mark.parametrize("omni_server", [...], indirect=True)` per function (usually **one** `OmniServerParams` per test). A loop inside one test is OK only when it serves **that function’s single intent** (e.g. three standard sizes in `test_*_sizes_256_512_1024`).
-- **Runtime fixture scope** (`tests/helpers/fixtures/runtime.py`): default **`omni_server` / `omni_runner`** (module) + matching client/handler; use **`omni_server_function` / `omni_runner_function`** only when each `test_*` must start a fresh instance (see below).
-- **L1 mocks**: never `unittest.mock`; use `mocker` or `monkeypatch` only (see below).
-- **API calls (L2+ e2e, online and offline)**: **reuse** `send_*_request` in `tests/helpers/runtime.py` when it exists; **otherwise add** the helper in `runtime.py` first, then call it from the test. General `assert_*` inside `send_*_request`; special `assert_*` only in the test. See **Runtime send helpers** below — do not call `omni.generate`, raw HTTP, or SDK clients from `test_*.py`.
-- **Response assertions**: reusable checks on API bodies / decoded media belong in **`tests/helpers/assertions.py`** — not as private `_assert_*` helpers inside `test_*.py` (see below).
-- **Model-specific payloads stay in test modules** — per-model `MODEL`, deploy path, `REF_AUDIO_URL`, `get_prompt()`, `_build_request()`, and inline `request_config` dicts live in `test_{slug}.py` / `test_{slug}_expansion.py` (and offline/L1 siblings). **Do not** create `tests/helpers/{slug}.py` to deduplicate them; a little copy across files is preferred (see `test_glm_tts.py`, `test_cosyvoice3_tts_expansion.py`). `tests/helpers/` is repo-wide harness only (`mark`, `media`, `runtime`, `stage_config`, `assertions`, `fixtures/`).
-
-#### L1 unit test constraints (mocking)
-
-L1 tests (`core_model and cpu`, under `tests/diffusion/`, `tests/engine/`, `tests/model_executor/`, etc.) must follow the repo’s **pytest-mock** convention (`pytest-mock>=3.10.0` in `pyproject.toml` `[project.optional-dependencies] dev`).
-
-| Do | Don't |
-|----|-------|
-| `def test_foo(mocker):` + `mocker.patch(...)`, `mocker.spy(...)`, `mocker.Mock()`, `mocker.MagicMock()`, `mocker.AsyncMock()` | `from unittest.mock import patch, MagicMock, Mock, AsyncMock` |
-| `def test_bar(monkeypatch):` + `monkeypatch.setattr` / `setenv` / `delenv` / `setitem` | `@patch(...)` decorator |
-| Let `mocker` auto-stop patches after the test | `with patch(...):` / `patch.object(...)` context managers |
-| Mirror neighboring L1 tests in the same directory | `unittest.mock.create_autospec` unless an existing file already documents an exception |
-
-**Rationale:** `mocker` ties patch lifecycle to pytest fixtures (no leaked patches across tests). `unittest.mock` decorators/context managers are easy to compose incorrectly with parametrized or async tests and are inconsistent with in-tree L1 style.
-
-**Minimal patterns:**
-
-```python
-def test_returns_cached_config(mocker):
-    loader = mocker.patch(
-        "vllm_omni.foo.load_yaml",
-        return_value={"stages": []},
-    )
-    result = get_deploy_config("ci/foo.yaml")
-    assert result["stages"] == []
-    loader.assert_called_once_with("ci/foo.yaml")
-
-
-def test_skips_when_env_unset(monkeypatch):
-    monkeypatch.delenv("VLLM_OMNI_FEATURE", raising=False)
-    assert should_enable_feature() is False
-```
-
-E2E levels (L2+) generally **avoid mocks**; if a rare L2 stub is unavoidable, still prefer `mocker` over `unittest.mock` for consistency.
-
-#### Runtime fixtures — scope (`tests/helpers/fixtures/runtime.py`)
-
-vLLM-Omni e2e tests start a real **OmniServer** (online) or **OmniRunner** (offline). Pick scope by **how often the process must be recreated**, not by test level alone.
-
-| Scope | Online fixtures | Offline fixtures | When to use |
-|-------|-----------------|------------------|-------------|
-| **Module** (default) | `omni_server` → `openai_client` | `omni_runner` → `omni_runner_handler` | **Default for L2/L3/L4 expansion** — amortize model/server init across `test_*` in the same module. Same `OmniServerParams` / runner config can be reused by multiple tests. |
-| **Function** | `omni_server_function` → `openai_client_function` | `omni_runner_function` → `omni_runner_handler_function` | **Only when required** — each `test_*` must get a **clean** server/runner (no shared engine/GPU state). Typical: `tests/dfx/reliability/`, sleep/wakeup, crash/restart, tests that mutate global server state. |
-
-**Rules:**
-
-1. **Default to module scope** (`omni_server` / `omni_runner`) unless the scenario **explicitly** needs a fresh instance per test function.
-2. **Indirect parametrize name must match the fixture name**: `@pytest.mark.parametrize("omni_server", ...)` with `omni_server` + `openai_client`; `@pytest.mark.parametrize("omni_server_function", ...)` with `omni_server_function` + `openai_client_function`. Do not mix module fixture with function client (or vice versa).
-3. Different `OmniServerParams` per `test_*` (e.g. default vs `--enable-cpu-offload`) is still OK with **module** `omni_server` — pytest parametrizes per test node; only switch to `_function` when isolation between tests matters, not merely because `server_args` differ.
-4. One `test_*` with **many** server configs (expansion matrix) → single function + `@pytest.mark.parametrize("omni_server", [...], indirect=True)` + module `omni_server` (see in-tree `test_qwen_image_expansion.py`).
-
-```python
-# Default — module-scoped server (L4 expansion)
-@pytest.mark.parametrize("omni_server", [pytest.param(OmniServerParams(model=MODEL), marks=H100)], indirect=True)
-def test_foo_images_generations_default_1024(omni_server, openai_client) -> None:
-    openai_client.send_images_generations_request({...})
-
-# Function-scoped — reliability / per-test clean state only
-@pytest.mark.parametrize(
-    "omni_server_function",
-    [pytest.param(OmniServerParams(model=MODEL), marks=H100)],
-    indirect=True,
-)
-def test_foo_sleep_wakeup_cycle(omni_server_function, openai_client_function) -> None:
-    openai_client_function.send_omni_sleep_http_request({...})
-    openai_client_function.send_omni_wakeup_http_request({...})
-```
-
-#### Runtime send helpers — online **and** offline (`tests/helpers/runtime.py`)
-
-**L2+ e2e (online serving and offline inference) must call APIs through `tests/helpers/runtime.py`.** Fixtures live in `tests/helpers/fixtures/runtime.py`; **send/assert implementation** lives in `tests/helpers/runtime.py` + `tests/helpers/assertions.py`.
-
-| Principle | Action |
-|-----------|--------|
-| **Reuse first** | Grep `runtime.py` for an existing **`send_*_request`** on `OpenAIClientHandler` (online) or `OmniRunnerHandler` (offline) that matches the endpoint / pipeline shape. |
-| **Extend when close** | If an existing `send_*_request` almost fits (e.g. missing one optional field), extend it in `runtime.py` — do not fork logic in the test file. |
-| **Add when missing** | No suitable helper → add **`send_<feature>_request`** (high-level: call + general `assert_*`) or **`send_<route>_<verb>_http_request`** (low-level HTTP for negative/dfx) in **`runtime.py` first**, then call it from tests. |
-| **Test module owns payload only** | In `test_*.py`: `MODEL`, deploy path, vendored media, `get_prompt()`, and inline **`request_config` dicts** only. **No** `omni.generate(...)`, raw `requests.post`, OpenAI SDK calls, or `_collect_audio()` / `_process_output()` in e2e tests. |
-
-**Online** (`openai_client` from `omni_server`): `OpenAIClientHandler.send_*_request`.
-
-**Offline** (`omni_runner_handler` from `omni_runner`): `OmniRunnerHandler.send_*_request`.
-
-| Do | Don't |
-|----|-------|
-| Online: `openai_client.send_omni_request`, `send_diffusion_request`, `send_audio_speech_request`, `send_video_diffusion_request`, `send_images_generations_request`, … | `requests.post(f"{base_url}/v1/...", json=…)` or `client.chat.completions.create(...)` inside a test |
-| Offline: `omni_runner_handler.send_omni_request`, `send_diffusion_request`, `send_audio_speech_request`, `send_single_stage_tts_request`, `send_single_stage_tts_batch_request`, … | `omni_runner.omni.generate(...)` + hand-rolled tensor/WAV extraction in `test_*.py` |
-| Add missing `send_*` to **`runtime.py`** first; bundle general `assert_*` inside the send helper | A one-off `def _post_*` / `def _collect_audio` at the bottom of a test module |
-| Mirror naming/style of neighboring `send_*` (docstring, `request_config` dict, optional `run_level`, `err_code` / `err_message` for negative cases) | Different parameter shapes per test file for the same endpoint |
-
-**Workflow when generating online/offline e2e tests:**
-
-1. Decide whether the needed check is **general** (every success call of this `send_*`) or **special** (one case / one parameter combo only). See **General vs special assert placement** below.
-2. **Search `runtime.py`** for a matching **`send_*_request`**; if missing, add it (with general `assert_*` inside) before writing the test body.
-3. In the test module: build `request_config` → call **`send_*_request` only** for the general contract; call **`assert_*` in the test file only** for special, case-specific checks (import from `assertions.py`).
-4. Reserve **low-level** `send_*_http_request` for negative/dfx tests (`err_code` / `err_message`) — not for ordinary L2+ success-path e2e.
-5. When wiring Buildkite `source_file_dependencies`, include **`tests/helpers/runtime.py`** and/or **`tests/helpers/assertions.py`** when new helpers were added.
-
-**Exceptions (document in the test docstring why):** models whose offline prompt path cannot go through existing handlers yet (e.g. `test_higgs_audio_v2.py`, `test_voxtral_tts.py` with custom tokenizer compose) may call `omni.generate` directly until a `send_*_request` is added to `runtime.py` — treat as **debt**, not the default for new TTS/diffusion/omni e2e.
-
-**General vs special assert placement:**
-
-| Kind | Definition | Where it lives | Test file calls |
-|------|------------|----------------|-----------------|
-| **General** | Default success contract for this endpoint/client method — e.g. HTTP 200, `data[]` shape, `n` count, `size` dimensions, decodable image/audio, bundled omni/diffusion fields | Implement in `assertions.py`, invoke **inside** `send_*_request` in `runtime.py` (after low-level send / SDK call, when `err_code` is not set) | **`send_*_request` only** — do **not** repeat the same `assert_*` |
-| **Special** | Extra check tied to **this test case only** — e.g. seed byte-identical replay, accuracy/CLIP threshold, perf ceiling, model-specific optional field | Add or reuse `assert_*` in `assertions.py` (never inline in test) | `send_*_request` **then** `assert_<special>(...)` **once** for that case |
-
-Changing `request_config` fields (`size`, `n`, `seed`, server flags) is **not** special validation — the general `assert_*` should read those from `request_config` inside `send_*`.
-
-**`send_*` ↔ `assert_*` pairing (online `OpenAIClientHandler`):**
-
-| High-level `send_*` (prefer in L2+ e2e) | Assert already invoked inside `runtime.py` | Low-level HTTP-only sibling |
-|----------------------------------------|--------------------------------------------|-----------------------------|
-| `send_omni_request` | `assert_omni_response` | `send_chat_completions_http_request` → `assert_http_error` only |
-| `send_diffusion_request` | `assert_diffusion_response` | — |
-| `send_audio_speech_request` | `assert_audio_speech_response` | `send_audio_speech_http_request` → `assert_http_error` only |
-| `send_video_diffusion_request` | `assert_diffusion_response` | `send_videos_*_http_request` → `assert_http_error` only |
-| `send_images_generations_request` *(add when needed)* | **`assert_images_generations_response`** *(general — inside send)* | `send_images_generations_http_request` → `assert_http_error` only |
-| `send_images_edits_request` *(add when needed)* | **`assert_images_edits_response`** *(general — inside send)* | `send_images_edits_http_request` → `assert_http_error` only |
-
-**Rule:** Tests call high-level `send_*_request` for the general contract. **Never** call the same bundled `assert_*` again. Call an extra `assert_*` in the test **only** for special, case-specific validation.
-
-**Common `OpenAIClientHandler` entry points** (non-exhaustive — grep `runtime.py` before adding):
-
-| Area | High-level (SDK + assert) | Low-level HTTP (`*_http_request`) |
-|------|---------------------------|-----------------------------------|
-| Omni chat | `send_omni_request` | `send_chat_completions_http_request` |
-| Diffusion T2I (chat route) | `send_diffusion_request` | — |
-| Diffusion X2V | `send_video_diffusion_request` | `send_videos_create_http_request`, `send_video_content_http_request`, … |
-| DALL-E T2I / edit | `send_images_generations_request`, `send_images_edits_request` | `send_images_generations_http_request`, `send_images_edits_http_request` |
-| TTS (online) | `send_audio_speech_request` | `send_audio_speech_http_request`, `send_audio_generate_http_request`, … |
-| Ops / meta | — | `send_health_http_request`, `send_models_http_request`, `send_omni_sleep_http_request`, … |
-
-**Common `OmniRunnerHandler` entry points** (offline — grep `runtime.py` before adding):
-
-| Area | High-level (`send_*_request` + assert) | Notes |
-|------|----------------------------------------|-------|
-| Omni multimodal | `send_omni_request` | `generate_multimodal` path |
-| Diffusion offline | `send_diffusion_request` | chat-route / `OmniTextPrompt` offline |
-| Qwen-style TTS | `send_audio_speech_request` | two-stage, `generate_multimodal` + `mm_processor_kwargs` |
-| Single-stage TTS (Coqui XTTS, MOSS-TTS-Nano, …) | `send_single_stage_tts_request`, `send_single_stage_tts_batch_request` | `prompt` + `additional_information` + `omni.generate` — **do not** duplicate in tests |
-| New model family | **Add** `send_<family>_request` here first | Then call from `tests/e2e/offline_inference/test_*.py` |
-
-L1 tests under `tests/entrypoints/` may use **FastAPI `TestClient`** or direct handler calls with mocks; they do **not** need `OpenAIClientHandler` / `OmniRunnerHandler`. **L2+ online and offline e2e** must use `runtime.py` `send_*` helpers.
-
-#### Invalid parameter validation (`tests/dfx/reliability/invalid_param_test/`)
-
-When the user asks for **invalid parameter validation**, **invalid request bodies**, **HTTP 4xx contract tests**, or any case that sends **malformed / out-of-range / mismatched** API payloads against a **live** server:
-
-1. **Do not** place these in `tests/e2e/online_serving/` or `*_expansion.py`. If already drafted there, **move** the `test_*` into the correct `invalid_param_test` script and delete the duplicate from e2e.
-2. **Pick the script by HTTP route** (extend an existing file; add a new `test_invalid_<area>.py` only when no in-tree script covers that route family):
-
-| API / area | Script |
-|------------|--------|
-| Omni chat completions, WebSocket video/realtime paths | `test_invalid_omni_chat.py` |
-| `POST /v1/audio/speech`, stream, batch, voices | `test_invalid_audio_speech.py` |
-| Audio diffusion endpoints | `test_invalid_audio_diffusion.py` |
-| `POST /v1/images/generations` | `test_invalid_image_generation.py` |
-| `POST /v1/images/edits` | `test_invalid_image_editing.py` |
-| `POST/GET/DELETE /v1/videos*` | `test_invalid_video_generation.py` |
-| Sleep / wakeup / server control | `test_invalid_server_control.py` |
-
-3. **Match in-tree style** in the chosen script:
-
-| Element | Convention |
-|---------|------------|
-| **Module markers** | `pytestmark = [pytest.mark.slow, pytest.mark.<omni or tts or diffusion>]` |
-| **Server fixture** | `_PARAMS` / `_QWEN3_TTS_SPEECH`-style list of `pytest.param(OmniServerParams(...), id="...", marks=hardware_marks(...))`; `@pytest.mark.parametrize("omni_server", _PARAMS, indirect=True)` |
-| **Hardware** | `hardware_marks(res={"cuda": "H100"})` for heavy diffusion/omni/video; `hardware_marks(res={"cuda": "L4"})` for smaller TTS models (must match weekly `-m "slow and L4"` step) |
-| **HTTP client** | **Low-level** `openai_client.send_*_http_request({..., "err_code": 400, "err_message": (...)} )` — **not** `send_*_request` (success path) |
-| **Case shape** | Prefer **one `test_*` per route family** + `@pytest.mark.parametrize("body_spec, err_message", [...])` with stable `id=` per case; or dedicated `test_<route>_malformed_json` when not parametrized |
-| **Body helpers** | `_minimal_<endpoint>_json(omni_server)` / `_minimal_*_form_data()`; merge overrides with `body.update(body_spec)` |
-| **Known gaps** | `pytest.mark.skip(reason="…#3649")` as `_SKIP_ISSUE_3649` when server validation is not yet strict (mirror neighboring cases) |
-| **Sections** | Route banner comments (`# ─── POST /v1/images/generations ───`) like existing files |
-| **Shared fixtures** | Reuse `tests/dfx/reliability/invalid_param_test/conftest.py` (`tiny_png_bytes`, env defaults) |
-
-4. **Adding a new model** to invalid-param coverage: append a `pytest.param(OmniServerParams(model="...", stage_config_path=..., server_args=...), ...)` entry to the script’s `_PARAMS` list — do **not** create `test_invalid_<model>.py` unless the route is new.
-
-5. **CI — `.buildkite/test-weekly.yml` only** (not `test-ready.yml` / `test-merge.yml` / `test-nightly.yml`):
-
-| Weekly step | Command | When your cases run |
-|-------------|---------|---------------------|
-| **Invalid parameters Test · H100** | `pytest -s -v tests/dfx/reliability/invalid_param_test/ -m "slow and H100"` | Diffusion / omni / video invalid-param tests with `H100` hardware mark |
-| **Invalid parameters Test · L4** | `pytest -s -v tests/dfx/reliability/invalid_param_test/ -m "slow and L4"` | TTS / lighter models marked `L4` |
-
-- **Trigger:** `build.env("WEEKLY") == "1"` or PR label `weekly-test`.
-- **Default:** extending an existing `invalid_param_test` script needs **no YAML edit** — the weekly steps already sweep the whole directory.
-- **Edit YAML only** when adding a **new hardware queue**, a **new top-level script** that must run in isolation, or a **model-specific weekly shard** (mirror neighboring reliability steps).
-- Weekly steps **do not** use `source_file_dependencies`.
-
-**Example — append to `test_invalid_image_generation.py`:**
-
-```python
-@pytest.mark.parametrize(
-    "body_spec, err_message",
-    [
-        pytest.param({"seed": -1}, ("seed", "greater_than_equal", "0"), id="seed_negative"),
-    ],
-)
-@pytest.mark.parametrize("omni_server", _PARAMS, indirect=True)
-def test_images_generations_invalid_requests(
-    omni_server: OmniServer,
-    openai_client: OpenAIClientHandler,
-    body_spec: dict[str, object],
-    err_message: str | tuple[str, ...],
-) -> None:
-    body = _minimal_images_gen_json(omni_server)
-    body.update(body_spec)
-    openai_client.send_images_generations_http_request(
-        {"json": body, "timeout": 300, "err_code": 400, "err_message": err_message}
-    )
-```
-
-See [references/test-routing.md](references/test-routing.md) **Invalid parameter / weekly CI**.
-
-#### Assertion helpers (`tests/helpers/assertions.py`)
-
-**Do not** add module-local helpers such as `_assert_images_generations_payload` or `_send_and_assert_*` in e2e test files. Response/media validation belongs in **`tests/helpers/assertions.py`**, grouped by **category**:
-
-| Category | Existing anchors | When to extend vs add |
-|----------|------------------|------------------------|
-| **Image** (chat diffusion + DALL-E JSON) | `assert_image_diffusion_response`, `assert_image_valid` | DALL-E `/v1/images/generations` JSON → add **`assert_images_generations_response`** beside image helpers (reuses `assert_image_valid`); do not fork decode logic into tests |
-| **Video** | `assert_video_diffusion_response`, `assert_video_valid` | Extend these for new video contracts |
-| **Audio** | `assert_audio_diffusion_response`, `assert_audio_speech_response`, `assert_audio_valid` | Extend for new TTS/audio endpoints |
-| **Omni multimodal** | `assert_omni_response` | Extend for new modality combos |
-| **HTTP errors** | `assert_http_error`, `assert_err_message_in_text` | Used inside low-level `send_*_http_request` and negative tests |
-
-| Do | Don't |
-|----|-------|
-| Put **general** `assert_*` inside `send_*_request` in `runtime.py`; tests call `send_*_request` only | Call `assert_diffusion_response` after `send_diffusion_request` (general assert already bundled) |
-| Put **special** `assert_*` in `assertions.py` and call it **in the test** after `send_*_request` when that case needs extra checks | Put general decode/count/size logic in the test because “this case uses `n=4`” (that belongs in general assert reading `request_config`) |
-| Extend the matching category function, or add `assert_<endpoint>_response` next to its category | `_send_and_assert_*` or PIL/base64 loops in `test_*.py` |
-| Use low-level `send_*_http_request` in dfx/negative tests (`err_code`) | Use low-level HTTP send + manual general assert in every L2+ expansion file |
-
-**Workflow when generating tests:**
-
-1. Implement or reuse **general** `assert_*` in `assertions.py` (by category).
-2. Wire it into **`send_*_request`** in `runtime.py` if not already bundled.
-3. In the test: `request_config` → `send_*_request`. Add a separate `assert_*` import/call **only** for special case validation.
-4. Update `source_file_dependencies` when `runtime.py` or `assertions.py` changes.
-
-**Example — DALL-E `/v1/images/generations`:**
-
-```python
-# tests/helpers/assertions.py — general contract (image category)
-def assert_images_generations_response(resp_body: dict, request_config: dict, *, run_level: str | None = None) -> None:
-    ...  # data[], n, size → assert_image_valid
-
-# tests/helpers/runtime.py — general assert INSIDE send
-def send_images_generations_request(self, request_config: dict[str, Any], ...) -> list[HttpResponse]:
-    responses = self.send_images_generations_http_request(request_config, ...)
-    if request_config.get("err_code") is None:
-        body = responses[0].json_body
-        assert isinstance(body, dict)
-        assert_images_generations_response(body, request_config, run_level=self.run_level)
-    return responses
-
-# tests/e2e/.../test_foo_expansion.py — one case per test_* (no case_id branching)
-@pytest.mark.parametrize("omni_server", [pytest.param(OmniServerParams(model=MODEL), marks=SINGLE_L4)], indirect=True)
-def test_foo_images_generations_default_1024(omni_server, openai_client) -> None:
-    openai_client.send_images_generations_request({"json": body, "timeout": 300})
-
-@pytest.mark.parametrize("omni_server", [pytest.param(OmniServerParams(model=MODEL), marks=SINGLE_L4)], indirect=True)
-def test_foo_images_generations_sizes_256_512_1024(omni_server, openai_client) -> None:
-    for size in ("256x256", "512x512", "1024x1024"):
-        openai_client.send_images_generations_request({"json": {**body, "size": size}, "timeout": 300})
-
-# chat diffusion — separate test; send_diffusion_request bundles assert_diffusion_response
-@pytest.mark.parametrize("omni_server", [pytest.param(OmniServerParams(model=MODEL), marks=SINGLE_L4)], indirect=True)
-def test_foo_chat_completions_t2i_fallback(omni_server, openai_client) -> None:
-    openai_client.send_diffusion_request({...})
-```
-
-L1 tests under `tests/entrypoints/` may keep **minimal** asserts next to the handler under test when validating a single branch; still prefer `assertions.py` when the same JSON/media check appears in more than one file or level (e.g. L1 protocol test + L4 e2e).
-
-#### Omni Test Writing Guidance (L1-L4 Layering)
-
-When the goal is a general Omni/multimodal test case, prioritize mapping the test to the correct purpose, directory, and resource assumptions aligned with the layers (see [CI_5levels.md](../../../docs/contributing/ci/CI_5levels.md)):
-
-- **L1**: Unit/logic validation on CPU (`core_model and cpu`). Cover input validation, branches, and exception paths (`tests/<component>/test_*.py`). **Mock with `mocker` / `monkeypatch` only — not `unittest.mock`.**
-- **L2**: Basic e2e (online/offline basic scenarios). Prefer dummy/lightweight models to validate the end-to-end request-to-output-structure/streaming chain (typically `tests/e2e/online_serving/` and `tests/e2e/offline_inference/`).
-- **L3/L4**: Important integration, performance, and accuracy validation. L4 emphasizes “full functional scenarios + performance/stress + runnable doc examples” (typically `*_expansion.py` plus related expansion cases).
-
-Also keep markers consistent with the run level: use `core_model` for L1/L2 and `advanced_model` for L3/L4, and pair with `--run-level` to select the intended CI strategy.
-
-#### Diffusion Test Writing Guidance (L4 Coverage Combinations)
-
-When the task involves diffusion models/features, organize L4 test cases following [`#1832`](https://github.com/vllm-project/vllm-omni/issues/1832): combine multiple diffusion features into as few test cases as possible to fit limited CI GPU resources.
-
-Implementation strategy:
-
-1. If “full L4 is too heavy”: first provide a **reduced local validation case in L1/L2** (so the key assertion and the contract fix point are covered deterministically).
-2. Then provide **CI/nightly-ready L3/L4 high-marked cases** (e.g. `advanced_model`) to broaden coverage under resource constraints.
-
-#### L4 nightly sub-pillars — Function vs Accuracy vs Perf
-
-**L4 is not only `test_*_expansion.py`.** In `test-nightly.yml`, each model-type group typically runs **separate jobs**:
-
-| Sub-pillar | Nightly step label pattern | What you add | Typical paths |
-|------------|----------------------------|--------------|---------------|
-| **Function** | `· Function Test with …` | E2e expansion / feature matrix | `tests/e2e/online_serving/test_<model>_expansion.py` (or offline) |
-| **Accuracy** | `· Accuracy Test` | Quality / similarity vs baseline | `tests/e2e/accuracy/test_<model>*.py` |
-| **Perf** | `· Perf Test · <Model>` | Throughput / latency / memory benchmarks | `tests/dfx/perf/tests/test_<model>_vllm_omni.json` + runner script |
-| **Doc** (optional) | `· Doc Test` | Runnable doc examples | `tests/examples/*/test_text_to_image.py`, … |
-
-**Default when the user asks for “L4 functional cases”:** deliver **Function** pillar only (`*_expansion.py` + Function Test shard in `test-nightly.yml`). **Do not** silently add Perf or Accuracy unless the user also asks for **performance** / **benchmark** / **accuracy** / **full L4** / **full L4 coverage**.
-
-**When the user explicitly asks for L4 perf (or full L4 including perf):**
-
-1. **Do not** put throughput/latency assertions inside `test_*_expansion.py` — perf uses the **dfx benchmark harness**, not `omni_server` e2e fixtures.
-2. Add or extend a **JSON config** under `tests/dfx/perf/tests/` (mirror in-tree names: `test_qwen_image_vllm_omni.json`, `test_bagel_vllm_omni.json`).
-3. Each JSON entry: `test_name`, `server_params` (`model`, `serve_args`), `benchmark_params[]` with explicit **`baseline`** (`throughput_qps`, `latency_mean`, `peak_memory_mb_mean`, …).
-4. Run locally via the matching script (model type → runner):
-
-| Model type | Runner script | Example config |
-|------------|---------------|----------------|
-| **Diffusion X2I/X2V** | `tests/dfx/perf/scripts/run_diffusion_benchmark.py` | `tests/dfx/perf/tests/test_qwen_image_vllm_omni.json` |
-| **TTS** | `tests/dfx/perf/scripts/run_benchmark.py` | `tests/dfx/perf/tests/test_tts.json` (shared) or `test_{slug}.json` (dedicated; use when the model must not join the shared nightly matrix before integration — e.g. `test_voxcpm2.json`, `test_coqui_tts.json`) |
-| **Omni** | (see existing Omni Perf steps — `run_benchmark.py` / dedicated omni configs in-tree) | `tests/dfx/perf/tests/test_omni*.json` |
-
-5. Wire **`test-nightly.yml` · Perf Test** step for that model (separate from Function Test): export `DIFFUSION_BENCHMARK_DIR` / `BENCHMARK_DIR`, run pytest on the script with `--test-config-file`, **upload artifacts** (`buildkite-agent artifact upload`), often **multi-GPU H100** for diffusion perf.
-6. **One benchmark scenario → one `test_name` block** in JSON (same spirit as one `test_*` per function case). Combine server `serve_args` + workload in one entry; use multiple `benchmark_params` rows for size/step sweeps under the same server config.
-
-**Perf local command (diffusion example):**
-
-```bash
-cd tests
-export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
-export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
-pytest -s -v dfx/perf/scripts/run_diffusion_benchmark.py \
-  --test-config-file dfx/perf/tests/test_<model>_vllm_omni.json
-```
-
-**Clarify in the test plan** which L4 pillars you deliver: `Function only` | `Function + Perf` | `Function + Accuracy + Perf`.
-
-See [references/test-routing.md](references/test-routing.md) **L4 nightly pillars** for Buildkite step patterns.
-
-### Step 5: Wire Buildkite (when CI must run the new test)
-
-If the test is not already collected by an existing pipeline command (for example, L1 tests marked `core_model and cpu` are already covered by the **Simple Unit Test** step in ready/merge), update the appropriate pipeline under `.buildkite/`:
-
-| Test level | Edit this file | Typical trigger / intent |
-|------------|----------------|---------------------------|
-| **L1** and **L2** | [`.buildkite/test-ready.yml`](../../../.buildkite/test-ready.yml) | PR **ready** label; L1 CPU + L2 GPU/basic e2e (steps labeled **Omni ·**, **TTS ·**, **Diffusion ·**) |
-| **L3** | [`.buildkite/test-merge.yml`](../../../.buildkite/test-merge.yml) | Post-merge; `advanced_model` integration per model type |
-| **L4** | [`.buildkite/test-nightly.yml`](../../../.buildkite/test-nightly.yml) | Nightly; grouped by model type (see below) |
-| **Invalid param / reliability (weekly)** | [`.buildkite/test-weekly.yml`](../../../.buildkite/test-weekly.yml) | Weekly; `tests/dfx/reliability/invalid_param_test/` — **not** L1–L4 e2e pipelines |
-
-**Level-specific Buildkite delivery (follow the user’s requested level):**
-
-| User asks for | Document / edit **only** | Do **not** add by default |
-|---------------|---------------------------|---------------------------|
-| **L4** (`*_expansion.py`, `full_model`) | `test-nightly.yml` — append file to the matching nightly shard (X2I / X2V / Omni / TTS) **or** note it is already collected by an existing `-m` / `-k` sweep | `test-merge.yml` (L3 / `advanced_model`) or `test-ready.yml` (L2) |
-| **L3** | `test-merge.yml` with `advanced_model` + `source_file_dependencies` | `test-nightly.yml` unless user also wants nightly |
-| **L2** | `test-ready.yml` with `core_model` + `source_file_dependencies` | merge / nightly pipelines |
-| **Invalid param** | `test-weekly.yml` — **Invalid parameters Test** group (`-m "slow and H100"` / `-m "slow and L4"`). Usually **no YAML edit** when appending cases to existing `invalid_param_test` scripts | `test-ready.yml`, `test-merge.yml`, `test-nightly.yml` |
-
-`test-nightly.yml` shards use **explicit pytest file paths** in `commands` (and PR labels like `diffusion-x2iat-test`); they generally **do not** use `source_file_dependencies`. Only suggest merge/ready YAML when the requested level is L3/L2 (or the user explicitly asks for multi-level CI).
-
-**`test-nightly.yml` top-level groups** (each uses `full_model` + type marker in pytest `-m`):
-
-| Group | Type marker | Notes |
-|-------|-------------|--------|
-| **Omni Model Test** | `omni` | Function / doc / accuracy / **perf** for Qwen-Omni family |
-| **TTS Model Test** | `tts` | `-m "full_model and L4 and tts"` (and H100 variants when enabled); **Perf Test** uses `run_benchmark.py` + `test_tts.json` |
-| **Diffusion X2I(&A&T) Model Test** | `diffusion` | Image/audio/text diffusion; explicit file shards for **Function**; **Perf Test · &lt;Model&gt;** uses `run_diffusion_benchmark.py` + JSON config |
-| **Diffusion X2V Model Test** | `diffusion` | Video-only (Wan, HunyuanVideo, …); PR label `diffusion-x2v-test`; separate **Perf** steps when in-tree |
-
-Guidelines when editing YAML:
-
-- **Match markers and `--run-level`** to the test: L1/L2 in `test-ready.yml` use `core_model` + `omni` / `tts` / `diffusion` as appropriate; L3 merge uses `advanced_model`; L4 nightly uses `full_model` with the same type marker.
-- **Diffusion L4**: add x2i/x2a/x2t expansion files to an **X2I(&A&T)** step; add x2v expansion files to **X2V** — never rely on a broad `tests/e2e/` sweep alone (shards exist to save GPU and avoid wrong queue).
-- **`source_file_dependencies` (required for new E2E jobs in `test-ready.yml` and `test-merge.yml`)**: every step under the **`:card_index_dividers: E2E Test`** group **must** declare `source_file_dependencies` so Buildkite only schedules the job when relevant paths change. Omitting it breaks path-based CI skipping for GPU e2e jobs.
-- **Reuse or extend an existing step** when the new test shares the same marker expression, queue, and timeout; otherwise add a new `steps:` entry with the correct `agents.queue`, `timeout_in_minutes`, and docker/kubernetes plugin blocks consistent with neighboring jobs.
-- **Job timeout — use `timeout_in_minutes` on the step, not `timeout` before pytest**: when adding or documenting a new E2E job in `test-ready.yml` / `test-merge.yml`, set `timeout_in_minutes` on the step and run `pytest` directly in `commands`. **Do not** wrap pytest in `timeout 40m bash -c "..."` / `timeout 20m bash -c "..."`; Buildkite already enforces the step deadline via `timeout_in_minutes`.
-- **Never omit `plugins` in skill examples or PR YAML snippets**: H100 E2E steps on `mithril-h100-pool` use the full `kubernetes` block (`resources`, `volumeMounts`, `env` with `HF_TOKEN` secret, `nodeSelector: gpu-h100-sxm`, `devshm` + `hf-cache` volumes). L4/docker steps on `gpu_1_queue` use the full `docker#v5.2.0` block (`shm-size`, `HF_HOME`, volumes). Do not write `# ... plugins unchanged` — paste the complete block from a sibling step.
-- **Platform forks** (e.g. AMD-ready / AMD-merge) live alongside these files; apply the same level → file mapping for those pipelines when the test is platform-specific.
-
-#### `source_file_dependencies` for E2E Test jobs (`test-ready.yml`, `test-merge.yml`)
-
-When adding or splitting a step inside the **E2E Test** group, **always** include `source_file_dependencies` on that step. List paths the job actually depends on — at minimum:
-
-1. **Test module(s)** — every `tests/e2e/...` file the `pytest` command runs (online and offline if both are in one step).
-2. **Model implementation** — `vllm_omni/model_executor/models/<family>/` and/or `vllm_omni/diffusion/models/<family>/` as applicable.
-3. **Stage / input plumbing** (omni & multi-stage TTS) — `vllm_omni/model_executor/stage_input_processors/<family>.py` when present.
-4. **Deploy / stage YAML** — `vllm_omni/deploy/<config>.yaml` or `vllm_omni/deploy/ci/<config>.yaml` referenced by the test’s `OmniServerParams` / `get_deploy_config_path(...)`.
-5. **Test helpers** (when changed in the same PR) — `tests/helpers/runtime.py` for new `send_*` client methods; `tests/helpers/assertions.py` for new shared `assert_*` helpers.
-
-Use **repo-relative paths** (no leading `./`). Prefer **directory** entries (`.../models/bagel/`) when the whole package matters; use **file** entries for single test modules and YAML configs. Mirror a neighboring step for the same model family (e.g. **Diffusion · Bagel Test** in `test-ready.yml`).
-
-**Example** (L2 ready — diffusion online smoke, H100 kubernetes):
-
-```yaml
-- 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
-  timeout_in_minutes: 40
-  commands:
-    - |
-      export VLLM_IMAGE_FETCH_TIMEOUT=60
-      pytest -s -v tests/e2e/online_serving/test_bagel.py -m 'core_model' --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:$BUILDKITE_COMMIT
-              resources:
-                limits:
-                  nvidia.com/gpu: 1
-              volumeMounts:
-                - name: devshm
-                  mountPath: /dev/shm
-                - name: hf-cache
-                  mountPath: /root/.cache/huggingface
-              env:
-                - name: HF_HOME
-                  value: /root/.cache/huggingface
-                - name: HF_TOKEN
-                  valueFrom:
-                    secretKeyRef:
-                      name: hf-token-secret
-                      key: token
-          nodeSelector:
-            node.kubernetes.io/instance-type: gpu-h100-sxm
-          volumes:
-            - name: devshm
-              emptyDir:
-                medium: Memory
-            - name: hf-cache
-              hostPath:
-                path: /mnt/hf-cache
-                type: DirectoryOrCreate
-```
-
-**Example** (L3 merge — TTS online + offline, L4 docker):
-
-```yaml
-- label: "TTS · Qwen3-TTS Base Test"
-  source_file_dependencies:
-    - tests/e2e/online_serving/test_qwen3_tts_base.py
-    - tests/e2e/offline_inference/test_qwen3_tts_base.py
-    - vllm_omni/model_executor/models/qwen3_tts/
-    - vllm_omni/model_executor/stage_input_processors/qwen3_tts.py
-    - vllm_omni/deploy/qwen3_tts.yaml
-  timeout_in_minutes: 20
-  commands:
-    - |
-      export VLLM_LOGGING_LEVEL=DEBUG
-      export VLLM_ALLOW_LONG_MAX_MODEL_LEN=1
-      pytest -s -v tests/e2e/online_serving/test_qwen3_tts_base.py tests/e2e/offline_inference/test_qwen3_tts_base.py -m 'advanced_model and cuda' --run-level 'advanced_model'
-  agents:
-    queue: "gpu_1_queue" # g6.4xlarge instance on AWS, has 1 L4 GPU
-  plugins:
-    - docker#v5.2.0:
-        image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-        always-pull: true
-        propagate-environment: true
-        shm-size: "8gb"
-        environment:
-          - "HF_HOME=/fsx/hf_cache"
-        volumes:
-          - "/fsx/hf_cache:/fsx/hf_cache"
-```
-
-**Example** (L3 merge — diffusion online + offline, H100 kubernetes):
-
-```yaml
-- label: "Diffusion · Qwen Image Test"
-  source_file_dependencies:
-    - tests/e2e/online_serving/test_qwen_image.py
-    - tests/e2e/offline_inference/test_qwen_image.py
-    - vllm_omni/diffusion/models/qwen_image/
-  timeout_in_minutes: 40
-  commands:
-    - |
-      export VLLM_IMAGE_FETCH_TIMEOUT=60
-      pytest -s -v \
-        tests/e2e/online_serving/test_qwen_image.py \
-        tests/e2e/offline_inference/test_qwen_image.py \
-        -m 'advanced_model and cuda' --run-level 'advanced_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: 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
-```
-
-When extending an **existing** E2E step to run an additional test file, **append** that file (and any new model/deploy paths) to `source_file_dependencies` on the same step. When authoring a PR, mention the dependency list in the **Buildkite change** section of your test plan.
-
-#### L3 / L4: validating Buildkite from a feature branch (root `pipeline.yml`)
-
-Production behavior is driven by [`.buildkite/pipeline.yml`](../../../.buildkite/pipeline.yml): it builds the CI image, then uploads **L2** (`test-ready.yml` on non-`main`), **L3** (`test-merge.yml` when `build.branch == "main"`), and **L4** (`test-nightly.yml` when `build.env("NIGHTLY") == "1"`).
-
-When you add or debug **L3** or **L4** tests and need those child pipelines to run **off `main`** or **without** `NIGHTLY=1`, apply **temporary** edits (revert before merge):
-
-1. **Point the upload at the right file (optional shortcut)**  
-   In the **Upload Ready Pipeline** step, the command is `buildkite-agent pipeline upload .buildkite/test-ready.yml`. For a one-off run you may change that path to `.buildkite/test-merge.yml` (L3) or `.buildkite/test-nightly.yml` (L4), and adjust the step’s `if:` so it does not conflict with the other upload steps (avoid double-uploading unless intentional).
-
-2. **L3 — merge pipeline**  
-   The gate `if: build.branch == "main"` lives on the **Upload Merge Pipeline** step in `pipeline.yml` (not inside `test-merge.yml`). **Comment out that `if` line** so `test-merge.yml` is uploaded on your feature branch. Alternatively rely on step (1) instead of the dedicated merge upload step.
-
-3. **L4 — nightly pipeline**  
-   **Comment out** `if: build.env("NIGHTLY") == "1"` on the **Upload Nightly Pipeline** step in `pipeline.yml` so the nightly definition is uploaded without setting the env var.  
-   Child steps in [`test-nightly.yml`](../../../.buildkite/test-nightly.yml) often repeat `if: build.env("NIGHTLY") == "1"`; **comment out those lines on the steps you need to run**, otherwise they will still be skipped after upload.
-
-### Step 6: Run Tests
-
-Pick one command path:
-
-- **Quick local regression (preferred first)**: single file or `file.py::test_name`
-- **CI-like level run**: marker expression + `--run-level`
-
-Full templates live in [references/test-routing.md](references/test-routing.md). **After authoring tests, always emit concrete commands** (see **Output Format**).
-
-**Typical copy-paste examples** (run from repo `tests/` directory; requires matching vLLM/vllm-omni install and hardware):
-
-| Area | Example |
-|------|---------|
-| **Omni offline L2** | `pytest -s -v e2e/offline_inference/test_qwen2_5_omni.py -m "core_model and omni and not cpu" --run-level=core_model` |
-| **Omni online L2** | `pytest -s -v e2e/online_serving/test_qwen3_omni.py -m "core_model and omni" --run-level=core_model` |
-| **TTS online L2** | `pytest -s -v e2e/online_serving/test_qwen3_tts_base.py -m "core_model and tts" --run-level=core_model` |
-| **Diffusion X2I L2 online** | `pytest -s -v e2e/online_serving/test_qwen_image.py -m "core_model and diffusion" --run-level=core_model` |
-| **Diffusion X2V L2 online** | `pytest -s -v e2e/online_serving/test_wan22_t2v.py -m "core_model and diffusion" --run-level=core_model` |
-| **Diffusion X2I L4 nightly** | `pytest -s -v e2e/online_serving/test_qwen_image_expansion.py -m "full_model and diffusion and H100" --run-level=full_model` |
-| **Diffusion X2I L4 perf (nightly)** | `pytest -s -v dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file dfx/perf/tests/test_qwen_image_vllm_omni.json` |
-| **Diffusion X2V L4 nightly** | `pytest -s -v e2e/online_serving/test_wan22_expansion.py -m "full_model and cuda" --run-level=full_model` |
-| **Invalid param (weekly H100)** | `pytest -s -v dfx/reliability/invalid_param_test/ -m "slow and H100"` |
-| **Invalid param (weekly L4)** | `pytest -s -v dfx/reliability/invalid_param_test/ -m "slow and L4"` |
-| **L1 CPU** | `pytest -s -v -m "core_model and cpu"` |
-
-**Prerequisites to mention when relevant**: GPU model (e.g. L4 vs H100), `HF_HOME` / token for hub weights, module-level `skipif` (NPU/XPU-only gaps), and whether CI already collects the path (e.g. `test_*_expansion.py` glob in `test-nightly.yml`).
-
-### Step 7: Validate Result Quality
-
-Before finishing:
-
-- Is the new assertion directly tied to the bug/feature contract?
-- Are API calls made via **`runtime.py` `send_*_request`** (not inline HTTP/SDK in the test file)?
-- Is the **general** `assert_*` bundled inside `send_*_request` (not duplicated in the test)?
-- Is each scenario a **separate `test_*` function** (no `case_id` / `if param.id` branching across cases)?
-- **Fixture scope**: default **`omni_server` / `omni_runner`** (module); **`_function`** variants only when each test must restart the instance?
-- Is the test deterministic (no fragile timing/network coupling)?
-- Is runtime appropriate for the selected level?
-- Are markers and `--run-level` consistent?
-- **Invalid-param cases** in `tests/dfx/reliability/invalid_param_test/` (not e2e), using `send_*_http_request` + `err_code`, with `pytest.mark.slow` and correct `H100` / `L4` hardware mark?
-
-## Output Format
-
-When completing a request, return:
-
-1. **Test plan** (level, markers, file target, **L4 pillar(s)** if applicable: Function / Accuracy / Perf / Doc; **or** **Invalid param** pillar with target `invalid_param_test/test_invalid_<area>.py`; module basename for e2e: `test_{slug}.py` / `test_{slug}_expansion.py`)
-2. **Generated/updated test file(s)** — model-specific constants and request payloads stay **inside** those test modules (**never** `tests/helpers/{slug}.py`). Extend **`tests/helpers/runtime.py`** / **`tests/helpers/assertions.py`** only when new **repo-wide** `send_*` or shared assert helpers are required (do not leave ad-hoc HTTP or `_assert_*` in test modules). **If L4 Perf was requested**, also list **`tests/dfx/perf/tests/*.json`**. **If invalid-param cases were requested**, list the **`invalid_param_test/` script** and parametrized `test_*` / new `id=` rows — **not** e2e paths.
-3. **Buildkite change** — **match the requested level and pillar**:
-   - **L4 Function**: `test-nightly.yml` **Function Test** shard (file list / note existing sweep). **No** `test-merge.yml` unless user also asked for L3.
-   - **L4 Perf**: `test-nightly.yml` **Perf Test · &lt;Model&gt;** step (new step or extend commands); include env exports + artifact upload pattern from a sibling perf job.
-   - **L4 Accuracy**: `test-nightly.yml` **Accuracy Test** step under the same model-type group.
-   - **L3**: `test-merge.yml` + full `source_file_dependencies` + `agents` + `plugins`.
-   - **L2**: `test-ready.yml` + same E2E block requirements.
-   - **Invalid param**: `test-weekly.yml` — note **Invalid parameters Test · H100/L4** group; usually **no YAML change** when only extending existing scripts.
-   If the user asked only for **L4 functional cases**, state explicitly that **Perf / Accuracy / Invalid param** were not included (offer to add if needed). If the plan mixes **success e2e** and **invalid param**, split deliverables across e2e vs `invalid_param_test/` and call out both CI files.
-4. **Run commands (required)** — always include, in fenced `bash` blocks:
-   - **Local — whole file**: `cd tests` then `pytest -s -v <path> …`
-   - **Local — single test** (optional but preferred when the change is one function): `pytest -s -v path::test_func …`
-   - **CI-like** (when not L1 CPU): the same **marker + `--run-level`** pairing the level uses (see [references/test-routing.md](references/test-routing.md) and **Step 6** table above)
-   - **Prerequisites** (one line): e.g. “Requires CUDA L4 + weights cached locally”; “Requires H100 + `HF_TOKEN`”; “Syntax-only validation when vllm is not installed locally”
-5. **Result summary** (pass/fail if you executed tests; if not executed, state that explicitly and what the user should run)
-
-## Additional Resources
-
-- Marker and command routing: [references/test-routing.md](references/test-routing.md)
-- CI pipelines (vllm-omni): [test-ready.yml](../../../.buildkite/test-ready.yml) (L1/L2), [test-merge.yml](../../../.buildkite/test-merge.yml) (L3), [test-nightly.yml](../../../.buildkite/test-nightly.yml) (L4), [test-weekly.yml](../../../.buildkite/test-weekly.yml) (invalid param / reliability)
-- L4 documentation example tests (naming, extraction vs copied scripts, output dirs, skips): [docs/contributing/ci/test_examples/l4_doc_example_tests.inc.md](../../../docs/contributing/ci/test_examples/l4_doc_example_tests.inc.md) — see also [PR #1910](https://github.com/vllm-project/vllm-omni/pull/1910)

