Generative Pre-trained Autoregressive Diffusion Transformer

In this work, we present GPDiT, a Generative Pre-trained Autoregressive Diffusion Transformer that unifies the strengths of diffusion and autoregressive modeling for long-range video synthesis, within a continuous latent space. Instead of predicting discrete tokens, GPDiT autoregressively predicts future latent frames using a diffusion loss, enabling natural modeling of motion dynamics and semantic consistency across frames. This continuous autoregressive framework not only enhances generation quality but also endows the model with representation capabilities. Additionally, we introduce a lightweight causal attention variant and a parameter-free rotation-based time-conditioning mechanism, improving both the training and inference efficiency. Extensive experiments demonstrate that GPDiT achieves strong performance in video generation quality, video representation ability, and few-shot learning tasks, highlighting its potential as an effective framework for video modeling in continuous space.

Authors

• Yuan Zhang Xiangtan University
• Jiacheng Jiang
• Guoqing Ma Laboratory of Brain Atlas and Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences School of Future Technology, University of Chinese Academy of Sciences
• Zhiying Lu
• Bo Wang
• Haoyang Huang
• Jianlong Yuan
• Nan Duan Microsoft

Keywords

No keywords are indexed for this paper.