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| license: apache-2.0 |
| base_model: |
| - stable-diffusion-v1-5/stable-diffusion-v1-5 |
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| <div align="center"> |
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| <h2>DynamicID: Zero-Shot Multi-ID Image Personalization with Flexible Facial Editability</h2> |
| <h3>[ICCV 2025]</h3> |
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| [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), |
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| [](https://arxiv.org/abs/2503.06505) |
| [](https://github.com/ByteCat-bot/DynamicID) |
| </div> |
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| --- |
| 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! |
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| ## 🔍 Abstract |
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| 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**. |
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| 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: |
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| - 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. |
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| - Identity-Motion Reconfigurator (IMR), which applies feature-space manipulation to effectively disentangle and reconfigure facial motion and identity features, supporting flexible facial editing. |
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| - A task-decoupled training paradigm that reduces data dependency |
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| - A curated VariFace-10k facial dataset, comprising 10k unique individuals, each represented by 35 distinct facial images. |
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| Experimental results demonstrate that DynamicID outperforms state-of-the-art methods in identity fidelity, facial editability, and multi-ID personalization capability. |
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| ## 💡 Method |
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| <div align="center"> |
| <img src="assets/pipeline.jpg", width="1000"> |
| </div> |
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| 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. |
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| ## 🚀 Checkpoint |
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| 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). |
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| 2. Download our SAA-related and IMR-related checkpoints from [DynamicID Checkpoints on Hugging Face](https://huggingface.co/meteorite2023/DynamicID). |
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| ## 🌈 Gallery |
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| <div align="center"> |
| <img src="assets/teaser.jpg", width="900"> |
| <br><br><br> |
| <img src="assets/single.jpg", width="900"> |
| <br><br><br> |
| <img src="assets/multi.jpg", width="900"> |
| </div> |
| |
| ## 📌 ToDo List |
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| - [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 |
|
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| ## 📖 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} |
| } |
| |
| ``` |
