Parallelized Autoregressive Visual Generation (2412.15119v3)

Abstract: Autoregressive models have emerged as a powerful approach for visual generation but suffer from slow inference speed due to their sequential token-by-token prediction process. In this paper, we propose a simple yet effective approach for parallelized autoregressive visual generation that improves generation efficiency while preserving the advantages of autoregressive modeling. Our key insight is that parallel generation depends on visual token dependencies-tokens with weak dependencies can be generated in parallel, while strongly dependent adjacent tokens are difficult to generate together, as their independent sampling may lead to inconsistencies. Based on this observation, we develop a parallel generation strategy that generates distant tokens with weak dependencies in parallel while maintaining sequential generation for strongly dependent local tokens. Our approach can be seamlessly integrated into standard autoregressive models without modifying the architecture or tokenizer. Experiments on ImageNet and UCF-101 demonstrate that our method achieves a 3.6x speedup with comparable quality and up to 9.5x speedup with minimal quality degradation across both image and video generation tasks. We hope this work will inspire future research in efficient visual generation and unified autoregressive modeling. Project page: https://yuqingwang1029.github.io/PAR-project.

• The paper proposes a novel method that selectively parallelizes token generation by separating weak and strong dependency tokens.
• It demonstrates significant speedups of 3.6x to 9.5x on ImageNet and UCF-101 datasets while maintaining output quality.
• The approach preserves standard model architectures, paving the way for real-time applications in autonomous driving, AR, and video synthesis.

Parallelized Autoregressive Visual Generation

The paper "Parallelized Autoregressive Visual Generation" addresses a critical bottleneck in the application of autoregressive models to visual generation: the inefficiency introduced by the sequential, token-by-token generation process. Autoregressive models have demonstrated significant promise in various domains, including language and visual data, thanks to their scalability and uniform modeling capabilities. However, the inherent sequential nature of these models limits their practicality for real-time applications, particularly in complex visual generation tasks such as image and video synthesis.

This paper proposes a novel approach aimed at enhancing the efficiency of autoregressive models through parallelized token generation. The key insight underpinning this work is the recognition that not all visual tokens are equally dependent on one another. Specifically, visual tokens exhibiting weak dependencies can be generated in parallel without substantial degradation in quality, whereas tokens with strong dependencies typically require sequential processing to maintain consistency.

To operationalize this insight, the authors develop a parallel generation strategy distinguished by the selective parallelization of tokens. Tokens likely to have weak dependencies are grouped for simultaneous generation, while those with strong dependencies are processed sequentially. This balancing act is achieved without altering the fundamental architecture or the tokenization process of standard autoregressive models, preserving their versatility and simplicity.

Empirical validation of the proposed method was conducted on both image and video datasets, specifically ImageNet and UCF-101, showcasing substantial speedup gains without compromising output quality. The experiments indicated a 3.6$\times$ increase in generation speed for images with quality maintained at comparable levels to the original sequential processes. In scenarios with minimal quality concessions, speedups reached up to 9.5$\times$. These results are particularly significant given that they were achieved without extensive modifications to existing model frameworks.

The implications of this research are manifold. Practically, the proposed method paves the way for more efficient use of autoregressive models in visual tasks, potentially expanding their applications in fields requiring real-time or near-real-time data processing, such as autonomous driving, augmented reality, and video game development. Theoretically, this work contributes to the understanding of dependency structures in visual data, offering a framework for further exploration into token correlations and their impact on generation strategies.

Looking forward, this research opens several avenues for future inquiry. One potential direction involves exploring the adaptability of this parallelization strategy across other machine learning models and tasks. Furthermore, refining the dependency estimation process could lead to even more substantial improvements in parallelization efficiency. As the landscape of artificial intelligence continues to evolve, integrating these findings with advancements in hardware acceleration and distributed computing may yield systems capable of handling ever-increasing volumes of complex visual data with unprecedented efficiency.

In conclusion, the paper provides a robust framework for enhancing the efficiency of autoregressive visual generation by leveraging token dependency structures to facilitate parallel processing. This advancement not only underscores the versatility of autoregressive models but also sets a precedent for future endeavors aiming to reconcile model performance with operational efficiency in AI-driven visual data processing.