Improving Autoregressive Visual Generation with Cluster-Oriented Token Prediction

• URL: http://arxiv.org/abs/2501.00880v1
• Date: Wed, 01 Jan 2025 15:58:51 GMT
• Title: Improving Autoregressive Visual Generation with Cluster-Oriented Token Prediction
• Authors: Teng Hu, Jiangning Zhang, Ran Yi, Jieyu Weng, Yabiao Wang, Xianfang Zeng, Zhucun Xue, Lizhuang Ma,
• Abstract summary: IAR is an Improved AutoRegressive Visual Generation Method. We propose a Codebook Rearrangement strategy that uses balanced k-means clustering algorithm. We also propose a Cluster-oriented Cross-entropy Loss that guides the model to correctly predict the cluster where the token is located.
• Score: 52.09472099976885
• License:
• Abstract: Employing LLMs for visual generation has recently become a research focus. However, the existing methods primarily transfer the LLM architecture to visual generation but rarely investigate the fundamental differences between language and vision. This oversight may lead to suboptimal utilization of visual generation capabilities within the LLM framework. In this paper, we explore the characteristics of visual embedding space under the LLM framework and discover that the correlation between visual embeddings can help achieve more stable and robust generation results. We present IAR, an Improved AutoRegressive Visual Generation Method that enhances the training efficiency and generation quality of LLM-based visual generation models. Firstly, we propose a Codebook Rearrangement strategy that uses balanced k-means clustering algorithm to rearrange the visual codebook into clusters, ensuring high similarity among visual features within each cluster. Leveraging the rearranged codebook, we propose a Cluster-oriented Cross-entropy Loss that guides the model to correctly predict the cluster where the token is located. This approach ensures that even if the model predicts the wrong token index, there is a high probability the predicted token is located in the correct cluster, which significantly enhances the generation quality and robustness. Extensive experiments demonstrate that our method consistently enhances the model training efficiency and performance from 100M to 1.4B, reducing the training time by half while achieving the same FID. Additionally, our approach can be applied to various LLM-based visual generation models and adheres to the scaling law, providing a promising direction for future research in LLM-based visual generation.

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