# Identity Decoupling for Multi-Subject Personalization of Text-to-Image Models

arXiv: https://arxiv.org/abs/2404.04243 · HTML: https://mlsanity.com/paper/2404.04243/

## Key facts

- Cited by 33 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 47 of its 56 references that are in the map.
- First posted on arXiv 5 Apr 2024.

## 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 47)

- [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)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [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)
- [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)
- [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)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [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)
- [T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.08453/) (2023)
- [Multi-Concept Customization of Text-to-Image Diffusion](https://mlsanity.com/paper/2212.04488/) (2022)
- [ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation](https://mlsanity.com/paper/2304.05977/) (2023)
- [Training Diffusion Models with Reinforcement Learning](https://mlsanity.com/paper/2305.13301/) (2023)
- [eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers](https://mlsanity.com/paper/2211.01324/) (2022)
- [Diffusion Model Alignment Using Direct Preference Optimization](https://mlsanity.com/paper/2311.12908/) (2023)
- [Directly Fine-Tuning Diffusion Models on Differentiable Rewards](https://mlsanity.com/paper/2309.17400/) (2023)
- [Aligning Text-to-Image Models using Human Feedback](https://mlsanity.com/paper/2302.12192/) (2023)
- [ELITE: Encoding Visual Concepts into Textual Embeddings for Customized Text-to-Image Generation](https://mlsanity.com/paper/2302.13848/) (2023)
- [AnyDoor: Zero-shot Object-level Image Customization](https://mlsanity.com/paper/2307.09481/) (2023)
- [Scaling Open-Vocabulary Object Detection](https://mlsanity.com/paper/2306.09683/) (2023)
- [SVDiff: Compact Parameter Space for Diffusion Fine-Tuning](https://mlsanity.com/paper/2303.11305/) (2023)
- [DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic Data](https://mlsanity.com/paper/2306.09344/) (2023)

## Built on by (most cited first, 30 of 33)

- [DreamStory: Open-Domain Story Visualization by LLM-Guided Multi-Subject Consistent Diffusion](https://mlsanity.com/paper/2407.12899/) (2024)
- [UniPortrait: A Unified Framework for Identity-Preserving Single- and Multi-Human Image Personalization](https://mlsanity.com/paper/2408.05939/) (2024)
- [StoryMaker: Towards Holistic Consistent Characters in Text-to-image Generation](https://mlsanity.com/paper/2409.12576/) (2024)
- [Movie Weaver: Tuning-Free Multi-Concept Video Personalization with Anchored Prompts](https://mlsanity.com/paper/2502.07802/) (2025)
- [FreeTuner: Any Subject in Any Style with Training-free Diffusion](https://mlsanity.com/paper/2405.14201/) (2024)
- [Personalized Image Generation with Deep Generative Models: A Decade Survey](https://mlsanity.com/paper/2502.13081/) (2025)
- [Personalized Multimodal Large Language Models: A Survey](https://mlsanity.com/paper/2412.02142/) (2024)
- [ClassDiffusion: More Aligned Personalization Tuning with Explicit Class Guidance](https://mlsanity.com/paper/2405.17532/) (2024)
- [AnyStory: Towards Unified Single and Multiple Subject Personalization in Text-to-Image Generation](https://mlsanity.com/paper/2501.09503/) (2025)
- [Self-Refining Video Sampling](https://mlsanity.com/paper/2601.18577/) (2026)
- [Mod-Adapter: Tuning-Free and Versatile Multi-concept Personalization via Modulation Adapter](https://mlsanity.com/paper/2505.18612/) (2025)
- [RePIC: Reinforced Post-Training for Personalizing Multi-Modal Language Models](https://mlsanity.com/paper/2506.18369/) (2025)
- [Silent Branding Attack: Trigger-free Data Poisoning Attack on Text-to-Image Diffusion Models](https://mlsanity.com/paper/2503.09669/) (2025)
- [LatexBlend: Scaling Multi-concept Customized Generation with Latent Textual Blending](https://mlsanity.com/paper/2503.06956/) (2025)
- [MultiHuman-Testbench: Benchmarking Image Generation for Multiple Humans](https://mlsanity.com/paper/2506.20879/) (2025)
- [LMMs Meet Object-Centric Vision: Understanding, Segmentation, Editing and Generation](https://mlsanity.com/paper/2604.11789/) (2026)
- [Controllable Human Image Generation with Personalized Multi-Garments](https://mlsanity.com/paper/2411.16801/) (2024)
- [TARA: Token-Aware LoRA for Composable Personalization in Diffusion Models](https://mlsanity.com/paper/2508.08812/) (2025)
- [SIGMA-GEN: Structure and Identity Guided Multi-subject Assembly for Image Generation](https://mlsanity.com/paper/2510.06469/) (2025)
- [MultiBind: A Benchmark for Attribute Misbinding in Multi-Subject Generation](https://mlsanity.com/paper/2603.21937/) (2026)
- [Improving Multi-Subject Consistency in Open-Domain Image Generation with Isolation and Reposition Attention](https://mlsanity.com/paper/2411.19261/) (2024)
- [Continual Personalization for Diffusion Models](https://mlsanity.com/paper/2510.02296/) (2025)
- [ShowFlow: From Robust Single Concept to Condition-Free Multi-Concept Generation](https://mlsanity.com/paper/2506.18493/) (2025)
- [Layout-and-Retouch: A Dual-stage Framework for Improving Diversity in Personalized Image Generation](https://mlsanity.com/paper/2407.09779/) (2024)
- [Griffin: Generative Reference and Layout Guided Image Composition](https://mlsanity.com/paper/2509.23643/) (2025)
- [AnyMS: Bottom-up Attention Decoupling for Layout-guided and Training-free Multi-subject Customization](https://mlsanity.com/paper/2512.23537/) (2025)
- [FaR: Enhancing Multi-Concept Text-to-Image Diffusion via Concept Fusion and Localized Refinement](https://mlsanity.com/paper/2504.03292/) (2025)
- [Parallel Rescaling: Rebalancing Consistency Guidance for Personalized Diffusion Models](https://mlsanity.com/paper/2506.00607/) (2025)
- [A Training-Free Approach for Multi-ID Customization via Attention Adjustment and Spatial Control](https://mlsanity.com/paper/2511.20401/) (2025)
- [C2P-VAR: Continual and Compositional Personalization in Visual Autoregressive Models](https://mlsanity.com/paper/2605.19750/) (2026)

## Abstract (arXiv)

Text-to-image diffusion models have shown remarkable success in generating personalized subjects based on a few reference images. However, current methods often fail when generating multiple subjects simultaneously, resulting in mixed identities with combined attributes from different subjects. In this work, we present MuDI, a novel framework that enables multi-subject personalization by effectively decoupling identities from multiple subjects. Our main idea is to utilize segmented subjects generated by a foundation model for segmentation (Segment Anything) for both training and inference, as a form of data augmentation for training and initialization for the generation process. Moreover, we further introduce a new metric to better evaluate the performance of our method on multi-subject personalization. Experimental results show that our MuDI can produce high-quality personalized images without identity mixing, even for highly similar subjects as shown in Figure 1. Specifically, in human evaluation, MuDI obtains twice the success rate for personalizing multiple subjects without identity mixing over existing baselines and is preferred over 70% against the strongest baseline.