.claude/skills/vllm-omni-test/references/test-routing.md

@@ -1,372 +0,0 @@
-# Test Routing Reference
-
-Use this reference to map testing goals to levels, markers, and runnable commands.
-
-**Repo paths** (`.buildkite/`, `docs/contributing/…`): link with repo-relative paths from this file (e.g. `../../../../.buildkite/test-ready.yml`, `../../../../docs/contributing/ci/CI_5levels.md`).
-
-## Model-centric e2e filename convention (L2–L4)
-
-When generating a **new** test module tied to a **specific model** under `tests/e2e/offline_inference/` or `tests/e2e/online_serving/`:
-
-| Level | Pattern | Example (`Qwen/Qwen2.5-Omni-7B`) |
-|-------|---------|----------------------------------|
-| **L2**, **L3** | `test_{lowercase_model_slug}.py` | `test_qwen2_5_omni.py` |
-| **L4** | `test_{lowercase_model_slug}_expansion.py` | `test_qwen2_5_omni_expansion.py` |
-
-**Slug rules for `{lowercase_model_slug}`** (canonical: [SKILL.md](../SKILL.md) § *Naming: generated test module files*):
-
-1. Start from the HuggingFace-style id (e.g. `Qwen/Qwen2.5-Omni-7B`), but **do not** put the org into the filename: use the **repo segment only** (`Qwen2.5-Omni-7B`), not `Qwen_...` / `qwen_qwen2_5_...`.
-2. **Lowercase**; replace `.`, `-`, and whitespace with a single `_` (e.g. `Qwen2.5-Omni-7B` → `qwen2_5_omni`). **Omit** trailing size tokens such as `7b` / `30b` in the basename when a single file covers that model line in the directory (matches `test_qwen2_5_omni.py` in-tree).
-3. If two checkpoints in the same folder need separate modules, add a **minimal** disambiguator (e.g. `_7b` vs `_3b`) **only then**.
-4. **L1** unit tests are **not** bound to this pattern; use `tests/<area>/test_<feature>.py` as today.
-
-## Model type markers (`omni` / `tts` / `diffusion`)
-
-Every model-centric e2e test must declare **exactly one** type marker:
-
-| Marker | Model family | Typical APIs |
-|--------|--------------|--------------|
-| `pytest.mark.omni` | Multimodal LLM (Qwen-Omni, …) | `send_omni_request`, `omni_runner`, stage YAML |
-| `pytest.mark.tts` | TTS / speech synthesis | `send_audio_speech_request`, `/v1/audio/speech` |
-| `pytest.mark.diffusion` | Diffusion generative models | `send_diffusion_request`, `send_video_diffusion_request`, `send_images_generations_http_request`, `OmniDiffusionSamplingParams` |
-
-**Diffusion** uses one marker for all output modalities. Nightly CI splits diffusion by **group**, not by extra markers:
-
-| Nightly group (`test-nightly.yml`) | Diffusion scope | Examples |
-|-----------------------------------|-----------------|----------|
-| **Diffusion X2I(&A&T) Model Test** | x2**i** / x2**a** / x2**t** — image, audio, text (non-video) | Qwen-Image*, BAGEL, FLUX, SD3, Z-Image, LongCat, DreamZero |
-| **Diffusion X2V Model Test** | x2**v** — video only | Wan2.2, HunyuanVideo 1.5, Wan VACE |
-
-PR labels for selective nightly runs: `diffusion-x2iat-test`, `diffusion-x2v-test` (plus `nightly-test` / `NIGHTLY=1`).
-
-## Level and Marker Mapping
-
-| Goal | Suggested Level | Marker baseline | Typical location |
-|------|------------------|-----------------|------------------|
-| Unit logic, regression on pure code path | L1 | `core_model and cpu` | `tests/<component>/test_*.py` |
-
-### L1 unit tests — mocking (`mocker`, not `unittest.mock`)
-
-L1 modules run in **Simple Test** (`test-ready.yml` / `test-merge.yml`: `-m 'core_model and cpu'`). When isolation requires doubles:
-
-- **Use** the **`mocker`** fixture (`pytest-mock`): `mocker.patch`, `mocker.spy`, `mocker.Mock`, `mocker.MagicMock`, `mocker.AsyncMock`.
-- **Use** **`monkeypatch`** for env vars and simple `setattr` / `delattr` without mock objects.
-- **Do not use** `unittest.mock` — no `from unittest.mock import ...`, no `@patch`, no `with patch(...)`.
-
-```python
-# Good (L1)
-def test_handler(mocker):
-    mocker.patch("vllm_omni.pkg.fn", return_value=0)
-
-# Bad (L1)
-from unittest.mock import patch
-
-@patch("vllm_omni.pkg.fn")
-def test_handler(mock_fn): ...
-```
-
-E2E (L2+) should not rely on mocks unless documenting a rare exception; prefer real (or lightweight) execution paths.
-| Basic integration/e2e | L2 | `core_model` + **one of** `omni` / `tts` / `diffusion` | `tests/e2e/...` |
-| Advanced integration | L3 | `advanced_model` + type marker | `tests/e2e/...` |
-| Full function / nightly | L4 | `full_model` (nightly) + type marker | **Function:** `tests/e2e/*_expansion.py`; **Perf:** `tests/dfx/perf/tests/*.json`; **Accuracy:** `tests/e2e/accuracy/` |
-| Invalid HTTP / param validation | Weekly (dfx) | `pytest.mark.slow` + type marker + `H100` or `L4` | `tests/dfx/reliability/invalid_param_test/test_invalid_*.py` |
-
-## Marker Selection Rules
-
-1. **Level** (pick one):
-   - `core_model` — L1/L2 (`test-ready.yml`)
-   - `advanced_model` — L3 (`test-merge.yml`)
-   - `full_model` — L4 nightly (`test-nightly.yml`); some expansion tests still carry both `advanced_model` and `full_model` during migration
-2. **Model type** (pick one): `omni`, `tts`, or `diffusion`
-3. **Cross-cutting** when relevant: `parallel`, `cache`, `example`, `benchmark`
-4. **Hardware**: `cpu`, `cuda`, `rocm`, `npu`, `L4`, `H100`, `distributed_cuda`, …
-5. Multi-card: `@hardware_test(...)` in `tests/helpers/mark.py`
-
-## Command Templates
-
-### Quick local checks
-
-```bash
-cd tests
-pytest -s -v test_xxxx.py
-```
-
-### L1
-
-```bash
-cd tests
-pytest -s -v -m "core_model and cpu"
-```
-
-### L2
-
-```bash
-cd tests
-pytest -s -v -m "core_model and not cpu" --run-level=core_model
-```
-
-### L3 (merge)
-
-```bash
-cd tests
-pytest -s -v -m "advanced_model" --run-level=advanced_model
-```
-
-### L4 (nightly)
-
-**Function** (e2e expansion — default when user asks for “L4 functional cases”):
-
-```bash
-cd tests
-pytest -s -v e2e/online_serving/test_qwen_image_expansion.py -m "full_model and diffusion and H100" --run-level=full_model
-```
-
-**Perf** (dfx benchmark harness — separate pillar; not `omni_server` fixtures):
-
-```bash
-cd tests
-export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
-export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
-pytest -s -v dfx/perf/scripts/run_diffusion_benchmark.py \
-  --test-config-file dfx/perf/tests/test_qwen_image_vllm_omni.json
-```
-
-Broad marker sweep (when no explicit file shard):
-
-```bash
-cd tests
-pytest -s -v -m "full_model" --run-level=full_model
-```
-
-## L4 nightly pillars (Function / Accuracy / Perf)
-
-Nightly **L4** for a model is often **multiple jobs** in `test-nightly.yml`, not a single pytest module:
-
-| Pillar | User says | Deliver | Nightly step |
-|--------|-----------|---------|--------------|
-| **Function** | “L4 functional cases” / “functional” / `*_expansion.py` | `tests/e2e/.../test_<model>_expansion.py` | `· Function Test with H100/L4` |
-| **Perf** | “performance” / “perf” / “benchmark” / “full L4” | `tests/dfx/perf/tests/test_<model>_vllm_omni.json` + runner | `· Perf Test · <Model>` |
-| **Accuracy** | “accuracy” / “similarity” | `tests/e2e/accuracy/test_<model>*.py` | `· Accuracy Test` |
-
-**Rule:** “L4 functional cases” → **Function only** by default. State that Perf/Accuracy are separate pillars; add them only when requested.
-
-**Diffusion perf JSON** (`tests/dfx/perf/tests/test_<slug>_vllm_omni.json`): array of objects with `test_name`, `server_params` (`model`, `serve_args`), `benchmark_params[]`, and per-workload **`baseline`** (`throughput_qps`, `latency_mean`, `peak_memory_mb_mean`). Copy structure from `test_qwen_image_vllm_omni.json` in-tree.
-
-**Diffusion perf nightly step** (under **Diffusion X2I(&A&T)**; copy full `kubernetes` plugins from `· Perf Test · Qwen-Image`):
-
-```yaml
-      - label: ":full_moon: Diffusion X2I(&A&T) · Perf Test · <Model-Display-Name>"
-        key: nightly-diffusion-x2iat-performance-<slug>
-        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_<slug>_vllm_omni.json
-            EXIT=$$?
-            buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
-            buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
-            exit $$EXIT
-        agents:
-          queue: "mithril-h100-pool"
-        plugins:
-          # … full kubernetes block (often multi-GPU) — copy from Perf Test · Qwen-Image
-```
-
-| Model type | Perf runner | Config example |
-|------------|-------------|----------------|
-| Diffusion X2I/X2V | `dfx/perf/scripts/run_diffusion_benchmark.py` | `test_qwen_image_vllm_omni.json` |
-| TTS | `dfx/perf/scripts/run_benchmark.py` | `test_tts.json` (`BENCHMARK_DIR`) |
-| Omni | `run_benchmark.py` / omni configs in-tree | `test_omni*.json` |
-
-Do **not** put throughput/latency baselines inside `test_*_expansion.py` — that belongs in the dfx perf JSON + nightly Perf job.
-
-## Invalid parameter validation (weekly / dfx)
-
-When the user’s test plan includes **invalid parameter validation**, **invalid request bodies**, or **HTTP 4xx** validation against a live server:
-
-1. **Do not** author these in `tests/e2e/online_serving/test_*.py` or `*_expansion.py`. **Move** any drafted `test_*` into `tests/dfx/reliability/invalid_param_test/`.
-2. **Pick script by route** (extend in-tree file):
-
-| Route family | Script |
-|--------------|--------|
-| Omni chat / WS video·realtime | `test_invalid_omni_chat.py` |
-| `/v1/audio/speech` (+ stream / batch / voices) | `test_invalid_audio_speech.py` |
-| Audio diffusion | `test_invalid_audio_diffusion.py` |
-| `/v1/images/generations` | `test_invalid_image_generation.py` |
-| `/v1/images/edits` | `test_invalid_image_editing.py` |
-| `/v1/videos*` | `test_invalid_video_generation.py` |
-| Sleep / wakeup / server control | `test_invalid_server_control.py` |
-
-3. **Style:** `pytestmark = [pytest.mark.slow, pytest.mark.<type>]`; `_PARAMS` + `hardware_marks`; `send_*_http_request` with `err_code` + `err_message`; parametrized `body_spec` rows with `id=`; `_minimal_*_json()` helpers; `_SKIP_ISSUE_3649` when tracked in [#3649](https://github.com/vllm-project/vllm-omni/issues/3649).
-4. **CI:** [`.buildkite/test-weekly.yml`](../../../../.buildkite/test-weekly.yml) group **Reliability Test - Invalid parameters Test** — **not** ready/merge/nightly.
-
-```bash
-# Weekly H100 (diffusion / omni / video invalid-param)
-cd tests
-pytest -s -v dfx/reliability/invalid_param_test/ -m "slow and H100"
-
-# Weekly L4 (e.g. Qwen3-TTS speech invalid-param)
-pytest -s -v dfx/reliability/invalid_param_test/ -m "slow and L4"
-
-# Single script / case
-pytest -s -v dfx/reliability/invalid_param_test/test_invalid_image_generation.py::test_images_generations_invalid_requests -m "slow and H100"
-```
-
-**Trigger:** `WEEKLY=1` or PR label `weekly-test`. Appending cases to existing scripts usually needs **no YAML edit** (directory sweep). No `source_file_dependencies` on weekly steps.
-
-### Platform-targeted examples
-
-```bash
-cd tests
-pytest -s -v -m "core_model and distributed_cuda and L4" --run-level=core_model
-```
-
-### Concrete e2e paths (common in-tree)
-
-Paths are relative to `tests/` after `cd tests`.
-
-| Scenario | Example command |
-|----------|------------------|
-| **Omni** offline L2 | `pytest -s -v e2e/offline_inference/test_qwen2_5_omni.py -m "core_model and omni and not cpu" --run-level=core_model` |
-| **Omni** offline L2 — one test | `pytest -s -v e2e/offline_inference/test_qwen2_5_omni.py::test_text_to_text -m "core_model and omni and not cpu" --run-level=core_model` |
-| **Omni** online L2 | `pytest -s -v e2e/online_serving/test_qwen3_omni.py -m "core_model and omni" --run-level=core_model` |
-| **Omni** offline L2 (Qwen3.5-9B VL) | `pytest -s -v e2e/offline_inference/test_qwen3_5_9b.py -m "core_model and omni and not cpu" --run-level=core_model` |
-| **TTS** online L2 | `pytest -s -v e2e/online_serving/test_qwen3_tts_base.py -m "core_model and tts" --run-level=core_model` |
-| **TTS** online L2 — one test | `pytest -s -v e2e/online_serving/test_qwen3_tts_base.py::test_text_to_audio_001 -m "core_model and tts" --run-level=core_model` |
-| **Diffusion X2I** offline L2 | `pytest -s -v e2e/offline_inference/test_t2i_model.py -m "core_model and diffusion and not cpu" --run-level=core_model` |
-| **Diffusion X2I** L2 online smoke | `pytest -s -v e2e/online_serving/test_qwen_image.py e2e/online_serving/test_bagel.py -m "core_model and diffusion" --run-level=core_model` |
-| **Diffusion X2V** L2 online smoke | `pytest -s -v e2e/online_serving/test_wan22_t2v.py -m "core_model and diffusion" --run-level=core_model` |
-| **Diffusion X2I** L4 online expansion (H100) | `pytest -s -v e2e/online_serving/test_qwen_image_expansion.py -m "full_model and diffusion and H100" --run-level=full_model` |
-| **Diffusion X2I** L4 perf (nightly) | `pytest -s -v dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file dfx/perf/tests/test_qwen_image_vllm_omni.json` |
-| **Diffusion X2I** L4 online expansion (Edit) | `pytest -s -v e2e/online_serving/test_qwen_image_edit_expansion.py -m "full_model and diffusion and H100" --run-level=full_model` |
-| **Diffusion X2V** L4 nightly | `pytest -s -v e2e/online_serving/test_wan22_expansion.py -m "full_model and cuda" --run-level=full_model` |
-| **Invalid param** weekly H100 | `pytest -s -v dfx/reliability/invalid_param_test/ -m "slow and H100"` |
-| **Invalid param** weekly L4 | `pytest -s -v dfx/reliability/invalid_param_test/ -m "slow and L4"` |
-
-### Agent / author completion checklist
-
-When adding or modifying tests, do not stop at “where the file lives” — also deliver:
-
-1. **Local**: `cd tests` + `pytest -s -v <path>` (and `path::test_func` when a single case is enough).
-2. **CI-like**: marker string + `--run-level` matching the pipeline (`core_model` / `advanced_model` / `full_model`).
-3. **L1 mocks**: `mocker` / `monkeypatch` only; no `unittest.mock`.
-4. **API client + assert placement**: **General** validation → implement in `assertions.py`, call **inside** `send_*_request` in `runtime.py`; tests call **`send_*_request` only**. **Special** case validation → `assert_*` in `assertions.py`, called **in the test** after `send_*_request`. Low-level `send_*_http_request` is for negative/dfx tests (`err_code`), not ordinary L2+ success e2e.
-5. **Shared assertions**: logic in **`tests/helpers/assertions.py`**; general checks inside `send_*_request`; special checks only in the test. No `_assert_*` in `test_*.py`.
-6. **One case → one `test_*`**: function name reflects what is validated; no `if case_id == ...` mega-test merging multiple scenarios.
-7. **Fixture scope**: default **`omni_server` + `openai_client`** / **`omni_runner` + `omni_runner_handler`** (module). Use **`omni_server_function` + `openai_client_function`** / **`omni_runner_function` + `omni_runner_handler_function`** only when each `test_*` must spawn a fresh instance. Parametrize name must match fixture (`omni_server` vs `omni_server_function`).
-8. **Type marker**: `omni`, `tts`, or `diffusion` on every model e2e module.
-9. **Diffusion L4 Function**: wire `*_expansion.py` into **X2I(&A&T)** or **X2V** **Function Test** in **`test-nightly.yml` only** — do not add `test-merge.yml` unless the user also requested L3.
-10. **Diffusion L4 Perf** (only when requested): add `tests/dfx/perf/tests/test_<slug>_vllm_omni.json` + **Perf Test · &lt;Model&gt;** step (artifact upload); not part of “L4 functional cases” by default.
-11. **E2E Buildkite (L2/L3 only)**: `test-ready.yml` / `test-merge.yml` steps need **`source_file_dependencies`** + full **`agents` + `plugins`**. **L4 nightly** uses explicit file lists or perf scripts in `test-nightly.yml` (no merge job).
-12. **Invalid param**: cases in **`tests/dfx/reliability/invalid_param_test/`** (route-matching script), `send_*_http_request` + `err_code`, `pytest.mark.slow` + `H100`/`L4`; CI = **`test-weekly.yml`** only — do not put in e2e or nightly.
-13. **Prerequisites**: GPU tier, HF cache/token, and any module `skipif` / platform-only YAML.
-
-## Buildkite pipeline mapping
-
-| Level | Repo file | Model-type grouping |
-|-------|-----------|---------------------|
-| L1, L2 | [`.buildkite/test-ready.yml`](../../../../.buildkite/test-ready.yml) | Steps prefixed **Omni ·**, **TTS ·**, **Diffusion ·** under **E2E Test** — **`source_file_dependencies` required** |
-| L3 | [`.buildkite/test-merge.yml`](../../../../.buildkite/test-merge.yml) | Per-model E2E steps; `-m "advanced_model and …"` — **`source_file_dependencies` required** |
-| L4 | [`.buildkite/test-nightly.yml`](../../../../.buildkite/test-nightly.yml) | **Omni / TTS / Diffusion X2I(&A&T) / X2V** — each group may have **Function**, **Accuracy**, **Perf**, **Doc** steps |
-| Invalid param (weekly) | [`.buildkite/test-weekly.yml`](../../../../.buildkite/test-weekly.yml) | **Invalid parameters Test · H100** / **· L4** — sweeps `tests/dfx/reliability/invalid_param_test/` |
-
-### `source_file_dependencies` (E2E Test only — ready & merge)
-
-Required on each step inside the **E2E Test** group in `test-ready.yml` and `test-merge.yml`. Typical entries:
-
-| Category | Path pattern |
-|----------|----------------|
-| Test module | `tests/e2e/online_serving/test_<slug>.py`, `tests/e2e/offline_inference/test_<slug>.py` |
-| API client helpers | `tests/helpers/runtime.py` (when the e2e job uses newly added `send_*` methods) |
-| Shared assert helpers | `tests/helpers/assertions.py` (when the e2e job uses newly added `assert_*` helpers) |
-| AR / omni model | `vllm_omni/model_executor/models/<family>/` |
-| Diffusion model | `vllm_omni/diffusion/models/<family>/` |
-| Stage processor | `vllm_omni/model_executor/stage_input_processors/<family>.py` |
-| Deploy config | `vllm_omni/deploy/<name>.yaml` or `vllm_omni/deploy/ci/<name>.yaml` |
-
-Copy the dependency block from the closest in-tree E2E step for the same model family. Update the list whenever the pytest command or server YAML changes.
-
-### E2E `agents` + `plugins` blocks (required — do not truncate)
-
-When generating or documenting E2E steps, **always include the complete `plugins` section**. Set **`timeout_in_minutes`** on the step for the job deadline; run **`pytest` directly** in `commands` (no `timeout 40m bash -c` wrapper around pytest).
-
-Common patterns in `test-ready.yml` / `test-merge.yml`:
-
-| Queue | Plugin | Required fields |
-|-------|--------|-----------------|
-| `mithril-h100-pool` | `kubernetes` | `resources.limits.nvidia.com/gpu`, `volumeMounts` (`devshm`, `hf-cache`), `env` (`HF_HOME`, `HF_TOKEN` via `secretKeyRef`), `nodeSelector: gpu-h100-sxm`, `volumes` (`devshm` emptyDir, `hf-cache` hostPath `/mnt/hf-cache`) |
-| `gpu_1_queue` / `gpu_4_queue` | `docker#v5.2.0` | `image`, `always-pull`, `propagate-environment`, `shm-size: "8gb"`, `environment: HF_HOME`, `volumes: /fsx/hf_cache` (add `HF_TOKEN` when the step needs hub access) |
-
-**H100 kubernetes template** (copy from **Diffusion · Bagel Test** / **Diffusion · Qwen Image Test** in-tree):
-
-```yaml
-  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:$BUILDKITE_COMMIT
-              resources:
-                limits:
-                  nvidia.com/gpu: 1
-              volumeMounts:
-                - name: devshm
-                  mountPath: /dev/shm
-                - name: hf-cache
-                  mountPath: /root/.cache/huggingface
-              env:
-                - name: HF_HOME
-                  value: /root/.cache/huggingface
-                - name: HF_TOKEN
-                  valueFrom:
-                    secretKeyRef:
-                      name: hf-token-secret
-                      key: token
-          nodeSelector:
-            node.kubernetes.io/instance-type: gpu-h100-sxm
-          volumes:
-            - name: devshm
-              emptyDir:
-                medium: Memory
-            - name: hf-cache
-              hostPath:
-                path: /mnt/hf-cache
-                type: DirectoryOrCreate
-```
-
-**L4 docker template** (copy from **TTS · Qwen3-TTS Base Test** in `test-merge.yml`):
-
-```yaml
-  agents:
-    queue: "gpu_1_queue"
-  plugins:
-    - docker#v5.2.0:
-        image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-        always-pull: true
-        propagate-environment: true
-        shm-size: "8gb"
-        environment:
-          - "HF_HOME=/fsx/hf_cache"
-        volumes:
-          - "/fsx/hf_cache:/fsx/hf_cache"
-```
-
-When extending an existing step (e.g. add `tests/e2e/offline_inference/test_qwen_image.py` to **Diffusion · Qwen Image Test**), update `source_file_dependencies` and `commands` only; **keep the existing `agents` + `plugins` block unchanged** unless the hardware tier changes.
-
-The root [`.buildkite/pipeline.yml`](../../../../.buildkite/pipeline.yml) decides **which** child file is uploaded. To run **L3** on a feature branch, comment out `if: build.branch == "main"` on the merge upload step. To run **L4** without `NIGHTLY=1`, comment out the nightly upload step’s `if: build.env("NIGHTLY") == "1"` and the same `if` on relevant steps inside `test-nightly.yml`. Revert such edits before merging.
-
-Platform-specific pipelines (e.g. AMD) follow the same level → file pairing under `.buildkite/`.
-
-## Diffusion RFC (#1832) Alignment Tips
-
-For **X2I(&A&T)** coverage planning:
-
-- Prioritize high-value feature combinations with minimal case count.
-- Split into lightweight L1/L2 validation plus L4 `*_expansion.py` under the **X2I** nightly shards.
-- **X2V** models (Wan, HunyuanVideo) stay in the **X2V** group — do not merge into X2I sweeps.
-
-If hardware is insufficient, provide an executable reduced case plus a deferred full CI/nightly plan.

