# Zero-shot Image Editing with Reference Imitation

arXiv: https://arxiv.org/abs/2406.07547 · HTML: https://mlsanity.com/paper/2406.07547/

## Key facts

- Cited by 65 papers in the map (data through 14 Oct 2026).
- Builds on 46 of its 59 references that are in the map.
- First posted on arXiv 11 Jun 2024.

## Builds on (most cited first, 30 of 46)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [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)
- [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://mlsanity.com/paper/2401.10891/) (2024)
- [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)
- [MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editing](https://mlsanity.com/paper/2304.08465/) (2023)
- [Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation](https://mlsanity.com/paper/2311.17117/) (2023)
- [Free-Form Image Inpainting with Gated Convolution](https://mlsanity.com/paper/1806.03589/) (2018)
- [Generative Image Inpainting with Contextual Attention](https://mlsanity.com/paper/1801.07892/) (2018)
- [Image Inpainting for Irregular Holes Using Partial Convolutions](https://mlsanity.com/paper/1804.07723/) (2018)
- [Paint by Example: Exemplar-based Image Editing with Diffusion Models](https://mlsanity.com/paper/2211.13227/) (2022)
- [Emergent Correspondence from Image Diffusion](https://mlsanity.com/paper/2306.03881/) (2023)
- [AnyDoor: Zero-shot Object-level Image Customization](https://mlsanity.com/paper/2307.09481/) (2023)
- [MagicAnimate: Temporally Consistent Human Image Animation using Diffusion Model](https://mlsanity.com/paper/2311.16498/) (2023)
- [A Tale of Two Features: Stable Diffusion Complements DINO for Zero-Shot Semantic Correspondence](https://mlsanity.com/paper/2305.15347/) (2023)
- [SmartBrush: Text and Shape Guided Object Inpainting with Diffusion Model](https://mlsanity.com/paper/2212.05034/) (2022)
- [Mix-of-Show: Decentralized Low-Rank Adaptation for Multi-Concept Customization of Diffusion Models](https://mlsanity.com/paper/2305.18292/) (2023)
- [Inpaint Anything: Segment Anything Meets Image Inpainting](https://mlsanity.com/paper/2304.06790/) (2023)
- [Break-A-Scene: Extracting Multiple Concepts from a Single Image](https://mlsanity.com/paper/2305.16311/) (2023)
- [MAT: Mask-Aware Transformer for Large Hole Image Inpainting](https://mlsanity.com/paper/2203.15270/) (2022)

