Veda attention (explicit SM89 option)

attention_backend="veda-sm89" selects learned sparse video attention for the original LightX v0.1 four-step keyframe profiles on one SM89 GPU. It also accepts the explicit FL/Ref HybridModel. auto remains dense for these profiles. Other model profiles and cooperating GPUs reject this option.

After installing Vflash's pipeline dependencies, run python -m vflash.install_veda. The installer fetches Veda revision fd59c7277ccc37ebf1a8f6823474b8c2ef2e33a0, packages its independent core and retains its MIT license and SageAttention 1.0.6 BSD notice. It does not install ComfyUI or download model weights. Supply the Veda H3 predictor separately under the MiniMax H3 Community License; the evaluated revision is 76f202874608115408d73280be9531c4ab888242.

pipeline = H3Pipeline(
    prepared, device=device, trust_local_code=True,
    attention_backend="veda-sm89",
    veda_predictor=Path("models/veda-predictor.safetensors"),
    hybrid_model=HybridModel(Path("models/MiniMax-H3/transformer_ref")),
)

generate and denoise accept --attention-backend veda-sm89 --veda-predictor FILE. For the native HTTP service set VFLASH_ATTENTION_BACKEND=veda-sm89 and VFLASH_VEDA_PREDICTOR to the mounted predictor. Missing or incompatible assets, a dependency revision mismatch, or a failed GPU self-test stops execution; there is no dense fallback. The predictor is session-owned; geometry and statistics reset before each serial request and resources are released on close.

Layers 0–4 and 45–49 retain Torch Flash attention. The other forty layers use Veda's actual triton-int8 kernel with generated keep budget 0.1. Text, audio and condition connections remain dense within that approximate kernel; this is not an exact-arithmetic guarantee. Receipts identify the actual kernel, 160 sparse and 40 dense calls per four-step request, target grid, predictor-plan match and observed sparse-layer work. The predictor's training schedule and geometry do not make this four-step combination numerically equivalent to dense H3.

An exploratory matched-input/actual-noise control on one RTX 4090 48 GB measured Hybrid denoising at 102.928 s dense and 72.867 s Veda; denoising plus media was 124.408 s versus 94.063 s. Workload: 1536×640, five seconds, four evaluations, BF16 trunk/runtime LoRA, two-slot block ring, Torch 2.11/CUDA 13.0, frozen clean conditioning and a common official decoder. Another GPU was active: these are bounded control timings, not isolated complete-request or same-quality claims. Eight sampled times and native face/fabric crops showed motion changes without new large structural failures; fine-detail softness remained. Audio semantics were not assessed. Independent complete-pipeline qualification is separate.

Six complete Python-pipeline requests additionally ran on the same single GPU, with the real official encoder and decoder: two source families, I2VA and L2VA paired controls, a larger I2VA canvas, and a one-image hybrid Ref request. At 896×512/5s, I2VA denoising was 37.507 s dense and 34.196 s Veda; L2VA was 36.582 s and 34.372 s. The matched warm L2VA request was 64.733 s versus 61.608 s (4.83% faster). Cold I2VA totals were 165.293 s and 171.789 s, including different 94.647/101.967 s initialization and first-use costs: no cold-start win is claimed. Veda I2VA at 1344×768 took 118.634 s warm, including 74.010 s denoising; hybrid Ref at 896×512 took 59.002 s warm. This shows why one speed ratio must not be applied to every canvas. Native-size crops showed more facial detail on the larger canvas, alongside the extra cost; existing object-contact errors persisted. Changed expressions/reflections are visible in the attention comparison. Audio semantic equivalence and universal quality gains remain unestablished.