.github/CODEOWNERS

@@ -22,30 +22,30 @@
 /.buildkite/ @yenuo26 @congw729
 
 # Engine and model_executor
-/vllm_omni/core/ @tzhouam @yinpeiqi @fake0fan @Sy0307
-/vllm_omni/engine/ @tzhouam @yinpeiqi @fake0fan @Sy0307
-/vllm_omni/entrypoints/ @tzhouam @yinpeiqi @fake0fan @alex-jw-brooks
+/vllm_omni/core/ @tzhouam @yinpeiqi @fake0fan
+/vllm_omni/engine/ @tzhouam @yinpeiqi @fake0fan
+/vllm_omni/entrypoints/ @tzhouam @yinpeiqi @fake0fan
 /vllm_omni/model_executor/ @tzhouam @gcanlin
 /vllm_omni/worker/ @tzhouam @gcanlin
 
 # Diffusion
-/vllm_omni/diffusion/ @Isotr0py @princepride @SamitHuang @wtomin @ZJY0516 @RuixiangMa @david6666666 @xuechendi
+/vllm_omni/diffusion/ @Isotr0py @princepride @SamitHuang @wtomin @ZJY0516 @RuixiangMa @david6666666
 /vllm_omni/lora/ @princepride @SamitHuang @wtomin @ZJY0516 @RuixiangMa @yuanheng-zhao
 
 # Diffusion CPU offloading
-/vllm_omni/diffusion/offloader/ @Isotr0py @princepride @SamitHuang @wtomin @ZJY0516 @RuixiangMa @yuanheng-zhao @xuechendi
+/vllm_omni/diffusion/offloader/ @Isotr0py @princepride @SamitHuang @wtomin @ZJY0516 @RuixiangMa @yuanheng-zhao
 
 # Omni and TTS support
 /vllm_omni/transformers_utils/ @tzhouam @yuanheng-zhao
-/vllm_omni/model_executor/models/ @linyueqian @ZeldaHuang @princepride @yuanheng-zhao @amy-why-3459 @Bounty-hunter @Sy0307 @gcanlin
+/vllm_omni/model_executor/models/ @linyueqian @ZeldaHuang @princepride @yuanheng-zhao @amy-why-3459 @Bounty-hunter
 
 # Benchmarks
 /benchmarks/accuracy/ @david6666666
 /benchmarks/build_dataset/ @linyueqian @yuanheng-zhao
-/benchmarks/diffusion/ @Isotr0py @princepride @SamitHuang @wtomin @ZJY0516 @RuixiangMa @xuechendi
+/benchmarks/diffusion/ @Isotr0py @princepride @SamitHuang @wtomin @ZJY0516 @RuixiangMa
 /benchmarks/distributed/ @princepride
 /benchmarks/glm_image/ @princepride @JaredforReal
-/benchmarks/tts/ @linyueqian @ZeldaHuang @princepride @Sy0307 @gcanlin
+/benchmarks/tts/ @linyueqian @ZeldaHuang @princepride
 
 # Usability and Observability
 /vllm_omni/deploy/ @tzhouam @lishunyang12
@@ -53,13 +53,13 @@
 /vllm_omni/profiler/ @gcanlin
 
 # ComfyUI
-/apps/ @fhfuih @alex-jw-brooks
+/apps/ @fhfuih
 
 # Large Scale Serving
 /vllm_omni/distributed/ @princepride @yuanheng-zhao @xuechendi @natureofnature @fake0fan
 
 # Quantization and configuration
-/vllm_omni/config/ @lishunyang12 @alex-jw-brooks
+/vllm_omni/config/ @lishunyang12
 /vllm_omni/quantization/ @david6666666 @Isotr0py @lishunyang12
 
 # Hardware plugins and accelerators

.github/ISSUE_TEMPLATE/400-bug-report.yml

@@ -19,7 +19,6 @@ body:
 
       Consider redacting or replacing sensitive values with placeholders like `<YOUR_TOKEN_HERE>` when sharing configuration or code examples.
 - type: textarea
-  id: current-environment
   attributes:
     label: Your current environment
     description: |
@@ -43,7 +42,6 @@ body:
     required: true
 
 - type: textarea
-  id: code-version
   attributes:
     label: Your code version
     description: |
@@ -68,7 +66,6 @@ body:
     required: true
 
 - type: textarea
-  id: bug-description
   attributes:
     label: 🐛 Describe the bug
     description: |

.gitignore

@@ -208,7 +208,6 @@ cache/
 .cache/
 diffusion_cache/
 kv_cache/
-!vllm_omni/experimental/ar_diffusion/kv_cache/
 
 # Logs
 logs/
@@ -242,7 +241,6 @@ datasets/
 *.json
 !tests/dfx/perf/tests/*.json
 !apps/ComfyUI-vLLM-Omni/example_workflows/*.json
-!tests/assets/soulxsinger/*.json
 *.jsonl
 *.parquet
 

README.md

@@ -16,7 +16,6 @@ Easy, fast, and cheap omni-modality model serving for everyone
 ---
 
 *Latest News* 🔥
-- [2026/07] We released [0.24.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.24.0) - aligned with the vLLM 0.24 release line, expanding production-ready coverage across TTS, speech, diffusion, image/video generation, and robot-policy serving, with major Omni stage runtime refactoring, diffusion request-level batching, async output materialization, quantization/cache/memory improvements, and broad CUDA/ROCm/XPU/NPU support.
 - [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.
@@ -32,9 +31,9 @@ Easy, fast, and cheap omni-modality model serving for everyone
 
 [vLLM](https://github.com/vllm-project/vllm) was originally designed to support large language models for text-based autoregressive generation tasks. vLLM-Omni is a framework that extends its support for omni-modality model inference and serving:
 
-- **Omni-modality**: Text, image, audio, video, and action data processing
+- **Omni-modality**: Text, image, video, and audio data processing
 - **Non-autoregressive Architectures**: extend the AR support of vLLM to Diffusion Transformers (DiT) and other parallel generation models
-- **Heterogeneous outputs**: from traditional text generation to multimodal and action outputs
+- **Heterogeneous outputs**: from traditional text generation to multimodal outputs
 
 <p align="center">
   <picture>
@@ -58,10 +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. Qwen3-Omni, Cosmos3, HunyuanImage, BAGEL)
+- **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)
-- **Robot-policy and action models** (e.g. GR00T-N1.7, DreamZero-DROID, InternVLA-A1, Cosmos3 action policy)
 
 ## Getting Started
 
@@ -70,7 +68,6 @@ Visit our [documentation](https://vllm-omni.readthedocs.io/en/latest/) to learn
 - [Installation](https://vllm-omni.readthedocs.io/en/latest/getting_started/installation/)
 - [Quickstart](https://vllm-omni.readthedocs.io/en/latest/getting_started/quickstart/)
 - [List of Supported Models](https://vllm-omni.readthedocs.io/en/latest/models/supported_models/)
-- [Deployment Recipes](https://recipes.vllm.ai) for vLLM-Omni model serving
 
 ## Contributing
 

apps/lvsa-omni-showcase/README.md

@@ -1,87 +0,0 @@
-# LVSA Long-Video Showcase
-
-**Training-free block-sparse attention for long-video diffusion**, running on
-vLLM-Omni via the public plugin entry points (no core changes). LVSA speeds up
-extended-horizon generation and prevents the freeze/loop failure mode dense
-attention exhibits beyond a model's training length — with no fine-tuning and no
-weight changes.
-
-Upstream: **[LongVideoSparseAttention](https://github.com/JiusiServe/LongVideoSparseAttention)**
-(algorithm, all model adapters, full docs).
-
-## Results
-
-Wan 2.1 1.3B, single A100 80 GB, mean over 5 prompts (Dense vs LVSA-FlashInfer):
-
-| Horizon | 1× | 2× | 3× | 4× | 5× | 6× |
-|---|---|---|---|---|---|---|
-| **Speedup** | 0.9× | 1.4× | 1.9× | 2.5× | 3.0× | **3.5×** |
-| **VQeval composite** (Dense→LVSA) | 63→63 | 59→63 | 61→63 | 61→64 | 59→64 | 59→63 |
-
-The speedup **grows with length** (LVSA is the dense regime at 1× by design, so
-the win starts at 2×), and quality *improves* at extension — LVSA's rotating
-keyframes prevent the looping/static output dense produces beyond its horizon
-(VQeval loop-quality +27 to +34 at ≥3×). At long horizons dense **OOMs on 80 GB**
-(HunyuanVideo ≥2×, Wan2.1-14B and Cosmos at 6×) where LVSA still generates.
-
-→ Full sweep (5 models × 6 horizons, VQeval + VBench-Long) and the
-quality-metric methodology:
-**[benchmarks/](https://github.com/JiusiServe/LongVideoSparseAttention/tree/main/benchmarks)**.
-
-## Quickstart
-
-```bash
-# 1. vLLM-Omni 0.22.0 (stable) + matching vLLM
-pip install "vllm==0.22.0"
-pip install --no-build-isolation \
-  "vllm-omni @ git+https://github.com/vllm-project/vllm-omni.git@v0.22.0"
-
-# 2. LVSA core + plugin
-git clone https://github.com/JiusiServe/LongVideoSparseAttention.git
-( cd LongVideoSparseAttention && pip install -e . && pip install -e lvsa-vllm-omni/ )
-
-# 3. this showcase's client dep
-pip install requests
-```
-
-FlashInfer (used by the default `flashinfer` backend) **ships with vLLM** — step 1
-already pulls the pinned `flashinfer-python` + `flashinfer-cubin`, so the fused
-backend works with no extra install. Two optional extras:
-
-```bash
-# (optional) faster startup — prebuilt JIT cache, matched to your CUDA + flashinfer
-CU=cu128   # match your CUDA: cu121 / cu124 / cu128 / …
-FIVER=$(python -c "import importlib.metadata as m; print(m.version('flashinfer-python'))")
-pip install --extra-index-url "https://flashinfer.ai/whl/${CU}" \
-  "flashinfer-jit-cache==${FIVER}+${CU}"
-
-# (fallback) no FlashInfer in your environment? run the SDPA backend instead:
-export LVSA_BACKEND=sdpa
-```
-
-```bash
-# Serve an LVSA-enabled endpoint (Wan 2.1 1.3B at 2× horizon):
-MODEL=/path/to/Wan2.1-T2V-1.3B-Diffusers MODEL_FAMILY=wan FRAMES=161 \
-  bash serve_lvsa.sh
-
-# In another shell — generate:
-python generate.py --frames 161 --height 480 --width 832 \
-  --prompt "A dog running through a sunlit forest." --out wan_lvsa.mp4
-```
-
-`serve_lvsa.sh` sets the LVSA env vars for the chosen `MODEL_FAMILY`
-(`wan` / `hunyuan`). Wan and HunyuanVideo run through the LVSA **attention
-backend** by default — it stays sparse even under sequence-parallel, unlike the
-monkey-patch hooks.
-
-## Configuration
-
-The one knob you must get right is the model's training horizon
-(`LVSA_REFERENCE_LATENT_FRAMES`: Wan = 21, HunyuanVideo = 33); `serve_lvsa.sh`
-sets it per family. For the full environment-variable reference, the multi-GPU
-support matrix, and tuning (`sparsity_scale`, rotating keyframes, non-standard
-resolutions), see the upstream docs:
-
-- **[lvsa-vllm-omni plugin reference](https://github.com/JiusiServe/LongVideoSparseAttention/blob/main/lvsa-vllm-omni/README.md)** — every `LVSA_*` env var
-- **[docs/parallelism.md](https://github.com/JiusiServe/LongVideoSparseAttention/blob/main/docs/parallelism.md)** — TP / Ulysses / HSDP support per model
-- **[docs/tuning.md](https://github.com/JiusiServe/LongVideoSparseAttention/blob/main/docs/tuning.md)** — picking knobs for your model and horizon

apps/lvsa-omni-showcase/generate.py

@@ -1,93 +0,0 @@
-#!/usr/bin/env python
-"""Generate a long video through an LVSA-enabled vLLM-Omni server.
-
-A thin client over the Omni video API — start the server first with
-``serve_lvsa.sh``. The sparse attention runs server-side; this just submits a
-job and saves the result.
-
-Uses the **asynchronous** endpoint (``POST /v1/videos`` → poll
-``GET /v1/videos/{id}`` → download ``GET /v1/videos/{id}/content``) rather than
-``POST /v1/videos/sync``. The sync endpoint is capped by a fixed server-side
-timeout (``VIDEO_SYNC_TIMEOUT_S``, 600 s) and is documented as
-"for benchmark and testing scenarios" — long videos (LVSA's use case) routinely
-exceed it. The async job has no such cap.
-
-  python generate.py --frames 481 --height 480 --width 832 \
-      --prompt "A golden retriever running through a sunlit forest." \
-      --out dog_forest_lvsa.mp4
-"""
-
-import argparse
-import os
-import time
-
-import requests
-
-
-def main():
-    ap = argparse.ArgumentParser()
-    ap.add_argument("--server", default=os.environ.get("OMNI_SERVER", "http://localhost:8000"))
-    ap.add_argument("--prompt", default="A dog running through a sunlit forest.")
-    ap.add_argument("--frames", type=int, default=161)  # Wan 2x horizon
-    ap.add_argument("--height", type=int, default=480)
-    ap.add_argument("--width", type=int, default=832)
-    ap.add_argument("--fps", type=int, default=16)
-    ap.add_argument("--steps", type=int, default=50)
-    ap.add_argument("--guidance", type=float, default=5.0)
-    ap.add_argument("--out", default="lvsa_long_video.mp4")
-    ap.add_argument("--poll-interval", type=float, default=5.0, help="Seconds between status polls.")
-    ap.add_argument(
-        "--max-wait", type=float, default=7200.0, help="Give up if the job hasn't finished after this many seconds."
-    )
-    args = ap.parse_args()
-
-    base = args.server.rstrip("/")
-    form = {
-        "prompt": args.prompt,
-        "size": f"{args.width}x{args.height}",
-        "num_frames": str(args.frames),
-        "fps": str(args.fps),
-        "num_inference_steps": str(args.steps),
-        "guidance_scale": str(args.guidance),
-    }
-
-    # ── 1. submit the async job ──────────────────────────────────────────────
-    print(f"[lvsa-app] POST {base}/v1/videos  {args.width}x{args.height} {args.frames}f steps={args.steps}")
-    resp = requests.post(f"{base}/v1/videos", data=form, timeout=120)
-    resp.raise_for_status()
-    job = resp.json()
-    vid = job["id"]
-    print(f"[lvsa-app] job {vid} ({job.get('status', '?')})")
-
-    # ── 2. poll until completed / failed ─────────────────────────────────────
-    deadline = time.time() + args.max_wait
-    last = None
-    while True:
-        if time.time() > deadline:
-            raise SystemExit(f"[lvsa-app] timed out after {args.max_wait:.0f}s (job {vid} still running)")
-        time.sleep(args.poll_interval)
-        r = requests.get(f"{base}/v1/videos/{vid}", timeout=60)
-        if r.status_code != 200:
-            # retrieve_video returns a non-200 JSON when the job has FAILED
-            raise SystemExit(f"[lvsa-app] job failed: {r.text}")
-        st = r.json()
-        status = st.get("status")
-        progress = st.get("progress", 0)
-        if (status, progress) != last:
-            print(f"[lvsa-app] {status}  {progress}%")
-            last = (status, progress)
-        if status == "completed":
-            break
-        if status == "failed":
-            raise SystemExit(f"[lvsa-app] job failed: {st.get('error')}")
-
-    # ── 3. download the rendered mp4 ─────────────────────────────────────────
-    c = requests.get(f"{base}/v1/videos/{vid}/content", timeout=600)
-    c.raise_for_status()
-    with open(args.out, "wb") as f:
-        f.write(c.content)
-    print(f"[lvsa-app] saved {args.out}")
-
-
-if __name__ == "__main__":
-    main()

apps/lvsa-omni-showcase/serve_lvsa.sh

@@ -1,45 +0,0 @@
-#!/usr/bin/env bash
-# Start an LVSA-enabled vLLM-Omni server for Wan or HunyuanVideo.
-#
-# LVSA is selected as the attention backend via --diffusion-attention-config (see
-# below); the `LVSA_*` env vars only tune it. The `lvsa-vllm-omni` plugin registers
-# itself through vLLM-Omni's `general_plugins` entry points — nothing in vLLM-Omni
-# core is modified. Watch the log for "[LVSA] Geometry detected" to confirm sparse
-# attention is active for your request geometry.
-#
-#   MODEL=/path/to/Wan2.1-T2V-1.3B-Diffusers MODEL_FAMILY=wan     FRAMES=161 bash serve_lvsa.sh
-#   MODEL=/path/to/HunyuanVideo-1.5-...       MODEL_FAMILY=hunyuan FRAMES=193 bash serve_lvsa.sh
-set -euo pipefail
-
-MODEL=${MODEL:?set MODEL=/path/to/Wan-or-HunyuanVideo-Diffusers}
-MODEL_FAMILY=${MODEL_FAMILY:-wan}     # wan | hunyuan
-PORT=${PORT:-8000}
-FRAMES=${FRAMES:-161}                 # target horizon in frames
-VAE_T=${VAE_T:-4}                     # VAE temporal compression (Wan/Hunyuan = 4)
-BACKEND=${LVSA_BACKEND:-flashinfer}   # flashinfer (fused, fastest) | sdpa
-
-T_LAT=$(( (FRAMES - 1) / VAE_T + 1 ))
-
-# Per-family training reference horizon (Wan train ref = 21, Hunyuan = 33).
-case "$MODEL_FAMILY" in
-  wan)     REFERENCE=${REFERENCE:-21} ;;
-  hunyuan) REFERENCE=${REFERENCE:-33} ;;
-  *) echo "MODEL_FAMILY must be 'wan' or 'hunyuan'"; exit 1 ;;
-esac
-export LVSA_BACKEND="$BACKEND"
-export LVSA_TOTAL_LATENT_FRAMES="$T_LAT"
-export LVSA_REFERENCE_LATENT_FRAMES="$REFERENCE"
-export LVSA_ROTATE_KEYFRAMES=${LVSA_ROTATE_KEYFRAMES:-1}
-
-echo "[lvsa-app] serving $MODEL ($MODEL_FAMILY) on :$PORT"
-echo "[lvsa-app]   LVSA backend=$BACKEND  T_lat=$T_LAT (frames=$FRAMES)  reference=$REFERENCE"
-echo "[lvsa-app]   confirm activation by grep '\\[LVSA\\] Geometry detected' in this log"
-
-# vLLM-Omni 0.22 selects the LVSA attention backend per-role via
-# --diffusion-attention-config. The backend is what drives sparse attention
-# here; it engages under tensor-parallel too, unlike the LVSA_*_HOOK monkey-patch
-# path (which falls back to dense under sequence-parallel), so we deliberately do
-# NOT set a hook.
-exec vllm serve "$MODEL" --omni --host 0.0.0.0 --port "$PORT" \
-     --diffusion-attention-config '{"per_role": {"self": {"backend": "LVSA"}}}' \
-     --init-timeout 1800 ${EXTRA_ARGS:-}

benchmarks/diffusion/diffusion_benchmark_serving.py

@@ -79,7 +79,6 @@ Usage:
 import argparse
 import ast
 import asyncio
-import base64
 import glob
 import json
 import logging
@@ -107,9 +106,6 @@ from tqdm.asyncio import tqdm
 
 logger = logging.getLogger(__name__)
 
-_STAGE_METRICS_ENDPOINTS = {"/v1/chat/completions"}
-_RETURN_STAGE_METRICS_FIELD = "return_stage_metrics"
-
 
 class BaseDataset(ABC):
     def __init__(self, args, api_url: str, model: str):
@@ -1017,81 +1013,6 @@ def calculate_metrics(
     return metrics
 
 
-def _save_generated_outputs(
-    outputs: list[RequestFuncOutput],
-    requests_list: list[RequestFuncInput],
-    save_dir: str,
-) -> None:
-    """Decode and save base64 images/videos from successful responses."""
-    os.makedirs(save_dir, exist_ok=True)
-    saved = 0
-    failed = 0
-
-    for idx, (req, out) in enumerate(zip(requests_list, outputs)):
-        if not out.success or not out.response_body:
-            continue
-
-        if isinstance(out.response_body, bytes):
-            fname = f"req_{idx:04d}.mp4"
-            fpath = os.path.join(save_dir, fname)
-            with open(fpath, "wb") as f:
-                f.write(out.response_body)
-            saved += 1
-            continue
-
-        media_urls: list[str] = []
-
-        # Chat-completions style: choices[*].message.content[*].image_url.url
-        choices = out.response_body.get("choices", [])
-        if isinstance(choices, list):
-            for choice in choices:
-                content = (choice or {}).get("message", {}).get("content")
-                if not isinstance(content, list):
-                    continue
-                for item in content:
-                    if not isinstance(item, dict) or item.get("type") != "image_url":
-                        continue
-                    url = (item.get("image_url") or {}).get("url", "")
-                    if isinstance(url, str) and url.startswith("data:"):
-                        media_urls.append(url)
-
-        # Images endpoint style: data[*].b64_json
-        data_items = out.response_body.get("data", [])
-        if isinstance(data_items, list):
-            for data_item in data_items:
-                if not isinstance(data_item, dict):
-                    continue
-                b64_json = data_item.get("b64_json", "")
-                if isinstance(b64_json, str) and b64_json:
-                    media_urls.append(f"data:image/png;base64,{b64_json}")
-
-        for img_idx, url in enumerate(media_urls):
-            if "," not in url:
-                continue
-
-            try:
-                header, b64_data = url.split(",", 1)
-                ext = "png"
-                if "image/jpeg" in header:
-                    ext = "jpg"
-                elif "image/webp" in header:
-                    ext = "webp"
-                elif "video/mp4" in header:
-                    ext = "mp4"
-
-                img_bytes = base64.b64decode(b64_data)
-                fname = f"req_{idx:04d}_{img_idx}.{ext}"
-                fpath = os.path.join(save_dir, fname)
-                with open(fpath, "wb") as f:
-                    f.write(img_bytes)
-                saved += 1
-            except Exception as e:
-                failed += 1
-                logger.warning(f"Failed to save image for request {idx}: {e}", exc_info=True)
-
-    logger.info(f"Saved {saved} generated image(s) to {save_dir}. Failed to save {failed} image(s).")
-
-
 def wait_for_service(base_url: str, timeout: int = 120) -> None:
     print(f"Waiting for service at {base_url}...")
     start_time = time.time()
@@ -1174,10 +1095,6 @@ async def benchmark(args):
     requests_list = dataset.get_requests()
     print(f"Prepared {len(requests_list)} requests from {args.dataset} dataset.")
 
-    if args.return_stage_metrics and args.endpoint in _STAGE_METRICS_ENDPOINTS:
-        for req in requests_list:
-            req.extra_body.setdefault(_RETURN_STAGE_METRICS_FIELD, True)
-
     if args.endpoint == "/v1/images/edits":
         for req in requests_list:
             req.default_bot_task = args.bot_task
@@ -1285,9 +1202,6 @@ async def benchmark(args):
 
     print("\n" + "=" * 60)
 
-    if args.save_dir:
-        _save_generated_outputs(outputs, requests_list, args.save_dir)
-
     if args.output_file:
         with open(args.output_file, "w") as f:
             json.dump(metrics, f, indent=2)
@@ -1364,18 +1278,11 @@ if __name__ == "__main__":
         default=1,
         help="Number of warmup requests to run before measurement.",
     )
-    # NOTE Changed default from 1 to 2 because some models (e.g., Bagel) run
-    # `num_timesteps - 1` denoising iterations. A default of 1 results in 0 steps,
-    # which causes errors.
-    # TODO If this slightly longer warmup causes regression issues for other
-    # diffusion pipelines in the future, consider implementing model-specific
-    # overrides instead of a global default.
     parser.add_argument(
         "--warmup-num-inference-steps",
         type=int,
-        default=2,
-        help="Number of inference steps used for warmup requests. "
-        "Default is 2 to ensure at least one denoising step is executed.",
+        default=1,
+        help="num_inference_steps used for warmup requests.",
     )
     parser.add_argument(
         "--warmup-concurrency",
@@ -1416,13 +1323,6 @@ if __name__ == "__main__":
         default=3.0,
         help="SLO target multiplier: slo_ms = estimated_exec_time_ms * slo_scale (default: 3).",
     )
-    parser.add_argument(
-        "--save-dir",
-        type=str,
-        default=None,
-        help="Directory to save generated images/outputs for visual inspection. "
-        "If not set, generated outputs are discarded after metric collection.",
-    )
     parser.add_argument("--disable-tqdm", action="store_true", help="Disable progress bar.")
     parser.add_argument(
         "--enable-negative-prompt",
@@ -1458,11 +1358,6 @@ if __name__ == "__main__":
         default="think",
         help=("bot_task form field for --endpoint /v1/images/edits (think, recaption, think_recaption, vanilla)."),
     )
-    parser.add_argument(
-        "--return-stage-metrics",
-        action="store_true",
-        help="Request stage duration metrics from endpoints that support return_stage_metrics.",
-    )
 
     args = parser.parse_args()
     asyncio.run(benchmark(args))

benchmarks/fish-speech/bench_speaker_cache.py

@@ -201,7 +201,6 @@ async def main():
             "input": prompt,
             "voice": "default",
             "stream": True,
-            "stream_format": "audio",
             "response_format": "pcm",
             "ref_audio": ref_audio_b64,
             "ref_text": args.ref_text,
@@ -241,7 +240,6 @@ async def main():
             "input": prompt,
             "voice": args.voice_name,
             "stream": True,
-            "stream_format": "audio",
             "response_format": "pcm",
             "ref_text": args.ref_text,
             "max_new_tokens": 2048,

benchmarks/tts/model_configs.yaml

@@ -80,17 +80,6 @@ models:
     task_extra_body:
       voice_clone: {}
 
-  # MOSS-TTS-Local-Transformer-v1.5: Qwen3 backbone + 1-layer GPT2-style local
-  # depth transformer (n_vq=12), paired with the 48 kHz stereo
-  # MOSS-Audio-Tokenizer-v2 codec. Same voice-clone request shape as the
-  # other MOSS-TTS variants.
-  OpenMOSS-Team/MOSS-TTS-Local-Transformer-v1.5:
-    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.

docker/Dockerfile.ci

@@ -1,8 +1,8 @@
-# Target vllm version: v0.24.0 (releases/v0.24.0 branch).
+# 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=v0.25.0
+ARG VLLM_BASE_TAG=v0.22.0
 FROM ${VLLM_BASE_IMAGE}:${VLLM_BASE_TAG}
 ARG APP_DIR=/workspace/vllm-omni
 WORKDIR ${APP_DIR}
@@ -16,7 +16,6 @@ RUN apt-get update && \
 
 RUN uv pip install --system ".[dev]"
 
-
 RUN ln -sf /usr/bin/python3 /usr/bin/python
 
 ENTRYPOINT []

docker/Dockerfile.cuda

@@ -1,4 +1,4 @@
-ARG BASE_IMAGE=vllm/vllm-openai:v0.24.0
+ARG BASE_IMAGE=vllm/vllm-openai:v0.22.0
 FROM ${BASE_IMAGE}
 
 ARG COMMON_WORKDIR=/app

docker/Dockerfile.npu

@@ -1,5 +1,5 @@
-ARG VLLM_ASCEND_IMAGE=quay.io/atlas-ci/vllm-ascend
-ARG VLLM_ASCEND_TAG=v0.24.0
+ARG VLLM_ASCEND_IMAGE=quay.io/ascend/vllm-ascend
+ARG VLLM_ASCEND_TAG=v0.18.0rc1
 FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}
 
 # WORKDIR /vllm-workspace/vllm

docker/Dockerfile.npu.a3

@@ -1,5 +1,5 @@
-ARG VLLM_ASCEND_IMAGE=quay.io/atlas-ci/vllm-ascend
-ARG VLLM_ASCEND_TAG=v0.24.0-a3
+ARG VLLM_ASCEND_IMAGE=quay.io/ascend/vllm-ascend
+ARG VLLM_ASCEND_TAG=v0.18.0rc1-a3
 FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}
 
 # WORKDIR /vllm-workspace/vllm

docker/Dockerfile.npu.ci

@@ -1,5 +1,5 @@
-ARG VLLM_ASCEND_IMAGE=quay.io/atlas-ci/vllm-ascend
-ARG VLLM_ASCEND_TAG=v0.24.0
+ARG VLLM_ASCEND_IMAGE=quay.io/ascend/vllm-ascend
+ARG VLLM_ASCEND_TAG=nightly-main
 FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}
 
 ARG APP_DIR=/vllm-workspace/vllm-omni

docker/Dockerfile.npu.ci.a3

@@ -1,5 +1,5 @@
-ARG VLLM_ASCEND_IMAGE=quay.io/atlas-ci/vllm-ascend
-ARG VLLM_ASCEND_TAG=v0.24.0-a3
+ARG VLLM_ASCEND_IMAGE=quay.io/ascend/vllm-ascend
+ARG VLLM_ASCEND_TAG=nightly-main-a3
 FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}
 
 ARG APP_DIR=/vllm-workspace/vllm-omni

docker/Dockerfile.rocm

@@ -1,4 +1,4 @@
-ARG BASE_IMAGE=vllm/vllm-openai-rocm:v0.24.0
+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=v0.24.0
+ARG VLLM_VERSION=v0.22.0
 RUN git clone -b ${VLLM_VERSION} https://github.com/vllm-project/vllm
 WORKDIR /workspace/vllm
 
@@ -101,15 +101,13 @@ RUN --mount=type=cache,target=/root/.cache/uv \
     source /opt/intel/oneapi/ccl/2021.15/env/vars.sh --force && \
     export CMAKE_PREFIX_PATH="$(python3 -c 'import site; print(site.getsitepackages()[0])'):${CMAKE_PREFIX_PATH}"
 
-ENV LD_LIBRARY_PATH="/opt/intel/oneapi/ccl/2021.15/lib:$LD_LIBRARY_PATH:/usr/local/lib/"
+ENV LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/lib/"
 
 ENV VLLM_TARGET_DEVICE=xpu
 ENV VLLM_WORKER_MULTIPROC_METHOD=spawn
 
 RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip uninstall triton triton-xpu && \
-    uv pip install triton-xpu==3.7.1 && \
-    uv pip install --no-build-isolation --no-deps .
+    uv pip install --no-build-isolation .
 
 CMD ["/bin/bash"]
 
@@ -144,12 +142,16 @@ WORKDIR /workspace/vllm-omni
 COPY . .
 
 ENV VLLM_OMNI_TARGET_DEVICE=xpu
+RUN uv pip install --no-cache-dir ".[dev]" --no-build-isolation
+
+# FIX triton
 RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip install --no-build-isolation \
-    --extra-index-url https://mirrors.ustc.edu.cn/pypi/simple/ \
-    ".[dev]" && \
-    uv pip uninstall triton oneccl oneccl-devel && \
-    uv pip install triton-xpu==3.7.1 --reinstall
+    uv pip uninstall triton triton-xpu && \
+    uv pip install triton-xpu==3.7.0
+
+# remove torch bundled oneccl to avoid conflicts
+RUN --mount=type=cache,target=/root/.cache/uv \
+    uv pip uninstall oneccl oneccl-devel
 
 FROM vllm-omni AS vllm-omni-openai
 

docs/.nav.yml

@@ -18,6 +18,7 @@ nav:
     - Offline Inference:
       - BAGEL-7B-MoT: user_guide/examples/offline_inference/bagel.md
       - GLM-Image Multistage End-to-End Inference: user_guide/examples/offline_inference/glm_image.md
+      - Helios Video Generation: user_guide/examples/offline_inference/helios.md
       - HunyuanImage-3.0 Image-to-Text Inference: user_guide/examples/offline_inference/hunyuan_image3.md
       - Image-To-Image: user_guide/examples/offline_inference/image_to_image.md
       - Image-To-Video: user_guide/examples/offline_inference/image_to_video.md
@@ -68,11 +69,10 @@ nav:
         - Hybrid Sharded Data Parallel: user_guide/diffusion/parallelism/hsdp.md
         - Sequence Parallel: user_guide/diffusion/parallelism/sequence_parallel.md
         - Tensor Parallel: user_guide/diffusion/parallelism/tensor_parallel.md
-        - VAE Parallelism: user_guide/diffusion/parallelism/vae_parallelism.md
+        - VAE Patch Parallel: user_guide/diffusion/parallelism/vae_patch_parallel.md
       - CPU Offloading: user_guide/diffusion/cpu_offload_diffusion.md
       - LoRA: user_guide/diffusion/lora.md
       - Custom Pipeline: features/custom_pipeline.md
-      - Request-Level Batching: user_guide/diffusion/request_batching.md
       - Step Execution: user_guide/diffusion/step_execution.md
     - Quantization:
       - Overview: user_guide/quantization/overview.md
@@ -119,7 +119,6 @@ nav:
       - design/feature/async_chunk.md
       - design/feature/vae_parallel.md
       - design/feature/diffusion_step_execution.md
-      - design/feature/diffusion_request_level_batching.md
       - design/feature/diffusion_continuous_batching.md
     - Module Design:
       - design/module/ar_module.md

docs/README.md

@@ -28,9 +28,9 @@ Easy, fast, and cheap omni-modality model serving for everyone
 
 [vLLM](https://github.com/vllm-project/vllm) was originally designed to support large language models for text-based autoregressive generation tasks. vLLM-Omni is a framework that extends its support for omni-modality model inference and serving:
 
-- **Omni-modality**: Text, image, audio, video, and action data processing
+- **Omni-modality**: Text, image, video, and audio data processing
 - **Non-autoregressive Architectures**: extend the AR support of vLLM to Diffusion Transformers (DiT) and other parallel generation models
-- **Heterogeneous outputs**: from traditional text generation to multimodal and action outputs
+- **Heterogeneous outputs**: from traditional text generation to multimodal outputs
 
 <p align="center">
   <picture>
@@ -55,10 +55,8 @@ 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. Qwen3-Omni, Cosmos3, 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)
-- Robot-policy and action models (e.g. GR00T-N1.7, DreamZero-DROID, InternVLA-A1, Cosmos3 action policy)
+- Omni-modality models (e.g. Qwen2.5-Omni, Qwen3-Omni)
+- Multi-modality generation models (e.g. Qwen-Image)
 
 For more information, checkout the following:
 

docs/community/governance.md

@@ -33,20 +33,16 @@ Lead maintainers are responsible for the overall direction and strategy of the p
 
 Committers have write access and merge rights. They typically have deep expertise in specific areas of this project and shepherd the community contributions:
 
-- [@alex-jw-brooks](https://github.com/alex-jw-brooks): Frontend, Configuration, and Entrypoint Support
-- [@amy-why-3459](https://github.com/amy-why-3459): Omni Modality Model and World Model Support
 - [@david6666666](https://github.com/david6666666): Quantization and Community Relationship
-- [@gcanlin](https://github.com/gcanlin): Hardware plugin, NPU integration, and TTS Model Support
+- [@gcanlin](https://github.com/gcanlin): Hardware plugin and NPU integration
 - [@Isotr0py](https://github.com/Isotr0py): Diffusion and Quantization
 - [@linyueqian](https://github.com/linyueqian): TTS and Omni Support
 - [@lishunyang12](https://github.com/lishunyang12): Quantization and Configuration
 - [@princepride](https://github.com/princepride): Diffusion and Omni Support
 - [@RuixiangMa](https://github.com/RuixiangMa): Diffusion models, parallel, cache, and docs
 - [@SamitHuang](https://github.com/SamitHuang): RL, Diffusion, and cache
-- [@Sy0307](https://github.com/Sy0307): TTS Model and AR Engine Support
 - [@tzhouam](https://github.com/tzhouam): Engine and New Model Support
 - [@wtomin](https://github.com/wtomin): Diffusion models, parallel, and docs
-- [@xuechendi](https://github.com/xuechendi): Diffusion and Intel GPU Support
 - [@ZeldaHuang](https://github.com/ZeldaHuang): Omni Support
 - [@ZJY0516](https://github.com/ZJY0516): Diffusion attention backend, kernel fusion, and CustomOp
 - [@yuanheng-zhao](https://github.com/yuanheng-zhao): Diffusion cache, offload, and Omni Support
@@ -57,7 +53,7 @@ This section breaks down diffusion-related responsibilities. If you have PRs tou
 
 ### Runtime-related
 
-- Diffusion models: @RuixiangMa, @wtomin, @xuechendi
+- Diffusion models: @RuixiangMa, @wtomin
 
