This paper presents Randomized AutoRegressive modeling (RAR) for visual generation, which sets a new state-of-the-art performance on the image generation task while maintaining full compatibility with language modeling frameworks. The proposed RAR is simple: during a standard autoregressive training process with a next-token prediction objective, the input sequence-typically ordered in raster form-is randomly permuted into different factorization orders with a probability r, where r starts at 1 and linearly decays to 0 over the course of training. This annealing training strategy enables the model to learn to maximize the expected likelihood over all factorization orders and thus effectively improve the model's capability of modeling bidirectional contexts. Importantly, RAR preserves the integrity of the autoregressive modeling framework, ensuring full compatibility with language modeling while significantly improving performance in image generation. On the ImageNet-256 benchmark, RAR achieves an FID score of 1.48, not only surpassing prior state-of-the-art autoregressive image generators but also outperforming leading diffusion-based and masked transformer-based methods. Code and models will be made available at https://github.com/bytedance/1d-tokenizer

本研究解决了图像生成任务中现有方法的性能瓶颈，提出了一种新的随机自回归建模（RAR）方法，通过在训练过程中随机排列输入序列，显著提高模型的双向上下文建模能力。该方法在ImageNet-256基准上的FID分数达到1.48，不仅超越了之前的自回归图像生成模型，还优于前沿的扩散基和掩码变换器方法，具有重要影响。

随机自回归视觉生成