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README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+---
+# FantasyPortrait: Enhancing Multi-Character Portrait Animation with Expression-Augmented Diffusion Transformers
+
+[![Home Page](https://img.shields.io/badge/Project-FantasyPortrait-blue.svg)](https://fantasy-amap.github.io/fantasy-portrait/)
+[![arXiv](https://img.shields.io/badge/Arxiv-2507.12956-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2507.12956)
+[![hf_dataset](https://img.shields.io/badge/🤗%20Dataset-FantasyPortrait-yellow.svg)](https://huggingface.co/datasets/acvlab/FantasyPortrait)
+[![hf_paper](https://img.shields.io/badge/🤗-FantasyPortrait-red.svg)](https://huggingface.co/papers/2507.12956)
+
+## 🔥 Latest News!!
+* August 10, 2025: We released the inference code, model weights and datasets.
+
+## Demo
+For more interesting results, please visit our [website](https://fantasy-amap.github.io/fantasy-portrait/).
+
+
+## Quickstart
+### 🛠️Installation
+
+Clone the repo:
+
+```
+git clone https://github.com/Fantasy-AMAP/fantasy-portrait.git
+cd fantasy-portrait
+```
+
+Install dependencies:
+```
+apt-get install ffmpeg
+# Ensure torch >= 2.0.0
+pip install -r requirements.txt
+# Note: flash attention must be installed
+pip install flash_attn
+```
+
+### 📦Multi-Expr Dataset
+We make public the first multi-portrait facial expression video dataset **Multi-Expr Dataset**, Please download it via the this [link](https://huggingface.co/datasets/acvlab/FantasyPortrait-Multi-Expr).
+
+### 🧱Model Download
+| Models        |                       Download Link                                           |    Notes                      |
+| --------------|-------------------------------------------------------------------------------|-------------------------------|
+| Wan2.1-I2V-14B-720P  |      🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-720P)    🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-720P)     | Base model
+| FantasyPortrait      |      🤗 [Huggingface](https://huggingface.co/acvlab/FantasyPortrait/)     🤖 [ModelScope](https://www.modelscope.cn/models/amap_cvlab/FantasyPortrait/)         | Our emo condition weights
+
+Download models using huggingface-cli:
+``` sh
+pip install "huggingface_hub[cli]"
+huggingface-cli download Wan-AI/Wan2.1-I2V-14B-720P --local-dir ./models/Wan2.1-I2V-14B-720P
+huggingface-cli download acvlab/FantasyPortrait --local-dir ./models
+```
+
+Download models using modelscope-cli:
+``` sh
+pip install modelscope
+modelscope download Wan-AI/Wan2.1-I2V-14B-720P --local_dir ./models/Wan2.1-I2V-14B-720P
+modelscope download amap_cvlab/FantasyPortrait  --local_dir ./models
+```
+
+### 🔑 Single-Portrait Inference
+``` sh
+bash infer_single.sh
+```
+
+### 🔑 Multi-Portrait Inference
+If you use input image and drive videos with multiple people, you can run as follows:
+``` sh
+bash infer_multi.sh
+```
+If you use input image with multiple people and different multiple single-human driven videos, you can run as follows:
+```sh
+bash infer_multi_diff.sh
+```
+
+### 📦Speed and VRAM Usage
+We present a detailed table here. The model is tested on a single A100.
+
+|`torch_dtype`|`num_persistent_param_in_dit`|Speed|Required VRAM|
+|-|-|-|-|
+|torch.bfloat16|None (unlimited)|15.5s/it|40G|
+|torch.bfloat16|7*10**9 (7B)|32.8s/it|20G|
+|torch.bfloat16|0|42.6s/it|5G|
+
+
+
+## 🧩 Community Works
+We ❤️ contributions from the open-source community! If your work has improved FantasyPortrait, please inform us.
+Or you can directly e-mail [frank.jf@alibaba-inc.com](mailto://frank.jf@alibaba-inc.com). We are happy to reference your project for everyone's convenience.
+
+## 🔗Citation
+If you find this repository useful, please consider giving a star ⭐ and citation
+```
+@article{wang2025fantasyportrait,
+  title={FantasyPortrait: Enhancing Multi-Character Portrait Animation with Expression-Augmented Diffusion Transformers},
+  author={Wang, Qiang and Wang, Mengchao and Jiang, Fan and Fan, Yaqi and Qi, Yonggang and Xu, Mu},
+  journal={arXiv preprint arXiv:2507.12956},
+  year={2025}
+}
+```
+
+## Acknowledgments
+Thanks to [Wan2.1](https://github.com/Wan-Video/Wan2.1), [PD-FGC](https://github.com/Dorniwang/PD-FGC-inference) and [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio) for open-sourcing their models and code, which provided valuable references and support for this project. Their contributions to the open-source community are truly appreciated.