 ### Optimization
 
@@ -65,7 +61,6 @@ This section breaks down diffusion-related responsibilities. If you have PRs tou
 - Attention backend: @ZJY0516
 - Cache: @yuanheng-zhao, @RuixiangMa, @SamitHuang
 - Offload: @yuanheng-zhao
-- Intel GPU support: @xuechendi
 - Quantization: @lishunyang12, @david6666666
 - Kernel fusion / communication-computation: @ZJY0516
 

docs/configuration/composable_parallel.md

@@ -1,239 +0,0 @@
-# Composable parallel strategies
-
-In vLLM-Omni, the `composable_parallel` system layers a declarative *parallel strategy* on top of the existing stage configs. A strategy YAML maps each pipeline stage (by its `model_stage` name) to a stack of axis declarations — one per parallelism dimension — that the engine translates into per-stage parallel sizing (`tensor_parallel_size`, `data_parallel_size`, `pipeline_parallel_size`, expert-parallel toggle, replica count) and, when relevant, an orchestrator-wide load-balancer policy.
-
-Strategies are intentionally narrow: each one only writes the axes it declares, leaving every other engine arg untouched. This makes a strategy file a small, reusable overlay you can swap independently of the deploy YAML (see `configuration/stage_configs.md`).
-
-!!! note
-    Composable parallel is **opt-in**. Passing `--strategy-config <path/to/strategy.yaml>` overlays the derived sizing onto the registry-merged stages *before* any CLI overrides are applied. When `--strategy-config` is omitted, stages remain exactly as their deploy YAML produces them.
-
-## Top-level schema reference
-
-A strategy file has a single top-level mapping, `strategies`, keyed by the `model_stage` name of each stage you want to overlay. Each value is a list of axis declarations (see [StrategySpec fields](#strategyspec-fields) below).
-
-| Field | Type | Required | Default | Description |
-|-------|------|----------|---------|-------------|
-| `strategies` | `dict[str, list]` | required | — | Maps `model_stage` name (e.g. `thinker`, `talker`, `code2wav`, `dit`) to a list of axis declarations. The key MUST be the stage name, not the integer `stage_id`. See `vllm_omni/config/composable_parallel/strategy_loader.py`. |
-
-### StrategySpec fields
-
-Each entry in a stage's list becomes one `StrategySpec` (see `vllm_omni/config/composable_parallel/spec.py`). The loader fills in `routing` and `aggregation` from per-kind defaults, so the file stays compact.
-
-| Field | Type | Required | Default | Description |
-|-------|------|----------|---------|-------------|
-| `axis` | str | required | — | Mesh-axis kind. One of `tp`, `dp`, `pp`, `ep`, `stage_replica` (wired today) or `sp_ulysses`, `sp_ring`, `cfg`, `vae_pp`, `hsdp`, `stage_pp`, `cp` (reserved; declaring one raises `AxisTranslationError` at translate time). See `MeshAxisKind` in `vllm_omni/config/composable_parallel/spec.py`. |
-| `size` | int | required | — | Axis degree. Must be `> 0`. |
-| `routing` | str | optional | per-kind default | Stateless routing policy. Accepted only for `dp` / `stage_replica`; other kinds reject it. Values: `random`, `round_robin`, `least_queue`. (`hash` is recognised but currently raises `NotImplementedError`.) See `RouteByStage` in `vllm_omni/config/composable_parallel/routing.py`. |
-| `l1_owner` | str | optional | per-kind default | Overrides which layer owns request routing for this axis. `engine` for `tp`/`dp`/`pp`/`ep`, `delegated` for `stage_replica`. Recorded into `shard_extension`. |
-| `name` | str | optional | axis kind | Human-readable label for this spec, used in error messages. |
-
-Two additional `StrategySpec` slots — `layer_hook_specs` and `kernel_specs` — let advanced authors bind layer-walker hook IDs (`LayerHookSpec`) or kernel IDs (`KernelSpec`) onto an axis. These are not exposed by the YAML loader today and are intended for programmatic construction. See `vllm_omni/config/composable_parallel/spec.py` for the dataclasses.
-
-### CLI flags
-
-| Flag | Description |
-|------|-------------|
-| `--strategy-config PATH` | Path to a `strategy.yaml`. Only takes effect on registry-based deploy paths (`--deploy-config` or the bundled default deploy YAML). Its derived sizing is overlaid onto the merged stages *before* CLI overrides. |
-| `--stage-configs-path PATH` | **Legacy.** Loads the old `stage_args` YAML schema for un-migrated models. Passing `--strategy-config` together with this path is silently inapplicable — vLLM-Omni emits a warning and the strategy is ignored (see `vllm_omni/entrypoints/utils.py`, lines 372–380). |
-| `--omni-lb-policy POLICY` | Orchestrator-wide load-balancer policy. Strategy-derived from any `stage_replica` axis; an explicit CLI value wins and must match the derived one or `AsyncOmniEngine` raises `Conflicting load-balancer policy` at construction. |
-
-!!! warning
-    `--strategy-config` cannot be used with `--stage-configs-path` (the legacy deploy path). If your model has not been migrated to the registry-based pipeline yet, the strategy is silently dropped with a warning. Migrate to a registry-based model or use `--deploy-config` to apply a strategy.
-
-## Sub-schemas
-
-### `axis`
-
-The set of recognised axis kinds is the `MeshAxisKind` enum in `vllm_omni/config/composable_parallel/spec.py`:
-
-```yaml
-# Translatable today (see `_SUPPORTED_KINDS` in translator.py).
-axis: tp              # tensor parallel — sets tensor_parallel_size
-axis: dp              # engine data parallel — sets data_parallel_size (vLLM's intra-engine DP LB)
-axis: pp              # pipeline parallel — sets pipeline_parallel_size
-axis: ep              # dense expert parallel — sets enable_expert_parallel=True; size must equal tp*dp
-axis: stage_replica   # omni-coordinator-level replica fan-out — sets num_replicas + omni_lb_policy
-
-# Reserved (raise AxisTranslationError today; declared in the type system for forward compat).
-axis: sp_ulysses
-axis: sp_ring
-axis: cfg
-axis: vae_pp
-axis: hsdp
-axis: stage_pp
-axis: cp
-```
-
-### `routing`
-
-The full closed hierarchy lives in `vllm_omni/config/composable_parallel/routing.py` (`Broadcast`, `PartitionByHash`, `ShardSequence`, `ShardSpatial`, `RouteByExpert`, `PipelineMicrobatch`, `DuplicateWithCondTag`, `RouteByStage`). For YAML authors only `RouteByStage` is exposed as a `routing:` string, and only for `dp` / `stage_replica` axes (other axes have fixed routing — `tp`/`ep` broadcast, `pp` microbatch). Recognised policies:
-
-```yaml
-routing: random         # uniform-random across replicas/DP ranks
-routing: round_robin    # cycle through replicas/DP ranks (the default for dp/stage_replica)
-routing: least_queue    # send to the replica with the smallest backlog
-# routing: hash         # recognised but raises NotImplementedError; no key-stable balancer yet
-```
-
-### `aggregation`
-
-Aggregation is filled in automatically by the loader (`_default_aggregation` in `vllm_omni/config/composable_parallel/strategy_loader.py`) from the axis kind, so users do not author it:
-
-| Axis kind | Aggregation pattern |
-|-----------|---------------------|
-| `tp`, `ep` | `TakeRank` (all ranks agree; pick one) |
-| `dp`, ep (in some flows) | `Union` (disjoint replica results) |
-| `pp` | `StitchPipeline` |
-| `stage_replica` | `FanInByStage` |
-
-Full pattern hierarchy: `TakeRank`, `Union`, `GatherDim`, `AllGather`, `StitchSpatial`, `StitchPipeline`, `Combine`, `FanInByStage` (see `vllm_omni/config/composable_parallel/aggregation.py`).
-
-## Precedence
-
-From highest to lowest, when the same axis is touched by multiple layers:
-
-1. **CLI overrides** (`--stage_N_tensor_parallel_size`, `--stage-overrides`, `--omni-lb-policy`, etc.). A CLI value always wins; a warning is emitted whenever it overrides a strategy-declared axis (see `_reconcile_strategy_with_cli` in `vllm_omni/config/config_factory.py`).
-2. **Strategy YAML** (`--strategy-config`). Derived sizing is written onto the merged stages with *conflict-on-explicit* semantics — if the deploy YAML already set a value to something different, application raises `StrategyApplyError`.
-3. **Deploy YAML** (`--deploy-config` or the bundled `vllm_omni/deploy/<model_type>.yaml`).
-4. **Parser defaults.**
-
-After CLI overrides land, the device-layout guard re-runs (`check_device_layout`, called from `_reconcile_strategy_with_cli`) against the *effective* `tp * dp * pp * num_replicas` and the resolved `devices` string, so a `--devices` or `--tensor-parallel-size` that conflicts with the strategy's world size cannot slip past the pre-spawn check.
-
-## Worked example
-
-### Example 1 — `strategy_tp2.yaml`
-
-Shard the `thinker` stage with TP=2 on top of the bundled Qwen2.5-Omni deploy YAML. Routing is fixed (broadcast) for TP, so no `routing:` field is needed.
-
-```yaml
-# examples/offline_inference/qwen2_5_omni/strategy_tp2.yaml
-strategies:
-  thinker:
-    - axis: tp
-      size: 2
-```
-
-Launch:
-
-```bash
-python3 end2end.py --query-type text --num-prompts 6 \
-    --strategy-config strategy_tp2.yaml \
-    --stage-overrides '{"0": {"devices": "0,1"}, "1": {"devices": "2"}, "2": {"devices": "2"}}'
-```
-
-Effective `OmniParallelConfig` per stage after the overlay (see `OmniParallelConfig` in `vllm_omni/config/composable_parallel/translator.py`):
-
-| Stage (`model_stage`) | `tensor_parallel_size` | `data_parallel_size` | `pipeline_parallel_size` | `stage_replica_size` | `omni_lb_policy` |
-|-----------------------|------------------------|----------------------|--------------------------|----------------------|------------------|
-| `thinker`             | **2** (from strategy)  | 1                    | 1                        | 1                    | —                |
-| `talker`              | 1                      | 1                    | 1                        | 1                    | —                |
-| `code2wav`            | 1                      | 1                    | 1                        | 1                    | —                |
-
-### Example 2 — `strategy_stage_replica.yaml`
-
-Replicate the `thinker` stage across two independent engines and let omni's StagePool round-robin requests across them.
-
-```yaml
-# examples/offline_inference/qwen2_5_omni/strategy_stage_replica.yaml
-strategies:
-  thinker:
-    - axis: stage_replica
-      size: 2
-      routing: round_robin
-```
-
-Launch:
-
-```bash
-python3 end2end.py --query-type text --num-prompts 6 \
-    --strategy-config strategy_stage_replica.yaml \
-    --stage-overrides '{"0": {"devices": "0,1"}, "1": {"devices": "2"}, "2": {"devices": "2"}}'
-```
-
-Effective per-stage config and the derived orchestrator knob:
-
-| Stage (`model_stage`) | `tensor_parallel_size` | `stage_replica_size` | `omni_lb_policy` (derived) |
-|-----------------------|------------------------|----------------------|----------------------------|
-| `thinker`             | 1                      | **2**                | **`round-robin`**          |
-| `talker`              | 1                      | 1                    | —                          |
-| `code2wav`            | 1                      | 1                    | —                          |
-
-The derived `omni_lb_policy = round-robin` is **not** written into any stage's engine args. Instead it is surfaced on `StrategyApplyResult.omni_lb_policy` and applied once at orchestrator construction by `AsyncOmniEngine._apply_strategy_lb_policy` (see `vllm_omni/engine/async_omni_engine.py`, lines 1107–1134). If you also pass `--omni-lb-policy round-robin` the values must match; passing a different value raises `Conflicting load-balancer policy`.
-
-## Per-field deep-dive
-
-### `name`
-
-Default: the axis kind (e.g. `"tp"`).
-
-Human-readable label that appears in `AxisTranslationError` and `StrategyApplyError` messages. Set it to disambiguate two stages declaring the same axis kind when scanning logs.
-
-### `mesh_axis` (`axis` + `size`)
-
-Default: no default — both fields are required.
-
-`axis` chooses the parallelism dimension; `size` is its degree. Five kinds are wired end-to-end today (`tp`, `dp`, `pp`, `ep`, `stage_replica`); the remaining kinds in `MeshAxisKind` are reserved and raise `AxisTranslationError` at translate time so a typo cannot silently become a no-op (see `_SUPPORTED_KINDS` in `vllm_omni/config/composable_parallel/translator.py`). For `ep`, the size MUST equal `tensor_parallel_size * data_parallel_size`.
-
-### `routing`
-
-Default: per-kind — `RouteByStage(round_robin)` for `dp` and `stage_replica`; `PipelineMicrobatch()` for `pp`; `Broadcast()` for `tp`/`ep` and any other kind.
-
-User-facing string only on `dp` and `stage_replica` axes. The four recognised values are `random`, `round_robin`, `least_queue`, `hash`; `hash` is rejected today with `NotImplementedError`. See `RouteByStage` in `vllm_omni/config/composable_parallel/routing.py`.
-
-### `l1_owner`
-
-Default: per-kind — `engine` for `tp`/`dp`/`pp`/`ep`, `delegated` for `stage_replica`.
-
-Overrides which layer owns request routing along this axis. `engine` means vLLM realizes routing intra-engine; `delegated` means the omni coordinator owns routing across independent replicas. Set this only when overriding the per-kind default in `_DEFAULT_L1_OWNER` (`vllm_omni/config/composable_parallel/translator.py`); invalid values fail fast in `_resolve_l1_owner`.
-
-### `stage_replica` and `omni_lb_policy`
-
-`stage_replica` is *not* a vLLM world dimension; it spins up `size` independent engine replicas of one stage, coordinated by the omni `StagePool` load balancer. The axis writes both:
-
-- The stage's `num_replicas` (a per-stage deploy knob).
-- The pipeline-wide `omni_lb_policy` string (an orchestrator-level knob).
-
-Because `omni_lb_policy` is read once at `AsyncOmniEngine` construction — not from per-stage engine args — only one value is allowed across the whole pipeline. If two stages declare conflicting policies, application raises (see `apply_strategy_specs` in `vllm_omni/config/composable_parallel/apply.py`, lines 253–260). To set this knob manually instead, use `--omni-lb-policy` on the CLI; see [the model stage docs](stage_configs.md) for how stage replicas plug into the orchestrator at runtime.
-
-### `model_stage` keys
-
-Default: no default — required for every entry under `strategies:`.
-
-Strategy keys MUST be `model_stage` *names* (strings), the same labels stage configs use for `engine_args.model_stage` (see [`model_stage` in `stage_configs.md`](stage_configs.md#engine_argsmodel_stage)). Integer `stage_id` values are not accepted; the resolver matches on `model_stage` exactly (`_resolve_stage` in `vllm_omni/config/composable_parallel/apply.py`).
-
-## Common pitfalls
-
-!!! warning "Using `stage_id` integers as keys"
-    Strategy keys are `model_stage` names (str), not integer stage IDs. A file that uses an integer index will raise at apply time:
-
-    ```text
-    StrategyApplyError: strategy role model_stage=0 did not match any stage;
-    available stages: 'thinker'(id=0), 'talker'(id=1), 'code2wav'(id=2)
-    ```
-
-    Use the `model_stage` name (e.g. `thinker`) instead.
-
-!!! warning "Mixing CLI overrides with a strategy on the same axis"
-    A strategy is meant to be the single writer for the axes it declares. If a CLI override (`--stage_0_tensor_parallel_size`, `--stage-overrides '{"0": {...}}'`) sets the same axis, the **CLI value wins** and a warning is emitted from `_reconcile_strategy_with_cli`. The device-layout guard then re-runs against the effective layout, so a CLI override that breaks `tp * dp * pp * num_replicas` will still fail fast at config time.
-
-!!! warning "Passing `--strategy-config` against the legacy `--stage-configs-path`"
-    Composable parallel only applies on the registry-based deploy path. If a model still resolves through the legacy `stage_args` YAML (loaded via `--stage-configs-path`), `--strategy-config` is silently inapplicable and emits:
-
-    ```text
-    --strategy-config (<path>) was provided but model '<model>' resolves via the
-    legacy stage_configs YAML path, which does not support composable-parallel
-    strategies; the strategy is ignored. Use a registry-based model to apply it.
-    ```
-
-    Migrate the model to the new deploy schema (see `configuration/stage_configs.md`) before applying a strategy.
-
-!!! warning "Conflicting `omni_lb_policy` across stages"
-    `omni_lb_policy` is a single pipeline-wide knob. If two `stage_replica` axes in different stages derive different policies, `apply_strategy_specs` raises:
-
-    ```text
-    StrategyApplyError: role 'talker' derives omni_lb_policy='least-queue-length'
-    but role 'thinker' already derived 'round-robin'. The load-balancer policy is
-    pipeline-wide; only one value is allowed.
-    ```
-
-    Make the policies match, drop one, or supply the value explicitly via `--omni-lb-policy`.

docs/configuration/pd_disaggregation.md

@@ -123,7 +123,7 @@ After inserting the extra thinker stage, renumber the remaining stages:
     runtime:
       devices: "2"
     engine_input_source: [1]
-    sync_process_input_func: vllm_omni.model_executor.stage_input_processors.qwen3_omni.thinker2talker_token_only
+    custom_process_input_func: vllm_omni.model_executor.stage_input_processors.qwen3_omni.thinker2talker
 
   - stage_id: 3
     runtime:
@@ -131,7 +131,7 @@ After inserting the extra thinker stage, renumber the remaining stages:
     engine_args:
       max_num_seqs: 1
     engine_input_source: [2]
-    sync_process_input_func: vllm_omni.model_executor.stage_input_processors.qwen3_omni.talker2code2wav_token_only
+    custom_process_input_func: vllm_omni.model_executor.stage_input_processors.qwen3_omni.talker2code2wav
 ```
 
 Compared with the default Qwen3-Omni config:

docs/configuration/stage_configs.md

@@ -290,6 +290,7 @@ stage_args:
       enable_prefix_caching: false
       engine_output_type: latent
     engine_input_source: [0]
+    custom_process_input_func: vllm_omni.model_executor.stage_input_processors.qwen2_5_omni.thinker2talker
     default_sampling_params:
       temperature: 0.9
       top_p: 0.8
@@ -378,20 +379,6 @@ The maximum number of sequences for concurrent processing in this stage. For LLM
 
 Default: `1`
 
-#### `engine_args.request_batch_max_wait_ms`
-
-The maximum time, in milliseconds, that a diffusion request-mode stage may wait
-before the first `schedule()` of a new scheduler wave so compatible requests can
-accumulate for request-level batching. This only applies to diffusion pipelines
-that support request-level batching with `step_execution` disabled.
-
-Use this together with `max_num_seqs > 1` for bursty serving traffic. `0`
-disables admission waiting and preserves the lowest first-request latency.
-For diffusion request-level batching tuning, see
-[Request-Level Batching](../user_guide/diffusion/request_batching.md).
-
-Default: `0.0`
-
 ### `engine_args`
 
 Engine arguments for configuring the LLM engine, diffusion engine, or other engine types used by this stage.

docs/contributing/README.md

@@ -129,27 +129,6 @@ The precheck covers five PR types: Bug Fix, Performance, New Model, Diffusion Mo
 ### 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.
 
-### Automatic skip-ci (docs and pytest skip marks)
-
-On pull requests and `main` pushes, the bootstrap step in [`.buildkite/pipeline.yml`](https://github.com/vllm-project/vllm-omni/blob/main/.buildkite/pipeline.yml) runs [`.buildkite/scripts/upload_pipeline.py`](https://github.com/vllm-project/vllm-omni/blob/main/.buildkite/scripts/upload_pipeline.py) against the git diff. When every changed file qualifies, **L2 (`ready`) and L3 (`merge-test`) pipelines are not uploaded**, so the default GPU CI jobs are skipped.
-
-| Change per file | Examples |
-| --- | --- |
-| Documentation | `docs/**`, any `*.md`, `mkdocs.yml` |
-| Pytest skip marks only (under `tests/`) | Add/remove/edit `@pytest.mark.skip`, `@pytest.mark.skipif`, or `pytest.skip(...)`; reformat `pytestmark` only to add a skip/skipif alongside existing `pytest.mark.*` entries |
-| New skipped test module | New `tests/**/*.py` whose `pytestmark` includes unconditional `pytest.mark.skip` |
-
-These PR shapes all trigger skip-ci:
-
-- Documentation only
-- Qualifying skip-mark edits in `tests/**/*.py` only
-- **A mix of documentation and qualifying skip-mark test edits**
-
-Skip-ci does **not** apply when the diff also touches product code (for example `vllm_omni/`), or when test files change assertions, imports, fixtures, or other non-skip logic. If the diff cannot be resolved (non-PR branches outside `main`), CI runs as usual.
-
-!!! note
-    Skipping L2/L3 does **not** disable the Docker image build step. Nightly (L4) upload can still run when the PR has a `nightly-test` label or on scheduled `main` builds with `NIGHTLY=1`. See [CI levels](./ci/CI_5levels.md) for how bootstrap skip-ci relates to diff-gated E2E jobs.
-
 ### Code Quality
 
 The PR needs to meet the following code quality standards:

docs/contributing/ci/CI_5levels.md

@@ -11,10 +11,6 @@ Through five levels (L1-L5) and common (Common) specifications, the system clari
     <tr>
       <th>Level</th>
       <th>Scope & Focus</th>
-      <th>Model Coverage Strategy</th>
-      <th>Feature Coverage Strategy</th>
-      <th>Interface Coverage Strategy</th>
-      <th>Tags</th>
       <th>Time Cost</th>
       <th>Test Dir</th>
       <th>Doc</th>
@@ -28,10 +24,6 @@ Through five levels (L1-L5) and common (Common) specifications, the system clari
       <td>Contribution Guideline & PR checklist</td>
       <td>/</td>
       <td>/</td>
-      <td>/</td>
-      <td>/</td>
-      <td>/</td>
-      <td>/</td>
       <td>.github/PULL_REQUEST_TEMPLATE.md <a href="../tests_style/"> Test Style (PR Checklist)</a></td>
       <td>/</td>
       <td>/</td>
@@ -40,10 +32,6 @@ Through five levels (L1-L5) and common (Common) specifications, the system clari
       <td>CI Failure Description</td>
       <td>/</td>
       <td>/</td>
-      <td>/</td>
-      <td>/</td>
-      <td>/</td>
-      <td>/</td>
       <td><a href="../failures/"> CI Failures</a></td>
       <td>/</td>
       <td>/</td>
@@ -51,10 +39,6 @@ Through five levels (L1-L5) and common (Common) specifications, the system clari
     <tr>
       <td><strong>L1</strong><br>(Unit & Logic)</td>
       <td>Unit tests for components like entrypoints, models</td>
-      <td>/</td>
-      <td>/</td>
-      <td>/</td>
-      <td><code>core_model and cpu</code></td>
       <td rowspan="2">&lt;15min</td>
       <td>/tests/{component_name}/test_xxx</td>
       <td>
@@ -66,18 +50,10 @@ Through five levels (L1-L5) and common (Common) specifications, the system clari
     </tr>
     <tr>
       <td><strong>L2</strong><br>(E2E across models & GPU-required UT)</td>
-      <td>Online (basic deployment scenarios):<br>dummy, normal inference function (output format, stream), some instance startup UT</td>
-      <td>High-priority models + online basic scenarios; request success, non-empty output, format match (no Whisper/accuracy)</td>
-      <td>High-priority features (using random lightweight models)</td>
-      <td>High-priority interfaces (using random lightweight models)</td>
-      <td><code>core_model and hardware_test(H100, L4, etc.) and omni/tts/diffusion</code></td>
+      <td>Online & Offline (basic deployment scenarios):<br>dummy, normal inference function (output format, stream), some instance startup UT</td>
       <td>
-        <strong>Model tests:</strong><br>
         /tests/e2e/online_serving/test_{model_name}.py<br>
-        <strong>Feature tests:</strong><br>
-        /tests/{component_name}/test_xxx<br>
-        <strong>Interface tests:</strong><br>
-        /tests/entrypoints/test_xxx
+        /tests/e2e/offline_inference/test_{model_name}.py
       </td>
       <td>
         <a href="#chapter-1-l1-l2-level-testing-unit-testing-and-basic-end-to-end-verification">Chapter 1</a><br>
@@ -89,19 +65,10 @@ Through five levels (L1-L5) and common (Common) specifications, the system clari
     <tr>
       <td><strong>L3</strong><br>(Important Perf & Integration & Accuracy)</td>
       <td>Online & Offline (multiple deployment scenarios):<br>real model, normal inference function, normal accuracy</td>
-      <td>High/medium-priority models + key online/offline scenarios; real weights, Whisper/similarity, preset voice gender, basic accuracy</td>
-      <td>Medium-priority features (using random lightweight models)</td>
-      <td>Medium-priority interfaces (using random lightweight models)</td>
-      <td><code>advanced_model and hardware_test(H100, L4, etc.) and omni/tts/diffusion</code></td>
       <td>&lt;30min</td>
       <td>
-        <strong>Model tests:</strong><br>
         /tests/e2e/online_serving/test_{model_name}.py<br>
-        /tests/e2e/offline_inference/test_{model_name}.py<br>
-        <strong>Feature tests:</strong><br>
-        /tests/{component_name}/test_xxx<br>
-        <strong>Interface tests:</strong><br>
-        /tests/entrypoints/test_xxx
+        /tests/e2e/offline_inference/test_{model_name}.py
       </td>
       <td>
         <a href="#chapter-2-l3-level-testing-core-integration-performance-and-accuracy-verification">Chapter 2</a><br>
@@ -112,27 +79,18 @@ Through five levels (L1-L5) and common (Common) specifications, the system clari
     </tr>
     <tr>
       <td><strong>L4</strong><br>(Perf & Integration & Accuracy)</td>
-      <td>Online: full functional scenarios + performance test + doc test + accuracy test</td>
-      <td>High-priority models: function, performance, accuracy, and doc testing<br>Medium-priority models: function and doc testing</td>
-      <td>Low-priority features (using real weights)</td>
-      <td>Low-priority interfaces (using real weights)</td>
-      <td><code>full_model and hardware_test(H100, L4, etc.) and omni/tts/diffusion</code></td>
+      <td>Online & Offline: full functional scenarios + performance test + doc test</td>
       <td>&lt;3 hour</td>
       <td>
-        <strong>Model tests:</strong><br>
+        <strong>Full Function:</strong><br>
         /tests/e2e/online_serving/test_{model_name}_expansion.py<br>
-        <strong>Feature tests:</strong><br>
-        /tests/{component_name}/test_xxx<br>
-        <strong>Interface tests:</strong><br>
-        /tests/entrypoints/test_xxx<br>
+        /tests/e2e/offline_inference/test_{model_name}_expansion.py<br>
         <strong>Performance:</strong><br>
         /tests/dfx/perf/tests/test_qwen_omni.json (Omni), test_tts.json (TTS),<br>
         and /tests/dfx/perf/tests/test_{diffusion_model}_vllm_omni.json (Diffusion)<br>
         <strong>Doc Test:</strong><br>
         tests/example/online_serving/test_{model_name}.py<br>
-        tests/example/offline_inference/test_{model_name}.py<br>
-        <strong>Accuracy Test:</strong><br>
-        /tests/e2e/accuracy/test_{model_name}.py
+        tests/example/offline_inference/test_{model_name}.py
       </td>
       <td>
         <a href="#chapter-3-l4-level-testing-full-functionality-performance-and-documentation-testing">Chapter 3</a><br>
@@ -143,11 +101,7 @@ Through five levels (L1-L5) and common (Common) specifications, the system clari
     </tr>
     <tr>
       <td><strong>L5</strong><br>(Stability & Reliability)</td>
-      <td>Online: long-term stability test + reliability test</td>
-      <td>Long-term stability and reliability testing for high-priority models<br>Low-priority models: function and doc testing</td>
-      <td>/</td>
-      <td>Invalid-parameter validation for high-priority interfaces</td>
-      <td><code>slow and hardware_test(H100, L4, etc.) and omni/tts/diffusion</code></td>
+      <td>Online & Offline: long-term stability test + reliability test</td>
       <td> Depends on reality </td>
       <td>
         <strong>Stability:</strong><br>
@@ -155,7 +109,7 @@ Through five levels (L1-L5) and common (Common) specifications, the system clari
         /tests/dfx/stability/tests/test_wan22.json<br>
         <strong>Reliability:</strong><br>
         tests/dfx/reliability/test_reliability_{model_key}.py<br>
-        (e.g. <code>test_reliability_qwen3_omni.py</code>, <code>test_reliability_wan22.py</code>, <code>test_reliability_hunyuan_image.py</code>, <code>test_reliability_voxcpm2.py</code>)
+        (e.g. <code>test_reliability_qwen3_omni.py</code>, <code>test_reliability_wan22.py</code>)
       </td>
       <td>
         <a href="#chapter-4-l5-level-testing-stability-and-reliability-testing">Chapter 4</a><br>
@@ -314,8 +268,8 @@ 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`.
@@ -348,17 +302,12 @@ Steps **without** `source_file_dependencies` are always uploaded (subject to the
 
 To balance CI cost and coverage:
 
-- **Always run** (no `source_file_dependencies`): baseline groups outside E2E Test—e.g. Simple Test, Diffusion unit tests, Engine/Model Executor, Distributed, Custom Pipeline, Entrypoints (L2), LoRA / Entrypoints (L3).
-- **Diff-gated** (`source_file_dependencies` set): **every leaf job under the E2E Test group** in `test-ready.yml` and `test-merge.yml`, regardless of queue (`mithril-h100-pool`, `gpu_1_queue`, or `gpu_4_queue`). Each step lists the smallest set of prefixes that should trigger it—typically:
-  - pytest file(s) exercised by the job (online and/or offline);
-  - model code under `vllm_omni/model_executor/models/` or `vllm_omni/diffusion/models/`;
-  - related `vllm_omni/model_executor/stage_input_processors/` and `vllm_omni/deploy/*.yaml` when applicable.
+- **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 E2E step: add `source_file_dependencies` on the leaf job with those prefixes. Prefer **per-step** deps rather than a broad group-level list unless every child shares the same 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 examples
-
-H100 E2E (kubernetes / `mithril-h100-pool`):
+#### YAML example
 
 ```yaml
       - label: "Diffusion · Qwen Image Test"
@@ -371,21 +320,6 @@ H100 E2E (kubernetes / `mithril-h100-pool`):
           queue: "mithril-h100-pool"
 ```
 
-Docker E2E (`gpu_1_queue` / `gpu_4_queue`)—same key, same upload-time filtering:
-
-```yaml
-      - label: "TTS · Qwen3-TTS CustomVoice Test"
-        source_file_dependencies:
-          - tests/e2e/online_serving/test_qwen3_tts_customvoice.py
-          - vllm_omni/model_executor/models/qwen3_tts/
-          - vllm_omni/model_executor/stage_input_processors/qwen3_tts.py
-          - vllm_omni/deploy/qwen3_tts.yaml
-        commands:
-          - pytest -s -v tests/e2e/online_serving/test_qwen3_tts_customvoice.py ...
-        agents:
-          queue: "gpu_4_queue"
-```
-
 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
@@ -405,7 +339,7 @@ On a PR build, Buildkite logs from `upload_pipeline.py` include lines such as `s
 
 - 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.
@@ -427,6 +361,7 @@ Example (Windows 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
 
@@ -446,11 +381,7 @@ L2 level testing builds upon L1 by introducing GPU resources and verifying that
     -   ***Time Cost***: Execution time is controlled within ***15 minutes*** to ensure fast feedback.
 -   ***L2 (Basic End-to-End Testing)***:
 -   -   ***Scope***: Covers two basic deployment scenarios: `online` (serving) and `offline` (inference).
-    -   ***Focus***: Uses `dummy` weights (via deploy YAML patching at `core_model`) or lightweight real models to verify that the entire chain from request input to result output works normally, including output data structure, streaming (stream) support, and **cheap payload checks** at `--run-level core_model` (see below). Also includes some unit tests that require launching independent service instances.
-    -   ***L2 response validation (`core_model`)***: Implemented in `tests/helpers/assertions.py` and invoked by `OpenAIClientHandler` based on `--run-level`. At L2 we require **request success** plus minimal output sanity—not full accuracy:
-        -   **Speech / TTS** (`assert_audio_speech_response`): decoded audio must be present and non-empty (or exceed `min_audio_bytes` when set in `request_config`); `response_format` must match the returned content-type (e.g. `wav`, `pcm`). Whisper transcript similarity, PCM HNR, and preset-voice gender checks run only at L3+.
-        -   **Diffusion** (`assert_diffusion_response`): at least one non-empty image, video, or audio artifact. Resolution/frame-count parity and other deep checks run only at L3+.
-        -   **Omni multimodal** (`assert_omni_response`): L2 asserts successful completion; keyword, transcript, and cross-modal similarity checks run only at L3+.
+    -   ***Focus***: Uses `dummy` models or lightweight real models to verify that the entire chain from request input to result output works normally, including output data structure, streaming (stream) support, etc. Also includes some unit tests that require launching independent service instances.
     -   ***Characteristic***: Requires ***GPU*** resources to perform model computations.
 
 ### 1.3 Test Directory and Execution Files
@@ -466,7 +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, **E2E Test** 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) prefixes (H100 and docker queues alike); non-E2E groups still always upload. See [Diff-aware Buildkite uploads](#diff-aware-buildkite-uploads-source_file_dependencies).
+-   ***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***:
 
@@ -559,11 +490,6 @@ L3 level testing executes after code is merged into the main branch. Its core pu
 -   1.  ***Inference Functionality***: Ensures real models can perform forward computation normally and return results.
     2.  ***Accuracy Compliance***: Verifies that the model's evaluation metrics (e.g., accuracy) meet the expected baseline, preventing code changes from introducing accuracy issues.
     3.  ***Important Performance***: Verifies whether performance (e.g., P99 latency, throughput) in core scenarios meets preset thresholds.
--   ***L3 response validation (`advanced_model`)***: At `--run-level advanced_model`, `tests/helpers/assertions.py` adds semantic checks on top of L2 payload gates (see Chapter 1):
-    -   **Speech / TTS** (`assert_audio_speech_response`): Whisper transcript of returned audio vs `request_config["input"]` (cosine similarity &gt; 0.9 when input text is set); optional `min_audio_bytes` floor; PCM harmonic-to-noise ratio when `response_format` is `pcm`; **preset voice gender** via `_assert_preset_voice_gender_from_audio` when `voice` matches a known preset in `_PRESET_VOICE_GENDER_MAP` (pitch/F0-based classifier; skipped for unknown voices or `pcm` output).
-    -   **Omni multimodal** (`assert_omni_response`): non-empty text/audio outputs; `key_words` in transcript or text; text–audio similarity / containment; preset **`speaker`** gender check (same helper as TTS).
-    -   **Diffusion** (`assert_*_diffusion_response`): image/video dimension and frame-count parity with request parameters where configured.
-    -   **Accuracy suites**: pixel/video similarity and metric baselines under `/tests/e2e/accuracy/` (separate from inline helper assertions).
 
 ### 2.3 Test Directory and Execution Files
 
@@ -575,7 +501,7 @@ 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 (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—**all E2E Test** leaf steps are diff-gated; other L3 groups always upload. See [Diff-aware Buildkite uploads](#diff-aware-buildkite-uploads-source_file_dependencies).
+-   ***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***:
 
@@ -591,9 +517,9 @@ L3 level testing executes after code is merged into the main branch. Its core pu
 
     **Explanation**:
 
-    @pytest.mark.advanced_model: Marks the test as L3 merge level (`--run-level advanced_model`): real weights, Whisper/similarity/keyword checks, diffusion deep checks, and **preset voice/speaker gender** validation where applicable. @pytest.mark.full_model: Marks L4 nightly-only suites (e.g. `test_*_expansion.py`, doc examples).
+    @pytest.mark.advanced_model: Marks the test as L3 merge level, indicating deep validation with real models. @pytest.mark.full_model: Marks L4 nightly-only suites (e.g. `test_*_expansion.py`, doc examples).
 
-    @pytest.mark.core_model: Marks the test as L1 or L2 level. At `--run-level core_model`, validation is limited to request success plus cheap payload checks (e.g. non-empty audio bytes, response format/content-type, non-empty diffusion outputs)—not Whisper, keyword, or accuracy gates. Deploy YAML may use `load_format: dummy` for fast PR feedback.
+    @pytest.mark.core_model: Marks the test as L1 or L2 level, indicating that this test case validates the basic functionality of the core model. It uses mock weights and only checks if the relevant interface functions correctly.
 
     @pytest.mark.parametrize: A parameterization decorator that allows abstracting test data into parameters, enabling reuse of the same test logic across different data configurations. indirect=True indicates that parameters will be passed to the fixture for processing.
 
@@ -689,7 +615,7 @@ L3 level testing executes after code is merged into the main branch. Its core pu
 
     **Single Request**: The comment clearly states this is a single-request completion test. For concurrent testing, it can be extended to multiple requests using request_num = n.
 
-    **Implicit Validation**: `send_omni_request`, `send_audio_speech_request`, and `send_diffusion_request` call `assert_*_response` helpers with the session `--run-level`. At `core_model`, checks are limited to success plus cheap payload sanity (non-empty audio/media, format/content-type, optional `min_audio_bytes`). At `advanced_model` and `full_model`, deep validation adds Whisper transcripts, keyword/similarity, diffusion dimension checks, PCM HNR, etc.
+    **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)).
 
@@ -750,16 +676,14 @@ L5 level testing focuses on the performance of model services under ***long-runn
 ### 4.2 Testing Content and Scope
 
 -   ***Long-term Stability (Stability) Testing***: Uses JSON under `tests/dfx/stability/tests/` (for example `test_qwen3_omni.json` and `test_wan22.json`) to run the service under moderate load for an extended period (e.g., over 12 hours), monitoring whether metrics like memory/VRAM usage, response time, and throughput degrade over time, and whether the service process remains stable.
--   ***Reliability Testing***: Uses pytest suites under `tests/dfx/reliability/` to inject controlled faults against a **live** `vllm_omni serve` instance (same **`omni_server` / `omni_server_function`** fixture style as E2E). Current suites emphasize **GPU memory pressure** (CUDA sidecar “memory hog”), **worker / runtime process kill** (`SIGKILL` on `VLLM::Worker` for Qwen3-Omni, `multiprocessing.spawn` for Wan2.2 video workers, or `vLLM-Omni::` for HunyuanImage DiT workers), **large multimodal chat**, **`/v1/videos`**, **`/v1/images/generations`**, or **`/v1/audio/speech`** jobs under OOM, **`/health` → 503** and **fast-fail / non-hanging concurrent** requests after kill, and **OpenAI-style 5xx error contracts** (e.g. text vs text+audio under OOM). **Post-fault recovery** checks exist where enabled (some cases may be `skip` while issues are tracked). See the Reliability `<details>` block in Section 4.4 for file-level responsibilities and CI markers (`slow`, `hardware_test`, POSIX-only kill).
+-   ***Reliability Testing***: Uses pytest suites under `tests/dfx/reliability/` to inject controlled faults against a **live** `vllm_omni serve` instance (same **`omni_server` / `omni_server_function`** fixture style as E2E). Current suites emphasize **GPU memory pressure** (CUDA sidecar “memory hog”), **worker / runtime process kill** (`SIGKILL` on `VLLM::Worker` for Qwen3-Omni or `multiprocessing.spawn` for Wan2.2 video workers), **large multimodal chat** or **`/v1/videos`** jobs under OOM, **`/health` → 503** and **fast-fail / non-hanging concurrent** requests after kill, and **OpenAI-style 5xx error contracts** (e.g. text vs text+audio under OOM). **Post-fault recovery** checks exist where enabled (some cases may be `skip` while issues are tracked). See the Reliability `<details>` block in Section 4.4 for file-level responsibilities and CI markers (`slow`, `hardware_test`, POSIX-only kill).
 
 ### 4.3 Test Directory and Execution Files
 
 -   ***Stability Test Configuration***: `tests/dfx/stability/tests/test_qwen3_omni.json`, `tests/dfx/stability/tests/test_wan22.json` (one JSON per model / runner family)
 -   ***Reliability Test Suite*** (`tests/dfx/reliability/`):
     -   `test_reliability_qwen3_omni.py` — Qwen3-Omni chat / multimodal reliability (GPU OOM, process kill, recovery, error contract under `--async-chunk` vs default).
-    -   `test_reliability_wan22.py` — Wan2.2 I2V video API reliability (`/v1/videos` under OOM and process kill, recovery).
-    -   `test_reliability_hunyuan_image.py` — HunyuanImage-3.0-Instruct DiT-only reliability (`/v1/images/generations` under OOM and process kill; deploy `hunyuan_image3_dit.yaml`, H100 × 4).
-    -   `test_reliability_voxcpm2.py` — VoxCPM2 TTS reliability (`/v1/audio/speech` under OOM and process kill; deploy `voxcpm2.yaml`, L4 × 1).
+    -   `test_reliability_wan22.py` — Wan2.2 T2V video API reliability (`/v1/videos` under OOM and process kill, recovery).
     -   `helpers.py` — Shared primitives used by current suites: raw HTTP probes for `/v1/chat/completions` and `/health`, OpenAI-style error parsing, GPU OOM sidecar (`inject_gpu_oom` / `stop_gpu_oom_hogs`), and `pgrep`-based process-kill injector construction (`make_process_kill_fault_injector`).
     -   `conftest.py` — `fault_injector` and `omni_server_after_fault` / `omni_server_after_fault_function` fixtures to run a callable **after** the server is ready.
     -   `README.md` — Short local run commands for this directory.
@@ -857,33 +781,22 @@ Reliability tests are **short fault-injection** integration runs (L5 **(b)** in
 | `helpers.py` | Shared helpers used by current reliability suites: raw `POST`/`GET` probes (`/v1/chat/completions`, `/health`), OpenAI error parsing (`extract_openai_error_contract_from_bytes`), GPU OOM sidecar lifecycle (`inject_gpu_oom`, `stop_gpu_oom_hogs`), and process-kill injector builder (`make_process_kill_fault_injector`). |
 | `conftest.py` | Pytest fixtures: indirect `fault_injector`, `omni_server_after_fault` / `omni_server_after_fault_function` (run injector after server is ready, then yield server). |
 | `test_reliability_qwen3_omni.py` | Qwen3-Omni: OOM vs **text vs text+audio** error contract, large multimodal chat under OOM, concurrent pressure, **SIGKILL** on `VLLM::Worker`, `/health` → 503 + fast-fail + concurrent chat; optional OOM recovery scenario (may be skipped while tracked in issues). |
-| `test_reliability_wan22.py` | Wan2.2 I2V: large `/v1/videos` under OOM, **SIGKILL** on `multiprocessing.spawn` chain, health / fast-fail / concurrent video requests; optional recovery test (may be skipped). |
-| `test_reliability_hunyuan_image.py` | HunyuanImage DiT-only: large `/v1/images/generations` under OOM, **SIGKILL** on `vLLM-Omni::` workers and serve/tree targets, health / fast-fail / concurrent image requests; some OOM/recovery cases may be skipped while tracked in issues. |
-| `test_reliability_voxcpm2.py` | VoxCPM2: `/v1/audio/speech` under OOM (error contract), **SIGKILL** on `VLLM::` workers and serve/tree targets, health / fast-fail / concurrent speech requests; some OOM cases may be skipped while tracked in issues. |
+| `test_reliability_wan22.py` | Wan2.2 T2V: large `/v1/videos` under OOM, **SIGKILL** on `multiprocessing.spawn` chain, health / fast-fail / concurrent video requests; optional recovery test (may be skipped). |
 | `README.md` | Minimal run / collect examples. |
 
 #### Parametrization and markers
 
 - Each test module defines a **`RELIABILITY_SCENARIOS`** list (`test_name`, `server_params`: model, `stage_config_name` or diffusion `server_args`, etc.). **`create_reliability_omni_server_params()`** in `tests/dfx/conftest.py` resolves stage paths (including XPU substitutions where applicable) and builds **`OmniServerParams`** lists consumed by **`@pytest.mark.parametrize(..., indirect=True)`** on `omni_server` or `omni_server_function`.
-- Cases are tagged **`@pytest.mark.slow`** for weekly / selective CI. GPU-heavy suites use **`@hardware_test(res={"cuda": "H100"}, num_cards=...)`** or **`@hardware_test(res={"cuda": "L4"}, num_cards=1)`** (Qwen3-Omni **2**× H100; Wan2.2 **1**× H100; HunyuanImage DiT **4**× H100; VoxCPM2 **1**× L4).
+- Cases are tagged **`@pytest.mark.slow`** for weekly / selective CI. GPU-heavy suites use **`@hardware_test(res={"cuda": "H100"}, num_cards=...)`** (Qwen3-Omni paths often require **2** cards; Wan2.2 video paths **1** card).
 - **Process-kill** tests use **`@pytest.mark.skipif(os.name == "nt", ...)`** because injection uses POSIX **`pgrep` / `kill`**.
 
 #### CI trigger
 
-Weekly Buildkite (`.buildkite/test-weekly.yml`) runs one step per model suite (trigger: `WEEKLY=1` or PR label `weekly-test`), for example:
-
-| Buildkite step | Test file | CI hardware |
-| -------------- | --------- | ----------- |
-| Reliability Test - qwen3-omni | `test_reliability_qwen3_omni.py` | H100 × 2 (`mithril-h100-pool`) |
-| Reliability Test - wan22 | `test_reliability_wan22.py` | H100 × 2 (`mithril-h100-pool`) |
-| Reliability Test - hunyuan-image | `test_reliability_hunyuan_image.py` | H100 × 4 (`mithril-h100-pool`) |
-| Reliability Test - voxcpm2 | `test_reliability_voxcpm2.py` | L4 × 1 (`gpu_1_queue`) |
+Weekly Buildkite (`.buildkite/test-weekly.yml`) runs, for example:
 
