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vllm-omni

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Aktualisiert7. Juni 2026 um 12:42

vLLM-Omni output-side multimodal generation — image (FLUX.1/2, Qwen-Image, GLM-Image, BAGEL, SD3.5, HunyuanImage-3.0), video (Wan2.1/2.2, LTX-2, HunyuanVideo-1.5), TTS (Qwen3-TTS, CosyVoice3, Voxtral-TTS), any-to-any omni (Qwen3-Omni, Qwen2.5-Omni, MiMo-Audio) via `vllm serve --omni`. Stage-based disaggregation (OmniConnector + Mooncake + RDMA), `/v1/images/generations`, async+sync `/v1/videos`, `/v1/audio/speech` with voice-upload, PCM16 WebSocket `/v1/realtime`, Ulysses/Ring SP + CFG-parallel, DiT FP8/INT8/GGUF, CUDA/ROCm/NPU/XPU/MUSA matrix, release pitfalls (v0.19.0rc1 FLUX regression, GLM-Image transformers>=5.0, Qwen3-TTS enforce-eager).

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vllm-deployment

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Use this skill when authoring, reviewing, or fixing a vLLM Kubernetes manifest, Docker/Podman pod, or OpenShift ServingRuntime — even when the user does not say "vllm". Triggers on: lab cluster performance practices, cache mount + survival across pod restarts (/root/.cache, VLLM_CACHE_ROOT, TORCHINDUCTOR_CACHE_DIR, TRITON_CACHE_DIR, "do we have caches saved"), HF_TOKEN secret in pod env, liveness + readiness probe tuning (initialDelaySeconds, failureThreshold, "pod takes 12 min to boot"), serve_args review, --enforce-eager rationale, MoE deployment ("ep2 dp2", --enable-expert-parallel, expert-parallel sizing), TP/PP sizing, ConfigMap parser-plugin mount, image tag selection, cold-boot reduction, multi-node LWS + Ray, control planes (llm-d, production-stack, AIBrix, NVIDIA Dynamo, KServe), KEDA autoscaling, GAIE routing, disaggregated prefill/decode (Nixl/Mooncake/LMCache/MORI-IO), RHAIIS on OpenShift (SCC, arbitrary UID, Routes 60s, ModelCar, air-gapped). Lead with operator intent, not vendor names.

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skill-improver

air-gapped/skills

Autoresearch loop for Claude Code skills — greedy keep/discard hill climbing on a 10-dimension quality rubric, with blind subagent validation for self-scoring bias, plus a `freshen` mode that probes external references (release notes, docs, deprecation signals) and applies verified updates, plus a `trigger` mode that measures and tunes the skill's frontmatter description until it reliably fires when it should and stays silent when it shouldn't (60/40 train/test split, 3 runs/query, blinded test scores).

2026-07-183

redis-to-valkey

air-gapped/skills

Migrate Redis deployments (especially Bitnami Redis Helm charts in Sentinel HA mode) to Valkey on Kubernetes, including fully air-gapped clusters. Core knowledge: the RDB-version wall (Valkey replicates/loads only from Redis ≤ 7.2.x; Redis 7.4+ writes RDB v12 which Valkey rejects; Valkey 9 writes its own v80 — a one-way door), the two transfer layers (version-bound REPLICAOF/DUMP-RESTORE vs version-agnostic logical replay with RedisShake or rdb-cli), a side-by-side cutover runbook, Valkey chart selection (groundhog2k / CloudPirates / official valkey-io tradeoffs), Bitnami-redis→valkey values translation, consumer-app reconnection (Sentinel discovery, master-set names, frozen redis_version 7.2.4), Prometheus exporter continuity, air-gap tool/image mirroring, and Argo CD source rewiring away from charts.bitnami.com. Part of the bitnami-exit suite.

