Latent Inter-Frame Pruning: A Training-Free Method Bridging Traditional Video Compression and Modern Diffusion Transformers for Efficient Generation

• URL: http://arxiv.org/abs/2604.23858v1
• Date: Sun, 26 Apr 2026 20:03:11 GMT
• Title: Latent Inter-Frame Pruning: A Training-Free Method Bridging Traditional Video Compression and Modern Diffusion Transformers for Efficient Generation
• Abstract summary: Video generation, while capable of generating realistic videos, is computationally expensive and slow, prohibiting real-time applications.<n>We observe that video latents encoded via an autoencoder contain redundancy along the temporal axis.<n>We propose the Latent Inter-frame Pruning framework to prune (skip the re-computation of) duplicated latent patches, thereby reducing computational burden and increasing throughput.
• Score: 2.639628765667673
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Video generation, while capable of generating realistic videos, is computationally expensive and slow, prohibiting real-time applications. In this paper, we observe that video latents encoded via an autoencoder under the Latent Diffusion Model (LDM) framework contain redundancy along the temporal axis. Analogous to how traditional video compression algorithms avoid transmitting redundant frame data, we propose the Latent Inter-frame Pruning framework to prune (skip the re-computation of) duplicated latent patches, thereby reducing computational burden and increasing throughput. However, direct pruning results in visual artifacts due to the discrepancy between full-sequence training and pruned inference. To resolve these artifacts, we propose an Attention Recovery mechanism to bridge the train-inference gap. With our proposed method, we increase video editing throughput by 1.44$\times$, achieving 12.44 FPS on an NVIDIA RTX 6000 while maintaining video quality. We hope our work inspires further research into integrating traditional video compression methods with modern video generation pipelines. This work is a preliminary work on Training-free Latent Inter-Frame Pruning with Attention Recovery.

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