RePlan: Reasoning-Guided Region Planning for Complex Instruction-Based Image Editing

Instruction-based image editing enables natural-language con-trol over visual modifications, yet existing models falter under Instruc-tion–Visual Complexity (IV-Complexity), where intricate instructionsmeet cluttered or ambiguous scenes. We introduce RePlan (Region-alignedPlanning), a plan-then-execute framework that couples a vision–languageplanner with a diffusion editor. The planner decomposes instructions viastep-by-step reasoning and explicitly grounds them to target regions;the editor then applies changes using a training-free attention-region in-jection mechanism, enabling precise, parallel multi-region edits withoutiterative inpainting. To strengthen planning, we apply GRPO-based rein-forcement learning using 1K instruction-only examples, yielding substan-tial gains in reasoning fidelity and format reliability. We further presentIV-Edit, a benchmark focused on fine-grained grounding and knowledge-intensive edits. Across IV-Complex settings, RePlan consistently outper-forms strong baselines trained on far larger datasets, improving regionalprecision and overall consistency. Project page: https://replan- iv-edit.github.io/