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

RainFusion Attention

RAINFUSION_ATTN runs MindIE-SD RainFusion (rf_v2) block-sparse video attention on Ascend NPU. It pools 128-token key blocks, ranks them per query block, and attends to the highest-scoring blocks after arranging video tokens in (t, h, w) order.

Only the video segment is sparse. Prefix rows such as text, visual conditions, and audio, plus first-frame blocks, remain dense. Unsupported calls—including warmup steps, skipped layers, missing video geometry, and video segments below 32 blocks—delegate to FLASH_ATTN, so compatible models can select RainFusion globally.

Configuration

Key	Valid values	Meaning
sparsity	finite, [0, 1]	Nominal dropped-key-block fraction; default 0.8; 0 disables sparsity
start_step	integer, >= 0	Number of early denoise steps kept dense
skip_layers	selector such as "0-3,38"	DiT blocks kept dense

vllm-omni serve MiniMaxAI/MiniMax-H3 \
  --diffusion-attention-config '{"default":{"backend":"RAINFUSION_ATTN",\
    "block_sparse":{"sparsity":0.8,"start_step":0}}}'

from vllm_omni.diffusion.data import AttentionConfig, AttentionSpec, BlockSparseSpec

config = AttentionConfig(
    default=AttentionSpec(
        backend="RAINFUSION_ATTN",
        block_sparse=BlockSparseSpec(
            sparsity=0.8,
            start_step=0,
            skip_layers="0-1",
        ),
    ),
)

Tune start_step, then sparsity, then skip_layers. Increasing the dense early-step window is usually the cheapest way to recover global structure; use layer exclusions only after a same-seed dense comparison identifies sensitive blocks.

Requirements and compatibility

RainFusion requires Ascend NPU and mindiesd. Selecting it on another platform raises. It is incompatible with ring sequence parallelism because the kernel needs the complete key sequence for ranking; use Ulysses sequence parallelism with ring_degree=1.

Geometry handling

RainFusion handles arbitrary video grids by rearranging video spatially and, when necessary, promoting a real-video suffix to the always-kept prefix. It does not pad because rf_v2 does not consume a padding attention mask.

For MiniMax-H3 at 1344x768, grid (62, 24, 42) produces 62,496 video rows. The implementation promotes 2,976 rows and leaves 59,520 sparse rows (465 x 128) so the mask and kernel tiling remain aligned.

Spatial grids aligned to 8x8 are still preferable. A latent height or width not divisible by 8 creates an always-kept suffix and reduces realized sparsity. At the image level, that means width and height divisible by 256. Protected video tails require a compatible MindIE-SD release.

For common configuration and selector behavior, see the attention backend overview and the backend selection design.