---
license: apache-2.0
base_model:
- stable-diffusion-v1-5/stable-diffusion-v1-5
---
DynamicID: Zero-Shot Multi-ID Image Personalization with Flexible Facial Editability
[ICCV 2025]
[Xirui Hu](https://openreview.net/profile?id=~Xirui_Hu1),
[Jiahao Wang](https://openreview.net/profile?id=~Jiahao_Wang14),
[Hao Chen](https://openreview.net/profile?id=~Hao_chen100),
[Weizhan Zhang](https://openreview.net/profile?id=~Weizhan_Zhang1),
[Benqi Wang](https://openreview.net/profile?id=~Benqi_Wang2),
[Yikun Li](https://openreview.net/profile?id=~Yikun_Li1),
[Haishun Nan](https://openreview.net/profile?id=~Haishun_Nan1),
[](https://arxiv.org/abs/2503.06505)
[](https://github.com/ByteCat-bot/DynamicID)
---
This is the official implementation of DynamicID, a framework that generates visually harmonious image featuring **multiple individuals**. Each person in the image can be specified through user-provided reference images, and most notably, our method enables **independent control of each individual's facial expression** via text prompts. Hope you have fun with this demo!
---
## 🔍 Abstract
Recent advancements in text-to-image generation have spurred interest in personalized human image generation. Although existing methods achieve high-fidelity identity preservation, they often struggle with **limited multi-ID usability** and **inadequate facial editability**.
We present DynamicID, a tuning-free framework that inherently facilitates both single-ID and multi-ID personalized generation with high fidelity and flexible facial editability. Our key innovations include:
- Semantic-Activated Attention (SAA), which employs query-level activation gating to minimize disruption to the original model when injecting ID features and achieve multi-ID personalization without requiring multi-ID samples during training.
- Identity-Motion Reconfigurator (IMR), which applies feature-space manipulation to effectively disentangle and reconfigure facial motion and identity features, supporting flexible facial editing.
- A task-decoupled training paradigm that reduces data dependency
- A curated VariFace-10k facial dataset, comprising 10k unique individuals, each represented by 35 distinct facial images.
Experimental results demonstrate that DynamicID outperforms state-of-the-art methods in identity fidelity, facial editability, and multi-ID personalization capability.
## 💡 Method
The proposed framework is architected around two core components: SAA and IMR. (a) In the anchoring stage, we jointly optimize the SAA and a face encoder to establish robust single-ID and multi-ID personalized generation capabilities. (b) Subsequently in the reconfiguration stage, we freeze these optimized components and leverage them to train the IMR for flexible and fine-grained facial editing.
## 🚀 Checkpoint
1. Download the pretrained Stable Diffusion v1.5 checkpoint from [Stable Diffusion v1.5 on Hugging Face](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5).
2. Download our SAA-related and IMR-related checkpoints from [DynamicID Checkpoints on Hugging Face](https://huggingface.co/meteorite2023/DynamicID).
## 🌈 Gallery
## 📌 ToDo List
- [x] Release technical report
- [x] Release **training and inference code**
- [x] Release **Dynamic-sd** (based on *stable diffusion v1.5*)
- [ ] Release **Dynamic-flux** (based on *Flux-dev*)
- [ ] Release a Hugging Face Demo Space
## 📖 Citation
If you are inspired by our work, please cite our paper.
```bibtex
@inproceedings{dynamicid,
title={DynamicID: Zero-Shot Multi-ID Image Personalization with Flexible Facial Editability},
author={Xirui Hu,
Jiahao Wang,
Hao Chen,
Weizhan Zhang,
Benqi Wang,
Yikun Li,
Haishun Nan
},
booktitle={International Conference on Computer Vision},
year={2025}
}
``` 
