lvsa-quickstart

Install LVSA, select a backend, and generate a first long video.

18|4|Updated May 21, 2026

Search Tags:#verification #installation #video-generation #diffusion-models #sparse-attention #backend-selection #lvsa

Ready to run

npx skills add https://github.com/JiusiServe/LongVideoSparseAttention --skill lvsa-quickstart

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: lvsa-quickstart
Source: https://github.com/JiusiServe/LongVideoSparseAttention/tree/main/skills/lvsa-quickstart
Command: npx skills add https://github.com/JiusiServe/LongVideoSparseAttention --skill lvsa-quickstart

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

LVSA Quickstart provides a straightforward path to install LVSA and generate your first long video, enabling rapid validation of sparse-attention acceleration on video diffusion models.

Core Features & Use Cases

Install LVSA and pick a backend (SDPA or FlashInfer) for long-video generation.
Configure LVSA_REFERENCE_LATENT_FRAMES per model and verify LVSA engagement via logs.
Run a first generation to benchmark speedups and ensure the sparse path is active.

Quick Start

Install LVSA, select a backend, configure LVSA_REFERENCE_LATENT_FRAMES for your model, run a test generation, and verify sparse-attention engagement in the logs.

Frequently Asked Questions about lvsa-quickstart

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

FAQPage Schema

How do I install and set up LVSA for long video generation?▼

To install LVSA for long video generation, you need Python 3.12 and a virtual environment. You then follow the installation steps, select a backend like SDPA or FlashInfer, and configure LVSA_REFERENCE_LATENT_FRAMES for your model.

How do I verify that sparse attention is engaged in LVSA video diffusion?▼

You can verify sparse attention engagement in LVSA by checking the LVSA log lines during your test generation. Confirming these log lines ensures the sparse path is active and accelerating your video diffusion model.

How do I choose between SDPA and FlashInfer backends for LVSA?▼

Choosing between SDPA and FlashInfer backends for LVSA involves installing the optional dependencies for your preferred backend. Both backends enable sparse-attention acceleration for long video generation, allowing you to benchmark speedups.

What is LVSA_REFERENCE_LATENT_FRAMES and how do I configure it?▼

LVSA_REFERENCE_LATENT_FRAMES is a configuration parameter you set per model when using LVSA. Configuring it correctly ensures the sparse-attention mechanism engages properly during long video generation.

Does LVSA work with vllm-omni for sparse attention acceleration?▼

Yes, LVSA works with vllm-omni by installing it as an optional dependency. This setup allows you to validate sparse-attention acceleration on video diffusion models and benchmark the resulting speedups.