 ```bash
 pytest -s -v tests/dfx/reliability/test_reliability_qwen3_omni.py -m "slow"
 pytest -s -v tests/dfx/reliability/test_reliability_wan22.py -m "slow"
-pytest -s -v tests/dfx/reliability/test_reliability_hunyuan_image.py -m "slow"
-pytest -s -v tests/dfx/reliability/test_reliability_voxcpm2.py -m "slow"
 ```
 
 #### Local commands
@@ -892,8 +805,6 @@ pytest -s -v tests/dfx/reliability/test_reliability_voxcpm2.py -m "slow"
 pytest --collect-only tests/dfx/reliability
 pytest -s -v tests/dfx/reliability/test_reliability_qwen3_omni.py -m slow
 pytest -s -v tests/dfx/reliability/test_reliability_wan22.py -m slow
-pytest -s -v tests/dfx/reliability/test_reliability_hunyuan_image.py -m slow
-pytest -s -v tests/dfx/reliability/test_reliability_voxcpm2.py -m slow
 ```
 
 #### Adding a new model suite
@@ -906,7 +817,7 @@ pytest -s -v tests/dfx/reliability/test_reliability_voxcpm2.py -m slow
 </details>
 
 -   -   ***Stability***: `pytest -s -v tests/dfx/stability/scripts/test_stability_qwen3_omni.py` or `pytest -s -v tests/dfx/stability/scripts/test_stability_wan22.py` (or add `test_stability_<model>.py` alongside a matching JSON config)
-    -   ***Reliability***: `pytest -s -v tests/dfx/reliability/test_reliability_<model>.py -m slow` (current suites: `qwen3_omni`, `wan22`, `hunyuan_image`, `voxcpm2`; add `test_reliability_<suite>.py` and a matching step in `.buildkite/test-weekly.yml` for new models)
+    -   ***Reliability***: `pytest -s -v tests/dfx/reliability/test_reliability_qwen3_omni.py -m slow` and/or `pytest -s -v tests/dfx/reliability/test_reliability_wan22.py -m slow` (add `test_reliability_<suite>.py` for new models)
 
 ## Summary
 

docs/contributing/ci/test_guide.md

@@ -96,21 +96,15 @@ Our test scripts use the pytest framework. First, please use `git clone https://
     # Stability: Wan2.2 (v1/videos diffusion benchmark loop)
     pytest -s -v dfx/stability/scripts/test_stability_wan22.py -m slow
 
-    # Reliability: Qwen3-Omni (H100 × 2)
+    # Reliability: Qwen3-Omni
     pytest -s -v dfx/reliability/test_reliability_qwen3_omni.py -m slow
 
-    # Reliability: Wan2.2 (H100)
+    # Reliability: Wan2.2
     pytest -s -v dfx/reliability/test_reliability_wan22.py -m slow
 
-    # Reliability: HunyuanImage DiT (H100 × 4)
-    pytest -s -v dfx/reliability/test_reliability_hunyuan_image.py -m slow
-
-    # Reliability: VoxCPM2 (L4)
-    pytest -s -v dfx/reliability/test_reliability_voxcpm2.py -m slow
-
     ```
 
-    The latest L5 CI jobs (reliability + invalid-parameter weekly steps) are in [test-weekly.yml](https://github.com/vllm-project/vllm-omni/blob/main/.buildkite/test-weekly.yml).
+    The latest L5 commands for CI can be found in the [pipeline](https://github.com/vllm-project/vllm-omni/blob/main/.buildkite/test-ready.yml).
 
 You can find more information about markers in the documentation: [marker doc](./tests_markers.md)
 

docs/contributing/model/adding_diffusion_model.md

@@ -349,14 +349,12 @@ class YourModelPipeline(nn.Module):
 - def __call__(
 + def forward(
     self,
-+   req: DiffusionRequestBatch,  # ← Add request-batch parameter here
++   req: OmniDiffusionRequest,  # ← Add request parameter here
 - ):
-+ ) -> list[DiffusionOutput]:  # ← Add return type
++ ) -> DiffusionOutput:  # ← Add return type
 ```
 
-[`OmniDiffusionRequest`](https://docs.vllm.ai/projects/vllm-omni/en/latest/api/vllm_omni/diffusion/request/#vllm_omni.diffusion.request.OmniDiffusionRequest) is a dataclass that contains one **prompt** and the **sampling parameters** [`OmniDiffusionSamplingParams`](https://docs.vllm.ai/projects/vllm-omni/en/latest/api/vllm_omni/inputs/data/#vllm_omni.inputs.data.OmniDiffusionSamplingParams) for one logical diffusion request. It also contains a request_id for other components to trace this request and its outputs. Before pipeline execution, the runner wraps one or more independent requests into `DiffusionRequestBatch`.
-
-[`DiffusionRequestBatch`](https://docs.vllm.ai/projects/vllm-omni/en/latest/api/vllm_omni/diffusion/worker/request_batch/#vllm_omni.diffusion.worker.request_batch.DiffusionRequestBatch) exposes compatibility properties such as `prompts`, `sampling_params`, and `request_id`. Pipelines that can execute the whole request batch in one forward pass should set `supports_request_batch = True`; other pipelines still receive a single-request batch and return a one-element output list.
+[`OmniDiffusionRequest`](https://docs.vllm.ai/projects/vllm-omni/en/latest/api/vllm_omni/diffusion/request/#vllm_omni.diffusion.request.OmniDiffusionRequest) is a dataclass that contains the **prompts** and **sampling parameters** [`OmniDiffusionSamplingParams`](https://docs.vllm.ai/projects/vllm-omni/en/latest/api/vllm_omni/inputs/data/#vllm_omni.inputs.data.OmniDiffusionSamplingParams) for the diffusion pipeline execution. It also contains a request_id for other components to trace this request and its outputs.
 
 See some parameters in `OmniDiffusionSamplingParams` as follows:
 
@@ -369,36 +367,35 @@ See some parameters in `OmniDiffusionSamplingParams` as follows:
 **Extract parameters from request:**
 
 ```python
+from vllm_omni.diffusion.request import OmniDiffusionRequest
 from vllm_omni.diffusion.data import DiffusionOutput
-from vllm_omni.diffusion.worker.request_batch import DiffusionRequestBatch
 
 def forward(
     self,
-    req: DiffusionRequestBatch,
-) -> list[DiffusionOutput]:
-    # Extract prompts from the request batch
-    prompts = [
-        p if isinstance(p, str) else (p.get("prompt") or "")
-        for p in req.prompts
-    ]
+    req: OmniDiffusionRequest,
+) -> DiffusionOutput:
+    # Extract prompts from request
+    if req.prompts is not None:
+        prompt = [
+            p if isinstance(p, str) else (p.get("prompt") or "")
+            for p in req.prompts
+        ]
 
-    # Extract common sampling parameters
+    # Extract sampling parameters
     sampling_params = req.sampling_params
     num_inference_steps = sampling_params.num_inference_steps or 50
     guidance_scale = sampling_params.guidance_scale or 7.5
     height = sampling_params.height or (self.default_sample_size * self.vae_scale_factor)
     width = sampling_params.width or (self.default_sample_size * self.vae_scale_factor)
 
-    # For image editing pipelines, extract media from each prompt dict
-    input_images = []
-    for p in req.prompts:
-        multi_modal_data = p.get("multi_modal_data", {}) if isinstance(p, dict) else {}
-        input_images.append(multi_modal_data.get("image"))
+    # For image editing pipelines, extract images from multi_modal_data
+    if hasattr(req, 'multi_modal_data') and req.multi_modal_data:
+        input_images = req.multi_modal_data.get('image', [])
 
     # ... rest of generation logic
 ```
 
-For an image editing model, the request `prompt` can be a dict like:
+For an image editing model, an example `OmniDiffusionRequest` is like:
 ```python
 {
     "prompt": "turn this cat to a dog",
@@ -475,12 +472,12 @@ def get_your_model_pre_process_func(
     def pre_process_func(
         request: OmniDiffusionRequest,
         ):
-        prompt = request.prompt
-        multi_modal_data = prompt.get("multi_modal_data", {}) if not isinstance(prompt, str) else None
-        raw_image = multi_modal_data.get("image", None) if multi_modal_data is not None else None
-        # image pre-processing
-        # after pre-processing, update the request attributes
-        ...
+        for i, prompt in enumerate(request.prompts):
+            multi_modal_data = prompt.get("multi_modal_data", {}) if not isinstance(prompt, str) else None
+            raw_image = multi_modal_data.get("image", None) if multi_modal_data is not None else None
+            # image pre-processing
+            # after pre-processing, update the request attributes
+            ...
         return request
 
     return pre_process_func
@@ -926,11 +923,11 @@ When implementing a new pipeline, avoid putting all logic inside a single functi
 
 For example:
 ```
-def forward(self, req: DiffusionRequestBatch) -> list[DiffusionOutput]:
+def forward(self, req: OmniDiffusionRequest):
     prompt_embeds = self.encode_prompt(req)
     latents = self.diffuse(prompt_embeds, req)
     images = self.vae.decode(latents)
-    return [DiffusionOutput(output=images)]
+    return DiffusionOutput(output=images)
 ```
 This allows the timing utility to measure each stage (e.g., encode_prompt, diffuse, vae.decode) separately and helps identify performance bottlenecks more easily.
 

docs/contributing/model/adding_omni_model.md

@@ -475,64 +475,92 @@ thinker_hidden_states = output.multimodal_output["24"]
 
 ### Implementation Example
 
-Create stage transition processors in `vllm_omni/model_executor/stage_input_processors/your_model_name.py`. Each inter-stage edge should provide a **coherent processor set** rather than a single monolithic function:
-
-| Suffix | Role | Runs when |
-|--------|------|-----------|
-| `*_full_payload` | Worker-side payload producer | `async_chunk=false`; accumulates tensors and ships via connector |
-| `*_async_chunk` | Scheduler-side streaming producer | `async_chunk=true`; emits per-chunk payloads |
-| `*_token_only` | Orchestrator placeholder builder | `async_chunk=false`; allocates downstream prompt slots only |
+Create stage transition processors in `vllm_omni/model_executor/stage_input_processors/your_model_name.py`:
 
 ```python
-# qwen3_omni.py (Thinker → Talker, non-async path)
+# qwen3_omni.py
 
-def thinker2talker_token_only(
-    source_outputs: list[Any],
+def thinker2talker(
+    stage_list: list[Any],
+    engine_input_source: list[int],
     prompt: OmniTokensPrompt | TextPrompt | None = None,
     requires_multimodal_data: bool = False,
-    streaming_context: Any | None = None,
 ) -> list[OmniTokensPrompt]:
-    """Allocate talker prefill slots; bulk tensors arrive via the connector."""
-    ...
+    """
+    Process thinker outputs to create talker inputs.
+
+    Args:
+        stage_list: List of stage objects
+        engine_input_source: Source stage IDs (typically [0] for thinker)
+        prompt: Original prompt data
+
+    Returns:
+        List of OmniTokensPrompt for talker stage
+    """
+    source_stage_id = engine_input_source[0]
+    thinker_outputs = stage_list[source_stage_id].engine_outputs
+    talker_inputs = []
+
+    for thinker_output in thinker_outputs:
+        output = thinker_output.outputs[0]
+        # Extract thinker embeddings and hidden states
+        thinker_prefill_embeddings = output.multimodal_output["0"].float().clone().detach().cuda()
+        thinker_hidden_states = output.multimodal_output["24"].float().clone().detach().cuda()
+
+        info = {
+            "thinker_prefill_embeddings": thinker_prefill_embeddings,
+            "thinker_hidden_states": thinker_hidden_states,
+            "thinker_sequences": thinker_output.prompt_token_ids + output.token_ids,
+            "thinker_input_ids": thinker_output.prompt_token_ids,
+        }
+
+        talker_inputs.append(
+            OmniTokensPrompt(
+                prompt_token_ids=[0] * computed_length,
+                additional_information=info,
+                multi_modal_data=None,
+            )
+        )
+
+    return talker_inputs
 
 
-def thinker2talker_full_payload(
-    transfer_manager: Any,
-    pooling_output: dict[str, Any],
-    request: OmniEngineCoreRequest,
-) -> dict[str, Any] | None:
-    """Pack accumulated thinker hidden states into OmniPayload for stage-1."""
-    ...
+def talker2code2wav(
+    stage_list: list[Any],
+    engine_input_source: list[int],
+    prompt: OmniTokensPrompt | TextPrompt | None = None,
+    requires_multimodal_data: bool = False,
+) -> list[OmniTokensPrompt]:
+    """
+    Process talker outputs to create code2wav inputs.
+    """
+    source_stage_id = engine_input_source[0]
+    talker_outputs = stage_list[source_stage_id].engine_outputs
+    code2wav_inputs = []
 
+    for talker_output in talker_outputs:
+        output = talker_output.outputs[0]
+        # Extract codec codes
+        codec_codes = (
+            output.multimodal_output["code_predictor_codes"]
+            .to(torch.long)
+            .transpose(0, 1)
+            .cpu()
+            .to(torch.long)
+            .reshape(-1)
+            .tolist()
+        )
 
-def thinker2talker_async_chunk(
-    transfer_manager: Any,
-    multimodal_output: OmniPayload | dict[str, Any],
-    request: OmniEngineCoreRequest,
-    is_finished: bool = False,
-) -> OmniPayloadStruct | None:
-    """Stream thinker rows to talker while async_chunk is enabled."""
-    ...
+        code2wav_inputs.append(
+            OmniTokensPrompt(
+                prompt_token_ids=codec_codes,
+                multi_modal_data=None,
+            )
+        )
+
+    return code2wav_inputs
 ```
 
-Wire these in `pipeline.py`:
-
-```python
-StagePipelineConfig(
-    stage_id=0,
-    custom_process_next_stage_input_func=f"{_PROC}.thinker2talker_full_payload",
-    async_chunk_process_next_stage_input_func=f"{_PROC}.thinker2talker_async_chunk",
-    ...
-),
-StagePipelineConfig(
-    stage_id=1,
-    sync_process_input_func=f"{_PROC}.thinker2talker_token_only",
-    ...
-),
-```
-
-Do **not** add a no-suffix `thinker2talker` when a `sync_process_input_func` is already declared — `_select_processor_funcs()` always prefers the `*_token_only` hook in non-async mode, so the bare function would never run. See `docs/design/rfc_stage_input_processors_refactor.md` for the full contract.
-
 ## Testing
 
 For comprehensive testing guidelines, please refer to the [Test File Structure and Style Guide](../ci/tests_style.md).

docs/contributing/profiling.md

@@ -286,54 +286,3 @@ Recommended viewers:
 - Nsight Systems GUI for CUDA kernel timelines
 
 For upstream background on the underlying vLLM profiling infrastructure, see the [vLLM profiling guide](https://docs.vllm.ai/en/stable/contributing/profiling/).
-
-## 7. Orchestrator Monitor
-
-> **Warning:** Diagnostic only. Adds lightweight counters on the orchestrator poll loop and per-replica queue depth. Disable in production unless you are actively debugging orchestrator saturation.
-
-Multi-stage omni pipelines route client-facing outputs and inter-stage connector traffic through a single-process orchestrator. When that loop is saturated (for example by large multimodal payloads on the poll path), TTFT and stability can degrade even when GPU stages are healthy. The orchestrator monitor records time-series signals to confirm whether the orchestrator or downstream stage queues are the bottleneck.
-
-Enable on the stage-0 (orchestrator) process:
-
-```bash
-vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091 \
-  --stage-id 0 \
-  --enable-orch-monitor
-```
-
-Optional output path override:
-
-```bash
-export VLLM_OMNI_ORCH_MONITOR_PATH=/tmp/vllm_omni_orch_monitor.json
-```
-
-If unset, the monitor writes `./vllm_omni_orch_monitor_<MMDDHHMM>.json` at shutdown.
-
-### Recorded metrics
-
-Each 1-second window records:
-
-| Series | Description |
-|---|---|
-| `windows.duration_s` | Wall time covered by the window |
-| `windows.loop_idle` / `windows.loop_active` | Orchestrator poll-loop iterations with no work vs. active forwarding |
-| `replicas.<stage,replica>.outputs_queue_size` | MP client `outputs_queue` backlog for that replica |
-| `replicas.<stage,replica>.inflight` | Requests currently bound/routed to the replica |
-
-On shutdown the server also logs a short summary (`loop_active_pct`, per-replica queue averages/maxima).
-
-### Relationship to other diagnostics
-
-This monitor is intentionally separate from the existing profiling tools:
-
-| Tool | Scope | Output |
-|---|---|---|
-| `--enable-diffusion-pipeline-profiler` | Diffusion stage functions (`vae.decode`, `diffuse`, …) | Per-request `stage_durations` / logs |
-| `--enable-ar-profiler` | AR stage generation time | Per-request `stage_durations` |
-| `profiler_config` (`torch` / `cuda`) | GPU/CPU kernels inside a stage worker | `trace.json`, Nsight, operator tables |
-| Prometheus `/metrics` (`vllm:omni_*`) | Pipeline SLOs and cross-stage transfer | Continuous scrape endpoint |
-| `--enable-orch-monitor` | Orchestrator poll loop + replica queue backlog | Single JSON file at shutdown |
-
-The orchestrator monitor does not use `torch.profiler` because the bottleneck signal lives in the orchestrator process (poll-loop duty cycle and queue depth), not inside stage workers. It complements Prometheus metrics when you need a post-run time series for a benchmark or regression run.
-
-Process liveness monitors (`monitor_engine_liveness`, worker/engine-core monitors) are unrelated: they detect crashed workers, not orchestrator load.

docs/design/architecture_overview.md

@@ -13,7 +13,7 @@ This document outlines the architectural design for vLLM-Omni.
 
 The primary goal of the vLLM-Omni project is to build the fastest and easiest-to-use open-source Omni-Modality model inference & serving engine. vLLM-Omni extends the original vLLM, which was created to support large language models for text-based autoregressive (AR) generation tasks. vLLM-Omni is designed to support:
 
-* **Non-textual Output:** Enables the integration, efficient processing and output of various data types, including but not limited to, images, audio, video, and action trajectories, alongside text.
+* **Non-textual Output:** Enables the integration, efficient processing and output of various data types, including but not limited to, images, audio, and video, alongside text.
 * **Non-Autoregressive Structure:** Support model structure beyond autoregressive, especially Diffusion Transformer (DiT), which is widely used in visual and audio generation.
 * **Integration with vLLM Core:** Maintain compatibility and leverage existing vLLM key modules and optimizations where applicable.
 * **Extensibility:** Design a modular and flexible architecture that can easily accommodate new modalities, model architectures, and output formats.

docs/design/feature/cfg_parallel.md

@@ -126,16 +126,16 @@ Call `self.diffuse` in your pipeline's forward function:
 
 ```python
 import torch.nn as nn
-from vllm_omni.diffusion.worker.request_batch import DiffusionRequestBatch
-
 class YourModelPipeline(nn.Module, CFGParallelMixin):
     def forward(
         self,
-        req: DiffusionRequestBatch,
-    ) -> list[DiffusionOutput]:
-        # Read prompts and sampling params from the request
-        ...
-        # Encode prompts, initialize latents, get timesteps
+        prompt: str,
+        negative_prompt: str | None = None,
+        guidance_scale: float = 3.5,
+        num_inference_steps: int = 50,
+        **kwargs,
+    ):
+        # Encode prompts, Initialize latents, Get timesteps
         ...
         # Run diffusion loop (calls the mixin's diffuse method)
         latents = self.diffuse(

docs/design/feature/diffusion_continuous_batching.md

@@ -38,13 +38,9 @@ With continuous batching enabled:
 The current implementation is conservative:
 
 - only compatible requests are batched together
+- request-mode diffusion still runs with `max_num_seqs=1`
 - per-request progress and completion remain independent
 
-Here, "continuous batching" means the step-wise path enabled by
-`step_execution=True`. Request-mode `DiffusionRequestBatch` is static
-request-level batching for one full pipeline `forward()` call; it does not
-admit or remove requests between denoise steps.
-
 ## Enablement
 
 Use `--step-execution` as the feature gate, then increase `--max-num-seqs`
@@ -76,7 +72,7 @@ which is built from shape-sensitive and CFG-sensitive sampling fields. This is
 the core correctness rule for batching: requests are only co-batched when they
 share the same denoise tensor contract.
 
-There are three important details:
+There are two important details:
 
 - `num_inference_steps` is not part of the key, so requests with different
   total step counts can still share a batch
@@ -92,10 +88,8 @@ key also covers LoRA identity (`lora_int_id`, `lora_scale`), so requests
 targeting different adapters or scales run in separate batches and the
 worker can activate exactly one adapter per step.
 
-The scheduler batching unit is one logical `OmniDiffusionRequest`. In the
-step-wise path, runtime tensor batching is represented as `StepInputBatch`. For
-request-mode prompt semantics, see
-[Request-Level Batching](../../user_guide/diffusion/request_batching.md).
+The current batching unit is one `OmniDiffusionRequest`. Requests with
+multiple prompts do not participate in batching today.
 
 ## Runner
 
@@ -132,15 +126,17 @@ request-local scheduler state and outputs.
 the background loop and async add-request path needed for multiple requests to
 accumulate in the scheduler.
 
-When `step_execution=True`, the engine routes work through the step-wise
-executor path. The continuous batching behavior is defined by scheduler-side
-compatibility gating and runner-side `StepInputBatch` packing.
+This is supporting infrastructure, not the main design point. The batching
+behavior is defined by scheduler-side compatibility gating and runner-side
+batch packing.
 
 ## Current Limitations
 
 - Experimental feature; use `max_num_seqs=1` for the older conservative path.
 - Only native pipelines that already support `step_execution=True`.
+- Request-mode diffusion still clamps `max_num_seqs` back to `1`.
 - Only homogeneous batches keyed by `SamplingParamsKey` are supported.
+- Multi-prompt requests are not batched.
 - `cache_backend`, KV transfer, and other request-mode extras are not wired
   into the batched step-wise path yet.
 - Future work can relax the current same-shape restriction with richer

docs/design/feature/diffusion_request_level_batching.md

@@ -1,175 +0,0 @@
-# Request-Level Batching for Diffusion
-
-This document describes the request-mode batching path for diffusion pipelines.
-For end-user enablement and tuning, see
-[Request-Level Batching](../../user_guide/diffusion/request_batching.md).
-
-This is separate from
-[Continuous Batching for Step-Wise Diffusion](diffusion_continuous_batching.md).
-Request-level batching runs one full pipeline `forward()` over a static batch of
-compatible requests. Step-wise continuous batching admits work between denoise
-steps when `step_execution=True`.
-
-## Why It Helps
-
-The request-level design avoids coupling several logical requests to one request
-object. This keeps request identity, abort/error handling, and per-request
-metadata unambiguous while still allowing one fused pipeline forward pass for
-bursty or concurrent traffic.
-
-## Overview
-
-With request-level batching enabled:
-
-- each `OmniDiffusionRequest` contains one `prompt` and one `request_id`
-- the scheduler groups compatible waiting requests into one scheduler wave
-- `DiffusionRequestBatch` wraps the scheduled requests for pipeline `forward()`
-- batch-capable pipelines return `list[DiffusionOutput]`, one output per
-  request
-- `BatchRunnerOutput` maps each result back to its original `request_id`
-
-Pipelines opt in with `supports_request_batch = True` and a `forward()` method
-that accepts `DiffusionRequestBatch` and returns `list[DiffusionOutput]`.
-Pipelines that do not opt in keep the existing per-request execution path.
-
-## Enablement
-
-Request-level batching is the request-mode path, so `step_execution` must remain
-disabled. Increase `max_num_seqs` above `1` to let the scheduler keep multiple
-compatible requests active:
-
-```bash
-vllm serve Qwen/Qwen-Image --omni \
-  --port 8091 \
-  --max-num-seqs 4
-```
-
-For bursty online ingress, `request_batch_max_wait_ms` can add a bounded
-admission wait before the first `schedule()` of a scheduler wave:
-
-```bash
-vllm serve Qwen/Qwen-Image --omni \
-  --port 8091 \
-  --max-num-seqs 4 \
-  --request-batch-max-wait-ms 20
-```
-
-`request_batch_max_wait_ms=0` disables this wait and is the default.
-
-## Request Contract
-
-`OmniDiffusionRequest` represents one logical request. It owns one prompt,
-sampling parameters, request id, and request-local metadata. Runtime batches are
-formed by the scheduler and represented separately from the request payload.
-
-Runtime batching is represented by:
-
-- [`DiffusionSchedulerOutput`](gh-file:vllm_omni/diffusion/sched/interface.py)
-  for scheduled request ids and request payloads
-- [`DiffusionRequestBatch`](gh-file:vllm_omni/diffusion/worker/request_batch.py)
-  for the pipeline-facing request batch
-- [`BatchRunnerOutput`](gh-file:vllm_omni/diffusion/worker/utils.py) for
-  per-request results
-
-`DiffusionRequestBatch` intentionally exposes compatibility properties such as
-`prompts`, `sampling_params`, `request_id`, and `kv_sender_info` so migrated
-pipelines can stay close to upstream code while using a batch-aware contract.
-
-## Scheduler
-
-The scheduler derives its capacity from `max_num_seqs` through
-`max_num_running_reqs`. It exposes waiting/running queue counters so the engine
-can decide whether admission wait is useful before scheduling a new wave.
-
-Batch compatibility is controlled by
-[`SamplingParamsKey`](gh-file:vllm_omni/diffusion/sched/interface.py). The key
-contains shape-sensitive and guidance-sensitive fields, including output count
-and LoRA identity. Requests with incompatible shapes, CFG settings, output
-counts, LoRA adapters, or LoRA scales are kept in separate batches.
-
-Admission is conservative:
-
-- the scheduler only batches compatible requests
-- FIFO ordering is preserved
-- an incompatible request at the head of the waiting queue blocks later
-  compatible requests
-
-## Engine
-
-[`DiffusionEngine`](gh-file:vllm_omni/diffusion/diffusion_engine.py) resolves
-request-batch capability during initialization from the configured pipeline
-class, including custom pipeline classes.
-
-The capability check uses the pipeline class attribute
-`supports_request_batch = True`. Pipelines that set this attribute must implement
-a request-batch-compatible `forward()` contract and return one
-`DiffusionOutput` per request; the runner validates that return shape at runtime.
-
-When the selected pipeline is batch-capable and `step_execution=False`, request
-mode routes scheduler waves through the batch executor path. Otherwise it keeps
-the per-request executor path.
-
-The optional admission wait runs only when:
-
-- request batching is supported
-- `step_execution=False`
-- `request_batch_max_wait_ms > 0`
-- no requests are currently running
-
-The wait exits early when the waiting queue reaches capacity, when the queue is
-stable for a short window, when the deadline expires, or when the engine stops.
-
-## Executor And Runner
-
-The executor exposes two request-mode entries:
-
-- `execute_request`: one worker call per scheduled request
-- `execute_batch`: one worker call for the whole `DiffusionSchedulerOutput`
-
-On the batch path, the worker builds a `DiffusionRequestBatch` and runs the
-pipeline once. Request-local setup remains per request:
-
-- KV transfer metadata
-- random generator and seed handling
-- request output/error/abort mapping
-
-Shared batch setup happens once per batch when possible:
-
-- cache refresh
-- LoRA activation for the homogeneous adapter key
-- pipeline `forward(req_batch)`
-
-Large tensor IPC still uses the shared-memory packing path. The packer traverses
-both normal `RunnerOutput.result` wrappers and nested batch results so batched
-outputs do not fall back to pickle IPC for tensor payloads.
-
-## Current Limitations
-
-- Only pipelines that declare the request-batch contract use fused batch
-  execution.
-- Batches are homogeneous under `SamplingParamsKey`; heterogeneous resolution or
-  incompatible guidance settings do not co-batch yet.
-- FIFO scheduling can reduce batching opportunities when an incompatible
-  request is at the front of the queue.
-- `request_batch_max_wait_ms` improves burst coalescing but can add latency to
-  the first request in a scheduler wave. Keep it small for latency-sensitive
-  serving.
-- Step-wise continuous batching is documented separately and only applies when
-  `step_execution=True`.
-
-## Related Files
-
-- Request object and request batch:
-  [`vllm_omni/diffusion/request.py`](gh-file:vllm_omni/diffusion/request.py)
-- Scheduler interface:
-  [`vllm_omni/diffusion/sched/interface.py`](gh-file:vllm_omni/diffusion/sched/interface.py)
-- Scheduler base:
-  [`vllm_omni/diffusion/sched/base_scheduler.py`](gh-file:vllm_omni/diffusion/sched/base_scheduler.py)
-- Engine:
-  [`vllm_omni/diffusion/diffusion_engine.py`](gh-file:vllm_omni/diffusion/diffusion_engine.py)
-- Worker runner:
-  [`vllm_omni/diffusion/worker/diffusion_model_runner.py`](gh-file:vllm_omni/diffusion/worker/diffusion_model_runner.py)
-- Executor interface:
-  [`vllm_omni/diffusion/executor/abstract.py`](gh-file:vllm_omni/diffusion/executor/abstract.py)
-- Tests:
-  [`tests/diffusion/test_diffusion_engine.py`](gh-file:tests/diffusion/test_diffusion_engine.py)

docs/design/feature/vae_parallel.md

@@ -448,42 +448,6 @@ Complete examples in the codebase:
 
 ---
 
-## Spatially-Sharded Decode (Wan)
-
-The tile-parallel executor above assigns independent spatial **tiles** to ranks. The Wan VAE additionally supports a **spatially-sharded decode** backend, selected through `DiffusionParallelConfig.vae_parallel_mode` (`"spatial_shard_height"` or `"spatial_shard_width"`; default is `"tile"`).
-
-### How it differs from tile parallel
-
-| Aspect | Tile parallel (`"tile"`) | Spatially-sharded (`"spatial_shard_height"`/`"spatial_shard_width"`) |
-|--------|--------------------------|-----------------------------------------------|
-| Unit of work | Independent overlapping tiles | A single global feature map sharded along H or W |
-| Cross-rank communication | Gather tiles to rank 0, stitch + blend | Per-conv **halo exchange** of boundary rows/cols (P2P) |
-| Output assembly | Blend overlapping tiles | All-gather shards on rank 0, trim padding (matches `broadcast_result=False`) |
-| Scope | Decode + encode | Decode only |
-
-Spatial-shard decode swaps the decoder's spatial convolutions/padding for halo-exchanging variants (`WanDistConv2d`, `WanDistCausalConv3d`, `WanDistZeroPad2d`) so each rank only holds a shard of the activations but still sees the correct receptive field at shard boundaries. Implementation lives in `vllm_omni/diffusion/distributed/autoencoders/wan_spatial_shard.py`.
-
-### Wiring
-
-`vae_parallel_mode` flows through the same path as `vae_patch_parallel_size`:
-
-```text
-serve.py (--vae-parallel-mode) / OmniEngineArgs
-  -> DiffusionParallelConfig.vae_parallel_mode
-  -> registry.py: model.vae.set_parallel_size(vae_pp_size, mode=...)
-  -> DistributedVaeExecutor.parallel_mode
-  -> DistributedAutoencoderKLWan.tiled_decode dispatch
-```
-
-`DistributedAutoencoderKLWan._spatial_shard_decode_enabled()` gates the path: it requires distributed decode to be enabled, a 5D latent, and `vae_patch_parallel_size == DiT group size`. Otherwise it logs a warning and falls back to tile-parallel decode.
-
-### Notes
-
-- The decoder is patched **in place** the first time spatial-shard decode runs and is bound to a single split dimension for the lifetime of the VAE instance.
-- Numerical correctness vs. single-GPU decode is covered by `tests/diffusion/distributed/test_wan_spatial_shard.py::test_spatial_shard_decode_matches_reference` (multi-GPU, nightly `full_model` + `distributed_cuda`).
-
----
-
 ## Summary
 
 Adding VAE Patch Parallel support to diffusion model:

docs/design/index.md

@@ -11,7 +11,6 @@ This section contains design documents and architecture specifications for vLLM-
 - [Disaggregated Inference](feature/disaggregated_inference.md)
 - [Ray-based Execution](feature/ray_based_execution.md)
 - [Adding Step Execution Support for Diffusion Pipelines](feature/diffusion_step_execution.md)
-- [Request-Level Batching for Diffusion](feature/diffusion_request_level_batching.md)
 - [Continuous Batching for Step-Wise Diffusion](feature/diffusion_continuous_batching.md)
 
 ## Infrastructure Design Documents

docs/design/module/dit_module.md

@@ -199,7 +199,7 @@ class _BaseScheduler(SchedulerInterface):
 - **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`. Request-mode diffusion can use that capacity for compatible independent requests when the configured pipeline declares `supports_request_batch = True`; step-wise diffusion uses the same scheduler capacity for compatible step batches.
+- **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.
 
