vllm-omni/docs/user_guide/diffusion/attention_backends/sage.md

SageAttention

SageAttention backends provide lossy low-precision attention for diffusion models. Validate output quality against TORCH_SDPA at the same seed before using either backend in production.

SAGE_ATTN

SAGE_ATTN uses SageAttention 2.2 with INT8-quantized attention and FP16 accumulation.

Installation

Install SageAttention into the same environment as vLLM-Omni:

git clone https://github.com/thu-ml/SageAttention.git
cd SageAttention
export EXT_PARALLEL=4 NVCC_APPEND_FLAGS="--threads 8" MAX_JOBS=32
pip install . --no-build-isolation

Verify the installation:

python -c "import sageattention; print(sageattention.__file__)"

Select it globally:

vllm-omni serve <model> --diffusion-attention-backend SAGE_ATTN

SAGE_ATTN_3

SAGE_ATTN_3 uses the SageAttention3 Blackwell implementation.

SageAttention3 installation

git clone https://github.com/thu-ml/SageAttention.git
cd SageAttention/sageattention3_blackwell
python setup.py install

Verify the installation:

python -c "import sageattn3; print(sageattn3.__file__)"

vllm-omni serve <model> --diffusion-attention-backend SAGE_ATTN_3

SAGE_ATTN_3 requires CUDA, an importable sageattn3, and a Blackwell-class GPU. Its kernel assumes the query-head count equals the key/value-head count. GQA and MQA diffusion calls therefore fall back to PyTorch SDPA for correctness.

For common configuration and platform routing, see the attention backend overview.