Jev optimizes the attention mechanism, reducing MiniMax H3's generation time by 41.7%.
42 minutes ago
Beating AI News reports that Japanese developer Kamimoto has integrated Jev into MiniMax H3’s video generation workflow. Based on the operational status of each layer, Jev selects the proportion of Attention to retain from four preset options (1%, 3%, 5%, 10%), while Sparse Attention determines which specific computations can be omitted. When generating a roughly 5-second video on an RTX 4070 (12GB), the original 4-step approach took 6 minutes and 7 seconds; adding Sparse Attention plus Jev’s dynamic control cut the total runtime to 3 minutes and 34 seconds, a 41.7% reduction. However, this 41.7% speedup is not solely attributable to Jev. Sparse Attention itself can boost speed by skipping some Attention computations, and fixed retention rates of 5% or 10% also reduce runtime. Jev’s function is dynamic adjustment: allocating more computations to important layers and less to less critical ones. To date, there is no conclusive evidence that this dynamic adjustment delivers better video quality than fixed ratios, and the developer has explicitly stated that the quality has not been fully validated. The experimental code has been open-sourced.
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