from vllm-omni-skills50
Generate videos (text→video, image→video, text+image→video) using vLLM-Omni and Wan2.2-style diffusion models, with guidance on parameters and performance trade
This skill documents how to use vLLM-Omni with Wan2.2 and related models to generate videos in three modes: text-to-video (T2V), image-to-video (I2V), and text+image-to-video (TI2V). It provides quick-start code samples for offline and API usage, model IDs, recommended VRAM, common generation parameters (steps, guidance scale, frames, fps), and troubleshooting tips for memory and performance.
Use it when you need programmatic video generation from prompts or reference images, when experimenting with diffusion transformer models for motion, or when building pipelines that convert text/image inputs into short video outputs. Suitable for researchers and engineers with GPU resources (24–48GB VRAM for larger models).
references/wan-models.md (has_references=true)Inferred: code and ML-focused agents (Copilot, Codex, Claude-Code) and orchestration tooling that can run vLLM or serve models behind APIs.
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