标题： 超越简单编辑：基于复杂指令的X-Planner图像编辑 显示英文标题

标题： Beyond Simple Edits: X-Planner for Complex Instruction-Based Image Editing

摘要： 基于扩散的图像编辑方法在文本引导任务上取得了显著进展，但通常难以理解复杂的、间接的指令。 此外，当前模型经常面临身份保留效果差、意外编辑或严重依赖手动掩码的问题。 为了解决这些挑战，我们引入了X-Planner，这是一个基于多模态大语言模型（MLLM）的规划系统，能够有效将用户意图与编辑模型能力相衔接。 X-Planner采用思维链推理，系统地将复杂指令分解为更简单、清晰的子指令。 对于每个子指令，X-Planner会自动生成精确的编辑类型和分割掩码，消除人工干预，确保局部化且保留身份的编辑。 此外，我们提出了一种新颖的自动化流程，用于生成大规模数据以训练X-Planner，在现有基准和我们新引入的复杂编辑基准上均取得了最先进的结果。 显示英文摘要

摘要： Recent diffusion-based image editing methods have significantly advanced text-guided tasks but often struggle to interpret complex, indirect instructions. Moreover, current models frequently suffer from poor identity preservation, unintended edits, or rely heavily on manual masks. To address these challenges, we introduce X-Planner, a Multimodal Large Language Model (MLLM)-based planning system that effectively bridges user intent with editing model capabilities. X-Planner employs chain-of-thought reasoning to systematically decompose complex instructions into simpler, clear sub-instructions. For each sub-instruction, X-Planner automatically generates precise edit types and segmentation masks, eliminating manual intervention and ensuring localized, identity-preserving edits. Additionally, we propose a novel automated pipeline for generating large-scale data to train X-Planner which achieves state-of-the-art results on both existing benchmarks and our newly introduced complex editing benchmark.