Training-free Latent Inter-Frame Pruning with Attention Recovery
Current video generation models suffer from high computational latency, making real-time applications prohibitively costly. In this paper, we address this limitation by exploiting the temporal redundancy inherent in video latent patches. To this end, we propose the Latent Inter-frame Pruning with Attention Recovery (LIPAR) framework, which detects and skips recomputing duplicated latent patches. Additionally, we introduce a novel Attention Recovery mechanism that approximates the attention values of pruned tokens, thereby removing visual artifacts arising from naively applying the pruning method. Empirically, our method increases video editing throughput by 1.45times, on average achieving 12.2 FPS on an NVIDIA A6000 compared to the baseline 8.4 FPS. The proposed method does not compromise generation quality and can be seamlessly integrated with the model without additional training. Our approach effectively bridges the gap between traditional compression algorithms and modern generative pipelines.
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Dennis Menn
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2026-03-10T18:01:59.865Z
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Dennis Menn
Dennis314
user
Dennis314
69b011889dbab2a62ec280ce
Yuedong Yang
69b011889dbab2a62ec280cf
Bokun Wang
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Xiwen Wei
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Mustafa Munir
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Feng Liang
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Radu Marculescu
69b011889dbab2a62ec280d4
Chenfeng Xu
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Diana Marculescu
Training-free Latent Inter-Frame Pruning with Attention Recovery
Current video generation models suffer from high computational latency, making real-time applications prohibitively costly. In this paper, we address this limitation by exploiting the temporal redundancy inherent in video latent patches. To this end, we propose the Latent Inter-frame Pruning with Attention Recovery (LIPAR) framework, which detects and skips recomputing duplicated latent patches. Additionally, we introduce a novel Attention Recovery mechanism that approximates the attention values of pruned tokens, thereby removing visual artifacts arising from naively applying the pruning method. Empirically, our method increases video editing throughput by 1.45times, on average achieving 12.2 FPS on an NVIDIA A6000 compared to the baseline 8.4 FPS. The proposed method does not compromise generation quality and can be seamlessly integrated with the model without additional training. Our approach effectively bridges the gap between traditional compression algorithms and modern generative pipelines.
Latent Inter-frame Pruning with Attention Recovery framework reduces video generation latency by identifying and skipping redundant latent patches while maintaining quality through attention approximation.
video generation models temporal redundancy latent patches Latent Inter-frame Pruning Attention Recovery video editing throughput generative pipelines