## Built on by (most cited first, 30 of 65)

- [UniReal: Universal Image Generation and Editing via Learning Real-world Dynamics](https://mlsanity.com/paper/2412.07774/) (2024)
- [Insert Anything: Image Insertion via In-Context Editing in DiT](https://mlsanity.com/paper/2504.15009/) (2025)
- [FreeEdit: Mask-free Reference-based Image Editing with Multi-modal Instruction](https://mlsanity.com/paper/2409.18071/) (2024)
- [Does FLUX Already Know How to Perform Physically Plausible Image Composition?](https://mlsanity.com/paper/2509.21278/) (2025)
- [RelationAdapter: Learning and Transferring Visual Relation with Diffusion Transformers](https://mlsanity.com/paper/2506.02528/) (2025)
- [Edit Transfer: Learning Image Editing via Vision In-Context Relations](https://mlsanity.com/paper/2503.13327/) (2025)
- [ObjectMate: A Recurrence Prior for Object Insertion and Subject-Driven Generation](https://mlsanity.com/paper/2412.08645/) (2024)
- [DreamFuse: Adaptive Image Fusion with Diffusion Transformer](https://mlsanity.com/paper/2504.08291/) (2025)
- [OmniTry: Virtual Try-On Anything without Masks](https://mlsanity.com/paper/2508.13632/) (2025)
- [CTRL-D: Controllable Dynamic 3D Scene Editing with Personalized 2D Diffusion](https://mlsanity.com/paper/2412.01792/) (2024)
- [FastFit: Accelerating Multi-Reference Virtual Try-On via Cacheable Diffusion Models](https://mlsanity.com/paper/2508.20586/) (2025)
- [Group Editing: Edit Multiple Images in One Go](https://mlsanity.com/paper/2603.22883/) (2026)
- [MIGE: Mutually Enhanced Multimodal Instruction-Based Image Generation and Editing](https://mlsanity.com/paper/2502.21291/) (2025)
- [IC-Custom: Diverse Image Customization via In-Context Learning](https://mlsanity.com/paper/2507.01926/) (2025)
- [RAGDiffusion: Faithful Cloth Generation via External Knowledge Assimilation](https://mlsanity.com/paper/2411.19528/) (2024)
- [Edicho: Consistent Image Editing in the Wild](https://mlsanity.com/paper/2412.21079/) (2024)
- [GeoRemover: Removing Objects and Their Causal Visual Artifacts](https://mlsanity.com/paper/2509.18538/) (2025)
- [Voost: A Unified and Scalable Diffusion Transformer for Bidirectional Virtual Try-On and Try-Off](https://mlsanity.com/paper/2508.04825/) (2025)
- [In-Context Brush: Zero-shot Customized Subject Insertion with Context-Aware Latent Space Manipulation](https://mlsanity.com/paper/2505.20271/) (2025)
- [Refine-by-Align: Reference-Guided Artifacts Refinement through Semantic Alignment](https://mlsanity.com/paper/2412.00306/) (2024)
- [PIXELS: Progressive Image Xemplar-based Editing with Latent Surgery](https://mlsanity.com/paper/2501.09826/) (2025)
- [Oscillation Inversion: Understand the structure of Large Flow Model through the Lens of Inversion Method](https://mlsanity.com/paper/2411.11135/) (2024)
- [Long-horizon Visual Instruction Generation with Logic and Attribute Self-reflection](https://mlsanity.com/paper/2503.13500/) (2025)
- [MIND-Edit: MLLM Insight-Driven Editing via Language-Vision Projection](https://mlsanity.com/paper/2505.19149/) (2025)
- [MagicQuillV2: Precise and Interactive Image Editing with Layered Visual Cues](https://mlsanity.com/paper/2512.03046/) (2025)
- [EmoAgent: A Multi-Agent Framework for Diverse Affective Image Manipulation](https://mlsanity.com/paper/2503.11290/) (2025)
- [FlexIP: Dynamic Control of Preservation and Personality for Customized Image Generation](https://mlsanity.com/paper/2504.07405/) (2025)
- [CoLoGen: Progressive Learning of Concept-Localization Duality for Unified Image Generation](https://mlsanity.com/paper/2602.22150/) (2026)
- [Crossroads of Continents: Automated Artifact Extraction for Cultural Adaptation with Large Multimodal Models](https://mlsanity.com/paper/2407.02067/) (2024)
- [EZIGen: Enhancing zero-shot personalized image generation with precise subject encoding and decoupled guidance](https://mlsanity.com/paper/2409.08091/) (2024)

## Abstract (arXiv)

Image editing serves as a practical yet challenging task considering the diverse demands from users, where one of the hardest parts is to precisely describe how the edited image should look like. In this work, we present a new form of editing, termed imitative editing, to help users exercise their creativity more conveniently. Concretely, to edit an image region of interest, users are free to directly draw inspiration from some in-the-wild references (e.g., some relative pictures come across online), without having to cope with the fit between the reference and the source. Such a design requires the system to automatically figure out what to expect from the reference to perform the editing. For this purpose, we propose a generative training framework, dubbed MimicBrush, which randomly selects two frames from a video clip, masks some regions of one frame, and learns to recover the masked regions using the information from the other frame. That way, our model, developed from a diffusion prior, is able to capture the semantic correspondence between separate images in a self-supervised manner. We experimentally show the effectiveness of our method under various test cases as well as its superiority over existing alternatives. We also construct a benchmark to facilitate further research.