2026-07-183

airgap-vetting

air-gapped/skills

Vet an open-source product for air-gap readiness BEFORE adoption. Answers eight questions — telemetry, does-opt-out-actually-work, proxy-in-disguise over a hosted API, runtime downloads, custom-CA support, feature-level content egress / offline degradation, day-two sustainment (feed mirroring, staleness), and a 4-grade verdict (air-gap-native / possible-with-mirror / proxy-in-disguise / no-go). Two-pass: static grep of source + container image (bundled fingerprint tables work offline), then an optional dynamic harness (--network=none, egress deny+log, mitmproxy CA injection, faketime). Writes AIRGAP-VETTING.json + .md. Use for any "should we adopt X?" question in a disconnected environment, not just when the user says "air gap".

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triage

air-gapped/skills

Triage a batch of raw security findings. Verify each is real, collapse duplicates, re-rank by impact-on-asset x exploitability, and tag with an owner. Takes a directory or file of scanner output and writes TRIAGE.json + TRIAGE.md sorted by what actually needs engineering attention. Use when asked to "triage findings", "validate scanner output", "prioritize vulns", or "review the backlog". Runs interactively by default; pass --auto to skip the interview.

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patch

air-gapped/skills

Generate candidate fixes for verified security findings. Consumes TRIAGE.json (preferred), VULN-FINDINGS.json, or an execution-harness results directory. Static-analysis input gets a per-finding patch subagent + an independent reviewer and is written as inert diffs for human review; results-directory input from an external execution harness (the defending-code reference pipeline, if installed) is delegated to its verified build→reproduce→regress→re-attack patch ladder. Writes PATCHES/bug_NN/{patch.diff,patch_result.json}, PATCHES.md, and PATCHES.json. Use when asked to "fix the findings", "patch these vulns", "generate fixes", or "close the loop on triage".

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name	vllm-omni
description	vLLM-Omni output-side multimodal generation — image (FLUX.1/2, Qwen-Image, GLM-Image, BAGEL, SD3.5, HunyuanImage-3.0), video (Wan2.1/2.2, LTX-2, HunyuanVideo-1.5), TTS (Qwen3-TTS, CosyVoice3, Voxtral-TTS), any-to-any omni (Qwen3-Omni, Qwen2.5-Omni, MiMo-Audio) via `vllm serve --omni`. Stage-based disaggregation (OmniConnector + Mooncake + RDMA), `/v1/images/generations`, async+sync `/v1/videos`, `/v1/audio/speech` with voice-upload, PCM16 WebSocket `/v1/realtime`, Ulysses/Ring SP + CFG-parallel, DiT FP8/INT8/GGUF, CUDA/ROCm/NPU/XPU/MUSA matrix, release pitfalls (v0.19.0rc1 FLUX regression, GLM-Image transformers>=5.0, Qwen3-TTS enforce-eager).
when_to_use	Trigger on any vLLM deployment producing non-text output (image/video/audio) or any-to-any omni model, or model names ending `-Image`/`-TTS`/`-Omni`/`-Video`. Keywords — `vllm serve --omni`, `vllm-omni`, `/v1/images/generations`, `/v1/videos`, `/v1/audio/speech`, `/v1/audio/voices`, `/v1/realtime`, `async_chunk`, `stage_configs_path`, OmniConnector, MooncakeStore, OmniDiffusionSamplingParams, FlowUniPC, TeaCache, Cache-DiT, Sage/Ring/Ulysses, `--ulysses-degree`, `--ring-degree`, Thinker/Talker/Code2Wav, BAGEL, Wan2.2, FLUX.2-klein, ComfyUI bridge, verl RL. Narrow phrasings — "serve Qwen-Image", "Qwen3-Omni streaming audio", "async video job". Also implicit — "deploy image gen", "TTS endpoint", "video gen pipeline", "audit omni", "deploy-memo for {model}-Image/-TTS/-Video". NOT for embeddings/reranking/STT/OCR (→ `vllm-input-modalities`).

vLLM-Omni — output-side multimodal serving

Target: operators who serve image / video / audio / any-to-any generation models with the vLLM-Omni fork of vLLM. vllm-omni extends upstream vLLM (same CUDA/ROCm/NPU/XPU runtime, same OpenAI-compat API server) to add non-autoregressive DiT models, multi-stage pipeline execution, diffusion schedulers, CFG plumbing, and real-time streaming audio I/O — things upstream vLLM does not ship.

