Multi-turn image editing is essential for iterative design, yet current models often struggle with identity drift and error accumulation over successive steps. While existing research leverages video priors for consistency, their reliance on bidirectional attention is fundamentally misaligned with the causal, sequential nature of interactive editing. In this paper, we propose AnchorEdit, the first autoregressive (AR) diffusion-based framework designed specifically for high-resolution, long-term multi-turn editing. AnchorEdit bridges the gap between video priors and causal inference through a three-stage training curriculum: identity-preserving sing-turn pretraining, causal AR forcing fine-tuning with a novel self-rollout strategy to mitigate exposure bias, and consistency distillation for efficient 4-step generation. During inference, we introduce a memory mechanism to anchor the initial subject identity and ensure stable extrapolation across extended editing trajectories. To evaluate performance, we provide a new high-resolution multi-turn editing benchmark designed to stress-test long-horizon stability. Extensive experiments demonstrate that AnchorEdit achieves state-of-the-art results, maintaining exceptional subject fidelity and instruction following even over 10+ interaction rounds.

翻译：[translated abstract in Chinese] 多轮图像编辑对于迭代设计至关重要，但现有模型常面临身份漂移和误差累积的问题。尽管现有研究利用视频先验信息来保持一致性，但这些方法依赖的双向注意力机制与交互式编辑的因果性和顺序性本质存在根本性错配。本文提出AnchorEdit——首个专为高分辨率、长序列多轮编辑设计的自回归（AR）扩散框架。AnchorEdit通过三阶段训练课程弥合视频先验与因果推理之间的鸿沟：保持身份的单轮预训练、采用新型自展开策略缓解曝光偏差的因果AR强制精调，以及用于高效四步生成的一致性蒸馏。在推理阶段，我们引入内存机制锚定初始主体身份，确保在扩展编辑轨迹上的稳定外推。为评估性能，我们构建了专为测试长程稳定性的新型高分辨率多轮编辑基准。大量实验表明，AnchorEdit在超过10轮交互中均能保持卓越的主体保真度和指令遵循能力，达到当前最优水平。