Upload .\assets with huggingface_hub

.//assets

@@ -0,0 +1,91 @@
+
+# DynamicID: Zero-Shot Multi-ID Image Personalization with Flexible Facial Editability
+
+<div align="center">
+    
+### [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),
+
+[![arXiv](https://img.shields.io/badge/arXiv-2503.06505-b31b1b.svg)](https://arxiv.org/abs/2503.06505)
+[![GitHub](https://img.shields.io/badge/GitHub-MTVCrafter-blue?logo=github)](https://github.com/ByteCat-bot/DynamicID)
+</div>
+
+---
+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
+
+<div align="center">
+    <img src="assets/pipeline.jpg", width="1000">
+</div>
+
+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
+
+<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
+
+- [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}
+    }
+    
+```