提出快速自回归模型FAR，实现高效连续图像生成。
Fast Autoregressive Models for Continuous Latent Generation
- 用轻量快捷头替代扩散头，实现少步采样
- 推理速度比MAR快2.3倍，保质保量
- 可无缝接入因果Transformer，适合图像生成研究者
自回归模型在序列数据生成中表现卓越，尤其在自然语言处理领域，但其向连续域图像生成的扩展面临重大挑战。近期的掩码自回归模型（MAR）通过使用扩散头在连续空间中建模每标记分布，绕过量化问题，但因迭代去噪过程计算开销大，导致推理缓慢。为此，我们提出快速自回归模型（FAR），用轻量快捷头替代MAR的扩散头，实现高效少步采样，同时保持自回归原则。此外，FAR可无缝集成至因果Transformer，无需架构修改即可从离散转向连续标记生成。实验表明，FAR推理速度较MAR提升2.3倍，同时维持竞争力的FID和IS分数。该工作首次建立高效自回归范式用于高保真连续空间图像生成，弥合了视觉自回归建模中质量与可扩展性的关键鸿沟。
原文摘要 · Abstract (English)
Autoregressive models have demonstrated remarkable success in sequential data generation, particularly in NLP, but their extension to continuous-domain image generation presents significant challenges. Recent work, the masked autoregressive model (MAR), bypasses quantization by modeling per-token distributions in continuous spaces using a diffusion head but suffers from slow inference due to the high computational cost of the iterative denoising process. To address this, we propose the Fast AutoRegressive model (FAR), a novel framework that replaces MAR's diffusion head with a lightweight shortcut head, enabling efficient few-step sampling while preserving autoregressive principles. Additionally, FAR seamlessly integrates with causal Transformers, extending them from discrete to continuous token generation without requiring architectural modifications. Experiments demonstrate that FAR achieves $2.3\times$ faster inference than MAR while maintaining competitive FID and IS scores. This work establishes the first efficient autoregressive paradigm for high-fidelity continuous-space image generation, bridging the critical gap between quality and scalability in visual autoregressive modeling.
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