This skill is a reference, not a tutorial. SKILL.md holds the mental model, quick-answer router, top pitfalls, and operator cheat sheet. The references/ files hold endpoint catalogs, supported-model tables, stage-config grammar, and the diffusion/DiT details. Read only the reference file that matches the question.

The one thing to know before anything else

vllm-omni is not a fork — it layers on top of upstream vLLM, registers OmniModelConfig, and adds one CLI flag: --omni. Adding --omni to vllm serve routes the server through vllm_omni.entrypoints. As of v0.20.0 the old vLLM entrypoint-hijack / patch.py early-import mechanism was removed — the v0.20.0 release notes state "removal of the old vLLM entrypoint hijack, and runtime changes needed for the 0.20.0 integration path (#3232, #3082, #3352, #3393, #2306)". The omni runtime is now rebased onto upstream vLLM v0.20.0 (rebase PR #3232) rather than monkey-patching it. The architectural claim is to decompose any-to-any models into a graph of disaggregated stages (Thinker / Talker / Code2Wav for Qwen3-Omni; AR-encoder / DiT for Qwen-Image) connected via OmniConnector, so each stage scales independently. The paper (arXiv:2602.02204) claims up to 91.4% JCT reduction vs an unspecified baseline — treat as an architectural argument, not a deployment benchmark.

Version alignment is strict: vllm-omni major.minor must match upstream vLLM major.minor. v0.20.0 (2026-05-07) is the current stable, rebased on upstream vLLM v0.20.0 (CUDA 13.0 / PyTorch 2.11). First stable was v0.14.0 (2026-01-31). Latest pre-release is v0.21.0rc1 (2026-05-25). The v0.19.0rc1 FLUX.1-dev regression (#2730) is fixed in v0.20.0 stable (PR #2760) — no version pin needed anymore.

Quick-answer router

Serving a specific endpoint → references/endpoints.md

/v1/images/generations, /v1/images/edits (DALL·E-shape)
/v1/videos (async job) + /v1/videos/sync (raw MP4, 1200s timeout)
/v1/audio/speech, /v1/audio/voices (list + upload), /v1/audio/speech/batch, /v1/audio/speech/stream (WebSocket)
/v1/realtime (WebSocket PCM16 in/out for Qwen3-Omni)
/v1/chat/completions with diffusion via extra_body

Picking a model → references/models.md

Full supported-architecture → HuggingFace-ID table
Per-model platform matrix (CUDA / ROCm / NPU / XPU / MUSA)
Known-issue flags per family

Writing / debugging stage configs → references/stage-config.md

OmniModelConfig + StageConfig YAML grammar
OmniConnector types (Shared-memory / Mooncake-Store / Mooncake-Transfer-Engine / RDMA / Yuanrong)
Pipeline edge validation, entry-point requirement
stage_id, model_stage, worker_type, engine_output_type, async_chunk

DiT-specific questions → references/diffusion.md

Schedulers (FlowUniPC + model-specific)
CFG plumbing (dual CFG for Wan2.2, true_cfg_scale for Qwen-Image, cfg_branch_past_key_values)
Caches: TeaCache / Cache-DiT / latent cache / noise_pred cache
Quantization: FP8 (Flux #1640), INT8 (Z-Image/Qwen-Image #1470), GGUF (#1755) — all per-component via ComponentQuantizationConfig
Ulysses / Ring sequence parallel, CFG-parallel merged-batch TP

Qwen3-Omni realtime + Qwen3-TTS → references/realtime-tts.md

PCM16 mono @ 16 kHz in / 24 kHz out, OpenAI realtime event shape
async_chunk: false requirement
Qwen3-TTS CustomVoice / VoiceDesign / Base modes, 12 Hz / 25 Hz tokenizers
Voice-upload surface (10 MB cap, consent/ref_text/speaker_description required)

