# RelationAdapter: Learning and Transferring Visual Relation with Diffusion Transformers

arXiv: https://arxiv.org/abs/2506.02528 · HTML: https://mlsanity.com/paper/2506.02528/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models.
- Builds on 59 of its 74 references that are in the map.
- First posted on arXiv 3 Jun 2025.

## Placed in

- Diffusion models / [Image generation, text-to-image and editing](https://mlsanity.com/diffusion/image-generation-text-to-image-and-editing/)

## Builds on (most cited first, 30 of 59)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [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)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [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)
- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://mlsanity.com/paper/2108.01073/) (2021)
- [T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.08453/) (2023)
- [Imagic: Text-Based Real Image Editing with Diffusion Models](https://mlsanity.com/paper/2210.09276/) (2022)
- [GLIGEN: Open-Set Grounded Text-to-Image Generation](https://mlsanity.com/paper/2301.07093/) (2023)
- [Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring](https://mlsanity.com/paper/1612.02177/) (2016)
- [MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editing](https://mlsanity.com/paper/2304.08465/) (2023)
- [Exploring the Limits of Weakly Supervised Pretraining](https://mlsanity.com/paper/1805.00932/) (2018)
- [Paint by Example: Exemplar-based Image Editing with Diffusion Models](https://mlsanity.com/paper/2211.13227/) (2022)
- [OminiControl: Minimal and Universal Control for Diffusion Transformer](https://mlsanity.com/paper/2411.15098/) (2024)
- [Images Speak in Images: A Generalist Painter for In-Context Visual Learning](https://mlsanity.com/paper/2212.02499/) (2022)
- [Taming Rectified Flow for Inversion and Editing](https://mlsanity.com/paper/2411.04746/) (2024)
- [Versatile Diffusion: Text, Images and Variations All in One Diffusion Model](https://mlsanity.com/paper/2211.08332/) (2022)
- [Concept Sliders: LoRA Adaptors for Precise Control in Diffusion Models](https://mlsanity.com/paper/2311.12092/) (2023)
- [SSR-Encoder: Encoding Selective Subject Representation for Subject-Driven Generation](https://mlsanity.com/paper/2312.16272/) (2023)
- [Lumina-Next: Making Lumina-T2X Stronger and Faster with Next-DiT](https://mlsanity.com/paper/2406.18583/) (2024)
- [EasyControl: Adding Efficient and Flexible Control for Diffusion Transformer](https://mlsanity.com/paper/2503.07027/) (2025)
- [Transparent Image Layer Diffusion using Latent Transparency](https://mlsanity.com/paper/2402.17113/) (2024)
- [HumanSD: A Native Skeleton-Guided Diffusion Model for Human Image Generation](https://mlsanity.com/paper/2304.04269/) (2023)
- [Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection](https://mlsanity.com/paper/1909.01955/) (2019)
- [UniReal: Universal Image Generation and Editing via Learning Real-world Dynamics](https://mlsanity.com/paper/2412.07774/) (2024)

## Built on by (most cited first, 26 of 26)

- [Controllable Video Generation: A Survey](https://mlsanity.com/paper/2507.16869/) (2025)
- [Mitty: Diffusion-based Human-to-Robot Video Generation](https://mlsanity.com/paper/2512.17253/) (2025)
- [OmniPSD: Layered PSD Generation with Diffusion Transformer](https://mlsanity.com/paper/2512.09247/) (2025)
- [WorldWander: Bridging Egocentric and Exocentric Worlds in Video Generation](https://mlsanity.com/paper/2511.22098/) (2025)
- [StreamingEffect: Real-Time Human-Centric Video Effect Generation](https://mlsanity.com/paper/2605.17019/) (2026)
- [VISTA: Triplet-Supervised Video Style Transfer with Diffusion Transformers](https://mlsanity.com/paper/2605.17312/) (2026)
- [Edit2Perceive: Image Editing Diffusion Models Are Strong Dense Perceivers](https://mlsanity.com/paper/2511.18673/) (2025)
- [OmniRefiner: Reinforcement-Guided Local Diffusion Refinement](https://mlsanity.com/paper/2511.19990/) (2025)
- [The Consistency Critic: Correcting Inconsistencies in Generated Images via Reference-Guided Attentive Alignment](https://mlsanity.com/paper/2511.20614/) (2025)
- [Spanning the Visual Analogy Space with a Weight Basis of LoRAs](https://mlsanity.com/paper/2602.15727/) (2026)
- [LaDe: Unified Multi-Layered Graphic Media Generation and Decomposition](https://mlsanity.com/paper/2603.17965/) (2026)
- [NumeriKontrol: Adding Numeric Control to Diffusion Transformers for Instruction-based Image Editing](https://mlsanity.com/paper/2511.23105/) (2025)
- [Loom: Diffusion-Transformer for Interleaved Generation](https://mlsanity.com/paper/2512.18254/) (2025)
- [TryOnCrafter: Unleashing Camera Trajectories for Realistic Video Virtual Try-on via a Renderable 4D Try-on Proxy](https://mlsanity.com/paper/2606.26092/) (2026)
- [Unified Long Video Inpainting and Outpainting via Overlapping High-Order Co-Denoising](https://mlsanity.com/paper/2511.03272/) (2025)
- [Image Generation as a Visual Planner for Robotic Manipulation](https://mlsanity.com/paper/2512.00532/) (2025)
- [TokenPure: Watermark Removal through Tokenized Appearance and Structural Guidance](https://mlsanity.com/paper/2512.01314/) (2025)
- [EditTransfer++: Toward Faithful and Efficient Visual-Prompt-Guided Image Editing](https://mlsanity.com/paper/2605.07455/) (2026)
- [Delta-Adapter: Scalable Exemplar-Based Image Editing with Single-Pair Supervision](https://mlsanity.com/paper/2605.07940/) (2026)
- [SWEET: Sparse World Modeling with Image Editing for Embodied Task Execution](https://mlsanity.com/paper/2605.19319/) (2026)
- [GenPuzzle: Benchmarking Visual Reasoning in Image Generation Models](https://mlsanity.com/paper/2609.05902/) (2026)
- [MirrorPPR: Exemplar-Based Portrait Photo Retouching](https://mlsanity.com/paper/2606.29308/) (2026)
- [Show Me Examples: Inferring Visual Concepts from Image Sets](https://mlsanity.com/paper/2607.02402/) (2026)
- [TransPhy: Visual In-Context Learning for Physically Grounded Image Editing](https://mlsanity.com/paper/2608.24119/) (2026)
- [PAI-Actor: Cinematic Multi-Character Replacement in Dynamic Scenes](https://mlsanity.com/paper/2609.05918/) (2026)
- [VectorHarness: Recovering Editable, Relation-Preserving Structure from Scientific Graphics](https://mlsanity.com/paper/2609.13294/) (2026)

## Abstract (arXiv)

Inspired by the in-context learning mechanism of large language models (LLMs), a new paradigm of generalizable visual prompt-based image editing is emerging. Existing single-reference methods typically focus on style or appearance adjustments and struggle with non-rigid transformations. To address these limitations, we propose leveraging source-target image pairs to extract and transfer content-aware editing intent to novel query images. To this end, we introduce RelationAdapter, a lightweight module that enables Diffusion Transformer (DiT) based models to effectively capture and apply visual transformations from minimal examples. We also introduce Relation252K, a comprehensive dataset comprising 218 diverse editing tasks, to evaluate model generalization and adaptability in visual prompt-driven scenarios. Experiments on Relation252K show that RelationAdapter significantly improves the model's ability to understand and transfer editing intent, leading to notable gains in generation quality and overall editing performance.