In-Context Edit: Enabling Instructional Image Editing with In-Context Generation in Large Scale Diffusion Transformer

• URL: http://arxiv.org/abs/2504.20690v1
• Date: Tue, 29 Apr 2025 12:14:47 GMT
• Title: In-Context Edit: Enabling Instructional Image Editing with In-Context Generation in Large Scale Diffusion Transformer
• Authors: Zechuan Zhang, Ji Xie, Yu Lu, Zongxin Yang, Yi Yang,
• Abstract summary: We present an in-context editing framework for zero-shot instruction compliance using in-context prompting.<n>We also present a LoRA-MoE hybrid tuning strategy that enhances flexibility with efficient adaptation and dynamic expert routing.<n>This work establishes a new paradigm that enables high-precision yet efficient instruction-guided editing.
• Score: 32.45070206621554
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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/.

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