The top operator mistakes this skill exists to prevent

/v1/realtime with async_chunk: true. The realtime WebSocket rejects at connection if async_chunk is enabled (api_server.py:1208). Use the default stage-config (vllm_omni/model_executor/stage_configs/qwen3_omni_moe.yaml) — not the ...moe_async_chunk.yaml variant — for realtime sessions. The async-chunk config is for higher-throughput non-realtime Qwen3-Omni serving.
Qwen3-TTS with CUDA graphs on (v0.18 only). Issue #2866: on v0.18 the code2wav stage crashed when enforce_eager: false, so --enforce-eager was mandatory. #2866 is CLOSED (2026-04-29) and v0.20.0 ships TTS CUDA-graph capture + shared memory pools (release notes cite #2690/#2758/#2803), lifting the requirement. On v0.20.0+ keep --trust-remote-code but --enforce-eager is no longer forced — drop it to regain CUDA-graph throughput, and re-test latency.
Running the v0.19.0rc1 FLUX artifacts. Issue #2730: FLUX.1-dev generated incorrect images in v0.19.0rc1 (T5 text-encoder bug). Fixed in v0.20.0 stable (PR #2760, merged 2026-04-24). The v0.19.0rc1 tag artifacts are still broken, so do not deploy that specific tag — use v0.20.0+ for any FLUX deployment.
GLM-Image on v0.18 without transformers>=5.0. On v0.18 GLM-Image required a manual pip install 'transformers>=5.0' before serving (the default wheel pinned transformers below 5.0 and GLM-Image silently failed to load). v0.20.0 ships Transformers 5.x compatibility fixes from the upstream rebase — verify whether the manual upgrade is still needed on v0.20.0+ before adding it.
PCM format on /v1/realtime. Qwen3-Omni realtime hard-expects 16-bit PCM mono @ 16 kHz input, outputs PCM at 24 kHz. Stereo, 8 kHz, 24-bit, or WAV-with-header inputs produce garbage or silent failures. Use the reference client in examples/online_serving/qwen3_omni/openai_realtime_client.py as a template.
Default guidance_scale=0.0 sentinel. OmniDiffusionSamplingParams treats guidance_scale=0.0 as "not provided" — passing 0.5 intending partial CFG gets coerced. To disable CFG, leave the field unset; to enable, pass > 1.0.
Prefix caching on a stage that emits latents. Any stage with engine_output_type: latent (thinker stages producing hidden states) must set enable_prefix_caching: false in its engine_args. Prefix cache reuses token-level blocks, which makes no sense for latent outputs — leaving it on surfaces as intermittent stale responses.
/v1/videos/sync for long jobs. The sync endpoint has a hardcoded VIDEO_SYNC_TIMEOUT_S (default ~1200s) and returns 504 past that. Long Wan2.2 / HunyuanVideo-1.5 jobs should use POST /v1/videos (async), then poll GET /v1/videos/{id} and fetch /content.
Orphan processes after a Wan2.2 crash. Issue #2768: killing one Wan2.2 worker leaves sibling stage processes alive. Wrap launches in a process group + pkill -9 sweep on failure, or use systemd's KillMode=control-group.
Assuming vllm-omni serves text-only models. If the model has no multimodal output, use stock vLLM — vllm-omni adds overhead for features a text-only model won't exercise, and the community skill explicitly recommends against it. The decision rule: output modality is non-text OR the model name ends -Omni/-Image/-TTS/-Video → vllm-omni; otherwise stock vLLM.

Operator cheat sheet

Install

uv venv --python 3.12 --seed
source .venv/bin/activate

# CUDA — pin upstream vLLM to the matching minor:
uv pip install vllm==0.20.0 --torch-backend=auto

# ROCm:
uv pip install vllm==0.20.0+rocm700 \
  --extra-index-url https://wheels.vllm.ai/rocm/0.20.0/rocm700

# Then the omni package (prebuilt wheel OR editable clone):
uv pip install vllm-omni==0.20.0
# OR: git clone https://github.com/vllm-project/vllm-omni && cd vllm-omni && uv pip install -e .

