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--- |
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license: apache-2.0 |
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base_model: |
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- stable-diffusion-v1-5/stable-diffusion-v1-5 |
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--- |
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<meta name="google-site-verification" content="-XQC-POJtlDPD3i2KSOxbFkSBde_Uq9obAIh_4mxTkM" /> |
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<div align="center"> |
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<h2>DynamicID: Zero-Shot Multi-ID Image Personalization with Flexible Facial Editability</h2> |
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<h3>[ICCV 2025]</h3> |
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[Xirui Hu](https://openreview.net/profile?id=~Xirui_Hu1), |
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[Jiahao Wang](https://openreview.net/profile?id=~Jiahao_Wang14), |
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[Hao Chen](https://openreview.net/profile?id=~Hao_chen100), |
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[Weizhan Zhang](https://openreview.net/profile?id=~Weizhan_Zhang1), |
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[Benqi Wang](https://openreview.net/profile?id=~Benqi_Wang2), |
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[Yikun Li](https://openreview.net/profile?id=~Yikun_Li1), |
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[Haishun Nan](https://openreview.net/profile?id=~Haishun_Nan1), |
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[](https://arxiv.org/abs/2503.06505) |
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[](https://github.com/ByteCat-bot/DynamicID) |
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</div> |
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--- |
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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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--- |
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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"> |
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<img src="assets/pipeline.jpg", width="1000"> |
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</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"> |
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<img src="assets/teaser.jpg", width="900"> |
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<br><br><br> |
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<img src="assets/single.jpg", width="900"> |
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<br><br><br> |
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<img src="assets/multi.jpg", width="900"> |
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</div> |
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## 📌 ToDo List |
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- [x] Release technical report |
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- [x] Release **training and inference code** |
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- [x] Release **Dynamic-sd** (based on *stable diffusion v1.5*) |
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- [ ] Release **Dynamic-flux** (based on *Flux-dev*) |
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- [ ] Release a Hugging Face Demo Space |
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## 📖 Citation |
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If you are inspired by our work, please cite our paper. |
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```bibtex |
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@inproceedings{dynamicid, |
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title={DynamicID: Zero-Shot Multi-ID Image Personalization with Flexible Facial Editability}, |
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author={Xirui Hu, |
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Jiahao Wang, |
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Hao Chen, |
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Weizhan Zhang, |
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Benqi Wang, |
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Yikun Li, |
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Haishun Nan |
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}, |
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booktitle={International Conference on Computer Vision}, |
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year={2025} |
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} |
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``` |
