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    <abstract>Image Editing has made incredible progress in recent years. Earliest work only supported caption-guided editing. Recently, free-form text instructions and reference images are incorporated to allow more flexibility. However, existing methods still struggle with complicated editing instructions involving multiple objects or reference images. We present InstructAny2Pix, a novel image editing model that leverages a multi-modal LLM to execute complicated edit instructions. Compared with previous, works, InstructAny2Pix extends the flexibility of edit instructions in three ways: First, it can perform complex instructions involving multiple object edits; Second, it supports interleaving text instructions with multiple reference images; Third, it supports audio and music inputs as part of edit prompts, unlocking many creative applications, such as album cover generation and music-inspired merchandise design. To evaluate the effectiveness of InstructAny2Pix, we propose two new benchmark datasets MM-Inst and Dream-booth++ consisting of human written, multi-modal prompts. InstructAny2Pix outperforms baselines in these two proposed multi-modal benchmarks, as well as conventional image editing benchmarks such as InstructPix2Pix.</abstract>
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%0 Conference Proceedings
%T InstructAny2Pix: Image Editing with Multi-Modal Prompts
%A Li, Shufan
%A Singh, Harkanwar
%A Grover, Aditya
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F li-etal-2025-instructany2pix
%X Image Editing has made incredible progress in recent years. Earliest work only supported caption-guided editing. Recently, free-form text instructions and reference images are incorporated to allow more flexibility. However, existing methods still struggle with complicated editing instructions involving multiple objects or reference images. We present InstructAny2Pix, a novel image editing model that leverages a multi-modal LLM to execute complicated edit instructions. Compared with previous, works, InstructAny2Pix extends the flexibility of edit instructions in three ways: First, it can perform complex instructions involving multiple object edits; Second, it supports interleaving text instructions with multiple reference images; Third, it supports audio and music inputs as part of edit prompts, unlocking many creative applications, such as album cover generation and music-inspired merchandise design. To evaluate the effectiveness of InstructAny2Pix, we propose two new benchmark datasets MM-Inst and Dream-booth++ consisting of human written, multi-modal prompts. InstructAny2Pix outperforms baselines in these two proposed multi-modal benchmarks, as well as conventional image editing benchmarks such as InstructPix2Pix.
%R 10.18653/v1/2025.findings-naacl.36
%U https://aclanthology.org/2025.findings-naacl.36/
%U https://doi.org/10.18653/v1/2025.findings-naacl.36
%P 594-619

Markdown (Informal)

[InstructAny2Pix: Image Editing with Multi-Modal Prompts](https://aclanthology.org/2025.findings-naacl.36/) (Li et al., Findings 2025)

• InstructAny2Pix: Image Editing with Multi-Modal Prompts (Li et al., Findings 2025)

ACL

• Shufan Li, Harkanwar Singh, and Aditya Grover. 2025. InstructAny2Pix: Image Editing with Multi-Modal Prompts. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 594–619, Albuquerque, New Mexico. Association for Computational Linguistics.