Beyond Simple Edits: X-Planner for Complex Instruction-Based Image
  Editing
Authors: name: Chun-Hsiao Yeh, name: Yilin Wang, name: Nanxuan Zhao, name: Richard Zhang, name: Yuheng Li, name: Yi Ma, name: Krishna Kumar Singh

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.