WF-VAE: Enhancing Video VAE by Wavelet-Driven Energy Flow for Latent Video Diffusion Model (2411.17459v3)

Abstract: Video Variational Autoencoder (VAE) encodes videos into a low-dimensional latent space, becoming a key component of most Latent Video Diffusion Models (LVDMs) to reduce model training costs. However, as the resolution and duration of generated videos increase, the encoding cost of Video VAEs becomes a limiting bottleneck in training LVDMs. Moreover, the block-wise inference method adopted by most LVDMs can lead to discontinuities of latent space when processing long-duration videos. The key to addressing the computational bottleneck lies in decomposing videos into distinct components and efficiently encoding the critical information. Wavelet transform can decompose videos into multiple frequency-domain components and improve the efficiency significantly, we thus propose Wavelet Flow VAE (WF-VAE), an autoencoder that leverages multi-level wavelet transform to facilitate low-frequency energy flow into latent representation. Furthermore, we introduce a method called Causal Cache, which maintains the integrity of latent space during block-wise inference. Compared to state-of-the-art video VAEs, WF-VAE demonstrates superior performance in both PSNR and LPIPS metrics, achieving 2x higher throughput and 4x lower memory consumption while maintaining competitive reconstruction quality. Our code and models are available at https://github.com/PKU-YuanGroup/WF-VAE.

• The paper introduces WF-VAE, a novel architecture leveraging wavelet transforms to efficiently encode video data for latent diffusion models.
• It employs a causal cache for block-wise inference, ensuring a seamless latent flow and mitigating spatial-temporal artifacts.
• Experimental results show state-of-the-art PSNR and LPIPS metrics, doubling throughput and reducing memory consumption by fourfold compared to traditional methods.

Overview of "WF-VAE: Enhancing Video VAE by Wavelet-Driven Energy Flow for Latent Video Diffusion Model"

The research paper "WF-VAE: Enhancing Video VAE by Wavelet-Driven Energy Flow for Latent Video Diffusion Model" presents an innovative approach to addressing the computational challenges in training Latent Video Diffusion Models (LVDMs). The work focuses on improving the efficiency of Video Variational Autoencoders (VAEs), which, when used in LVDMs, typically become a bottleneck due to increased video resolution and duration.

Innovations and Methodology

The paper introduces WF-VAE (Wavelet Flow Variational Autoencoder), a novel autoencoder architecture that leverages wavelet transforms for efficient video encoding. Unlike traditional methods, which primarily rely on convolutional operations, WF-VAE employs a multi-level wavelet transform to decompose videos into various frequency-domain components. This decomposition allows for more efficient encoding of critical video information, particularly capturing and preserving low-frequency content via a defined energy flow pathway.

Additionally, the paper presents a "Causal Cache" method to enhance block-wise inference. This is essential for maintaining the continuity of the latent space without the spatial and temporal artifacts typical in conventional tiling strategies. The approach uses causal convolution to ensure a seamless flow of information across video chunks, achieving lossless inference results.

Results and Implications

WF-VAE demonstrates significant improvements over existing video VAEs in terms of both computational efficiency and reconstruction quality. Experimentally, WF-VAE achieves state-of-the-art performance metrics, such as superior PSNR (Peak Signal-to-Noise Ratio) and LPIPS (Learned Perceptual Image Patch Similarity), while doubling the throughput and reducing memory consumption by fourfold compared to traditional methods.

Practically, WF-VAE's enhancements could lead to more efficient video generation and compression technologies. Theoretically, this approach suggests a paradigm shift towards frequency-domain processing in VAEs, emphasizing the potential of wavelet transforms in AI model architectures. The implications extend to various applications, including real-time video processing, remote video analytics, and more scalable systems for large-scale video data handling.

Future Directions

The paper hints at future research avenues, such as optimizing and adapting the WF-VAE framework to other forms of media beyond video, potentially improving efficiency in image generation and 3D data encoding. Further exploration into parameter reductions, hinted at as a limitation regarding redundant parameters in the decoder, may optimize the WF-VAE's architectural efficiency.

Overall, WF-VAE presents a compelling advancement in video processing within AI, suggesting that computational efficiency and quality need not be mutually exclusive. This work has the potential to inform and inspire future developments in video VAEs and latent diffusion modeling, contributing to more scalable and efficient AI solutions.