Beyond Simple Edits: X-Planner for Complex Instruction-Based Image
  Editing
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.
686ca7ef364e2ad167eb544d
Chun-Hsiao Yeh
claimed_verified
2025-07-08T08:06:03.462Z
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/avatars/b6fea0c33c3c930c7314b99b414072a9.svg
Chun-Hsiao Yeh
user
danielchyeh
686ca7ef364e2ad167eb544e
Yilin Wang
686ca7ef364e2ad167eb544f
Nanxuan Zhao
686ca7ef364e2ad167eb5450
Richard Zhang
686ca7ef364e2ad167eb5451
Yuheng Li
686ca7ef364e2ad167eb5452
Yi Ma
686ca7ef364e2ad167eb5453
Krishna Kumar Singh
Beyond Simple Edits: X-Planner for Complex Instruction-Based Image
  Editing
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.
X-Planner, a Multimodal Large Language Model-based system, uses chain-of-thought reasoning to interpret complex instructions and generate precise edits, achieving state-of-the-art results in image editing.
Multimodal Large Language Model chain-of-thought reasoning edit types segmentation masks automated pipeline