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arXiv · 2505.16324

From Prediction to Perfection: Introducing Refinement to Autoregressive Image Generation

Cheng Cheng·Lin Song·Di An·Yicheng Xiao·Xuchong Zhang·Hongbin Sun·Ying Shan
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Abstract

Autoregressive (AR) image generators offer a language-model-friendly approach to image generation by predicting discrete image tokens in a causal sequence. However, unlike diffusion models, AR models lack a mechanism to refine previous predictions, limiting their generation quality. In this paper, we introduce TensorAR, a new AR paradigm that reformulates image generation from next-token prediction to next-tensor prediction. By generating overlapping windows of image patches (tensors) in a sliding fashion, TensorAR enables iterative refinement of previously generated content. To prevent information leakage during training, we propose a discrete tensor noising scheme, which perturbs input tokens via codebook-indexed noise. TensorAR is implemented as a plug-and-play module compatible with existing AR models. Extensive experiments on LlamaGEN, Open-MAGVIT2, and RAR demonstrate that TensorAR significantly improves the generation performance of autoregressive models.

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Cheng Cheng, Lin Song, Di An, Yicheng Xiao, Xuchong Zhang, Hongbin Sun, Ying Shan. 2025-05-22. From Prediction to Perfection: Introducing Refinement to Autoregressive Image Generation. https://arxiv.org/abs/2505.16324

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