README.md

---
license: apache-2.0
---
[中文阅读](./README_zh.md)
# FantasyTalking: Realistic Talking Portrait Generation via Coherent Motion Synthesis

[![Home Page](https://img.shields.io/badge/Project-<Website>-blue.svg)](https://fantasy-amap.github.io/fantasy-talking/) 
[![arXiv](https://img.shields.io/badge/Arxiv-2504.04842-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2504.04842) 
[![hf_paper](https://img.shields.io/badge/🤗-Paper%20In%20HF-red.svg)](https://huggingface.co/papers/2504.04842)

## 🔥 Latest News!!
* April 28, 2025: We released the inference code and model weights for audio conditions.


<!-- ![Fig.1](https://github.com/Fantasy-AMAP/fantasy-talking/blob/main/assert/fig0_1_0.png) -->


## Quickstart
### 🛠️Installation

Clone the repo:

```
git clone https://github.com/Fantasy-AMAP/fantasy-talking.git
cd fantasy-talking
```

Install dependencies:
```
# Ensure torch >= 2.0.0
pip install -r requirements.txt
# Optional to install flash_attn to accelerate attention computation
pip install flash_attn
```

### 🧱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
| Wav2Vec |      🤗 [Huggingface](https://huggingface.co/facebook/wav2vec2-base-960h)    🤖 [ModelScope](https://modelscope.cn/models/AI-ModelScope/wav2vec2-base-960h)      | Audio encoder
| FantasyTalking model      |      🤗 [Huggingface](https://huggingface.co/acvlab/FantasyTalking/)     🤖 [ModelScope](https://www.modelscope.cn/models/amap_cvlab/FantasyTalking/)         | Our audio 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 facebook/wav2vec2-base-960h --local-dir ./models/wav2vec2-base-960h
huggingface-cli download acvlab/FantasyTalking --files fantasytalking_model.ckpt --local-dir ./models/fantasytalking_model.ckpt
```

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 AI-ModelScope/wav2vec2-base-960h --local_dir ./models/wav2vec2-base-960h
modelscope download amap_cvlab/FantasyTalking --files fantasytalking_model.ckpt --local-dir ./models/fantasytalking_model.ckpt
```

### 🔑 Inference
``` sh
python infer.py  --image_path ./assets/images/woman.png --audio_path ./assets/audios/woman.wav 
```
You can control the character's behavior through the prompt. The recommended range for prompt and audio cfg is [3-7].
``` sh
python infer.py  --image_path ./assets/images/woman.png --audio_path ./assets/audios/woman.wav --prompt "The person is speaking enthusiastically, with their hands continuously waving." --prompt_cfg_scale 5.0 --audio_cfg_scale 5.0
```

We present a detailed table here. The model is tested on a single A100.(512x512, 81 frames).

|`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|

### Gradio Demo
We construct an online demo in Huggingface.
For the local gradio demo, you can run:
``` sh
pip install gradio spaces
python app.py
```

## 🧩 Community Works
We ❤️ contributions from the open-source community! If your work has improved FantasyTalking, please inform us.
## 🔗Citation
If you find this repository useful, please consider giving a star ⭐ and citation
```
@article{wang2025fantasytalking,
   title={FantasyTalking: Realistic Talking Portrait Generation via Coherent Motion Synthesis},
   author={Wang, Mengchao and Wang, Qiang and Jiang, Fan and Fan, Yaqi and Zhang, Yunpeng and Qi, Yonggang and Zhao, Kun and Xu, Mu},
   journal={arXiv preprint arXiv:2504.04842},
   year={2025}
 }
```

## Acknowledgments
Thanks to [Wan2.1](https://github.com/Wan-Video/Wan2.1), [HunyuanVideo](https://github.com/Tencent/HunyuanVideo), 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.