RePlan：基于推理引导的区域规划技术——面向复杂指令的图像编辑新方法

摘要

基于指令的图像编辑技术实现了自然语言对视觉修改的控制，但现有模型在指令-视觉复杂度（IV-Complexity）场景下表现不佳——当复杂指令遇到杂乱或模糊的图像时尤其明显。我们提出RePlan（区域对齐规划）框架，采用“先规划后执行”策略，将视觉语言规划器与扩散编辑器相结合。规划器通过逐步推理分解指令并显式定位目标区域；编辑器随后采用免训练的注意力区域注入机制实施修改，无需迭代修复即可实现精准的并行多区域编辑。为强化规划能力，我们基于GRPO强化学习算法，仅使用1,000条纯指令样本进行训练，显著提升了推理准确性与格式可靠性。我们还构建了IV-Edit基准数据集，专注于细粒度区域定位和知识密集型编辑任务。在IV-Complex场景测试中，RePlan在区域精度和整体保真度上持续超越基于海量数据训练的基线模型。项目页面：https://replan-iv-edit.github.io

English

Instruction-based image editing enables natural-language control over visual modifications, yet existing models falter under Instruction-Visual Complexity (IV-Complexity), where intricate instructions meet cluttered or ambiguous scenes. We introduce RePlan (Region-aligned Planning), a plan-then-execute framework that couples a vision-language planner with a diffusion editor. The planner decomposes instructions via step-by-step reasoning and explicitly grounds them to target regions; the editor then applies changes using a training-free attention-region injection mechanism, enabling precise, parallel multi-region edits without iterative inpainting. To strengthen planning, we apply GRPO-based reinforcement learning using 1K instruction-only examples, yielding substantial gains in reasoning fidelity and format reliability. We further present IV-Edit, a benchmark focused on fine-grained grounding and knowledge-intensive edits. Across IV-Complex settings, RePlan consistently outperforms strong baselines trained on far larger datasets, improving regional precision and overall fidelity. Our project page: https://replan-iv-edit.github.io

RePlan：基于推理引导的区域规划技术——面向复杂指令的图像编辑新方法

RePlan: Reasoning-guided Region Planning for Complex Instruction-based Image Editing

摘要

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