# Insert Anything: Image Insertion via In-Context Editing in DiT

arXiv: https://arxiv.org/abs/2504.15009 · HTML: https://mlsanity.com/paper/2504.15009/

## Key facts

- Cited by 61 papers in the map (data through 14 Oct 2026).
- Builds on 47 of its 55 references that are in the map.
- First posted on arXiv 21 Apr 2025.

## Builds on (most cited first, 30 of 47)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [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)
- [Sigmoid Loss for Language Image Pre-Training](https://mlsanity.com/paper/2303.15343/) (2023)
- [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (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)
- [MagicBrush: A Manually Annotated Dataset for Instruction-Guided Image Editing](https://mlsanity.com/paper/2306.10012/) (2023)
- [LightGlue: Local Feature Matching at Light Speed](https://mlsanity.com/paper/2306.13643/) (2023)
- [Paint by Example: Exemplar-based Image Editing with Diffusion Models](https://mlsanity.com/paper/2211.13227/) (2022)
- [InstantID: Zero-shot Identity-Preserving Generation in Seconds](https://mlsanity.com/paper/2401.07519/) (2024)
- [AnyDoor: Zero-shot Object-level Image Customization](https://mlsanity.com/paper/2307.09481/) (2023)
- [OminiControl: Minimal and Universal Control for Diffusion Transformer](https://mlsanity.com/paper/2411.15098/) (2024)
- [Inversion-Based Style Transfer with Diffusion Models](https://mlsanity.com/paper/2211.13203/) (2022)
- [VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalization](https://mlsanity.com/paper/2103.16874/) (2021)
- [SmartBrush: Text and Shape Guided Object Inpainting with Diffusion Model](https://mlsanity.com/paper/2212.05034/) (2022)
- [AnyEdit: Mastering Unified High-Quality Image Editing for Any Idea](https://mlsanity.com/paper/2411.15738/) (2024)
- [MVImgNet: A Large-scale Dataset of Multi-view Images](https://mlsanity.com/paper/2303.06042/) (2023)
- [Taming Rectified Flow for Inversion and Editing](https://mlsanity.com/paper/2411.04746/) (2024)
- [StyleDrop: Text-to-Image Generation in Any Style](https://mlsanity.com/paper/2306.00983/) (2023)
- [Style Injection in Diffusion: A Training-free Approach for Adapting Large-scale Diffusion Models for Style Transfer](https://mlsanity.com/paper/2312.09008/) (2023)
- [In-Context LoRA for Diffusion Transformers](https://mlsanity.com/paper/2410.23775/) (2024)
- [OOTDiffusion: Outfitting Fusion based Latent Diffusion for Controllable Virtual Try-on](https://mlsanity.com/paper/2403.01779/) (2024)

