Ca2-VDM: Efficient Autoregressive Video Diffusion Model with Causal Generation and Cache Sharing (2411.16375v2)

Abstract: With the advance of diffusion models, today's video generation has achieved impressive quality. To extend the generation length and facilitate real-world applications, a majority of video diffusion models (VDMs) generate videos in an autoregressive manner, i.e., generating subsequent clips conditioned on the last frame(s) of the previous clip. However, existing autoregressive VDMs are highly inefficient and redundant: The model must re-compute all the conditional frames that are overlapped between adjacent clips. This issue is exacerbated when the conditional frames are extended autoregressively to provide the model with long-term context. In such cases, the computational demands increase significantly (i.e., with a quadratic complexity w.r.t. the autoregression step). In this paper, we propose Ca2-VDM, an efficient autoregressive VDM with Causal generation and Cache sharing. For causal generation, it introduces unidirectional feature computation, which ensures that the cache of conditional frames can be precomputed in previous autoregression steps and reused in every subsequent step, eliminating redundant computations. For cache sharing, it shares the cache across all denoising steps to avoid the huge cache storage cost. Extensive experiments demonstrated that our Ca2-VDM achieves state-of-the-art quantitative and qualitative video generation results and significantly improves the generation speed. Code is available: https://github.com/Dawn-LX/CausalCache-VDM

• The paper demonstrates causal generation to precompute conditional frame caches, reducing redundant processing in autoregressive video diffusion models.
• It introduces cache sharing that cuts storage costs and extends conditional frame length while maintaining processing efficiency.
• Extensive experiments on datasets like MSR-VTT and UCF-101 show improved inference speed and competitive FVD scores compared to state-of-the-art models.

An Insightful Overview of "Ca2-VDM: Efficient Autoregressive Video Diffusion Model with Causal Generation and Cache Sharing"

The paper "Ca2-VDM: Efficient Autoregressive Video Diffusion Model with Causal Generation and Cache Sharing" addresses the computational inefficiencies encountered in existing autoregressive Video Diffusion Models (VDMs) by introducing two key innovations: causal generation and cache sharing. This work is positioned within the broader context of video generation which has gained significant traction through advancements in diffusion models akin to their successes in static image applications.

Key Contributions and Methods

1. Causal Generation: The authors propose a model architecture which employs unidirectional feature computations. This approach ensures that the cache for conditional frames can be precomputed during earlier autoregressive steps and can be reused in subsequent iterations. This strategic move alleviates the redundancy typically found in temporal attention mechanisms across overlapping frames among adjacent chunks in traditional autoregressive VDMs. It notably addresses issues of quadratic complexity traditionally encountered due to the repeated processing of these frames.
2. Cache Sharing: By leveraging causal generation, the paper introduces a mechanism to share the cache across all denoising steps effectively. This approach significantly reduces the substantial storage costs associated with maintaining separate caches for each denoising step. Furthermore, this storage efficiency is aided by a queue design that permits the cache length (of conditional frames) to extend while still maintaining processing efficiency.
3. Experimental Validation: Through a rigorous set of experiments, Ca2-VDM has demonstrated state-of-the-art performance both qualitatively and quantitatively. On multiple datasets, including MSR-VTT, UCF-101, and Sky Timelapse, Ca2-VDM shows superior inference speed while maintaining output quality on par with or better than existing state-of-the-art models. Particularly illustrative is the fact that the FVD scores, a critical measure of generated video quality, remain competitive in numerous challenging generation contexts.

Practical and Theoretical Implications

From a practical perspective, the model’s design principle serves as a template for resource-efficient video generation systems suitable for real-time applications and contexts where long video sequences require handling. Instantaneous video generation in scenarios such as live-streaming or interactive media could significantly benefit from such efficient autoregressive mechanisms.

From a theoretical standpoint, the paper’s approach underscores the importance of causality and state management in diffusion-based temporal systems. By decoupling the process and cache mechanisms from the linear progression of time steps, the proposed model reflects a larger thematic shift towards efficiency in model architectures, common in other domains like sequence modeling in NLP but still nascent in video processing.

Future Directions

The implementation of causal generation and cache sharing opens potential avenues for future research, especially within long-term sequence prediction and video generation without requiring extensive back-propagation through time. This work can inspire future models to consider similar efficiencies, potentially integrating adaptive cache mechanisms or hybrid attention systems. Further exploration could also delve into extending these approaches for even higher resolution outputs or for non-rectilinear video formats seen in AR/VR applications.

In conclusion, Ca2-VDM significantly advances the field of efficient video generation by targeting the inherent inefficiencies in autoregressive VDMs. This is accomplished by wisely re-architecting essential components of the autoregressive sequence synthesis through causal arrangements, thereby setting a new direction for practical and efficient latent space diffusion strategies in multimedia contexts.