 #### 2.4 Current `RequestScheduler` Policy
 
@@ -215,7 +215,7 @@ class RequestScheduler(_BaseScheduler):
 
 - **FIFO request scheduling**: Waiting requests are promoted in queue order.
 
-- **Compatible request admission**: `RequestScheduler` admits waiting requests while capacity remains and the request's `SamplingParamsKey` is compatible with the active batch. Request-mode execution keeps each logical request independent, while batch-capable pipelines receive the scheduled requests as a runner-side `DiffusionRequestBatch`.
+- **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 request ids.
 
@@ -235,12 +235,7 @@ while True:
 
 - **No scheduler-owned IPC**: Scheduler no longer talks to workers directly.
 
-- **Split concurrency model**: Request-mode diffusion can schedule a static
-  batch of compatible independent requests for one full pipeline forward when
-  the pipeline supports request-level batching; the step-wise path can also
-  admit or remove compatible requests between denoise steps. See
-  [Request-Level Batching](../feature/diffusion_request_level_batching.md) and
-  [Continuous Batching for Step-Wise Diffusion](../feature/diffusion_continuous_batching.md).
+- **Split concurrency model**: Request-mode diffusion remains single-active-request, while the step-wise path can keep multiple compatible requests running and advance them independently between denoise steps.
 
 ---
 

docs/features/custom_pipeline.md

@@ -54,24 +54,20 @@ Create a custom pipeline class that extends an existing pipeline. In this exampl
 
 ```python
 # custom_pipeline.py
-from vllm_omni.diffusion.data import OmniDiffusionConfig
+from vllm_omni.diffusion.data import DiffusionOutput, OmniDiffusionConfig
 from vllm_omni.diffusion.models.qwen_image.pipeline_qwen_image_edit import QwenImageEditPipeline
-from vllm_omni.diffusion.worker.request_batch import DiffusionRequestBatch
 import torch
 
 class CustomPipeline(QwenImageEditPipeline):
     def __init__(self, *, od_config: OmniDiffusionConfig, prefix: str = ""):
         super().__init__(od_config=od_config, prefix=prefix)
 
-    def forward(self, req: DiffusionRequestBatch):
-        # Optionally customize sampling parameters
-        actual_num_steps = req.sampling_params.num_inference_steps or 50
-        req.sampling_params.num_inference_steps = actual_num_steps
-
+    def forward(self, req, prompt=None, negative_prompt=None, **kwargs):
         # Call parent's forward to get normal output
-        output = super().forward(req=req)
+        output = super().forward(req=req, prompt=prompt, negative_prompt=negative_prompt, **kwargs)
 
         # Add custom trajectory data
+        actual_num_steps = req.sampling_params.num_inference_steps or kwargs.get('num_inference_steps', 50)
         output.trajectory_timesteps = torch.linspace(1000, 0, actual_num_steps, dtype=torch.float32)
         output.trajectory_latents = torch.randn(actual_num_steps, 1, 16, 64, 64, dtype=torch.float32)
 

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, 0.21.0, 0.22.0, 0.23.0, 0.24.0, and 0.25.0. 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"
 
@@ -94,10 +94,6 @@ Note: Pre-built wheels are currently available for vLLM-Omni 0.11.0rc1, 0.12.0rc
 
 ### Build your own docker image
 
-=== "NVIDIA CUDA"
-
-    --8<-- "docs/getting_started/installation/gpu/cuda.inc.md:build-docker"
-
 === "AMD ROCm"
 
     --8<-- "docs/getting_started/installation/gpu/rocm.inc.md:build-docker"

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==0.25.0 --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 0.25.0 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==0.25.0 --torch-backend=auto
+uv pip install vllm==0.22.0 --torch-backend=auto
 ```
 
-The 0.25.0 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 v0.25.0
+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 v0.25.0; the wheel filename has no `+cu130` suffix)
-export VLLM_PRECOMPILED_WHEEL_LOCATION=https://github.com/vllm-project/vllm/releases/download/v0.25.0/vllm-0.25.0-cp38-abi3-manylinux_2_28_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:v0.25.0 \
+    vllm/vllm-omni:v0.22.0 \
     vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091
 ```
 
@@ -110,53 +110,3 @@ docker run --runtime nvidia --gpus 2 \
     The CUDA image does not define a default entrypoint, so include `vllm serve ... --omni` after the image name.
 
 # --8<-- [end:pre-built-images]
-
-# --8<-- [start:build-docker]
-
-#### Build docker image
-
-```bash
-DOCKER_BUILDKIT=1 docker build -f docker/Dockerfile.cuda -t vllm-omni-cuda .
-```
-
-If you want to specify the base vLLM version:
-
-```bash
-DOCKER_BUILDKIT=1 docker build \
-  -f docker/Dockerfile.cuda \
-  --build-arg BASE_IMAGE=vllm/vllm-openai:v0.22.1 \
-  -t vllm-omni-cuda .
-```
-
-#### Launch the docker image
-
-##### Launch with OpenAI API Server
-
-!!! note
-    The model `Qwen/Qwen3-Omni-30B-A3B-Instruct` requires significant GPU memory. The example below has been verified on 2 x H100's.
-
-```bash
-docker run --runtime nvidia --gpus 2 \
-  -v ${HF_HOME:-$HOME/.cache/huggingface}:/root/.cache/huggingface \
-  --env "HF_TOKEN=$HF_TOKEN" \
-  -p 8091:8091 \
-  --ipc=host \
-  vllm-omni-cuda \
-  vllm serve --omni --model Qwen/Qwen3-Omni-30B-A3B-Instruct --port 8091
-```
-
-By default, this mounts `$HOME/.cache/huggingface` as the model cache directory. To use a custom location, set the `HF_HOME` environment variable before running the command (e.g., `export HF_HOME=/data/models`).
-
-##### Launch with interactive session for development
-
-```bash
-docker run --runtime nvidia --gpus all -it --rm \
-  -v ${HF_HOME:-$HOME/.cache/huggingface}:/root/.cache/huggingface \
-  --env "HF_TOKEN=$HF_TOKEN" \
-  -p 8091:8091 \
-  --ipc=host \
-  --entrypoint bash \
-  vllm-omni-cuda
-```
-
-# --8<-- [end:build-docker]

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==0.25.0+rocm723 --extra-index-url https://wheels.vllm.ai/rocm/0.25.0/rocm723
+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 0.25.0 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==0.25.0+rocm723 --extra-index-url https://wheels.vllm.ai/rocm/0.25.0/rocm723
+uv pip install vllm==0.22.0+rocm722 --extra-index-url https://wheels.vllm.ai/rocm/0.22.0/rocm722
 ```
 
-The pre-built 0.25.0 vLLM wheel targets ROCm 7.2.3. 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 v0.25.0
+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:v0.25.0 \
+  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:v0.25.0
+  vllm/vllm-omni-rocm:v0.22.0
 ```
 
 # --8<-- [end:pre-built-images]

docs/getting_started/installation/npu/npu.inc.md

@@ -1,16 +1,19 @@
 # --8<-- [start:requirements]
 
-For detailed hardware and software requirements, please refer to the [vLLM-Ascend installation documentation](https://docs.vllm.ai/projects/ascend/en/latest/installation.html).
+For detailed hardware and software requirements, please refer to the [vllm-ascend installation documentation](https://docs.vllm.ai/projects/ascend/en/latest/installation.html).
 
 # --8<-- [end:requirements]
 # --8<-- [start:installation-release]
 
-The recommended way to use vLLM-Omni on NPU is through the vLLM-Ascend pre-built Docker images:
+The recommended way to use vLLM-Omni on NPU is through the vllm-ascend pre-built Docker images:
 
 ```bash
-# vLLM-Ascend image
-# Atlas A2: quay.io/atlas-ci/vllm-ascend:v0.24.0
-# Atlas A3: quay.io/atlas-ci/vllm-ascend:v0.24.0-a3
+# Update the vllm-ascend image
+# Atlas A2:
+# export IMAGE=quay.io/ascend/vllm-ascend:v0.18.0rc1
+# Atlas A3:
+# export IMAGE=quay.io/ascend/vllm-ascend:v0.18.0rc1-a3
+export IMAGE=quay.io/ascend/vllm-ascend:v0.18.0rc1
 docker run --rm \
     --name vllm-omni-npu \
     --shm-size=1g \
@@ -28,11 +31,11 @@ docker run --rm \
     -v /etc/ascend_install.info:/etc/ascend_install.info \
     -v /root/.cache:/root/.cache \
     -p 8000:8000 \
-    -it quay.io/atlas-ci/vllm-ascend:0.24.0-a3 bash
+    -it $IMAGE bash
 
 # Inside the container, install vLLM-Omni from source
 cd /vllm-workspace
-git clone -b v0.24.0 https://github.com/vllm-project/vllm-omni.git
+git clone -b v0.18.0 https://github.com/vllm-project/vllm-omni.git
 cd vllm-omni
 pip install -v -e . --no-build-isolation
 # or VLLM_OMNI_TARGET_DEVICE=npu pip install -v -e .
@@ -84,11 +87,14 @@ Supported images as following.
 Here's an example deployment command that has been verified on 4 x NPUs:
 
 ```bash
-# Atlas A2: quay.io/ascend/vllm-omni:v0.24.0
-# Atlas A3: quay.io/ascend/vllm-omni:v0.24.0-a3
+# Atlas A2:
+# export IMAGE=quay.io/ascend/vllm-omni:v0.18.0
+# Atlas A3:
+# export IMAGE=quay.io/ascend/vllm-omni:v0.18.0-a3
+export IMAGE=quay.io/ascend/vllm-omni:v0.18.0
 docker run --rm \
-    --name vllm-omni-a3 \
-    --shm-size=4g \
+    --name vllm-omni-npu \
+    --shm-size=1g \
     --device /dev/davinci0 \
     --device /dev/davinci1 \
     --device /dev/davinci2 \
@@ -100,17 +106,10 @@ docker run --rm \
     -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
     -v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
     -v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
-    --device /dev/davinci_manager \
-    --device /dev/devmm_svm \
-    --device /dev/hisi_hdc \
-    -v /usr/local/dcmi:/usr/local/dcmi \
-    -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-    -v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
-    -v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
     -v /etc/ascend_install.info:/etc/ascend_install.info \
-    -v ~/.cache:/root/.cache \
-    -p 8091:8091 \
-    -it quay.io/ascend/vllm-omni:v0.24.0-a3 bash
+    -v /root/.cache:/root/.cache \
+    -p 8000:8000 \
+    -it $IMAGE bash
 ```
 
 !!! tip

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==0.25.0 --torch-backend=auto
+uv pip install vllm==0.22.0 --torch-backend=auto
 
 # On ROCm
-uv pip install vllm==0.25.0+rocm723 --extra-index-url https://wheels.vllm.ai/rocm/0.25.0/rocm723
+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
@@ -31,10 +31,11 @@ uv pip install -e .
 
 For additional installation methods — please see the [installation guide](installation/README.md).
 
+
 !!! 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 < `0.25.0` and vLLM Omni `0.25.0`, 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
 
@@ -51,14 +52,14 @@ if __name__ == "__main__":
     images[0].save("coffee.png")
 ```
 
-You can pass a list of prompts and wait for the independent requests to finish,
-as shown below.
+You can pass a list of prompts and wait for them to process altogether, shown below.
 
 !!! info
 
-    For diffusion pipelines, each prompt becomes a separate logical request.
-    The runtime may automatically batch compatible in-flight requests through
-    the scheduler and runner.
+    However, it is not currently recommended to do so
+    because not all models support batch inference,
+    and batch requesting mostly does not provide significant performance improvement (despite the impression that it does).
+    This feature is primarily for the sake of interface compatibility with vLLM and to allow for future improvements.
 
 ```python
 from vllm_omni.entrypoints.omni import Omni
@@ -87,9 +88,9 @@ if __name__ == "__main__":
 
 !!! info
 
-    For diffusion request-level batching controls such as `max_num_seqs` and
-    `request_batch_max_wait_ms`, see
-    [Request-Level Batching](../user_guide/diffusion/request_batching.md).
+    For diffusion pipelines, the stage config field `stage_args.[].engine_args.max_num_seqs` is 1 by default, and the input
+    list is sliced into single-item requests before feeding into the diffusion pipeline. For models that do internally support
+    batched inputs, you can [modify this configuration](../configuration/stage_configs.md) to let the model accept a longer batch of prompts.
 
 For more usages, please refer to [offline inference](../user_guide/examples/offline_inference/qwen2_5_omni.md)
 

docs/mkdocs/hooks/generate_api_readme.py

@@ -144,7 +144,6 @@ def scan_package(package_name: str = "vllm_omni") -> dict[str, list[str]]:
                 "vllm_omni.entrypoints.async_diffusion",
                 "vllm_omni.entrypoints.openai",
                 "vllm_omni.model_executor.models.voxtral_tts.configuration_voxtral_tts",
-                "vllm_omni.experimental",  # optional serving deps not installed in docs build
             ]
             if any(module_path.startswith(prefix) for prefix in excluded_prefixes):
                 continue

docs/models/supported_models.md

@@ -6,8 +6,6 @@ vLLM-Omni supports unified multimodal comprehension and generation models across
 
 If vLLM-Omni natively supports a model, its implementation can be found in <gh-file:vllm_omni/model_executor/models> and <gh-file:vllm_omni/diffusion/models>.
 
-For deployment recipes (TTS and multimodal), see [recipes.vllm.ai](https://recipes.vllm.ai) and the in-repo [`recipes/`](https://github.com/vllm-project/vllm-omni/tree/main/recipes) directory.
-
 ## List of Supported Models
 
 <style>
@@ -24,7 +22,6 @@ th {
 | `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` | ✅︎ | ✅︎ | | |
-| `Gr00tN1d7Pipeline` | GR00T N1.7 | `nvidia/GR00T-N1.7-3B` | ✅︎ | | | |
 | `HunyuanImage3ForCausalMM` | HunyuanImage3.0 (DiT-only) | `tencent/HunyuanImage-3.0`, `tencent/HunyuanImage-3.0-Instruct` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
 | `QwenImagePipeline` | Qwen-Image | `Qwen/Qwen-Image` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
 | `QwenImagePipeline` | Qwen-Image-2512 | `Qwen/Qwen-Image-2512` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
@@ -34,26 +31,23 @@ th {
 | `QwenImageEditPlusPipeline` | Qwen-Image-Edit-2511 | `Qwen/Qwen-Image-Edit-2511` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
 | `GlmImagePipeline` | GLM-Image | `zai-org/GLM-Image` | ✅︎ | ✅︎ | | |
 | `ZImagePipeline` | Z-Image | `Tongyi-MAI/Z-Image-Turbo` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
-| `Krea2Pipeline` | Krea 2 (Raw + Turbo) | `krea/Krea-2-Raw`, `krea/Krea-2-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`, `nvidia/Cosmos3-Super` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
-| `Wan22S2VPipeline` | Wan2.2-S2V | `Wan-AI/Wan2.2-S2V-14B` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
+| `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` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
-| `Wan22VACEPipeline` | Wan2.2-VACE | `Pyros13/Wan2.2-VACE-Fun-A14B-Diffusers` | ✅︎ |   |   |   |
 | `LTX2Pipeline` | LTX-2-T2V | `Lightricks/LTX-2` | ✅︎ | ✅︎ | | |
 | `LTX2ImageToVideoPipeline` | LTX-2-I2V | `Lightricks/LTX-2` | ✅︎ | ✅︎ | | |
 | `LTX2TwoStagesPipeline` | LTX-2-T2V | `rootonchair/LTX-2-19b-distilled` | ✅︎ | ✅︎ | | |
 | `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` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
+| `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` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
-| `StableDiffusionXLPipeline` | Stable-Diffusion-XL | `stabilityai/stable-diffusion-xl-base-1.0` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
 | `StableDiffusion3Pipeline` | Stable-Diffusion-3 | `stabilityai/stable-diffusion-3.5-medium` | ✅︎ | ✅︎ | | ✅︎ |
 | `CosyVoice3Model` | CosyVoice3 | `FunAudioLLM/Fun-CosyVoice3-0.5B-2512` | ✅︎ | ✅︎ | | ✅︎ |
 | `OmniVoiceModel` | OmniVoice | `k2-fsa/OmniVoice` | ✅︎ | | | |
@@ -65,20 +59,16 @@ th {
 | `FluxPipeline` | FLUX.1-schnell | `black-forest-labs/FLUX.1-schnell` | ✅︎ | ✅︎ | | ✅︎ |
 | `OmniGen2Pipeline` | OmniGen2 | `OmniGen2/OmniGen2` | ✅︎ | ✅︎ | | ✅︎ |
 | `StableAudioPipeline` | Stable-Audio-Open | `stabilityai/stable-audio-open-1.0` | ✅︎ | ✅︎ | | ✅︎ |
-| `SoulXSingerPipeline` | SoulX-Singer (SVS) | `Soul-AILab/SoulX-Singer` | ✅︎ | | | |
-| `SoulXSingerSVCPipeline` | SoulX-Singer-SVC | `Soul-AILab/SoulX-Singer` (`model-svc.pt`) | ✅︎ | | | |
 | `AudioXPipeline` | AudioX | `zhangj1an/AudioX` | ✅︎ | ✅︎ | | |
 | `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-1.7B-Base` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
+| `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` | ✅︎ | | | |
-| `HiggsMultimodalQwen3ForConditionalGeneration` | Higgs-Audio v3 (TTS) | `bosonai/higgs-audio-v3-tts-4b` | ✅︎ | | | |
-| `IndexTTS2TalkerForConditionalGeneration` | IndexTTS-2 | `IndexTeam/IndexTTS-2` | ✅︎ | | | |
 | `NextStep11Pipeline` | NextStep-1.1 | `stepfun-ai/NextStep-1.1` | ✅︎ | ✅︎ | | ✅︎ |
 | `MiMoAudioModel` | MiMo-Audio-7B-Instruct | `XiaomiMiMo/MiMo-Audio-7B-Instruct` | ✅︎ | ✅︎ | | |
 | `MiMoV2ASRForCausalLM` | MiMo-V2.5-ASR | `XiaomiMiMo/MiMo-V2.5-ASR` | ✅︎ | ✅︎ | | |

docs/serving/image_generation_api.md

@@ -32,20 +32,6 @@ curl -X POST http://localhost:8000/v1/images/generations \
   }' | jq -r '.data[0].b64_json' | base64 -d > dragon.png
 ```
 
-**Using curl save to file:**
-
-```bash
-curl -o dragon.png -X POST http://localhost:8000/v1/images/generations \
-  -H "Content-Type: application/json" \
-  -d '{
-    "prompt": "a dragon laying over the spine of the Green Mountains of Vermont",
-    "size": "1024x1024",
-    "seed": 42,
-    "response_format":"file"
-  }'
-```
-
-
 **Using Python:**
 
 ```python
@@ -71,36 +57,6 @@ img = Image.open(io.BytesIO(img_bytes))
 img.save("cat.png")
 ```
 
-**Using Python save to file:**
-
-```python
-import requests
-import base64
-from PIL import Image
-import io
-import re
-
-response = requests.post(
-    "http://localhost:8000/v1/images/generations",
-    json={
-        "prompt": "a black and white cat wearing a princess tiara",
-        "size": "1024x1024",
-        "num_inference_steps": 50,
-        "seed": 42,
-        "response_format":"file"
-    }
-)
-
-# save to file
-content_disposition = response.headers.get("Content-Disposition", "")
-match = re.search(r'filename="?(.+)"?', content_disposition)
-filename = match.group(1) if match else "save.png"
-with open(filename, "wb") as f:
-    for chunk in response.iter_content(8192):
-        f.write(chunk)
-print("saved:", filename)
-```
-
 **Using OpenAI SDK:**
 
 ```python

docs/serving/speech_api.md

@@ -9,13 +9,6 @@ vLLM-Omni provides an OpenAI-compatible API for text-to-speech (TTS) generation.
 
 See the [Supported Models](#supported-models) section below for the full list, including OmniVoice, VoxCPM2, and MOSS-TTS-Nano.
 
-!!! tip "Deployment recipes"
-    TTS deployment recipes are published at
-    [recipes.vllm.ai](https://recipes.vllm.ai) (e.g.
-    [Qwen3-TTS](https://recipes.vllm.ai/Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice),
-    [Higgs-Audio v3](https://recipes.vllm.ai/bosonai/higgs-audio-v3-tts-4b)).
-    The in-repo runbooks live under [`recipes/`](https://github.com/vllm-project/vllm-omni/tree/main/recipes).
-
 Each server instance runs a single model (specified at startup via `vllm serve <model> --omni`).
 
 ## Quick Start
@@ -122,8 +115,7 @@ Content-Type: application/json
 | `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 | When true, stream OpenAI `speech.audio.*` SSE events (requires `response_format="pcm"` or `"wav"`). For raw PCM/WAV byte streaming, set `stream_format="audio"`. |
-| `stream_format` | string | null | Streaming output format. `"audio"` streams raw audio bytes as they are decoded; `"sse"` streams OpenAI `speech.audio.*` Server-Sent Events. If omitted, `stream=true` selects SSE and `stream=false` remains non-streaming. See [Response Format](#response-format). |
+| `stream` | bool | false | Stream raw PCM chunks as they are decoded (requires `response_format="pcm"`) |
 
 **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.
 
@@ -137,57 +129,7 @@ Content-Type: application/json
 
 ### Response Format
 
-The response shape depends on the streaming parameters:
-
-**Non-streaming (default).** With `stream=false` and no `stream_format`, returns the
-complete clip as binary audio data with an appropriate `Content-Type` header (e.g.
-`audio/wav`). The raw-bytes body has no JSON carrier, so no `usage` is reported.
-
-**Raw audio stream** (`stream_format="audio"`). Streams raw audio bytes (PCM or
-WAV) as they are decoded.
-
-Both streaming modes (`stream_format="audio"` and `"sse"`) require
-`response_format="pcm"` or `"wav"`, and `speed` must be `1.0` (or omitted).
-
-**SSE stream** (`stream=true` or `stream_format="sse"`). Streams [OpenAI
-`speech.audio.*` Server-Sent Events](https://platform.openai.com/docs/api-reference/audio-streaming).
-Each event has an `event:` line and a JSON `data:` line:
-
-- `speech.audio.delta` — a base64 audio chunk:
-
-    ```json
-    { "type": "speech.audio.delta", "audio": "<base64>", "response_format": "pcm" }
-    ```
-
-- `speech.audio.done` — terminal event, carrying token `usage`:
-
-    ```json
-    {
-        "type": "speech.audio.done",
-        "usage": {
-            "input_tokens": 119,
-            "output_tokens": 77,
-            "total_tokens": 196,
-            "input_token_details": { "text_tokens": 18, "audio_tokens": 101 }
-        }
-    }
-    ```
-
-- `speech.audio.error` — emitted instead of `speech.audio.done` if generation fails:
-
-    ```json
-    { "type": "speech.audio.error", "error": { "message": "...", "type": "server_error", "param": null, "code": 500 } }
-    ```
-
-The `usage` object on `speech.audio.done` is the same shape returned per item by the
-[batch endpoint](#batch-speech-generation):
-
-- `input_tokens` = `text_tokens` + `audio_tokens`
-    - `text_tokens`: tokens of the synthesized text (`input` plus `instructions`)
-    - `audio_tokens`: reference-audio codec frames, non-zero only for in-context
-      voice cloning (Base task); `0` for CustomVoice/VoiceDesign or x-vector-only
-- `output_tokens`: generated codec tokens
-- `total_tokens` = `input_tokens` + `output_tokens`
+Returns binary audio data with appropriate `Content-Type` header (e.g., `audio/wav`).
 
 ### Voices Endpoint
 
@@ -284,9 +226,9 @@ Server -> Client:
 
 | Message | Description |
 |---------|-------------|
-| `{"type": "audio.start", "sentence_index": 0, "sentence_text": "...", "format": "pcm", "sample_rate": 24000}` | Audio generation starting for the buffered input |
+| `{"type": "audio.start", "sentence_index": 0, "sentence_text": "...", "format": "pcm", "sample_rate": 24000}` | Audio generation starting for a sentence |
 | Binary frame | Raw audio bytes (one or more PCM chunks when `stream_audio=true`) |
-| `{"type": "audio.done", "sentence_index": 0, "total_bytes": 96000, "error": false}` | Audio complete for the buffered input |
+| `{"type": "audio.done", "sentence_index": 0, "total_bytes": 96000, "error": false}` | Audio complete for a sentence |
 | `{"type": "session.done", "total_sentences": N}` | Session complete |
 | `{"type": "error", "message": "..."}` | Non-fatal error |
 
@@ -296,7 +238,8 @@ All REST API parameters are supported, plus:
 
 | Parameter | Type | Default | Description |
 |-----------|------|---------|-------------|
-| `stream_audio` | bool | false | Stream one or more PCM chunks for the buffered input over WebSocket |
+| `stream_audio` | bool | false | Stream one or more PCM chunks per sentence over WebSocket |
+| `split_granularity` | string | "sentence" | Text splitting granularity |
 
 
 ```bash
@@ -541,13 +484,7 @@ Each item in the `items` array requires only `input` (the text). All other field
             "index": 0,
             "status": "success",
             "audio_data": "<base64-encoded audio>",
-            "media_type": "audio/wav",
-            "usage": {
-                "input_tokens": 119,
-                "output_tokens": 77,
-                "total_tokens": 196,
-                "input_token_details": { "text_tokens": 18, "audio_tokens": 101 }
-            }
+            "media_type": "audio/wav"
         },
         {
             "index": 1,
@@ -561,18 +498,6 @@ Each item in the `items` array requires only `input` (the text). All other field
 }
 ```
 
-Each successful item carries a `usage` object (errored items omit it):
-
-- `input_tokens` = `text_tokens` + `audio_tokens`
-    - `text_tokens`: tokens of the synthesized text (`input` plus `instructions`)
-    - `audio_tokens`: reference-audio codec frames, non-zero only for in-context
-      voice cloning (Base task); `0` for CustomVoice/VoiceDesign or x-vector-only
-- `output_tokens`: generated codec tokens
-- `total_tokens` = `input_tokens` + `output_tokens`
-
-This is the same `usage` object emitted on the terminal `speech.audio.done` event
-of the single endpoint's [SSE stream](#response-format) (`stream_format="sse"`).
-
 ### Examples
 
 **Basic batch with shared defaults:**

docs/serving/videos_api.md

@@ -160,12 +160,7 @@ 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. Cosmos3 transfer mode
-also accepts `edge`, `blur`, `depth`, `seg`, or `wsm` control hints plus
-transfer options such as `control_path`, `control_guidance`,
-`control_guidance_interval`, `num_video_frames_per_chunk`,
-`num_conditional_frames`, `show_control_condition`, and `show_input`; see the
-Cosmos3 recipe for complete examples.
+decoded reference frames are used as clean conditioning.
 
 ### Speech-to-Video
 
@@ -200,16 +195,13 @@ curl -X POST http://localhost:8091/v1/videos/sync \
 
 ## Storage
 
-Set `VLLM_OMNI_SERVER_STORAGE__PATH` to control where asynchronous video outputs are
+Set `VLLM_OMNI_STORAGE_PATH` to control where asynchronous video outputs are
 stored:
 
 ```bash
-export VLLM_OMNI_SERVER_STORAGE__PATH=/var/tmp/vllm-omni-videos
+export VLLM_OMNI_STORAGE_PATH=/var/tmp/vllm-omni-videos
 ```
 
-> `VLLM_OMNI_STORAGE_PATH` is deprecated and will be removed in a future release;
-> use `VLLM_OMNI_SERVER_STORAGE__PATH` instead.
-
 ## Model-Specific Examples
 
 For complete text-to-video, image-to-video, and model-specific video-to-video

docs/user_guide/diffusion/attention_backends.md

@@ -20,8 +20,6 @@ The full set of backends and their platform defaults is in the **Backend Options
 | `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. |
-| `FLASH_ATTN_HUB` | FlashAttention 2 from HuggingFace `kernels` library. Useful for train/rollout alignment. |
-| `FLASH_ATTN_3_HUB` | FlashAttention 3 from HuggingFace `kernels` library. CUDA Hopper (sm_90+) only; falls back to `FLASH_ATTN_HUB` on older GPUs. |
 
 
 ## Configuration
@@ -212,36 +210,6 @@ 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.
 
-## HuggingFace Kernels Hub Backends
-
-To achieve perfect numerical consistency between **training** (typically using HuggingFace Diffusers with Hub kernels) and **serving/rollout** (using vLLM-Omni), you can use the Hub-based attention backends. This eliminates numerical drift / sampling divergence caused by executing different local kernel versions during rollout.
-
-The following backend options are supported:
-- `FLASH_ATTN_HUB` (HuggingFace `kernels-community/flash-attn2`)
-- `FLASH_ATTN_3_HUB` (HuggingFace `kernels-community/flash-attn3`, Hopper sm_90+ only)
-
-### Installation
-
-To use these backends, you must install the `kernels` library:
-
-```bash
-pip install kernels==0.14.1
-```
-
-If the `kernels` library is not available in the environment, vLLM-Omni will log a warning and fall back gracefully to the corresponding local backend implementations (`FLASH_ATTN`). On CUDA GPUs below Hopper (compute capability < 9.0), `FLASH_ATTN_3_HUB` falls back to `FLASH_ATTN_HUB`.
-
-### Usage
-
-Select a Hub backend using the global CLI flag or environment variables:
-
-```bash
-# Environment variable
-export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN_HUB
-
-# CLI flag
-vllm-omni serve <model> --diffusion-attention-backend FLASH_ATTN_HUB
-```
-
 ## Usage Examples
 
 ### Default (auto-route)

docs/user_guide/diffusion/cpu_offload_diffusion.md

@@ -193,11 +193,9 @@ Factory function `get_offload_backend()` selects the appropriate backend based o
 | NextStep11Pipeline | `stepfun-ai/NextStep-1.1` | `NextStepModel` | - | ✓ | `"layers"` |
 | OvisImagePipeline | `AIDC-AI/Ovis-Image-7B` | `OvisImageTransformer2DModel` | - | ✓ | `"transformer"` |
 | QwenImagePipeline | `Qwen/Qwen-Image` | `QwenImageTransformer2DModel` | ✓ | ✓ | `"transformer_blocks"` |
-| StableDiffusionXLPipeline | `stabilityai/stable-diffusion-xl-base-1.0` | `SDXLUNet2DConditionModel` | ✓ | ✓ | `"down_blocks"`, `"up_blocks"` |
 | StableDiffusion3Pipeline | `stabilityai/stable-diffusion-3.5-medium` | `SD3Transformer2DModel` | - | ✓ | `"transformer_blocks"` |
 | Wan22I2VPipeline | `Wan-AI/Wan2.2-I2V-A14B-Diffusers` | `WanTransformer3DModel` | ✓ | ✓ | `"blocks"` |
 | Wan22Pipeline | `Wan-AI/Wan2.2-T2V-A14B-Diffusers` | `WanTransformer3DModel` | ✓ | ✓ | `"blocks"` |
-| SoulXSingerPipeline / SoulXSingerSVCPipeline | `Soul-AILab/SoulX-Singer` | `DiffLlama` (`cfm_decoder.model.diff_estimator`) | ✓ | ✓ | `"layers"` |
 | BagelPipeline | `ByteDance-Seed/BAGEL-7B-MoT` | `Qwen2MoTModel` | - | ✓ | `"layers"`, `"customized modules"` |
 
 **Notes:**

docs/user_guide/diffusion/mot_config.md

@@ -1,50 +0,0 @@
-This directory contains auto-tuned Triton kernel configurations for the
-MoT (Mixture-of-Tokens) GEMM and RMSNorm operators used by BAGEL and other
-MoT-architecture diffusion models.
-
-File naming convention:
-    device_name=<GPU_NAME>,dtype=<DTYPE>.json
-
-For example:
-    device_name=NVIDIA_A100-SXM4-80GB,dtype=w16a16.json
-
-Each JSON file maps (K, N) matrix shapes to a dictionary of batch sizes (M)
-and their optimal Triton tile configurations:
-
-    {
-        "3584_4608": {              // K=3584, N=4608 (QKV projection)
-            "1024": {
-                "BLOCK_SIZE_M": 64,
-                "BLOCK_SIZE_N": 128,
-                "BLOCK_SIZE_K": 64,
-                "GROUP_SIZE_M": 8,
-                "num_warps": 4,
-                "num_stages": 3
-            },
-            ...
-        },
-        "3584_3584": { ... }        // K=3584, N=3584 (output projection)
-    }
-
-Config loading order (3-tier, see ops/mot_gemm.py):
-    1. $VLLM_TUNED_CONFIG_FOLDER/<filename>  (env override)
-    2. This directory: vllm_omni/diffusion/layers/mot/configs/<filename>
-    3. Conservative default config (compiles everywhere, sub-optimal perf)
-
-If no config file matches the current device, a warning is printed with
-instructions to run the auto-tuning benchmark.
-
-To generate configs for your hardware:
-
-    python benchmarks/kernels/mot_linear_benchmarks.py \
-        --model ByteDance-Seed/BAGEL-7B-MoT \
-        --tp-size 1 --dtype w16a16 --tune \
-        --save-dir vllm_omni/diffusion/layers/mot/configs/
-
-For multi-GPU tuning (uses Ray for parallel search):
-
-    python benchmarks/kernels/mot_linear_benchmarks.py \
-        --model ByteDance-Seed/BAGEL-7B-MoT \
-        --tp-size 2 --tune
-
-See benchmarks/kernels/mot_linear_benchmarks.py for full options.

docs/user_guide/diffusion/parallelism/overview.md

@@ -10,7 +10,7 @@ This guide covers the parallelism methods in vLLM-Omni for speeding up diffusion
 | **[Sequence Parallelism](sequence_parallel.md)**   | Splits sequence dimension across GPUs (Ulysses-SP, Ring-Attention, or hybrid) for high-resolution images and videos |
 | **[CFG-Parallel](cfg_parallel.md)**                | Runs CFG positive/negative branches on separate GPUs for ~1.8x speedup on guided generation                         |
 | **[Pipeline Parallelism](pipeline_parallel.md)**   | Splits the denoising transformer block-wise across sequential GPU stages to reduce per-GPU model memory             |
-| **[VAE Parallelism](vae_parallelism.md)** | Distributes VAE decode spatially across GPUs to reduce peak VAE memory                                              |
+| **[VAE Patch Parallelism](vae_patch_parallel.md)** | Distributes VAE decode spatially across GPUs to reduce peak VAE memory                                              |
 | **[HSDP](hsdp.md)**                                | Shards full model weights via PyTorch FSDP2 to enable large-model inference on memory-constrained GPUs              |
 | **[Expert Parallelism](expert_parallel.md)**       | Shards MoE expert blocks across GPUs for MoE models (e.g. HunyuanImage3.0)                                          |
 

docs/user_guide/diffusion/parallelism/vae_patch_parallel.md

@@ -1,4 +1,4 @@
-# VAE Parallelism Guide
+# VAE Patch Parallelism Guide
 
 
 ## Table of Content
@@ -15,7 +15,7 @@
 
 ## Overview
 
-VAE parallelism distributes VAE (Variational AutoEncoder) decode/encode work across multiple GPUs. This guide covers VAE patch/tile parallelism, which splits latent space into spatial tiles or patches, and Wan spatial-shard decode, which shards decoder feature maps along height or width.
+VAE Patch Parallelism distributes the VAE (Variational AutoEncoder) decode/encode computation across multiple GPUs by splitting the latent space into spatial tiles or patches. Each GPU processes a subset of tiles in parallel, significantly reducing peak memory consumption during the VAE decode stage while maintaining output quality.
 
