Jev integrated into MiniMax H3 video generation pipeline, test shows 41.7% reduction in time

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Jev begins managing Attention, reducing MiniMax H3 generation time by 41.7%

In a Beating AI news flash, Japanese developer Kamimoto integrated Jev into the MiniMax H3 video generation pipeline. Based on the operational status of each layer, it selects the Attention retention rate from four tiers: 1%, 3%, 5%, and 10%. Sparse Attention then determines which specific computations can be omitted.

On an RTX 4070 12GB, generating a video of approximately 5 seconds took 6 minutes and 07 seconds with the original 4-step method. After adding Sparse Attention and Jev dynamic control, the time dropped to 3 minutes and 34 seconds, reducing total duration by 41.7%.

However, this 41.7% improvement cannot be attributed entirely to Jev. Sparse Attention itself accelerates processing by computing less Attention, and fixed retention rates of 5% or 10% also yield speedups. Jev performs dynamic adjustment: computing more for important layers and less for less important ones.

It is not yet proven that this dynamic adjustment yields better image quality than fixed ratios, and the author explicitly stated that quality has not been fully verified. The experimental code has been open-sourced.

Original link https://m.theblockbeats.info/flash/368437

Key points

Kamimoto integrated Jev into the MiniMax H3 video generation pipeline.
Jev selects the Attention retention ratio from four tiers (1%, 3%, 5%, 10%) based on the operational status of each layer.
On an RTX 4070 12GB, the original 4-step method took 6 minutes and 07 seconds, while adding Sparse Attention and Jev dynamic control reduced the time to 3 minutes and 34 seconds.
Total duration was reduced by 41.7%.
The article states that the 41.7% improvement cannot be entirely attributed to Jev, as Sparse Attention itself also contributes to speedups.
The author notes that image quality has not yet been fully verified, and the experimental code has been open-sourced.

AI-assisted interpretation

The following is analysis, separate from reported facts. Verify important claims independently.

Jev dynamically adjusts the Attention computation load based on the importance of different layers: important layers retain more computation, while less important layers retain less.

Why it matters to readers

This method reduced video generation time in the tests described in the article, but the specific impact of Jev alone and its effect on image quality remain undetermined.

It demonstrates a method for accelerating AI video generation by reducing computation in certain parts.

Risks and unknowns

It is currently not proven that dynamic adjustment yields better image quality than a fixed ratio.
The authors explicitly state that quality has not been fully verified.
The 41.7% speedup cannot be entirely attributed to Jev.

Jev integrated into MiniMax H3 video generation pipeline, test shows 41.7% reduction in time

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