Latent-Compressed Variational Autoencoder for Video Diffusion Models

2026-04-12

AI Summary

This work addresses the instability and degraded generation performance in video variational autoencoders when used with latent diffusion models, which arises from an excessive number of latent channels. To mitigate this issue, the authors propose a frequency-aware latent space compression method that selectively attenuates high-frequency components in the video latent representations, replacing conventional channel pruning. This approach preserves essential structural information while achieving the same compression ratio. The proposed method substantially improves reconstruction fidelity, enhances training stability of the diffusion model, and outperforms strong baseline methods in generation quality, thereby demonstrating the efficacy and advantages of frequency-guided compression for video generation tasks.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Generation

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Video variational autoencoders (VAEs) used in latent diffusion models typically require a sufficiently large number of latent channels to ensure high-quality video reconstruction. However, recent studies have revealed that an excessive number of latent channels can impede the convergence of latent diffusion models and deteriorate their generative performance, even when reconstruction quality remains high. We propose a latent compression method that removes high-frequency components in video latent representations rather than directly reducing the number of channels, which often compromises reconstruction fidelity. Experimental results demonstrate that the proposed method achieves superior video reconstruction quality compared to strong baselines while maintaining the same overall compression ratio.

Problem

• video VAE
• latent diffusion models
• latent channels
• reconstruction quality
• generative performance

Innovation

• latent compression
• video VAE
• high-frequency removal
• latent diffusion models
• reconstruction fidelity