 This is particularly useful for:
 - **High-resolution image generation** where VAE decode can become a memory bottleneck
@@ -25,7 +25,7 @@ This is particularly useful for:
 See supported models list in [Supported Models](../../diffusion_features.md#supported-models).
 
 
-VAE patch parallelism uses two strategies based on image size:
+VAE Patch Parallelism uses two strategies based on image size:
 
 | Strategy | Use Case | How It Works | Overlap Handling | Output Quality |
 |----------|----------|--------------|------------------|----------------|
@@ -113,7 +113,6 @@ In `DiffusionParallelConfig`:
 | Parameter | Type | Default | Description |
 |-----------|------|---------|-------------|
 | `vae_patch_parallel_size` | int | 1 | Number of GPUs for VAE patch/tile parallelism. Set to 2 or higher to enable. Should typically match `tensor_parallel_size` as they share the same process group. |
-| `vae_parallel_mode` | str | `"tile"` | VAE parallel decode strategy: `"tile"` (default tile/patch parallel decode), `"spatial_shard_height"`, or `"spatial_shard_width"` (spatially-sharded decode, Wan only). See [Spatially-Sharded Decode](#spatially-sharded-decode-wan). |
 
 Additional requirements:
 
@@ -126,51 +125,6 @@ Additional requirements:
 
 ---
 
-## Spatially-Sharded Decode (Wan)
-
-The default `vae_parallel_mode="tile"` distributes whole tiles across ranks. For the **Wan** VAE there is an alternative decode strategy, **spatially-sharded decode**, selected via `vae_parallel_mode="spatial_shard_height"` or `vae_parallel_mode="spatial_shard_width"`.
-
-Instead of assigning independent tiles to ranks, spatial-shard decode shards the decoder feature maps along the height (`spatial_shard_height`) or width (`spatial_shard_width`) dimension and exchanges halo rows/columns between neighboring ranks around the spatial convolutions. This keeps the receptive field correct across shard boundaries, so the result matches the single-GPU decode within numerical tolerance.
-
-```python
-from vllm_omni import Omni
-from vllm_omni.diffusion.data import DiffusionParallelConfig
-
-omni = Omni(
-    model="Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
-    parallel_config=DiffusionParallelConfig(
-        tensor_parallel_size=2,
-        vae_patch_parallel_size=2,               # must match the DiT group size
-        vae_parallel_mode="spatial_shard_width", # or "spatial_shard_height"
-    ),
-)
-```
-
-Or from the CLI / serving entrypoint:
-
-```bash
-vllm serve Wan-AI/Wan2.1-T2V-1.3B-Diffusers --omni \
-    --tensor-parallel-size 2 \
-    --vae-patch-parallel-size 2 \
-    --vae-parallel-mode spatial_shard_width
-```
-
-**Constraints and behavior:**
-
-- Spatial-shard decode is **decode-only** and currently implemented for the **Wan** VAE. Other models ignore `spatial_shard_*` modes.
-- It requires `vae_patch_parallel_size` to **match the DiT process group size**. If it does not, the VAE logs a warning and **falls back to tile-parallel decode** at runtime.
-- `spatial_shard_height` and `spatial_shard_width` are mutually exclusive for a given VAE instance (the decoder is patched in place for a single split dimension).
-
-For end-to-end latency/throughput, launch serving with the desired `vae_parallel_mode` and use the existing diffusion serving benchmark:
-
-```bash
-python3 benchmarks/diffusion/diffusion_benchmark_serving.py \
-    --endpoint /v1/videos --dataset random --task t2v --num-prompts 1 \
-    --height 480 --width 832 --num-frames 17 --max-concurrency 1
-```
-
----
-
 ## Best Practices
 
 ### When to Use

docs/user_guide/diffusion/request_batching.md

@@ -1,110 +0,0 @@
-# Request-Level Batching
-
-Request-level batching lets diffusion serving combine multiple compatible
-logical requests into one pipeline `forward()` call. Each prompt remains a
-separate request with its own `request_id`, sampling parameters, seed, output,
-error, and abort state. The scheduler decides which requests can run together.
-
-!!! warning "Prompt List Semantics"
-
-    Diffusion request-level batching does not support a top-level packed
-    list-prompt request. Submit multiple prompts as independent requests and let
-    the scheduler batch compatible in-flight requests. Multimodal payloads stay
-    inside a single prompt dict, for example
-    `{"prompt": "...", "multi_modal_data": {"image": image}}`.
-
-## Enablement
-
-Increase `max_num_seqs` above `1` to allow the request scheduler to keep more
-than one compatible request active:
-
-```bash
-vllm serve Qwen/Qwen-Image --omni \
-  --port 8091 \
-  --max-num-seqs 4
-```
-
-For bursty online traffic, you can also set a small admission wait window. This
-lets the engine wait briefly before the first `schedule()` of a new wave so
-nearby compatible requests can arrive and share the same fused forward pass:
-
-```bash
-vllm serve Qwen/Qwen-Image --omni \
-  --port 8091 \
-  --max-num-seqs 4 \
-  --request-batch-max-wait-ms 20
-```
-
-`--request-batch-max-wait-ms 0` is the default and disables admission waiting,
-so there is no added wait latency.
-
-For deploy YAMLs, configure the diffusion stage engine args:
-
-```yaml
-stage_args:
-  - stage_id: 0
-    stage_type: diffusion
-    engine_args:
-      max_num_seqs: 4
-      request_batch_max_wait_ms: 20
-```
-
-## Compatibility
-
-Only pipelines that declare request-batch support use the fused request-batch
-path. The engine validates that the pipeline `forward()` uses the request-batch
-contract and returns `list[DiffusionOutput]`. Pipelines that do not support this
-contract do not use fused `pipeline.forward(batch)`; scheduled requests are
-executed through per-request worker calls.
-
-The scheduler batches only compatible requests. Compatibility is based on
-shape-sensitive and guidance-sensitive sampling fields, including resolution,
-frame count, CFG settings, output count, and LoRA identity. Requests with
-different LoRA adapters or scales are kept in separate batches so the worker
-activates one adapter per batch.
-
-Request-level batching applies when `step_execution=False`. For the separate
-step-wise runtime, see [Step Execution](step_execution.md).
-
-## Tuning
-
-- `max_num_seqs` caps the number of active compatible requests in one scheduler
-  wave.
-- `request_batch_max_wait_ms` is an upper bound on extra admission wait before a
-  new wave starts. Keep it small for latency-sensitive serving; values such as
-  `10` to `50` ms are a practical starting range for bursty HTTP ingress.
-- `0` disables admission waiting and preserves the lowest first-request latency.
-- FIFO ordering is conservative: an incompatible request at the front of the
-  waiting queue can block later compatible requests from joining the current
-  batch.
-
-## Python API
-
-When constructing `Omni`, pass the same engine arguments:
-
-```python
-from vllm_omni.entrypoints.omni import Omni
-
-omni = Omni(
-    model="Qwen/Qwen-Image",
-    max_num_seqs=4,
-    request_batch_max_wait_ms=20.0,
-)
-
-outputs = omni.generate(
-    [
-        "a cup of coffee on a table",
-        "a toy dinosaur on a sandy beach",
-        "a fox waking up in bed and yawning",
-    ]
-)
-```
-
-`Omni.generate([...])` submits each list item as its own logical diffusion
-request. The runtime may batch those requests internally when their sampling
-parameters are compatible.
-
-## For Contributors
-
-For implementation details and model-author guidance, see
-[Request-Level Batching for Diffusion](../../design/feature/diffusion_request_level_batching.md).

docs/user_guide/diffusion_features.md

@@ -61,7 +61,7 @@ Memory optimization methods help reduce GPU memory usage, enabling inference on
 |--------|-------------|----------|
 | **[CPU Offload](diffusion/cpu_offload_diffusion.md)** | Offloads model components to CPU memory | Limited VRAM, large models on consumer GPUs |
 | **[Quantization](quantization/overview.md)** | Reduces transformer stages from BF16 to FP8/INT8/etc. | Limited VRAM, minimal accuracy loss    |
-| **[VAE Parallelism](diffusion/parallelism/vae_parallelism.md)** | Distributes VAE decode work across GPUs | High-resolution generation with reduced VAE memory peak |
+| **[VAE Patch Parallelism](diffusion/parallelism/vae_patch_parallel.md)** | Distributes VAE decode tiling across GPUs | High-resolution generation with reduced VAE memory peak |
 
 ### Extensions
 
@@ -75,17 +75,13 @@ Extension methods add specialized capabilities to diffusion models beyond standa
 
 ### Execution Modes
 
-Execution modes control how the diffusion pipeline processes requests and
-denoise steps.
+Execution modes control how the diffusion pipeline processes denoise steps.
 
 | Method | Description | Best For |
 |--------|-------------|----------|
-| **[Request-Level Batching](diffusion/request_batching.md)** | Scheduler batches compatible independent diffusion requests into one pipeline forward pass | Bursty online serving and multi-request throughput |
 | **[Step Execution](diffusion/step_execution.md)** | Per-step denoise execution with mid-request abort support | Request cancellation between denoise steps, fine-grained execution control |
 
-**Note:** Request-level batching is available for pipelines that declare the
-request-batch forward contract. Step execution is currently supported by
-QwenImagePipeline only. See [Supported Models](#supported-models) for details.
+**Note:** Step execution is currently supported by QwenImagePipeline only. See [Supported Models](#supported-models) for details.
 
 ### Quantization Methods
 
@@ -121,21 +117,19 @@ The following tables show which models support each feature:
 | **GLM-Image**            |     ❌     |     ❌      |           ❌           |       ✅        |         ✅         |          ❌          |   ✅    |             ❌             |          ❌           |       ❌        |        ❌         |
 | **Hidream-I1-Full**        |     ❌     |     ❌      |           ❌           |       ❌        |         ✅         |          ❌          |   ❌    |             ❌             |          ❌           |       ❌        |        ❌         |
 | **HunyuanImage3**        |     ❌     |     ✅      |           ❌           |       ❌        |         ✅         |          ❌          |   ❌    |             ❌             |          ❌           |       ✅        |        ❌         |
-| **Krea 2**               |     ❌     |     ✅      |           ❌           |       ❌        |         ❌         |          ❌          |   ✅    |             ✅             |      ✅ (decode)      |       ❌        |        ❌         |
 | **LongCat-Image**        |     ✅     |     ✅      |           ✅           |       ✅        |         ✅         |          ❌          |   ❌    |             ✅             |          ❌           |       ❌        |        ❌         |
 | **LongCat-Image-Edit**   |     ✅     |     ✅      |           ✅           |       ✅        |         ✅         |          ❌          |   ❌    |             ✅             |          ❌           |       ❌        |        ❌         |
 | **MagiHuman**            |     ❌     |     ❌      |           ❌           |       ❓        |         ✅         |          ❌          |   ❌    |             ✅             |          ❌           |       ❌        |        ❌         |
 | **MammothModa2(T2I)**    |     ❌     |     ❌      |           ❌           |       ❌        |         ❌         |          ❌          |   ❌    |             ❌             |          ❌           |       ❌        |        ❌         |
 | **Nextstep_1(T2I)**      |     ❓     |     ❓      |           ❌           |       ✅        |         ✅         |          ❌          |   ❌    |             ✅             |          ❌           |       ❌        |        ❌         |
-| **OmniGen2**             |     ❌     |     ✅      |           ✅           |       ❌        |         ✅         |          ❌          |   ❌    |             ❌             |          ❌           |       ❌        |        ❌         |
+| **OmniGen2**             |     ❌     |     ✅      |           ❌           |       ❌        |         ✅         |          ❌          |   ❌    |             ❌             |          ❌           |       ❌        |        ❌         |
 | **Ovis-Image**           |     ❌     |     ✅      |           ❌           |       ✅        |         ❌         |          ❌          |   ❌    |             ✅             |          ❌           |       ❌        |        ❌         |
 | **Qwen-Image**           |     ✅     |     ✅      |           ✅           |       ✅        |         ✅         |          ❌          |   ✅    |             ✅             |      ✅ (decode)      |       ✅        |        ✅         |
 | **Qwen-Image-2512**      |     ✅     |     ✅      |           ✅           |       ✅        |         ✅         |          ❌          |   ✅    |             ✅             |      ✅ (decode)      |       ✅        |        ✅         |
 | **Qwen-Image-Edit**      |     ✅     |     ✅      |           ✅           |       ✅        |         ✅         |          ❌          |   ✅    |             ✅             |      ✅ (decode)      |       ❌        |        ❌         |
 | **Qwen-Image-Edit-2509** |     ✅     |     ✅      |           ✅           |       ✅        |         ✅         |          ❌          |   ✅    |        ✅ (decode)         |          ✅           |       ❌        |        ❌         |
 | **Qwen-Image-Layered**   |     ✅     |     ✅      |           ✅           |       ✅        |         ✅         |          ❌          |   ✅    |             ✅             |      ✅ (decode)      |       ❌        |        ❌         |
-| **SenseNova-U1**         |     ❌     |     ✅      |           ❌           |       ✅        |         ✅         |          ❌          |   ❌    |             ✅             |          ❌           |       ❌        |        ❌         |
-| **Stable-Diffusion-XL**  |     ❌     |     ❌      |           ✅           |       ✅        |         ✅         |          ❌          |   ✅    |             ✅             |      ✅ (decode)      |       ❌        |        ❌         |
+| **SenseNova-U1**         |     ❌     |     ✅      |           ❌           |       ❌        |         ✅         |          ❌          |   ❌    |             ✅             |          ❌           |       ❌        |        ❌         |
 | **Stable-Diffusion3.5**  |     ❌     |     ✅      |           ❌           |       ✅        |         ✅         |          ❌          |   ❌    |             ✅             |      ✅ (decode)      |       ❌        |        ❌         |
 | **Z-Image**              |     ✅     |     ✅      |           ✅           |       ❓        |   ✅ (TP=2 only)   |          ❌          |   ✅    |             ❌             |      ✅ (decode)      |       ✅        |        ❌         |
 | **ERNIE-Image**          |     ❌     |     ✅      |           ✅           |       ❓        |         ✅         |          ❌          |   ✅    |             ✅             |          ❌           |       ❌        |        ❌         |
@@ -145,14 +139,13 @@ The following tables show which models support each feature:
 > 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.
-> 4. Krea 2 currently supports single-GPU inference plus LoRA, Cache-DiT, HSDP, CPU/layerwise offload, and VAE-patch-parallel (decode). TP/SP/CFG-Parallel are not yet wired. The few-step distilled (Turbo) checkpoint uses `is_distilled=true` (fixed timestep shift `mu=1.15`); generate at 2048x2048 by default with `num_inference_steps≈8` and `guidance_scale=0`. The Raw checkpoint uses 1024x1024, `num_inference_steps=28`, and `guidance_scale=4.5`.
 
 ### 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)   |       ❌        |        ❌         |
+| **Wan2.2-S2V**               |     ❌     |     ✅      |           ❌           |       ❌        |         ✅         |         ❌         |   ❌    |             ✅             |  ✅ (encode/decode)   |       ❌        |        ❌         |
 | **Wan2.1-VACE**              |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ✅    |             ✅             |      ✅ (decode)      |       ❌        |        ❌         |
 | **LTX-2**                    |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ✅    |             ✅             |          ❌           |       ❌        |        ❌         |
 | **LTX-2.3**                  |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ❌    |             ✅             |      ✅ (decode)      |       ❌        |        ❌         |
@@ -276,7 +269,7 @@ Measured on NVIDIA H800:
 **Memory Optimization:**
 
 - **[CPU Offload Guide](diffusion/cpu_offload_diffusion.md)** - Offload model components to CPU, reduce GPU memory usage
-- **[VAE Parallelism Guide](diffusion/parallelism/vae_parallelism.md)** - Distribute VAE decode work across GPUs for high-resolution images and videos
+- **[VAE Patch Parallelism Guide](diffusion/parallelism/vae_patch_parallel.md)** - Distribute VAE decode tiling across GPUs for high-resolution images
 - **[Quantization Overview](quantization/overview.md)** - Overview of quantization methods for diffusion, multi-stage omni/TTS, and multi-stage diffusion models
 
 **Extensions:**

docs/user_guide/examples/offline_inference/helios.md

@@ -0,0 +1,203 @@
+# Helios Video Generation
+
+Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/helios>.
+
+
+Helios is a text-to-video (T2V), image-to-video (I2V), and video-to-video (V2V) diffusion model. This example demonstrates end-to-end video generation using vLLM-Omni with three model variants:
+
+| Variant | Description | Key Features |
+|---------|-------------|--------------|
+| **Helios-Base** | Base model, Stage 1 only | Single-stage denoising, `guidance_scale=5.0` |
+| **Helios-Mid** | Mid model, Stage 2 pyramid | Multi-stage pyramid denoising, CFG-Zero* support |
+| **Helios-Distilled** | Distilled model, Stage 2+3 | Few-step inference with DMD, `guidance_scale=1.0` |
+
+## Setup
+
+Please refer to the [stage configuration documentation](https://docs.vllm.ai/projects/vllm-omni/en/latest/configuration/stage_configs/) to configure memory allocation appropriately for your hardware setup.
+
+## Run Examples
+
+Get into the example folder:
+```bash
+cd examples/offline_inference/helios
+```
+
+### Text-to-Video (T2V)
+
+**Helios-Base** (Stage 1 only):
+```bash
+python end2end.py \
+    --model BestWishYsh/Helios-Base \
+    --sample-type t2v \
+    --prompt "A dynamic time-lapse video showing the rapidly moving scenery from the window of a speeding train." \
+    --guidance-scale 5.0 \
+    --output helios_t2v_base.mp4
+```
+
+**Helios-Mid** (Stage 2 + CFG-Zero*):
+```bash
+python end2end.py \
+    --model BestWishYsh/Helios-Mid \
+    --sample-type t2v \
+    --prompt "A dynamic time-lapse video showing the rapidly moving scenery from the window of a speeding train." \
+    --guidance-scale 5.0 \
+    --is-enable-stage2 \
+    --pyramid-num-inference-steps-list 20 20 20 \
+    --use-cfg-zero-star --use-zero-init --zero-steps 1 \
+    --output helios_t2v_mid.mp4
+```
+
+**Helios-Distilled** (Stage 2 pyramid + DMD):
+```bash
+python end2end.py \
+    --model BestWishYsh/Helios-Distilled \
+    --sample-type t2v \
+    --prompt "A dynamic time-lapse video showing the rapidly moving scenery from the window of a speeding train." \
+    --num-frames 240 \
+    --guidance-scale 1.0 \
+    --is-enable-stage2 \
+    --pyramid-num-inference-steps-list 2 2 2 \
+    --is-amplify-first-chunk \
+    --output helios_t2v_distilled.mp4
+```
+
+### Image-to-Video (I2V)
+
+**Helios-Base**:
+```bash
+python end2end.py \
+    --model BestWishYsh/Helios-Base \
+    --sample-type i2v \
+    --image-path /path/to/image.jpg \
+    --prompt "A towering emerald wave surges forward, its crest curling with raw power and energy." \
+    --guidance-scale 5.0 \
+    --output helios_i2v_base.mp4
+```
+
+**Helios-Mid** (Stage 2 + CFG-Zero*):
+```bash
+python end2end.py \
+    --model BestWishYsh/Helios-Mid \
+    --sample-type i2v \
+    --image-path /path/to/image.jpg \
+    --prompt "A towering emerald wave surges forward, its crest curling with raw power and energy." \
+    --guidance-scale 5.0 \
+    --is-enable-stage2 \
+    --pyramid-num-inference-steps-list 20 20 20 \
+    --use-cfg-zero-star --use-zero-init --zero-steps 1 \
+    --output helios_i2v_mid.mp4
+```
+
+**Helios-Distilled**:
+```bash
+python end2end.py \
+    --model BestWishYsh/Helios-Distilled \
+    --sample-type i2v \
+    --image-path /path/to/image.jpg \
+    --prompt "A towering emerald wave surges forward, its crest curling with raw power and energy." \
+    --num-frames 240 \
+    --guidance-scale 1.0 \
+    --is-enable-stage2 \
+    --pyramid-num-inference-steps-list 2 2 2 \
+    --is-amplify-first-chunk \
+    --output helios_i2v_distilled.mp4
+```
+
+### Video-to-Video (V2V)
+
+**Helios-Base**:
+```bash
+python end2end.py \
+    --model BestWishYsh/Helios-Base \
+    --sample-type v2v \
+    --video-path /path/to/video.mp4 \
+    --prompt "A bright yellow Lamborghini speeds along a curving mountain road." \
+    --guidance-scale 5.0 \
+    --output helios_v2v_base.mp4
+```
+
+**Helios-Mid** (Stage 2 + CFG-Zero*):
+```bash
+python end2end.py \
+    --model BestWishYsh/Helios-Mid \
+    --sample-type v2v \
+    --video-path /path/to/video.mp4 \
+    --prompt "A bright yellow Lamborghini speeds along a curving mountain road." \
+    --guidance-scale 5.0 \
+    --is-enable-stage2 \
+    --pyramid-num-inference-steps-list 20 20 20 \
+    --use-cfg-zero-star --use-zero-init --zero-steps 1 \
+    --output helios_v2v_mid.mp4
+```
+
+**Helios-Distilled**:
+```bash
+python end2end.py \
+    --model BestWishYsh/Helios-Distilled \
+    --sample-type v2v \
+    --video-path /path/to/video.mp4 \
+    --prompt "A bright yellow Lamborghini speeds along a curving mountain road." \
+    --num-frames 240 \
+    --guidance-scale 1.0 \
+    --is-enable-stage2 \
+    --pyramid-num-inference-steps-list 2 2 2 \
+    --is-amplify-first-chunk \
+    --output helios_v2v_distilled.mp4
+```
+
+## Common Parameters
+
+| Parameter | Default | Description |
+|-----------|---------|-------------|
+| `--model` | `BestWishYsh/Helios-Base` | Model ID or local path |
+| `--sample-type` | `t2v` | Generation mode: `t2v`, `i2v`, or `v2v` |
+| `--prompt` | — | Text prompt describing the video |
+| `--negative-prompt` | *(see source)* | Negative prompt for CFG (includes anti-static terms) |
+| `--image-path` | — | Input image (required for `i2v`) |
+| `--video-path` | — | Input video (required for `v2v`) |
+| `--height` | `384` | Video height in pixels |
+| `--width` | `640` | Video width in pixels |
+| `--num-frames` | `99` | Number of output frames |
+| `--num-inference-steps` | `50` | Denoising steps (Stage 1 only) |
+| `--guidance-scale` | `5.0` | CFG scale (`1.0` for Distilled) |
+| `--seed` | `42` | Random seed |
+| `--fps` | `16` | Output video frame rate |
+| `--output` | `helios_output.mp4` | Output file path |
+
+### Stage 2 / Pyramid Parameters
+
+| Parameter | Default | Description |
+|-----------|---------|-------------|
+| `--is-enable-stage2` | off | Enable pyramid multi-stage denoising |
+| `--pyramid-num-stages` | `3` | Number of pyramid stages |
+| `--pyramid-num-inference-steps-list` | `10 10 10` | Steps per pyramid stage |
+| `--is-amplify-first-chunk` | off | DMD amplification (Distilled) |
+
+### CFG-Zero* Parameters
+
+| Parameter | Default | Description |
+|-----------|---------|-------------|
+| `--use-cfg-zero-star` | off | Enable CFG-Zero* guidance (Mid) |
+| `--use-zero-init` | off | Zero init for first steps |
+| `--zero-steps` | `1` | Number of zero-init steps |
+
+### Memory & Parallelism
+
+| Parameter | Default | Description |
+|-----------|---------|-------------|
+| `--vae-use-slicing` | off | Enable VAE slicing |
+| `--vae-use-tiling` | off | Enable VAE tiling |
+| `--enforce-eager` | off | Disable torch.compile |
+| `--enable-cpu-offload` | off | CPU offloading |
+| `--enable-layerwise-offload` | off | Layerwise offloading |
+| `--ulysses-degree` | `1` | Ulysses SP degree |
+| `--ring-degree` | `1` | Ring SP degree |
+| `--cfg-parallel-size` | `1` | CFG parallel size (1 or 2) |
+| `--tensor-parallel-size` | `1` | Tensor parallelism size |
+
+## Example materials
+
+??? abstract "end2end.py"
+    ``````py
+    --8<-- "examples/offline_inference/helios/end2end.py"
+    ``````

docs/user_guide/examples/offline_inference/image_to_video.md

@@ -3,9 +3,7 @@
 Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/image_to_video>.
 
 
-This example demonstrates how to generate videos from images using Wan2.2,
-LTX-2/LTX-2.3, and HunyuanVideo-1.5 Image-to-Video models with vLLM-Omni's
-offline inference API.
+This example demonstrates how to generate videos from images using Wan2.2 Image-to-Video models with vLLM-Omni's offline inference API.
 
 ## Local CLI Usage
 
@@ -53,41 +51,13 @@ python image_to_video.py \
   --output i2v_output.mp4
 ```
 
-### LTX-2.3
-
-```bash
-python image_to_video.py \
-  --model dg845/LTX-2.3-Diffusers \
-  --model-class-name LTX23ImageToVideoPipeline \
-  --image cherry_blossom.jpg \
-  --prompt "Cherry blossoms swaying gently in the breeze with synchronized ambient sound" \
-  --negative-prompt "worst quality, inconsistent motion, blurry, jittery, distorted" \
-  --height 384 \
-  --width 512 \
-  --num-frames 25 \
-  --guidance-scale 4.0 \
-  --num-inference-steps 20 \
-  --frame-rate 24 \
-  --fps 24 \
-  --output ltx23_i2v_output.mp4
-```
-
-Use a Diffusers-format checkpoint such as `dg845/LTX-2.3-Diffusers`; the
-upstream `Lightricks/LTX-2.3` raw safetensors repo is not directly loadable by
-this pipeline. Pass `--model-class-name LTX23ImageToVideoPipeline` to select
-the LTX-2.3 image-to-video pipeline.
-
 Key arguments:
 
-- `--model`: Model ID (I2V-A14B for MoE, TI2V-5B for unified T2V+I2V, or
-  LTX-2/LTX-2.3).
+- `--model`: Model ID (I2V-A14B for MoE, TI2V-5B for unified T2V+I2V).
 - `--image`: Path to input image (required).
 - `--prompt`: Text description of desired motion/animation.
-- `--height/--width`: Output resolution (auto-calculated from image if not set).
-  Wan dimensions should be multiples of 16; LTX dimensions should be multiples
-  of 32.
-- `--num-frames`: Number of frames (model-specific default; LTX-style models
-  work best with `8k + 1`).
+- `--height/--width`: Output resolution (auto-calculated from image if not set). Dimensions should be multiples of 16.
+- `--num-frames`: Number of frames (default 81).
 - `--guidance-scale` and `--guidance-scale-high`: CFG scale (applied to low/high-noise stages for MoE).
 - `--negative-prompt`: Optional list of artifacts to suppress.
 - `--boundary-ratio`: Boundary split ratio for two-stage MoE models.
@@ -95,7 +65,6 @@ Key arguments:
 - `--sample-solver`: Wan2.2 sampling solver. Use `unipc` for the default multistep solver, or `euler` for Lightning/Distill checkpoints.
 - `--num-inference-steps`: Number of denoising steps (default 50).
 - `--fps`: Frames per second for the saved MP4 (requires `diffusers` export_to_video).
-- `--audio-sample-rate`: fallback audio sample rate for embedded audio.
 - `--output`: Path to save the generated video.
 - `--vae-use-slicing`: Enable VAE slicing for memory optimization.
 - `--vae-use-tiling`: Enable VAE tiling for memory optimization.

docs/user_guide/examples/offline_inference/mammothmodal2_preview.md

@@ -7,22 +7,29 @@ Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inf
 
 Download model
 ```bash
-hf download bytedance-research/MammothModa2-Preview --local-dir ./MammothModa2-Preview
+hf bytedance-research/MammothModa2-Preview --local-dir ./MammothModa2-Preview
 ```
 
 ### Text-to-Image (T2I)
 
-Text-to-image now runs through the shared offline image example
-(`examples/offline_inference/text_to_image/text_to_image.py`). See the recipe
-`recipes/MammothModa2/MammothModa2-Preview.md` for the full command and the
-`extra_body` knobs (`text_guidance_scale`, `cfg_range`, `num_inference_steps`).
+```bash
+python examples/offline_inference/mammothmodal2_preview/run_mammothmoda2_t2i.py \
+  --model ./MammothModa2-Preview \
+  --stage-config ./vllm_omni/model_executor/stage_configs/mammoth_moda2.yaml \
+  --prompt "A stylish woman riding a motorcycle in NYC, movie poster style" \
+  --height 1024 \
+  --width 1024 \
+  --num-inference-steps 50 \
+  --text-guidance-scale 4.0 \
+  --out output.png
+```
 
 ### Image Summary
 
 ```bash
 python examples/offline_inference/mammothmodal2_preview/run_mammothmoda2_image_summarize.py \
   --model ./MammothModa2-Preview \
-  --deploy-config ./vllm_omni/deploy/mammoth_moda2_ar.yaml \
+  --stage-config ./vllm_omni/model_executor/stage_configs/mammoth_moda2_ar.yaml \
   --question "Summarize this image." \
   --image ./image.png
 ```
@@ -33,3 +40,7 @@ python examples/offline_inference/mammothmodal2_preview/run_mammothmoda2_image_s
     ``````py
     --8<-- "examples/offline_inference/mammothmodal2_preview/run_mammothmoda2_image_summarize.py"
     ``````
+??? abstract "run_mammothmoda2_t2i.py"
+    ``````py
+    --8<-- "examples/offline_inference/mammothmodal2_preview/run_mammothmoda2_t2i.py"
+    ``````

docs/user_guide/examples/offline_inference/text_to_image.md

@@ -160,11 +160,9 @@ python examples/offline_inference/text_to_image/text_to_image.py \
   --output flux2-dev.png
 ```
 
-### Multiple Prompts
+### Batch Requests (Multiple Prompts)
 
-You can pass multiple prompts in a single `generate` call. For diffusion
-pipelines, each prompt is submitted as a separate logical request; compatible
-requests may be automatically batched by the scheduler and runner.
+You can pass multiple prompts in a single `generate` call.
 
 ```python
 from vllm_omni.entrypoints.omni import Omni
@@ -183,8 +181,17 @@ if __name__ == "__main__":
 
 !!! info
 
-    For diffusion request-level batching controls such as `max_num_seqs`, see
-    [Request-Level Batching](../../diffusion/request_batching.md).
+    Not all models support batch inference, and batch requesting mostly does not provide significant
+    performance improvement. This feature is primarily for interface compatibility with vLLM and to
+    allow for future improvements.
+
+!!! info
+
+    For diffusion pipelines, the stage config field `stage_args.[].runtime.max_batch_size` is 1 by
+    default, and the input list is sliced into single-item requests before feeding into the diffusion
+    pipeline. For models that do internally support batched inputs, you can
+    [modify this configuration](https://github.com/vllm-project/vllm-omni/tree/main/configuration/stage_configs.md) to let the model accept a
+    longer batch of prompts.
 
 ### Negative Prompts
 

docs/user_guide/examples/offline_inference/text_to_video.md

@@ -12,7 +12,6 @@ For backend selection and SageAttention usage, see the [Diffusion Attention Back
 | Model | Default Resolution | Default Frames | Default Steps | Guidance | VRAM (BF16) |
 |---|---|---|---|---|---|
 | `Wan-AI/Wan2.2-T2V-A14B-Diffusers` | 720x1280 | 81 | 40 | 4.0 | ~60 GiB |
-| `dg845/LTX-2.3-Diffusers` | 384x512 | 25 | 20 | 4.0 | 96GB-class GPU |
 | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v` | 480x832 | 121 | 50 | 6.0 | 1×A100 80GB |
 | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v` | 720x1280 | 121 | 50 | 6.0 | FP8 + VAE tiling required |
 
@@ -51,30 +50,6 @@ python text_to_video.py \
   --output ltx2_out.mp4
 ```
 