## Built on by (most cited first, 30 of 61)

- [Does FLUX Already Know How to Perform Physically Plausible Image Composition?](https://mlsanity.com/paper/2509.21278/) (2025)
- [ContextFlow: Training-Free Video Object Editing via Adaptive Context Enrichment](https://mlsanity.com/paper/2509.17818/) (2025)
- [RelationAdapter: Learning and Transferring Visual Relation with Diffusion Transformers](https://mlsanity.com/paper/2506.02528/) (2025)
- [DiffDecompose: Layer-Wise Decomposition of Alpha-Composited Images via Diffusion Transformers](https://mlsanity.com/paper/2505.21541/) (2025)
- [FullDiT2: Efficient In-Context Conditioning for Video Diffusion Transformers](https://mlsanity.com/paper/2506.04213/) (2025)
- [ContextGen: Contextual Layout Anchoring for Identity-Consistent Multi-Instance Generation](https://mlsanity.com/paper/2510.11000/) (2025)
- [Query-Kontext: An Unified Multimodal Model for Image Generation and Editing](https://mlsanity.com/paper/2509.26641/) (2025)
- [IC-Custom: Diverse Image Customization via In-Context Learning](https://mlsanity.com/paper/2507.01926/) (2025)
- [MICo-150K: A Comprehensive Dataset Advancing Multi-Image Composition](https://mlsanity.com/paper/2512.07348/) (2025)
- [SRA 2: Variational Autoencoder Self-Representation Alignment for Efficient Diffusion Training](https://mlsanity.com/paper/2601.17830/) (2026)
- [In-Context Brush: Zero-shot Customized Subject Insertion with Context-Aware Latent Space Manipulation](https://mlsanity.com/paper/2505.20271/) (2025)
- [InnoAds-Composer: Efficient Condition Composition for E-Commerce Poster Generation](https://mlsanity.com/paper/2603.05898/) (2026)
- [SIGMA-GEN: Structure and Identity Guided Multi-subject Assembly for Image Generation](https://mlsanity.com/paper/2510.06469/) (2025)
- [KnowVal: A Knowledge-Augmented and Value-Guided Autonomous Driving System](https://mlsanity.com/paper/2512.20299/) (2025)
- [HiFi-Inpaint: Towards High-Fidelity Reference-Based Inpainting for Generating Detail-Preserving Human-Product Images](https://mlsanity.com/paper/2603.02210/) (2026)
- [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)
- [Efficient Text-Guided Convolutional Adapter for the Diffusion Model](https://mlsanity.com/paper/2602.14514/) (2026)
- [DriveCombo: Benchmarking Compositional Traffic Rule Reasoning in Autonomous Driving](https://mlsanity.com/paper/2603.01637/) (2026)
- [Is This Edit Correct? A Multi-Dimensional Benchmark for Reasoning-Aware Image Editing](https://mlsanity.com/paper/2606.05172/) (2026)
- [Direct 3D-Aware Object Insertion via Decomposed Visual Proxies](https://mlsanity.com/paper/2606.06601/) (2026)
- [DreamOmni3: Scribble-based Editing and Generation](https://mlsanity.com/paper/2512.22525/) (2025)
- [A2-Edit: Precise Reference-Guided Image Editing of Arbitrary Objects and Ambiguous Masks](https://mlsanity.com/paper/2603.10685/) (2026)
- [TRACE: High-Fidelity 3D Scene Editing via Tangible Reconstruction and Geometry-Aligned Contextual Video Masking](https://mlsanity.com/paper/2604.01207/) (2026)
- [InnoText: A Unified Model for Visual Text Generation and Editing](https://mlsanity.com/paper/2607.22101/) (2026)
- [Geometry-Editable and Appearance-Preserving Object Compositon](https://mlsanity.com/paper/2505.20914/) (2025)
- [PosBridge: Multi-View Positional Embedding Transplant for Identity-Aware Image Editing](https://mlsanity.com/paper/2508.17302/) (2025)
- [Durian: Dual Reference Image-Guided Portrait Animation with Attribute Transfer](https://mlsanity.com/paper/2509.04434/) (2025)
- [Insert In Style: A Zero-Shot Generative Framework for Harmonious Cross-Domain Object Composition](https://mlsanity.com/paper/2511.15197/) (2025)
- [Dynamic Image Prompt Adapter for Scalable Zero-shot Personalized Text-to-Image Generation](https://mlsanity.com/paper/2512.09814/) (2025)

## Abstract (arXiv)

This work presents Insert Anything, a unified framework for reference-based image insertion that seamlessly integrates objects from reference images into target scenes under flexible, user-specified control guidance. Instead of training separate models for individual tasks, our approach is trained once on our new AnyInsertion dataset--comprising 120K prompt-image pairs covering diverse tasks such as person, object, and garment insertion--and effortlessly generalizes to a wide range of insertion scenarios. Such a challenging setting requires capturing both identity features and fine-grained details, while allowing versatile local adaptations in style, color, and texture. To this end, we propose to leverage the multimodal attention of the Diffusion Transformer (DiT) to support both mask- and text-guided editing. Furthermore, we introduce an in-context editing mechanism that treats the reference image as contextual information, employing two prompting strategies to harmonize the inserted elements with the target scene while faithfully preserving their distinctive features. Extensive experiments on AnyInsertion, DreamBooth, and VTON-HD benchmarks demonstrate that our method consistently outperforms existing alternatives, underscoring its great potential in real-world applications such as creative content generation, virtual try-on, and scene composition.