# A Benchmark and Baseline for Language-Driven Image Editing

arXiv: https://arxiv.org/abs/2010.02330 · HTML: https://mlsanity.com/paper/2010.02330/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2020.

## Built on by (most cited first, 25 of 25)

- [DiffEdit: Diffusion-based semantic image editing with mask guidance](https://mlsanity.com/paper/2210.11427/) (2022)
- [Guiding Instruction-based Image Editing via Multimodal Large Language Models](https://mlsanity.com/paper/2309.17102/) (2023)
- [InstructDiffusion: A Generalist Modeling Interface for Vision Tasks](https://mlsanity.com/paper/2309.03895/) (2023)
- [Talk-to-Edit: Fine-Grained Facial Editing via Dialog](https://mlsanity.com/paper/2109.04425/) (2021)
- [Improving Generation and Evaluation of Visual Stories via Semantic Consistency](https://mlsanity.com/paper/2105.10026/) (2021)
- [PixWizard: Versatile Image-to-Image Visual Assistant with Open-Language Instructions](https://mlsanity.com/paper/2409.15278/) (2024)
- [HumanEdit: A High-Quality Human-Rewarded Dataset for Instruction-based Image Editing](https://mlsanity.com/paper/2412.04280/) (2024)
- [FlexIT: Towards Flexible Semantic Image Translation](https://mlsanity.com/paper/2203.04705/) (2022)
- [Learning by Planning: Language-Guided Global Image Editing](https://mlsanity.com/paper/2106.13156/) (2021)
- [Towards Generic Image Manipulation Detection with Weakly-Supervised Self-Consistency Learning](https://mlsanity.com/paper/2309.01246/) (2023)
- [Embedding Arithmetic of Multimodal Queries for Image Retrieval](https://mlsanity.com/paper/2112.03162/) (2021)
- [REALEDIT: Reddit Edits As a Large-scale Empirical Dataset for Image Transformations](https://mlsanity.com/paper/2502.03629/) (2025)
- [Lumina-OmniLV: A Unified Multimodal Framework for General Low-Level Vision](https://mlsanity.com/paper/2504.04903/) (2025)
- [MIGE: Mutually Enhanced Multimodal Instruction-Based Image Generation and Editing](https://mlsanity.com/paper/2502.21291/) (2025)
- [DocEdit-v2: Document Structure Editing Via Multimodal LLM Grounding](https://mlsanity.com/paper/2410.16472/) (2024)
- [Towards Scalable Human-aligned Benchmark for Text-guided Image Editing](https://mlsanity.com/paper/2505.00502/) (2025)
- [What's Producible May Not Be Reachable: Measuring the Steerability of Generative Models](https://mlsanity.com/paper/2503.17482/) (2025)
- [Text-to-Image Cross-Modal Generation: A Systematic Review](https://mlsanity.com/paper/2401.11631/) (2024)
- [Understanding Generative AI Capabilities in Everyday Image Editing Tasks](https://mlsanity.com/paper/2505.16181/) (2025)
- [CAISE: Conversational Agent for Image Search and Editing](https://mlsanity.com/paper/2202.11847/) (2022)
- [SpaceEdit: Learning a Unified Editing Space for Open-Domain Image Editing](https://mlsanity.com/paper/2112.00180/) (2021)
- [What Changed? Detecting and Evaluating Instruction-Guided Image Edits with Multimodal Large Language Models](https://mlsanity.com/paper/2505.20405/) (2025)
- [VIBE: Visual Instruction Based Editor](https://mlsanity.com/paper/2601.02242/) (2026)
- [IEA: Amateur-Friendly Conversational Image Editing Agent via Three Stages of Multitask Alignment](https://mlsanity.com/paper/2606.08016/) (2026)
- [UIP2P: Unsupervised Instruction-based Image Editing via Edit Reversibility Constraint](https://mlsanity.com/paper/2412.15216/) (2024)

## Abstract (arXiv)

Language-driven image editing can significantly save the laborious image editing work and be friendly to the photography novice. However, most similar work can only deal with a specific image domain or can only do global retouching. To solve this new task, we first present a new language-driven image editing dataset that supports both local and global editing with editing operation and mask annotations. Besides, we also propose a baseline method that fully utilizes the annotation to solve this problem. Our new method treats each editing operation as a sub-module and can automatically predict operation parameters. Not only performing well on challenging user data, but such an approach is also highly interpretable. We believe our work, including both the benchmark and the baseline, will advance the image editing area towards a more general and free-form level.