-### LTX-2.3
-
-```bash
-python text_to_video.py \
-  --model dg845/LTX-2.3-Diffusers \
-  --model-class-name LTX23Pipeline \
-  --prompt "Cherry blossoms swaying gently in the breeze with synchronized ambient sound" \
-  --negative-prompt "worst quality, inconsistent motion, blurry, jittery, distorted" \
-  --height 384 \
-  --width 512 \
-  --num-frames 25 \
-  --num-inference-steps 20 \
-  --guidance-scale 4.0 \
-  --frame-rate 24 \
-  --fps 24 \
-  --audio-sample-rate 48000 \
-  --output ltx23_t2v_output.mp4
-```
-
-Use a Diffusers-format checkpoint such as `dg845/LTX-2.3-Diffusers`; the
-upstream `Lightricks/LTX-2.3` raw safetensors repo is not directly loadable by
-this pipeline. Pass `--model-class-name LTX23Pipeline` to select the LTX-2.3
-text-to-video pipeline explicitly.
-
 ### HunyuanVideo-1.5 (480p)
 
 ```bash
@@ -135,8 +110,6 @@ python text_to_video.py \
 ### Common
 
 - `--model`: Diffusers model ID or local path.
-- `--model-class-name`: Optional explicit pipeline class. Use `LTX23Pipeline`
-  for LTX-2.3 text-to-video.
 - `--prompt`: text description (string).
 - `--height/--width`: output resolution. Default depends on model.
 - `--num-frames`: number of frames. Default depends on model.
@@ -151,8 +124,7 @@ python text_to_video.py \
 - `--enable-cpu-offload`: enable CPU offloading for diffusion models.
 - `--enable-layerwise-offload`: enable layerwise offloading on DiT modules.
 - `--frame-rate`: generation FPS for pipelines that require it (e.g., LTX2).
-- `--audio-sample-rate`: audio sample rate for embedded audio (when the
-  pipeline returns audio; LTX-2.3 outputs 48kHz audio).
+- `--audio-sample-rate`: audio sample rate for embedded audio (when the pipeline returns audio).
 - `--quantization`: quantization method (`fp8` for FP8, `gguf` for GGUF).
 - `--flow-shift`: scheduler flow_shift parameter.
 

docs/user_guide/examples/online_serving/aura_omni.md

@@ -1,65 +0,0 @@
-# AURA Omni Pipeline
-
-`aura_omni` wires ASR, AURA, and Qwen3-TTS into one vLLM-Omni pipeline:
-
-```text
-ASR -> AURA -> Qwen3-TTS Talker -> Code2Wav
-```
-
-Qwen3-TTS remains two engine stages so the pipeline reuses the existing native
-Talker and Code2Wav implementation.
-
-```bash
-vllm serve aurateam/AURA \
-  --omni \
-  --deploy-config vllm_omni/deploy/aura_omni.yaml \
-  --served-model-name aurateam/AURA \
-  --trust-remote-code
-```
-
-Configure local checkpoints by editing per-stage `model` values in
-`vllm_omni/deploy/aura_omni.yaml`. The deploy file sets
-`pipeline: aura_omni`, so the four-stage topology is used even if the
-command-line `--model` points at one of the component checkpoints.
-
-Send requests with `"model": "aurateam/AURA"`. The ASR, AURA, and Qwen3-TTS
-checkpoint paths are internal stage models from the deploy YAML, not the
-OpenAI-facing served model name.
-
-The AURA stage can emit `<|silent|>`. Silent outputs are treated as a gate:
-they produce no Qwen3-TTS Talker input, so no audio is synthesized for that
-turn.
-
-## GPU Utilization Recommendation
-
-`gpu_memory_utilization` in `vllm_omni/deploy/aura_omni.yaml` controls how much
-VRAM each stage can reserve. Start with this split for a single GPU:
-
-- Stage 0 (ASR): `0.10`
-- Stage 1 (AURA): `0.40`
-- Stage 2 (Qwen3-TTS Talker): `0.20`
-- Stage 3 (Qwen3-TTS Code2Wav): `0.20`
-
-## TTS Modes
-
-`aura_omni` can pass AURA text to Qwen3-TTS in two task modes:
-
-- `Base`: voice clone from `tts_ref_audio` with ICL enabled in the AURA
-  pipeline. Provide both `tts_ref_audio` and `tts_ref_text`. Set
-  `tts_x_vector_only_mode=true` to disable ICL and use speaker embedding only.
-- `CustomVoice`: predefined speaker mode. Use a Qwen3-TTS CustomVoice
-  checkpoint for stages 2 and 3 in `aura_omni.yaml`, then pass
-  `tts_task_type=CustomVoice` and `tts_speaker`.
-
-By default, AURA responses are passed to Qwen3-TTS as text. Set
-`additional_information.tts_pass_token_ids=true` to pass AURA-generated
-assistant token ids directly instead. Even when token passthrough is disabled,
-the stage processor uses AURA token ids when available to estimate the Talker
-prefill length, so scheduling does not rely on raw character length.
-
-The example client exposes this as:
-
-```bash
-python examples/online_serving/aura_omni/openai_chat_completion_client.py \
-  --tts-pass-token-ids
-```

docs/user_guide/examples/online_serving/diffusers_pipeline_adapter.md

@@ -9,8 +9,7 @@ vLLM-Omni supports running diffusion models with the diffusers backend, directly
 
 The diffusers backend is a black-box adapter. Its primary focus is to serve diffusion models online.
 Currently, the following features are NOT yet supported.
-Community contributions are welcome to add these features when they can be
-implemented with clear Diffusers or vLLM-Omni behavior.
+It is not guaranteed whether they will be supported in the future.
 
 - CFG parallel execution
 - Sequence parallel execution
@@ -67,35 +66,6 @@ This is suitable for sampling parameters not available through the vLLM-Omni int
 When a parameter is available in the vLLM-Omni interface, it will be adapted here.
 But if that parameter is simultaneously set in both the vLLM-Omni interface and `diffusers_call_kwargs`, the **former** will take precedence (because it is set at request time).
 
-### Quantization
-
-Use `diffusers_load_kwargs` for Diffusers-native quantization options. If
-`diffusers_load_kwargs["quantization_config"]` is provided as a dictionary, the
-diffusers backend builds a Diffusers quantization config and lets Diffusers
-validate it before calling `DiffusionPipeline.from_pretrained()`.
-
-The diffusers backend also provides a small compatibility shortcut for
-vLLM-Omni quantization configs when `diffusers_load_kwargs` does not already
-contain `quantization_config`. Currently this maps online/dynamic `fp8` and
-online/dynamic `int8` to Diffusers/TorchAO dynamic quantization for transformer
-components such as `transformer` or `transformer_2`. This path requires
-`torchao` to be installed.
-
-For example, the CLI can request dynamic FP8 through the vLLM-Omni interface:
-
-```bash
-vllm serve "Qwen/Qwen-Image" \
-    --omni \
-    --diffusion-load-format diffusers \
-    --quantization-config '{"method": "fp8"}'
-```
-
-Other vLLM-Omni quantization methods, such as `gguf`, `modelopt`, `mxfp4`,
-`mxfp8`, serialized checkpoints, static FP8 configs, and layer-name skip lists
-such as `ignored_layers`, are intentionally not translated by this compatibility
-shortcut. Use Diffusers-native configuration through `diffusers_load_kwargs` or
-a native vLLM-Omni pipeline for those cases.
-
 ### Attention Backends
 
 The diffusers backend converts

docs/user_guide/examples/online_serving/glm_image.md

@@ -19,7 +19,7 @@ both stages on a single GPU, override per stage:
 
 ```bash
 vllm serve zai-org/GLM-Image --omni --port 8091 \
-    --stage-overrides '{"0": {"devices": "0"}, "1": {"devices": "0"}}'
+    --stage-0-devices 0 --stage-1-devices 0
 ```
 
 ## API Calls

docs/user_guide/examples/online_serving/image_to_video.md

@@ -84,26 +84,16 @@ curl -X POST http://localhost:8091/v1/videos/sync \
 Generated video files are stored on local disk by the async video API.
 Local file storage behavior can be controlled via the following environment variables:
 
-- `VLLM_OMNI_SERVER_STORAGE__PATH`: directory used for generated files
-  (default: `/tmp/storage`)
-- `VLLM_OMNI_SERVER_STORAGE__FILE_CONCURRENCY`: max concurrent save/delete/open
-  operations (default: `4`)
-- `VLLM_OMNI_SERVER_STORAGE__FILE_TTL`: optional TTL for generated files in
-  seconds
-- `VLLM_OMNI_SERVER_STORAGE__TTL_SWEEP_INTERVAL`: optional sweep interval in
-  seconds for enforcing file TTL; defaults to `300` when TTL is set
+- `VLLM_OMNI_STORAGE_PATH`: directory used for generated files (default: `/tmp/storage`)
+- `VLLM_OMNI_STORAGE_MAX_CONCURRENCY`: max concurrent save/delete operations (default: `4`)
 
 Example:
 
 ```bash
-export VLLM_OMNI_SERVER_STORAGE__PATH=/var/tmp/vllm-omni-videos
-export VLLM_OMNI_SERVER_STORAGE__FILE_CONCURRENCY=8
-export VLLM_OMNI_SERVER_STORAGE__FILE_TTL=86400
-export VLLM_OMNI_SERVER_STORAGE__TTL_SWEEP_INTERVAL=300
+export VLLM_OMNI_STORAGE_PATH=/var/tmp/vllm-omni-videos
+export VLLM_OMNI_STORAGE_MAX_CONCURRENCY=8
 ```
 
-See also: [Configuration Options](../../../configuration/README.md)
-
 ## API Calls
 
 ### Method 1: Using curl

docs/user_guide/examples/online_serving/text_to_speech.md

@@ -79,7 +79,7 @@ response = client.audio.speech.create(
 response.stream_to_file("output.wav")
 ```
 
-Streaming PCM output (where supported) — set `stream=true`, `stream_format="audio"`, and `response_format="pcm"`:
+Streaming PCM output (where supported) — set `stream=true` with `response_format="pcm"`:
 
 ```bash
 curl -X POST http://localhost:8091/v1/audio/speech \
@@ -88,7 +88,6 @@ curl -X POST http://localhost:8091/v1/audio/speech \
         "input": "Hello, how are you?",
         "voice": "default",
         "stream": true,
-        "stream_format": "audio",
         "response_format": "pcm"
     }' --no-buffer | play -t raw -r 24000 -e signed -b 16 -c 1 -
 ```
@@ -415,11 +414,10 @@ curl -X POST http://localhost:8091/v1/audio/speech \
         "voice": "vivian",
         "language": "English",
         "stream": true,
-        "stream_format": "audio",
         "response_format": "pcm"
     }' --no-buffer | play -t raw -r 24000 -e signed -b 16 -c 1 -
 ```
-Raw PCM streaming requires `stream_format="audio"`, `response_format="pcm"`, and `async_chunk: true` on the stage config (default in `qwen3_tts.yaml`). `speed` is not supported when streaming.
+Streaming requires `response_format="pcm"` and `async_chunk: true` on the stage config (default in `qwen3_tts.yaml`). `speed` is not supported when streaming.
 
 ### Streaming WebSocket
 The `/v1/audio/speech/stream` endpoint accepts text incrementally, splits it at sentence boundaries, and emits one PCM stream per sentence:
@@ -498,7 +496,7 @@ After startup, `/v1/audio/voices` lists `alice`, and `/v1/audio/speech` can use
 ```bash
 python voxcpm2/gradio_demo.py
 ```
-Uses an AudioWorklet-based player adapted from the Qwen3-TTS demo for gap-free playback. Raw PCM audio is streamed from the OpenAI Speech endpoint with `stream=true` and `stream_format="audio"`.
+Uses an AudioWorklet-based player adapted from the Qwen3-TTS demo for gap-free playback. Audio is streamed from the OpenAI Speech endpoint with `stream=true`.
 
 ---
 

docs/user_guide/examples/online_serving/text_to_video.md

@@ -90,26 +90,16 @@ curl -X POST http://localhost:8091/v1/videos/sync \
 Generated video files are stored on local disk by the async video API.
 Local file storage behavior can be controlled via the following environment variables:
 
-- `VLLM_OMNI_SERVER_STORAGE__PATH`: directory used for generated files
-  (default: `/tmp/storage`)
-- `VLLM_OMNI_SERVER_STORAGE__FILE_CONCURRENCY`: max concurrent save/delete/open
-  operations (default: `4`)
-- `VLLM_OMNI_SERVER_STORAGE__FILE_TTL`: optional TTL for generated files in
-  seconds
-- `VLLM_OMNI_SERVER_STORAGE__TTL_SWEEP_INTERVAL`: optional sweep interval in
-  seconds for enforcing file TTL; defaults to `300` when TTL is set
+- `VLLM_OMNI_STORAGE_PATH`: directory used for generated files (default: `/tmp/storage`)
+- `VLLM_OMNI_STORAGE_MAX_CONCURRENCY`: max concurrent save/delete operations (default: `4`)
 
 Example:
 
 ```bash
-export VLLM_OMNI_SERVER_STORAGE__PATH=/var/tmp/vllm-omni-videos
-export VLLM_OMNI_SERVER_STORAGE__FILE_CONCURRENCY=8
-export VLLM_OMNI_SERVER_STORAGE__FILE_TTL=86400
-export VLLM_OMNI_SERVER_STORAGE__TTL_SWEEP_INTERVAL=300
+export VLLM_OMNI_STORAGE_PATH=/var/tmp/vllm-omni-videos
+export VLLM_OMNI_STORAGE_MAX_CONCURRENCY=8
 ```
 
-See also: [Configuration Options](../../../configuration/README.md)
-
 ## API Calls
 
 ### Method 1: Using curl

docs/user_guide/fault_injection_reliability_matrix.md

@@ -65,7 +65,7 @@ More failure modes (for example, network jitter and network interruption) will b
 
 ## Source of Conclusions
 
-The behaviors and conclusions above are summarized from current fault injection validation results on **`Qwen3-Omni`**, **`Wan2.2`**, **`HunyuanImage-3.0-Instruct`** (DiT-only, `/v1/images/generations`), and **`VoxCPM2`** (`/v1/audio/speech`). Automated coverage lives under `tests/dfx/reliability/` (`test_reliability_qwen3_omni.py`, `test_reliability_wan22.py`, `test_reliability_hunyuan_image.py`, `test_reliability_voxcpm2.py`) and runs weekly via `.buildkite/test-weekly.yml`.
+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)
 

docs/user_guide/quantization/fp8.md

@@ -44,16 +44,10 @@ pip install vllm-omni[quack]
 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 **datacenter
-Blackwell only** (`sm_100` / `sm_101` / `sm_103`, compute capability `10.x`, e.g.
-B200): quack's CuteDSL GEMM uses the 5th-gen `tcgen05` tensor-core MMA, which exists
-only on those parts. On Hopper/Ada the CUTLASS FP8 kernel already fuses bias, and on
-workstation/consumer Blackwell (`sm_120` / `sm_121`, compute capability `12.x`, e.g.
-RTX PRO 6000 / RTX 50-series) `tcgen05` is absent — so quack is **not** auto-enabled
-there and FlashInfer's native FP8 path is used instead. Set
-`VLLM_OMNI_USE_QUACK_FP8=1` to force quack on, or `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.
+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
 
@@ -91,7 +85,6 @@ warmup_quack_fp8([(14040, 2048, 6144), (14040, 2048, 2048)])
 | FLUX.2-klein | `black-forest-labs/FLUX.2-klein-4B` | Yes | Yes | All layers | None | |
 | HunyuanImage-3.0 | `tencent/HunyuanImage-3.0`, `tencent/HunyuanImage-3.0-Instruct` | Yes | Yes | All layers; use the Hunyuan stage config for multi-stage runs | None | |
 | HunyuanVideo-1.5 | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v`, `720p_t2v`, `480p_i2v` | Yes | Yes | All layers | None | |
-| Cosmos3 | `nvidia/Cosmos3-Nano`, `nvidia/Cosmos3-Super` | Yes | Not validated | All layers | None | |
 
 ### Multi-Stage Omni/TTS Model (Qwen3-Omni, Qwen3-TTS)
 

docs/user_guide/quantization/gguf.md

@@ -9,15 +9,6 @@ for the transformer.
 
 GGUF is static quantization: the quantized weights are produced before serving.
 
-!!! note
-    GGUF support has migrated to OOT [vllm-gguf-plugin](https://github.com/vllm-project/vllm-gguf-plugin). Make sure you have GGUF plugin installed before serving a GGUF model.
-
-Before serving a GGUF model, make sure to install the [vllm-gguf-plugin](https://github.com/vllm-project/vllm-gguf-plugin):
-
-```bash
-uv pip install vllm-gguf-plugin>=0.0.3
-```
-
 ## Hardware Support
 
 | Device | Support |

docs/user_guide/quantization/modelopt.md

@@ -89,54 +89,11 @@ and
 | Model | Scope | Status |
 |-------|-------|--------|
 | Qwen3-Omni | Thinker language-model stage | ModelOpt FP8 checkpoint path |
-| Qwen3-Omni | Thinker language-model stage (W4A4 NVFP4) | Validated; see [Qwen3-Omni NVFP4 W4A4](#qwen3-omni-nvfp4-w4a4-thinker) below |
 | Qwen3-TTS | TTS language-model stage | Not validated |
 
 Audio encoder, vision encoder, talker, and code2wav stages stay in BF16 unless
 a model-specific guide documents otherwise.
 
-#### Qwen3-Omni NVFP4 W4A4 (thinker)
-
-vLLM-Omni serves ModelOpt NVFP4 W4A4 quantizations of the
-Qwen3-Omni-30B-A3B-Instruct thinker language model. The thinker text body
-(attention + MoE experts) is quantized to NVFP4 with FP8 per-tensor input
-scales; the audio encoder, vision encoder, talker, and code2wav stay in BF16.
-
-| Variant | HF checkpoint | Hardware |
-|---------|---------------|----------|
-| W4A4 NVFP4 (full thinker) | `YihongJin/Qwen3-Omni-30B-A3B-Instruct-NVFP4-W4A4-full-thinker-awqclip` | sm_100+ (Blackwell, FlashInfer FP4 GEMM) |
-
-Calibration uses ModelOpt `mtq.NVFP4_DEFAULT_CFG` with the `awq_clip` algorithm
-on 1024 ultrachat samples chat-templated through the Qwen3-Omni tokenizer.
-Excluded modules: `*audio_tower*`, `*visual*`, `*talker*`, `*code2wav*`,
-`*lm_head*`, `*mlp.gate*`. See `scripts/nvfp4/calibrate.py` for the reference
-recipe.
-
-!!! note "ModelOpt 0.44 NaN regression workaround"
-    ModelOpt 0.44's float32 -> FP8 E4M3 cast of per-block weight scales
-    occasionally emits literal NaN bytes (E4M3 encoding 0x7F / 0xFF) for
-    blocks whose pre-cast scale rounds above the FP8 max of 448 after the
-    global-scale division. A single NaN byte in any `weight_scale`
-    propagates through the FP4 GEMM and collapses the served model output
-    to `!!!!`. vLLM-Omni's `vllm_omni.patch` installs a defensive override
-    of `ModelOptNvFp4LinearMethod.process_weights_after_loading` that
-    clamps these bytes to the FP8 E4M3 max at load time. The override
-    self-extinguishes once vllm-omni's vllm pin moves to a release
-    containing the upstream fix. Set `VLLM_OMNI_SKIP_NVFP4_NAN_CLAMP=1`
-    to disable the override for diagnostics.
-
-Serving:
-
-```bash
-vllm serve YihongJin/Qwen3-Omni-30B-A3B-Instruct-NVFP4-W4A4-full-thinker-awqclip \
-    --omni --port 8000
-```
-
-> Do **not** pass `--enforce-eager` for production / benchmarks. CUDA graphs
-> amortize launch overhead and unlock the FP4 throughput wins; with
-> `--enforce-eager` set, W4A4 TPOT degrades ~10x relative to the CUDA-graph
-> configuration.
-
 ### Multi-Stage Diffusion Model
 
 ModelOpt checkpoints must be routed to the stage whose checkpoint contains the

docs/user_guide/quantization/mxfp4.md

@@ -39,7 +39,7 @@ vLLM-Omni provides two quantization methods with different scale structures:
 | NVIDIA Blackwell GPU (SM 100+) | ⭕ |
 | NVIDIA Ada/Hopper GPU (SM 89+) | ⭕ |
 | NVIDIA Ampere GPU (SM 80+) | ⭕ |
-| AMD ROCm (gfx950 / MI355X) | ✅ |
+| AMD ROCm | ⭕ |
 | Intel XPU | ⭕ |
 | Ascend NPU (Atlas 950 A5) | ✅ |
 

docs/user_guide/quantization/overview.md

@@ -20,7 +20,7 @@ type has a different quantization scope.
 | NVIDIA Blackwell GPU (SM 100+) | ✅ | ✅ | ✅ | ⭕ | ⭕ | ✅ | ✅ | ❌ |
 | NVIDIA Ada/Hopper GPU (SM 89+) | ✅ | ✅ | ✅ | ⭕ | ⭕ | ✅ | ✅ | ❌ |
 | NVIDIA Ampere GPU (SM 80+) | ✅ | ✅ | ⭕ | ⭕ | ⭕ | ✅ | ✅ | ❌ |
-| AMD ROCm | ⭕ | ⭕ | ⭕ | ⭕ | ✅ | ⭕ | ⭕ | ❌ |
+| AMD ROCm | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ | ❌ |
 | Intel XPU | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ | ✅ | ❌ |
 | Ascend NPU | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ |
 

examples/offline_inference/audiox/README.md

@@ -0,0 +1,40 @@
+# AudioX offline inference
+
+Generate audio with the [AudioX](https://zeyuet.github.io/AudioX/) MMDiT diffusion
+pipeline (`AudioXPipeline`). Six tasks: `t2a`, `t2m`, `v2a`, `v2m`, `tv2a`, `tv2m`.
+
+## Prerequisites
+
+Download a vLLM-Omni weight bundle (component-sharded safetensors):
+
+```bash
+huggingface-cli download zhangj1an/AudioX --local-dir ./audiox_weights
+```
+
+The Hugging Face id `zhangj1an/AudioX` also works directly without prefetching.
+
+## Usage
+
+```bash
+# Text-to-audio only (default uses zhangj1an/AudioX from the Hub):
+python end2end.py --tasks t2a
+
+# All six tasks against a local bundle and a sample video for v2*/tv2*:
+python end2end.py \
+  --model ./audiox_weights \
+  --video https://zeyuet.github.io/AudioX/static/samples/V2M/1XeBotOFqHA.mp4
+
+# Subset of tasks, custom seed and steps:
+python end2end.py --tasks t2a tv2a --num-inference-steps 100 --seed 0
+```
+
+## Arguments
+
+- `--model`: HF id or local bundle path (default: `zhangj1an/AudioX`).
+- `--tasks`: any subset of `t2a t2m v2a v2m tv2a tv2m` (default: all).
+- `--video`: video file/URL — required for `v2*` and `tv2*`.
+- `--reference-audio`: optional audio prompt (audio-conditioned generation).
+- `--num-inference-steps`, `--guidance-scale`, `--seed`, `--seconds-total`,
+  `--sample-rate`, `--output-dir`: generation knobs.
+
+Outputs land in `<output-dir>/<task>.wav` as 16-bit stereo WAV.

examples/offline_inference/audiox/end2end.py

@@ -0,0 +1,110 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""End-to-end AudioX offline example covering the 6 t2*/v2*/tv2* tasks.
+
+Provide a directory with the **vLLM-Omni AudioX safetensors bundle** (e.g. from
+``zhangj1an/AudioX`` on Hugging Face)::
+
+    huggingface-cli download zhangj1an/AudioX --local-dir ./audiox_weights
+    python end2end.py --model ./audiox_weights
+    python end2end.py --model ./audiox_weights --tasks t2a tv2a
+"""
+
+from __future__ import annotations
+
+import argparse
+import time
+from pathlib import Path
+
+import soundfile
+import torch
+import torchaudio.functional as TF
+
+from vllm_omni.entrypoints.omni import Omni
+from vllm_omni.inputs.data import OmniDiffusionSamplingParams
+from vllm_omni.platforms import current_omni_platform
+
+ROOT = Path(__file__).resolve().parent
+
+SAMPLE_PROMPTS: dict[str, str] = {
+    "t2a": "Fireworks burst twice, followed by a period of silence before a clock begins ticking.",
+    "t2m": "Uplifting ukulele tune for a travel vlog",
+    "v2a": "",
+    "v2m": "",
+    "tv2a": "drum beating sound and human talking",
+    "tv2m": "uplifting music matching the scene",
+}
+
+ALL_TASKS = ("t2a", "t2m", "v2a", "v2m", "tv2a", "tv2m")
+VIDEO_TASKS = frozenset({"v2a", "v2m", "tv2a", "tv2m"})
+TEXT_TASKS = frozenset({"t2a", "t2m", "tv2a", "tv2m"})
+
+
+def parse_args() -> argparse.Namespace:
+    p = argparse.ArgumentParser(description="AudioX offline end-to-end (6 t2*/v2*/tv2* tasks).")
+    p.add_argument("--model", default="zhangj1an/AudioX", help="HF id or local AudioX bundle path.")
+    p.add_argument("--tasks", nargs="+", default=list(ALL_TASKS), choices=ALL_TASKS)
+    p.add_argument("--video", default="", help="Video path / URL (required for v2*/tv2*).")
+    p.add_argument("--reference-audio", default="", help="Optional audio prompt for audio-conditioned generation.")
+    p.add_argument("--output-dir", default=str(ROOT / "audiox_task_outputs"))
+    p.add_argument("--num-inference-steps", type=int, default=250)
+    p.add_argument("--seconds-total", type=float, default=10.0)
+    p.add_argument("--guidance-scale", type=float, default=6.0)
+    p.add_argument("--seed", type=int, default=42)
+    p.add_argument("--sample-rate", type=int, default=48000, help="Output WAV rate (resampled if != model rate).")
+    return p.parse_args()
+
+
+def save_wav(audio: torch.Tensor, path: Path, sample_rate: int) -> None:
+    """Write 16-bit PCM WAV. ``audio`` is ``[channels, samples]`` float in [-1, 1]."""
+    path.parent.mkdir(parents=True, exist_ok=True)
+    soundfile.write(str(path), audio.clamp(-1.0, 1.0).cpu().T.numpy(), sample_rate, subtype="PCM_16")
+
+
+def main() -> None:
+    args = parse_args()
+
+    omni = Omni(model=args.model, model_class_name="AudioXPipeline")
+
+    for task in args.tasks:
+        if task in VIDEO_TASKS and not args.video:
+            raise SystemExit(f"task={task!r} requires --video")
+        prompt = SAMPLE_PROMPTS[task] if task in TEXT_TASKS else ""
+        extra: dict = {"audiox_task": task, "seconds_start": 0.0, "seconds_total": float(args.seconds_total)}
+        if task in VIDEO_TASKS:
+            extra["video_path"] = args.video
+        if args.reference_audio:
+            extra["audio_path"] = args.reference_audio
+
+        generator = torch.Generator(device=current_omni_platform.device_type).manual_seed(args.seed)
+        t0 = time.perf_counter()
+        outputs = omni.generate(
+            prompt,
+            OmniDiffusionSamplingParams(
+                generator=generator,
+                guidance_scale=args.guidance_scale,
+                num_inference_steps=args.num_inference_steps,
+                seed=args.seed,
+                extra_args=extra,
+            ),
+        )
+        audio = outputs[0].request_output.multimodal_output.get("audio")
+        if audio is None:
+            raise RuntimeError(f"No audio produced for task {task!r}")
+        audio = torch.as_tensor(audio).detach().cpu().float()
+        if audio.ndim == 3:
+            audio = audio[0]
+
+        model_sr = int(outputs[0].request_output.multimodal_output.get("audio_sample_rate") or 44100)
+        if model_sr != args.sample_rate:
+            audio = TF.resample(audio, model_sr, args.sample_rate)
+
+        out_path = Path(args.output_dir) / f"{task}.wav"
+        save_wav(audio, out_path, args.sample_rate)
+        print(f"[{task}] saved {out_path} ({time.perf_counter() - t0:.2f}s)")
+
+    omni.close()
+
+
+if __name__ == "__main__":
+    main()

examples/offline_inference/aura_omni/README.md

@@ -1,61 +0,0 @@
-# AURA Omni
-
-This example runs the native AURA Omni pipeline offline:
-
-```text
-Qwen3-ASR -> AURA/Qwen3-VL -> Qwen3-TTS Talker -> Qwen3-TTS Code2Wav
-```
-
-The first stage consumes speech and produces a transcript. The AURA stage then combines the transcript with the video frames from the original request and returns text or `<|silent|>`. Non-silent responses are passed to Qwen3-TTS as AURA-generated token ids.
-
-## Run
-
-```bash
-cd examples/offline_inference/aura_omni
-bash run_single_prompt.sh
-```
-
-Use local media:
-
-```bash
-python end2end.py \
-  --audio-path /path/to/input.wav \
-  --video-path /path/to/video.mp4 \
-  --modalities text,audio
-```
-
-Base voice clone mode is the default. It uses the AURA reference audio unless
-you override it:
-
-```bash
-python end2end.py \
-  --tts-task-type Base \
-  --tts-ref-audio /data/yrr/rein_test/shuhan.mp3 \
-  --tts-x-vector-only-mode
-```
-
-CustomVoice mode requires stages 2 and 3 in `vllm_omni/deploy/aura_omni.yaml`
-to point at a Qwen3-TTS CustomVoice checkpoint:
-
-```bash
-python end2end.py \
-  --tts-task-type CustomVoice \
-  --tts-speaker Vivian
-```
-
-For local checkpoints, edit the stage `model` entries in
-`vllm_omni/deploy/aura_omni.yaml` or pass a copied deploy config with
-`--deploy-config`.
-
-## GPU Utilization Recommendation
-
-Set `gpu_memory_utilization` in `vllm_omni/deploy/aura_omni.yaml` by stage.
-Suggested starting point:
-
-- Stage 0 (ASR): `0.10`
-- Stage 1 (AURA): `0.4`
-- Stage 2 (Qwen3-TTS Talker): `0.20`
-- Stage 3 (Qwen3-TTS Code2Wav): `0.20`
-
-Generated text and audio are written to `--output-dir`
-(default: `output_aura_omni`).

examples/offline_inference/aura_omni/end2end.py

@@ -1,236 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Offline AURA Omni example.
-
-The native AURA pipeline is:
-
-    Qwen3-ASR -> AURA/Qwen3-VL -> Qwen3-TTS Talker -> Code2Wav
-
-Stage 0 consumes audio and emits a transcript. The stage input processor then
-combines that transcript with the original visual payload for AURA.
-"""
-
-from __future__ import annotations
-
-import os
-from typing import NamedTuple
-
-import numpy as np
-import soundfile as sf
-import vllm
-from vllm import SamplingParams
-from vllm.assets.audio import AudioAsset
-from vllm.assets.video import VideoAsset, video_to_ndarrays
-from vllm.multimodal.media.audio import load_audio
-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.model_executor.stage_input_processors.aura_omni import (
-    DEFAULT_QWEN3_TTS_REF_TEXT,
-    default_qwen3_tts_ref_audio_path,
-)
-
-SEED = 42
-DEFAULT_MODEL = "aurateam/AURA"
-DEFAULT_DEPLOY_CONFIG = "vllm_omni/deploy/aura_omni.yaml"
-
-
-class QueryResult(NamedTuple):
-    inputs: dict
-    limit_mm_per_prompt: dict[str, int]
-
-
-def _load_audio(audio_path: str | None, sampling_rate: int) -> tuple[np.ndarray, int]:
-    if audio_path:
-        if not os.path.exists(audio_path):
-            raise FileNotFoundError(f"Audio file not found: {audio_path}")
-        audio_signal, sr = load_audio(audio_path, sr=sampling_rate)
-        return audio_signal.astype(np.float32), int(sr)
-    audio_signal, sr = AudioAsset("mary_had_lamb").audio_and_sample_rate
-    return audio_signal.astype(np.float32), int(sr)
-
-
-def _load_video(video_path: str | None, num_frames: int) -> list[np.ndarray]:
-    if video_path:
-        if not os.path.exists(video_path):
-            raise FileNotFoundError(f"Video file not found: {video_path}")
-        return video_to_ndarrays(video_path, num_frames=num_frames)
-    return VideoAsset(name="baby_reading", num_frames=num_frames).np_ndarrays
-
-
-def build_query(args) -> QueryResult:
-    prompt = (
-        "<|im_start|>user\n"
-        "<|audio_start|><|audio_pad|><|audio_end|>"
-        "Please transcribe this speech and use the video context to decide whether a reply is needed.<|im_end|>\n"
-        "<|im_start|>assistant\n"
-    )
-    audio_data = _load_audio(args.audio_path, args.sampling_rate)
-    video_frames = _load_video(args.video_path, args.num_frames)
-    return QueryResult(
-        inputs={
-            "prompt": prompt,
-            "multi_modal_data": {
-                "audio": audio_data,
-                "video": video_frames,
-            },
-            "additional_information": {
-                "aura_system_prompt": args.aura_system_prompt,
-                "tts_task_type": args.tts_task_type,
-                "tts_language": args.tts_language,
-                "tts_speaker": args.tts_speaker,
-                "tts_instruct": args.tts_instruct,
-                "tts_ref_audio": args.tts_ref_audio,
-                "tts_ref_text": args.tts_ref_text,
-                "tts_x_vector_only_mode": args.tts_x_vector_only_mode,
-                "tts_pass_token_ids": args.tts_pass_token_ids,
-            },
-        },
-        limit_mm_per_prompt={"audio": 1, "video": 1},
-    )
-
-
-def build_sampling_params() -> list[SamplingParams]:
-    asr_sampling = SamplingParams(
-        temperature=0.0,
-        top_p=1.0,
-        top_k=-1,
-        max_tokens=256,
-        detokenize=True,
-        seed=SEED,
-    )
-    aura_sampling = SamplingParams(
-        temperature=0.5,
-        top_p=1.0,
-        top_k=-1,
-        max_tokens=256,
-        detokenize=True,
-        repetition_penalty=1.0,
-        seed=SEED,
-    )
-    talker_sampling = SamplingParams(
-        temperature=0.9,
-        top_k=50,
-        max_tokens=4096,
-        detokenize=False,
-        repetition_penalty=1.05,
-        stop_token_ids=[2150],
-        seed=SEED,
-    )
-    code2wav_sampling = SamplingParams(
-        temperature=0.0,
-        top_p=1.0,
-        top_k=-1,
-        max_tokens=65536,
-        detokenize=True,
-        repetition_penalty=1.0,
-        seed=SEED,
-    )
-    return [asr_sampling, aura_sampling, talker_sampling, code2wav_sampling]
-
-
-def save_stage_output(stage_outputs, output_dir: str) -> None:
-    output = stage_outputs.request_output
-    request_id = output.request_id
-    if stage_outputs.final_output_type == "text":
-        text_output = output.outputs[0].text
-        out_txt = os.path.join(output_dir, f"{request_id}.txt")
-        with open(out_txt, "w", encoding="utf-8") as f:
-            f.write(str(text_output).strip() + "\n")
-        print(f"Request ID: {request_id}, text saved to {out_txt}")
-        print(text_output)
-        return
-
-    if stage_outputs.final_output_type == "audio":
-        mm = output.outputs[0].multimodal_output
-        if not mm or "audio" not in mm:
-            print(f"Request ID: {request_id}, no audio output")
-            return
-        audio = mm["audio"]
-        if isinstance(audio, list):
-            import torch
-
-            audio = torch.cat([(item if hasattr(item, "detach") else torch.tensor(item)).flatten() for item in audio])
-        sample_rate = int(mm.get("sr", 24000))
-        audio_numpy = audio.float().detach().cpu().numpy().flatten()
-        out_wav = os.path.join(output_dir, f"{request_id}.wav")
-        sf.write(out_wav, audio_numpy, samplerate=sample_rate, format="WAV")
-        print(f"Request ID: {request_id}, audio saved to {out_wav}")
-
-
-def main(args) -> None:
-    print("=" * 20, "\n", f"vllm version: {vllm.__version__}", "\n", "=" * 20)
-    os.makedirs(args.output_dir, exist_ok=True)
-
-    query = build_query(args)
-    prompts = [query.inputs for _ in range(args.num_prompts)]
-    if args.modalities:
-        modalities = [item.strip() for item in args.modalities.split(",") if item.strip()]
-        for prompt in prompts:
-            prompt["modalities"] = modalities
-
-    omni_kwargs = vars(args).copy()
-    omni_kwargs["model"] = args.model
-    omni = Omni(**omni_kwargs)
-    sampling_params_list = build_sampling_params()[: omni.num_stages]
-
-    try:
-        for stage_outputs in omni.generate(prompts, sampling_params_list):
-            save_stage_output(stage_outputs, args.output_dir)
-    finally:
-        omni.close()
-
-
-def parse_args():
-    parser = FlexibleArgumentParser(description="AURA Omni offline inference example")
-    parser.add_argument("--model", default=DEFAULT_MODEL, help="Top-level model name/path.")
-    parser.add_argument("--deploy-config", default=DEFAULT_DEPLOY_CONFIG, help="AURA Omni deploy config.")
-    parser.add_argument("--audio-path", "-a", default=None, help="Local audio file. Uses a built-in asset by default.")
-    parser.add_argument("--video-path", "-v", default=None, help="Local video file. Uses a built-in asset by default.")
-    parser.add_argument("--num-frames", type=int, default=16, help="Number of video frames to sample.")
-    parser.add_argument("--sampling-rate", type=int, default=16000, help="Audio sampling rate for ASR input.")
-    parser.add_argument("--num-prompts", type=int, default=1, help="Number of repeated prompts.")
-    parser.add_argument("--modalities", default="text,audio", help="Comma-separated final modalities.")
-    parser.add_argument("--output-dir", default="output_aura_omni", help="Directory for generated files.")
-    parser.add_argument(
-        "--aura-system-prompt",
-        default=(
-            "You are receiving a live video stream where the final frame is the present moment. "
-            "Respond only when a response is needed. Otherwise output '<|silent|>'. Respond in Chinese."
-        ),
-        help="System prompt for the AURA stage.",
-    )
-    parser.add_argument("--tts-task-type", default="Base", choices=["Base", "CustomVoice"], help="Qwen3-TTS task.")
-    parser.add_argument("--tts-language", default="Chinese", help="Qwen3-TTS language.")
-    parser.add_argument("--tts-speaker", default="Vivian", help="Qwen3-TTS speaker.")
-    parser.add_argument("--tts-instruct", default="", help="Optional Qwen3-TTS style instruction.")
-    parser.add_argument(
-        "--tts-ref-audio",
-        default=default_qwen3_tts_ref_audio_path(),
-        help="Base-mode reference audio path/URL.",
-    )
-    parser.add_argument(
-        "--tts-ref-text",
-        default=DEFAULT_QWEN3_TTS_REF_TEXT,
-        help="Base-mode reference audio transcript.",
-    )
-    parser.add_argument(
-        "--tts-x-vector-only-mode",
-        action="store_true",
-        help="Use speaker embedding only for Base mode (disable ICL ref_text conditioning).",
-    )
-    parser.add_argument(
-        "--tts-pass-token-ids",
-        action="store_true",
-        help="Pass AURA-generated assistant token ids directly to Qwen3-TTS. Defaults to sending text.",
-    )
-    parser.add_argument("--init-timeout", type=int, default=2000, help="Overall initialization timeout.")
-    parser.add_argument("--stage-init-timeout", type=int, default=2000, help="Per-stage initialization timeout.")
-    parser.add_argument("--log-stats", action="store_true", default=False, help="Enable stats logging.")
-    nullify_stage_engine_defaults(parser)
-    return parser.parse_args()
-
-
-if __name__ == "__main__":
-    main(parse_args())