vllm-omni

Operate disaggregated multimodal vLLM-Omni deployments across image, video, and audio endpoints.

5|1|Updated Apr 19, 2026
One-click install
npx skills add https://github.com/air-gapped/skills --skill vllm-omni
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: vllm-omni
Source: https://github.com/air-gapped/skills/tree/main/.claude/skills/vllm-omni
Command: npx skills add https://github.com/air-gapped/skills --skill vllm-omni

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

vLLM-Omni enables operators to deploy and operate disaggregated, multimodal inference pipelines that support image, video, and audio modalities at scale.

Core Features & Use Cases

  • End-to-end multimodal serving: /v1/images/generations, /v1/videos, /v1/audio/speech, /v1/realtime.
  • Stage-based disaggregation: Thinker → Talker → Code2Wav (or AR → DiT) with OmniConnector.
  • Model support and config: supports Qwen3-Omni, FLUX, Wan2.2, BAGEL, GLM-Image, etc; quantization, LoRA, frame interpolation, diffusion schedulers.
  • Reference materials: endpoints, models, stage-configs, diffusion docs.

Quick Start

Start a local vLLM-Omni deployment using a prepared stage-config YAML and point clients at the Omni-enabled endpoint.

Frequently Asked Questions about vllm-omni

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I deploy multimodal serving for image, video, and audio endpoints?

Deploy multimodal serving by using vLLM-Omni stage-config YAML files to disaggregate pipelines across endpoints like /v1/images/generations, /v1/videos, and /v1/audio/speech for scalable inference.

What is stage-based disaggregation in multimodal inference pipelines?

Stage-based disaggregation splits multimodal inference into sequential phases like Thinker, Talker, and Code2Wav, connected by OmniConnector to optimize processing across distinct model architectures.

Can I use vLLM-Omni to serve Qwen3-Omni and FLUX models simultaneously?

Yes, vLLM-Omni supports serving multiple architectures including Qwen3-Omni, FLUX, and Wan2.2, allowing operators to configure distinct diffusion and CFG pipelines across the same disaggregated deployment.

How do I configure diffusion schedulers and LoRA for multimodal endpoints?

Configure diffusion schedulers and LoRA by modifying the stage-config YAML files, which expose operator settings for quantization, frame interpolation, and diffusion pipelines across the supported endpoints.

Does vLLM-Omni require a root SKILL.md for endpoint deployment?

Yes, vLLM-Omni enforces the official Skill Unit definition by requiring a root SKILL.md with a name and description to validate the deployment and expose metadata for dependencies and components.

What are the limitations of disaggregated multimodal serving?

Disaggregated multimodal serving requires careful configuration of stage-based pipelines and OmniConnector, meaning operators must manage complex YAML configs to align quantization, diffusion schedulers, and model architectures correctly.