# FreeEdit: Mask-free Reference-based Image Editing with Multi-modal Instruction

arXiv: https://arxiv.org/abs/2409.18071 · HTML: https://mlsanity.com/paper/2409.18071/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- Builds on 66 of its 74 references that are in the map.
- First posted on arXiv 26 Sep 2024.

## Builds on (most cited first, 30 of 66)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022)
- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2308.06721/) (2023)
- [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022)
- [Null-text Inversion for Editing Real Images using Guided Diffusion Models](https://mlsanity.com/paper/2211.09794/) (2022)
- [Imagic: Text-Based Real Image Editing with Diffusion Models](https://mlsanity.com/paper/2210.09276/) (2022)

## Built on by (most cited first, 30 of 36)

- [KRIS-Bench: Benchmarking Next-Level Intelligent Image Editing Models](https://mlsanity.com/paper/2505.16707/) (2025)
- [Insert Anything: Image Insertion via In-Context Editing in DiT](https://mlsanity.com/paper/2504.15009/) (2025)
- [UniREditBench: A Unified Reasoning-based Image Editing Benchmark](https://mlsanity.com/paper/2511.01295/) (2025)
- [Edit Transfer: Learning Image Editing via Vision In-Context Relations](https://mlsanity.com/paper/2503.13327/) (2025)
- [PlanGen: Towards Unified Layout Planning and Image Generation in Auto-Regressive Vision Language Models](https://mlsanity.com/paper/2503.10127/) (2025)
- [ObjectMate: A Recurrence Prior for Object Insertion and Subject-Driven Generation](https://mlsanity.com/paper/2412.08645/) (2024)
- [X-Prompt: Towards Universal In-Context Image Generation in Auto-Regressive Vision Language Foundation Models](https://mlsanity.com/paper/2412.01824/) (2024)
- [InfiniDreamer: Arbitrarily Long Human Motion Generation via Segment Score Distillation](https://mlsanity.com/paper/2411.18303/) (2024)
- [MIGE: Mutually Enhanced Multimodal Instruction-Based Image Generation and Editing](https://mlsanity.com/paper/2502.21291/) (2025)
- [Image Editing with Diffusion Models: A Survey](https://mlsanity.com/paper/2504.13226/) (2025)
- [Re-Align: Structured Reasoning-guided Alignment for In-Context Image Generation and Editing](https://mlsanity.com/paper/2601.05124/) (2026)
- [In-Context Brush: Zero-shot Customized Subject Insertion with Context-Aware Latent Space Manipulation](https://mlsanity.com/paper/2505.20271/) (2025)
- [Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation](https://mlsanity.com/paper/2508.08949/) (2025)
- [FreqEdit: Preserving High-Frequency Features for Robust Multi-Turn Image Editing](https://mlsanity.com/paper/2512.01755/) (2025)
- [TAG-MoE: Task-Aware Gating for Unified Generative Mixture-of-Experts](https://mlsanity.com/paper/2601.08881/) (2026)
- [BIFR"OST: 3D-Aware Image compositing with Language Instructions](https://mlsanity.com/paper/2410.19079/) (2024)
- [MIND-Edit: MLLM Insight-Driven Editing via Language-Vision Projection](https://mlsanity.com/paper/2505.19149/) (2025)
- [GaussianPainter: Painting Point Cloud into 3D Gaussians with Normal Guidance](https://mlsanity.com/paper/2412.17715/) (2024)
- [AvatarMakeup: Realistic Makeup Transfer for 3D Animatable Head Avatars](https://mlsanity.com/paper/2507.02419/) (2025)
- [CameraMaster: Unified Camera Semantic-Parameter Control for Photography Retouching](https://mlsanity.com/paper/2511.21024/) (2025)
- [Semantic Mismatch and Perceptual Degradation: A New Perspective on Image Editing Immunity](https://mlsanity.com/paper/2512.14320/) (2025)
- [Meta-CoT: Enhancing Granularity and Generalization in Image Editing](https://mlsanity.com/paper/2604.24625/) (2026)
- [DDA-Thinker: Decoupled Dual-Atomic Reinforcement Learning for Reasoning-Driven Image Editing](https://mlsanity.com/paper/2604.25477/) (2026)
- [A2-Edit: Precise Reference-Guided Image Editing of Arbitrary Objects and Ambiguous Masks](https://mlsanity.com/paper/2603.10685/) (2026)
- [Forge-and-Quench: Enhancing Image Generation for Higher Fidelity in Unified Multimodal Models](https://mlsanity.com/paper/2601.04706/) (2026)
- [InEdit-Bench: Benchmarking Intermediate Logical Pathways for Intelligent Image Editing Models](https://mlsanity.com/paper/2603.03657/) (2026)
- [EditTransfer++: Toward Faithful and Efficient Visual-Prompt-Guided Image Editing](https://mlsanity.com/paper/2605.07455/) (2026)
- [Pinco: Position-induced Consistent Adapter for Diffusion Transformer in Foreground-conditioned Inpainting](https://mlsanity.com/paper/2412.03812/) (2024)
- [Borrowing from anything: A generalizable framework for reference-guided instance editing](https://mlsanity.com/paper/2512.15138/) (2025)
- [InpaintDPO: Mitigating Spatial Relationship Hallucinations in Foreground-conditioned Inpainting via Diverse Preference Optimization](https://mlsanity.com/paper/2512.15644/) (2025)

## Abstract (arXiv)

Introducing user-specified visual concepts in image editing is highly practical as these concepts convey the user's intent more precisely than text-based descriptions. We propose FreeEdit, a novel approach for achieving such reference-based image editing, which can accurately reproduce the visual concept from the reference image based on user-friendly language instructions. Our approach leverages the multi-modal instruction encoder to encode language instructions to guide the editing process. This implicit way of locating the editing area eliminates the need for manual editing masks. To enhance the reconstruction of reference details, we introduce the Decoupled Residual ReferAttention (DRRA) module. This module is designed to integrate fine-grained reference features extracted by a detail extractor into the image editing process in a residual way without interfering with the original self-attention. Given that existing datasets are unsuitable for reference-based image editing tasks, particularly due to the difficulty in constructing image triplets that include a reference image, we curate a high-quality dataset, FreeBench, using a newly developed twice-repainting scheme. FreeBench comprises the images before and after editing, detailed editing instructions, as well as a reference image that maintains the identity of the edited object, encompassing tasks such as object addition, replacement, and deletion. By conducting phased training on FreeBench followed by quality tuning, FreeEdit achieves high-quality zero-shot editing through convenient language instructions. We conduct extensive experiments to evaluate the effectiveness of FreeEdit across multiple task types, demonstrating its superiority over existing methods. The code will be available at: https://freeedit.github.io/.