Abstract
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: this https URL
Abstract (translated)
指令驱动的图像编辑允许通过自然语言对视觉修改进行控制，但现有的模型在处理复杂指令与混乱或含糊场景相结合的情况（即Instruction-Visual Complexity, IV-Complexity）时会遇到困难。我们提出了RePlan（Region-aligned Planning），这是一种“先规划后执行”的框架，它结合了视觉语言规划器和扩散编辑器。该规划器通过逐步推理分解指令，并明确将其与目标区域对齐；随后，编辑器利用无需训练的注意力-区域注入机制进行更改操作，从而支持精确且并行的多区域修改，而不需要迭代修复过程。 为了增强规划效果，我们应用了基于GRPO（Graph Regularized Policy Optimization）的强化学习方法，并使用仅包含1000个指令示例的数据集进行了训练，这种方法在推理准确性和格式可靠性方面取得了显著提升。此外，我们还介绍了IV-Edit，这是一个专注于细粒度对齐和知识密集型编辑任务的新基准测试。 在复杂的IV设置下，RePlan框架相对于那些基于更大数据集（远超过1000个样本）训练的强大基线模型而言，在区域精度和整体精确性方面表现出持续的优越性能。欲了解更多详情，请访问我们的项目页面：[点击这里](this https URL)。
URL
https://arxiv.org/abs/2512.16864
