Pyramidal Flow Matching for Efficient Video Generative Modeling (2410.05954v2)

Abstract: Video generation requires modeling a vast spatiotemporal space, which demands significant computational resources and data usage. To reduce the complexity, the prevailing approaches employ a cascaded architecture to avoid direct training with full resolution latent. Despite reducing computational demands, the separate optimization of each sub-stage hinders knowledge sharing and sacrifices flexibility. This work introduces a unified pyramidal flow matching algorithm. It reinterprets the original denoising trajectory as a series of pyramid stages, where only the final stage operates at the full resolution, thereby enabling more efficient video generative modeling. Through our sophisticated design, the flows of different pyramid stages can be interlinked to maintain continuity. Moreover, we craft autoregressive video generation with a temporal pyramid to compress the full-resolution history. The entire framework can be optimized in an end-to-end manner and with a single unified Diffusion Transformer (DiT). Extensive experiments demonstrate that our method supports generating high-quality 5-second (up to 10-second) videos at 768p resolution and 24 FPS within 20.7k A100 GPU training hours. All code and models are open-sourced at https://pyramid-flow.github.io.

• The paper introduces a pyramidal flow matching algorithm that reduces computational redundancy by operating with spatial and temporal pyramids.
• It unifies video generative modeling into a single Diffusion Transformer architecture for end-to-end optimization.
• Experimental results show efficient training with significantly reduced GPU hours while generating high-quality videos.

Pyramidal Flow Matching for Efficient Video Generative Modeling

The paper "Pyramidal Flow Matching for Efficient Video Generative Modeling" addresses a significant challenge in video synthesis: the computational demands required to model extensive spatiotemporal spaces. Traditional video generation involves complex data handling due to high-dimensional video content, primarily because current models utilize cascaded architectures that handle different resolutions separately. This work proposes a novel solution, the pyramidal flow matching framework, which enhances efficiency in video generative modeling by introducing both spatial and temporal pyramids.

Overview of Contributions

1. Pyramidal Flow Matching Algorithm: The proposed method reinterprets the conventional generation trajectory as a series of pyramid stages. This new framework operates only at full resolution during the final pyramid stage, significantly reducing computational redundancy. By utilizing piecewise flow, the approach interpolates between compressed (lower resolution) and clearer (higher resolution) latent spaces, facilitating efficient data handling in earlier stages.
2. Unified Model Training: A key aspect of the framework is its unified model architecture, which ensures continuity and maintains a single model's efficiency across all pyramid stages. The integration into a unified Diffusion Transformer (DiT) allows end-to-end optimization, enabling simultaneous generation and decomposition without the need for employing multiple models at different stages.
3. Temporal Pyramid Design: To further address computational efficiency, the method compresses full-resolution historical data when predicting future frames. This approach reduces token counts during training significantly, thereby enhancing training expedience without sacrificing quality. The temporal pyramid design ensures the redundancy in historical frames is minimized by progressively using lower-resolution histories.

Experimental Results

The proposed methodology was rigorously tested using large-scale datasets and evaluated on standardized benchmarks like VBench and EvalCrafter. It demonstrated notable efficiency improvements, generating high-quality 5-second to 10-second videos with reduced computational costs. Specifically, the framework required only 20.7k A100 GPU training hours, which is a significant reduction compared to existing technologies.

Implications and Future Work

The efficient modeling introduced by pyramidal flow matching opens new avenues for scalable video generative models. The unified framework is poised to impact practical applications, particularly in scenarios demanding real-time video synthesis. Theoretically, this paper contributes a new perspective on handling high-dimensional data sets by leveraging compressed latent spaces, thus offering insights for future architectures in AI that could adopt similar hierarchical approaches.

Future research might focus on enhancing the semantic alignment and scene transition capabilities within the proposed model. Additionally, exploring more sophisticated temporal compression methods can further refine the temporal pyramid's effectiveness, potentially leading to broader applicability in more diverse video generative tasks.

This work presents a valuable contribution to the field of AI, particularly within video generative modeling, offering an efficient and robust framework to tackle the inherent challenges presented by high-dimensional video data.