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name: offline-inference description: "Use vLLM-Omni local Python APIs for offline Omni and AsyncOmni inference, prompt dictionaries, diffusion sampling parameters, batched/generator flows, and multimodal output access." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Offline inference

Use this sub-skill when a user needs local Python inference with vLLM-Omni rather than an HTTP server. It covers the synchronous Omni entrypoint, the asynchronous AsyncOmni entrypoint, prompt dictionaries, OmniDiffusionSamplingParams, batched or generator-style request flows, and safe access to text, image, audio, video, latent, and trajectory outputs.

Route here for

• Building a local Python script with Omni(model=..., **kwargs) or AsyncOmni(model=..., **kwargs).
• Creating prompt dictionaries for text-to-image, image-to-image/edit, image/video/audio chat, Qwen3-Omni-style text/audio responses, or TTS-style additional_information payloads.
• Supplying diffusion sampling controls such as height, width, num_frames, num_inference_steps, guidance_scale, seed, output_type, lora_request, lora_scale, extra_args, and trajectory-return flags.
• Iterating over synchronous py_generator=True outputs or async for outputs from AsyncOmni.generate(...).
• Debugging where generated content lives on OmniRequestOutput objects.

Route elsewhere

• OpenAI-compatible HTTP serving, curl payloads, OpenAI SDK calls, realtime clients, or vllm serve ... --omni: use the sibling online-serving sub-skill.
• Deploy YAML topology, connector choice, stage placement, memory overlays, or distributed launch planning: use the sibling stage-configuration sub-skill.
• Model family selection, recipe adaptation, offload/parallel/quantization tradeoffs, cache backends, or hardware-specific model choices: use the sibling model-recipes sub-skill.
• Adding new model implementations, custom pipeline internals, adapter contracts, or maintainer tests: use the sibling model-integration sub-skill.

Always read first

1. API reference for verified signatures, prompt keys, sampling parameters, and output fields.
2. Workflows for copyable local-script patterns.
3. Troubleshooting before running large models or debugging missing modalities/OOM/version warnings.

Safety and execution checklist

• Confirm the environment can import compatible vllm and vllm_omni packages before loading a model.
• Treat model execution as GPU/accelerator- and model-cache-dependent. Do not assume network access, checkpoint availability, or gated-repo permission.
• Prefer local model paths when the user has already cached weights.
• For generated examples, start from the safe bundled helper: python scripts/build_offline_request.py --help. The helper only prints code; it does not import vLLM-Omni, contact a server, download weights, or instantiate a model.
• Keep original full-model examples as evidence only. Do not tell a future agent to run repository examples as a prerequisite for using this skill.