lvsa-add-model

LVSA — Long-Video Sparse Attention

Training-free block-sparse attention for video diffusion transformers. Speeds up long-video generation by up to 3.81× on HunyuanVideo at 1.5× the training horizon and 3.14× on Wan 1.3B at 6× horizon, and enables generation beyond the training horizon at lengths where dense attention OOMs on 80 GB GPUs — all with no fine-tuning, no model modifications.

Features

• Training-free: drop-in replacement for the attention layer; no weight changes.
• Model-agnostic: same engine drives single-stream (Wan), dual-stream (HunyuanVideo), and joint-attention (CogVideoX) DiTs via a thin per-model adapter.
• Single-GPU and multi-GPU: context-parallel (Ulysses) on top of standard PyTorch distributed primitives.
• vLLM-Omni integration: production-ready plugin for the vLLM-Omni serving framework — enable LVSA with one environment variable.
• Two backends: SDPA (always-on) and FlashInfer (block-sparse CSR, fastest at long sequences).
• Hardware-agnostic: SDPA path runs on CUDA and Ascend NPU (via torch_npu). FlashInfer is CUDA-only.
• Quality-positive at extended lengths: rotating-keyframe pattern actively prevents the looping/static-output failure mode dense attention exhibits beyond the training horizon.
• Composable with RIFLEx: optional RoPE-frequency rescaling for additional extrapolation headroom.

Headline numbers

SotA comparison — Wan 2.1 1.3B, 5 prompts × 3 horizons, single A100 80GB, 50 steps

Method	$2\times$ (165f)	$3\times$ (249f)	$4\times$ (333f)	Quality (VQeval comp.) at 4×
Dense	566 s	1145 s	1930 s	52.4
RIFLEx	564 s	1149 s	1931 s	53.6
UltraViCo (arXiv 2511.20123)	741 s	1544 s	2621 s	58.8
LVSA (SDPA)	502 s	796 s	1021 s	62.4
LVSA + FlashInfer	395 s	621 s	802 s	62.3

• LVSA-FI is 1.43× / 1.84× / 2.41× faster than Dense at 2× / 3× / 4×.
• LVSA-FI is 1.88× / 2.49× / 3.27× faster than UltraViCo — the strongest published baseline for length extrapolation.
• VQeval composite Δ vs Dense: +6.5 / +11.2 / +9.9. VBench-Long imaging_quality Δ vs Dense: +0.09 / +0.04 / +0.10.

HunyuanVideo 1.5 (single A100, 50 steps, 5-prompt mean)

Frame count	Ratio	Dense	LVSA	Speedup
65	0.5×	804 s	459 s	1.75×
129	1× (training reference)	2476 s	932 s	2.66×
193	1.5×	5191 s	1361 s	3.81×
257	2×	OOM	1617 s	LVSA-only (capability)

Wan 2.1 1.3B latency scaling (single A100, 50 steps, single-prompt sweep)

Frame count	Ratio	Dense	LVSA	Speedup
81	1×	168 s	194 s	0.87× (no benefit at training horizon)
161	2×	492 s	347 s	1.42×
321	4×	1610 s	700 s	2.30×
481	6×	3369 s	1072 s	3.14×

Installation

# Core library
pip install -e .

# vLLM-Omni plugin (optional — for serving)
pip install -e lvsa-vllm-omni/
pip install "vllm==0.18.0" "vllm-omni==0.18.0"   # validated pair

# VQeval (optional — for evaluating generated videos)
pip install -e vqeval/

For Docker-based deployment, see docs/install.md.

Quick start

Single-GPU generation with Wan 2.1 1.3B at 4× horizon

python examples/wan_generate.py \
    --model /path/to/Wan2.1-T2V-1.3B-Diffusers \
    --prompt "A dog running in the forest." \
    --num-frames 321 \
    --lvsa --flashinfer --rotate-keyframes --auto-keyframes \
    --output-name dog_4x.mp4

vLLM-Omni serving with HunyuanVideo 1.5

docker run --gpus '"device=0"' --ipc=host --shm-size=2g \
    -v /path/to/models:/models \
    -e DIFFUSION_ATTENTION_BACKEND=LVSA \
    -e LVSA_AUTO_KEYFRAMES=1 \
    -e LVSA_REFERENCE_LATENT_FRAMES=33 \
    lvsa-vllm-omni:latest \
    python lvsa-vllm-omni/scripts/gen_hunyuan.py \
        --model /models/HunyuanVideo-1.5-Diffusers-480p_t2v \
        --num-frames 193 \
        --prompt "Ocean waves crashing on a rocky coastline." \
        --output-name ocean_193f.mp4

See docs/quickstart.md for the complete first-run walkthrough.

How it works (one-paragraph version)