需先手动下载 Wan2.1 底座、Wav2Vec 和 FantasyTalking 三份权重到 ./models 才能推理;要求 torch >= 2.0.0,flash_attn 为可选加速项;显存不足时需通过 num_persistent_param_in_dit 限制常驻参数,但会明显变慢。
fantasy-talking
Fantasy-AMAPApache-2.0更新放缓基于扩散模型生成逼真说话人像视频,通过连贯运动合成让口型与头部动作自然同步,适合做数字人播报和虚拟主播。
[ACM MM 2025] FantasyTalking: Realistic Talking Portrait Generation via Coherent Motion Synthesis
git clone https://github.com/Fantasy-AMAP/fantasy-talking.git
cd fantasy-talking
pip install -r requirements.txt
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 fantasytalking_model.ckpt --local-dir ./models
python infer.py --image_path ./assets/images/woman.png --audio_path ./assets/audios/woman.wavREADME 在单张 A100 上测试(512x512、81 帧):不限制常驻参数约需 40G 显存,限制 7B 约需 20G,限制为 0 约需 5G;未说明 CPU 运行或其他 GPU 型号。
基于 Wan2.1 的音频驱动说话人像视频生成工具,输入一张图和一段音频即可生成口型同步、动作自然的说话视频
基于扩散模型生成逼真说话人像视频,通过连贯运动合成让口型与头部动作自然同步,适合做数字人播报和虚拟主播。
核心能力
- 以 Wan2.1-I2V-14B-720P 为底座做音频条件微调
- 用 Wav2Vec 音频编码器提取语音特征驱动口型
- 通过 prompt 控制人物动作与说话风格
- 支持调节 prompt 与 audio cfg 提升口型一致性
- 提供 Gradio 本地演示与 ComfyUI-Wan 集成
容易踩的坑
暂无 release 数据(可能没有发版,或尚未同步)。
用户点评
登录用户的真实使用记录,可按版本和硬件筛选还没有人点评。第一个说出真实体验的人最有价值。
FantasyTalking: Realistic Talking Portrait Generation via Coherent Motion Synthesis
🔥 Latest News!!
- July 7, 2025: Released a user guide to solve the common issues reported by recent researchers and the community.
- July 5, 2025: Our work is accepted by ACM MM 2025!
- April 29, 2025: Our work is merged to ComfyUI-Wan ! Thank kijai for the update 👏!
- April 28, 2025: We released the inference code and model weights for audio conditions.
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 🤖 ModelScope | Base model |
| Wav2Vec | 🤗 Huggingface 🤖 ModelScope | Audio encoder |
| FantasyTalking model | 🤗 Huggingface 🤖 ModelScope | Our audio condition weights |
Download models using huggingface-cli:
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 fantasytalking_model.ckpt --local-dir ./models
Download models using modelscope-cli:
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 fantasytalking_model.ckpt --local_dir ./models
🔑 Inference
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]. You can increase the audio cfg to achieve more consistent lip-sync.
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:
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. Or you can directly e-mail [email protected]. 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{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, HunyuanVideo, and 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.