Parallelized Autoregressive Visual Generation
This work addresses efficiency bottlenecks for researchers and practitioners using autoregressive models in visual generation, offering a simple integration method that is incremental but impactful for specific applications.
The paper tackles the slow inference speed of autoregressive visual generation models by proposing a parallelized approach that selectively generates tokens in parallel based on dependency strength, achieving a 3.6x speedup with comparable quality and up to 9.5x speedup with minimal degradation on ImageNet and UCF-101 datasets.
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.