Python 3.12 is required (3.11 is not supported). Docker image: vllm/vllm-omni:0.20.0.

Serving canonical forms

# Text-to-image (default Z-Image-Turbo quickstart):
vllm serve Tongyi-MAI/Z-Image-Turbo --omni --port 8091

# Qwen-Image with tensor parallelism:
vllm serve Qwen/Qwen-Image --omni --tensor-parallel-size 2 --port 8091

# Qwen3-Omni realtime (default stage config, async_chunk OFF):
vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni \
  --tensor-parallel-size 2 --gpu-memory-utilization 0.9 --port 8091

# Qwen3-Omni high-throughput non-realtime (async_chunk ON):
vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni \
  --stage-configs-path vllm_omni/model_executor/stage_configs/qwen3_omni_moe_async_chunk.yaml

# Qwen3-TTS (trust-remote-code; --enforce-eager only required on v0.18, lifted by TTS CUDA-graph capture in v0.20.0+):
vllm serve Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice --omni \
  --trust-remote-code --task-type CustomVoice

# Wan2.2 T2V with Ulysses sequence parallel:
vllm serve Wan-AI/Wan2.2-T2V-A14B-Diffusers --omni \
  --ulysses-degree 4 --ulysses-mode strict --port 8091

Common extra flags

Flag	Purpose
`--omni`	Enable vllm-omni entrypoint (load-bearing)
`--stage-configs-path`	Override default stage-config YAML
`--task-type`	Qwen3-TTS: `CustomVoice` \| `VoiceDesign` \| `Base`
`--ulysses-degree` / `--usp`	Ulysses sequence parallelism for DiT
`--ulysses-mode`	`strict` (divisibility) \| `advanced_uaa` (uneven shapes)
`--ring-degree`	Ring-based parallelism
`--num-gpus`	GPUs allocated to diffusion pipeline
`--omni-master-address` / `-oma`	Orchestrator hostname (multi-node)
`--omni-master-port` / `-omp`	Orchestrator port
`--stage-id`	Single-stage mode (requires master address)
`--worker-backend`	`multi_process` \| `ray`
`--model-class-name`	Override diffusion pipeline class

Key numbers to memorize

Metric	Value
Current stable	v0.20.0 (2026-05-07, rebased on vLLM v0.20.0, CUDA 13.0 / PyTorch 2.11)
Latest pre-release	v0.21.0rc1 (2026-05-25)
First stable	v0.14.0 (2026-01-31)
Minimum Python	3.12
`/v1/realtime` input	PCM16 mono @ 16 kHz
Qwen3-Omni audio output rate	24 kHz
Qwen3-TTS tokenizer rate	12 Hz or 25 Hz
`/v1/videos/sync` timeout	~1200s (hard)
Voice upload size cap	10 MB
Paper claim	up to 91.4% JCT reduction vs "baseline" (unspecified)
Qwen3-TTS published RTF (v0.16)	0.22–0.45
MiMo-Audio published RTF (v0.16)	~0.2 (11× baseline)

Paired skills

vllm-input-modalities — the complement: text embeddings, reranking, STT (Whisper/Voxtral-STT/Qwen3-ASR), OCR (DeepSeek-OCR). Trigger together when the deployment does both input and output non-text modalities.
vllm-nvidia-hardware — for sizing GB300/NVL72/Rubin capacity for diffusion + CFG-parallel + Ulysses footprints.
vllm-caching — OmniConnector borrows Mooncake from upstream vLLM; the caching skill has the connector-config surface.
vllm-observability — vllm-omni inherits upstream /metrics; profiler hooks (OmniTorchProfilerWrapper) add stage_id + rank awareness to trace files.

Source policy

All claims are cited with file:line, release-note PR refs, or issue IDs. Full anchor list + community channels + third-party plugin catalog in references/sources.md. Compiled 2026-04-18 against v0.18.0; last freshened 2026-05-28 (rebased to v0.20.0 stable; refresh again when the next upstream-rebase release ships).