36a5e9cfd2
Signed-off-by: Gael Glorian <gael.glorian@gmail.com> Signed-off-by: Ioannis Lamprou <yiannis.lamprou@gmail.com> Signed-off-by: Zhen Zhang <zhen.zhang.fr@huawei.com> Co-authored-by: Ioannis Lamprou <yiannis.lamprou@gmail.com> Co-authored-by: Zhen Zhang <zhen.zhang.fr@huawei.com>
46 lines
2.2 KiB
Bash
46 lines
2.2 KiB
Bash
#!/usr/bin/env bash
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# Start an LVSA-enabled vLLM-Omni server for Wan or HunyuanVideo.
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#
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# LVSA is selected as the attention backend via --diffusion-attention-config (see
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# below); the `LVSA_*` env vars only tune it. The `lvsa-vllm-omni` plugin registers
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# itself through vLLM-Omni's `general_plugins` entry points — nothing in vLLM-Omni
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# core is modified. Watch the log for "[LVSA] Geometry detected" to confirm sparse
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# attention is active for your request geometry.
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#
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# MODEL=/path/to/Wan2.1-T2V-1.3B-Diffusers MODEL_FAMILY=wan FRAMES=161 bash serve_lvsa.sh
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# MODEL=/path/to/HunyuanVideo-1.5-... MODEL_FAMILY=hunyuan FRAMES=193 bash serve_lvsa.sh
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set -euo pipefail
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MODEL=${MODEL:?set MODEL=/path/to/Wan-or-HunyuanVideo-Diffusers}
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MODEL_FAMILY=${MODEL_FAMILY:-wan} # wan | hunyuan
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PORT=${PORT:-8000}
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FRAMES=${FRAMES:-161} # target horizon in frames
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VAE_T=${VAE_T:-4} # VAE temporal compression (Wan/Hunyuan = 4)
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BACKEND=${LVSA_BACKEND:-flashinfer} # flashinfer (fused, fastest) | sdpa
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T_LAT=$(( (FRAMES - 1) / VAE_T + 1 ))
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# Per-family training reference horizon (Wan train ref = 21, Hunyuan = 33).
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case "$MODEL_FAMILY" in
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wan) REFERENCE=${REFERENCE:-21} ;;
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hunyuan) REFERENCE=${REFERENCE:-33} ;;
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*) echo "MODEL_FAMILY must be 'wan' or 'hunyuan'"; exit 1 ;;
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esac
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export LVSA_BACKEND="$BACKEND"
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export LVSA_TOTAL_LATENT_FRAMES="$T_LAT"
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export LVSA_REFERENCE_LATENT_FRAMES="$REFERENCE"
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export LVSA_ROTATE_KEYFRAMES=${LVSA_ROTATE_KEYFRAMES:-1}
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echo "[lvsa-app] serving $MODEL ($MODEL_FAMILY) on :$PORT"
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echo "[lvsa-app] LVSA backend=$BACKEND T_lat=$T_LAT (frames=$FRAMES) reference=$REFERENCE"
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echo "[lvsa-app] confirm activation by grep '\\[LVSA\\] Geometry detected' in this log"
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# vLLM-Omni 0.22 selects the LVSA attention backend per-role via
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# --diffusion-attention-config. The backend is what drives sparse attention
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# here; it engages under tensor-parallel too, unlike the LVSA_*_HOOK monkey-patch
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# path (which falls back to dense under sequence-parallel), so we deliberately do
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# NOT set a hook.
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exec vllm serve "$MODEL" --omni --host 0.0.0.0 --port "$PORT" \
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--diffusion-attention-config '{"per_role": {"self": {"backend": "LVSA"}}}' \
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--init-timeout 1800 ${EXTRA_ARGS:-}
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