title: In-Context Edit: Enabling Instructional Image Editing with In-Context
    Generation in Large Scale Diffusion Transformer
summary: Instruction-based image editing enables robust image modification via natural
  language prompts, yet current methods face a precision-efficiency tradeoff.
  Fine-tuning methods demand significant computational resources and large
  datasets, while training-free techniques struggle with instruction
  comprehension and edit quality. We resolve this dilemma by leveraging
  large-scale Diffusion Transformer (DiT)' enhanced generation capacity and
  native contextual awareness. Our solution introduces three contributions: (1)
  an in-context editing framework for zero-shot instruction compliance using
  in-context prompting, avoiding structural changes; (2) a LoRA-MoE hybrid tuning
  strategy that enhances flexibility with efficient adaptation and dynamic expert
  routing, without extensive retraining; and (3) an early filter inference-time
  scaling method using vision-language models (VLMs) to select better initial
  noise early, improving edit quality. Extensive evaluations demonstrate our
  method's superiority: it outperforms state-of-the-art approaches while
  requiring only 0.5% training data and 1% trainable parameters compared to
  conventional baselines. This work establishes a new paradigm that enables
  high-precision yet efficient instruction-guided editing. Codes and demos can be
  found in https://river-zhang.github.io/ICEdit-gh-pages/.
paper_authors._id: 6811c39584adfa26b82a4370
paper_authors.name: Zechuan Zhang
paper_authors._id: 6811c39584adfa26b82a4371
paper_authors.name: Ji Xie
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-02T06:36:45.182Z
paper_authors._id: 64e99fc07e2ec711a7138262
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64e99fc07e2ec711a7138262/FmP3F8_UXgh9K-0gwS99A.jpeg
paper_authors.fullname: Ji Xie
paper_authors.type: user
paper_authors.user: sanaka87
paper_authors._id: 6811c39584adfa26b82a4372
paper_authors.name: Yu Lu
paper_authors._id: 6811c39584adfa26b82a4373
paper_authors.name: Zongxin Yang
paper_authors._id: 6811c39584adfa26b82a4374
paper_authors.name: Yi Yang
paper_title: In-Context Edit: Enabling Instructional Image Editing with In-Context
    Generation in Large Scale Diffusion Transformer
paper_summary: Instruction-based image editing enables robust image modification via natural
  language prompts, yet current methods face a precision-efficiency tradeoff.
  Fine-tuning methods demand significant computational resources and large
  datasets, while training-free techniques struggle with instruction
  comprehension and edit quality. We resolve this dilemma by leveraging
  large-scale Diffusion Transformer (DiT)' enhanced generation capacity and
  native contextual awareness. Our solution introduces three contributions: (1)
  an in-context editing framework for zero-shot instruction compliance using
  in-context prompting, avoiding structural changes; (2) a LoRA-MoE hybrid tuning
  strategy that enhances flexibility with efficient adaptation and dynamic expert
  routing, without extensive retraining; and (3) an early filter inference-time
  scaling method using vision-language models (VLMs) to select better initial
  noise early, improving edit quality. Extensive evaluations demonstrate our
  method's superiority: it outperforms state-of-the-art approaches while
  requiring only 0.5% training data and 1% trainable parameters compared to
  conventional baselines. This work establishes a new paradigm that enables
  high-precision yet efficient instruction-guided editing. Codes and demos can be
  found in https://river-zhang.github.io/ICEdit-gh-pages/.
paper_ai_summary: A novel in-context editing framework using a Diffusion Transformer enhances instruction-following precision and efficiency without extensive retraining or large datasets.
paper_ai_keywords: instruction-based image editing natural language prompts precision-efficiency tradeoff fine-tuning training-free techniques Diffusion Transformer (DiT) in-context editing framework LoRA-MoE hybrid tuning vision-language models (VLMs) early filter inference-time scaling