<div align="center"> <img src="assets/header.svg" alt="PersonaLive" width="100%"> <h2>Expressive Portrait Image Animation for Live Streaming</h2>

Zhiyuan Li<sup>1,2,3</sup> · Chi-Man Pun<sup>1,📪</sup> · Chen Fang<sup>2</sup> · Jue Wang<sup>2</sup> · Xiaodong Cun<sup>3,📪</sup>

<sup>1</sup> University of Macau <sup>2</sup> Dzine.ai <sup>3</sup> GVC Lab, Great Bay University

</div>

📋 TODO

[ ] If you find PersonaLive useful or interesting, please give us a Star🌟! Your support drives us to keep improving.
[ ] Fix bugs (If you encounter any issues, please feel free to open an issue or contact me! 🙏)
[ ] Release training code.
[x] [2026.02.21] 🥳 PersonaLive is accepted by CVPR2026 🎉.
[x] [2025.12.29] 🔥 Enhance WebUI (Support reference image replacement).
[x] [2025.12.22] 🔥 Supported streaming strategy in offline inference to generate long videos on 12GB VRAM!
[x] [2025.12.17] 🔥 ComfyUI-PersonaLive is now supported! (Thanks to @okdalto)
[x] [2025.12.15] 🔥 Release paper!
[x] [2025.12.12] 🔥 Release inference code, config, and pretrained weights!

⚖️ Disclaimer

[x] This project is released for academic research only.
[x] Users must not use this repository to generate harmful, defamatory, or illegal content.
[x] The authors bear no responsibility for any misuse or legal consequences arising from the use of this tool.
[x] By using this code, you agree that you are solely responsible for any content generated.

⚙️ Framework

We present PersonaLive, a real-time and streamable diffusion framework capable of generating infinite-length portrait animations.

🚀 Getting Started

🛠 Installation

# clone this repo
git clone https://github.com/GVCLab/PersonaLive
cd PersonaLive

# Create conda environment
conda create -n personalive python=3.10
conda activate personalive

# Install packages with pip
pip install -r requirements_base.txt

⏬ Download weights

Option 1: Download pre-trained weights of base models and other components (sd-image-variations-diffusers and sd-vae-ft-mse). You can run the following command to download weights automatically:

python tools/download_weights.py

Option 2: Download pre-trained weights into the ./pretrained_weights folder from one of the below URLs:

Finally, these weights should be organized as follows:

pretrained_weights
├── onnx
│   ├── unet_opt
│   │   ├── unet_opt.onnx
│   │   └── unet_opt.onnx.data
│   └── unet
├── personalive
│   ├── denoising_unet.pth
│   ├── motion_encoder.pth
│   ├── motion_extractor.pth
│   ├── pose_guider.pth
│   ├── reference_unet.pth
│   └── temporal_module.pth
├── sd-vae-ft-mse
│   ├── diffusion_pytorch_model.bin
│   └── config.json
├── sd-image-variations-diffusers
│   ├── image_encoder
│   │   ├── pytorch_model.bin
│   │   └── config.json
│   ├── unet
│   │   ├── diffusion_pytorch_model.bin
│   │   └── config.json
│   └── model_index.json
└── tensorrt
    └── unet_work.engine

🎞️ Offline Inference

Run offline inference with the default configuration:

python inference_offline.py

-L: Max number of frames to generate. (Default: 100)
--use_xformers: Enable xFormers memory efficient attention. (Default: True)
--stream_gen: Enable streaming generation strategy. (Default: True)
--reference_image: Path to a specific reference image. Overrides settings in config.
--driving_video: Path to a specific driving video. Overrides settings in config.

⚠️ Note for RTX 50-Series (Blackwell) Users: xformers is not yet fully compatible with the new architecture. To avoid crashes, please disable it by running:

python inference_offline.py --use_xformers False

📸 Online Inference

📦 Setup Web UI

# install Node.js 18+
curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.39.1/install.sh | bash
nvm install 18

source web_start.sh

🏎️ Acceleration (Optional)

Converting the model to TensorRT can significantly speed up inference (~ 2x ⚡️). Building the engine may take about 20 minutes depending on your device. Note that TensorRT optimizations may lead to slight variations or a small drop in output quality.

# Install packages with pip
pip install -r requirements_trt.txt

# Converting the model to TensorRT
python torch2trt.py

💡 PyCUDA Installation Issues: If you encounter a "Failed to build wheel for pycuda" error during the installation above, please follow these steps:

# Install PyCUDA manually using Conda (avoids compilation issues):
conda install -c conda-forge pycuda "numpy<2.0"

# Open requirements_trt.txt and comment out or remove the line "pycuda==2024.1.2"

# Install other packages with pip
pip install -r requirements_trt.txt

# Converting the model to TensorRT
python torch2trt.py

⚠️ The provided TensorRT model is from an H100. We recommend ALL users (including H100 users) re-run python torch2trt.py locally to ensure best compatibility.

▶️ Start Streaming

python inference_online.py --acceleration none (for RTX 50-Series) or xformers or tensorrt

Then open http://0.0.0.0:7860 in your browser. (*If http://0.0.0.0:7860 does not work well, try http://localhost:7860)

How to use: Upload Image ➡️ Fuse Reference ➡️ Start Animation ➡️ Enjoy! 🎉

Regarding Latency: Latency varies depending on your device's computing power. You can try the following methods to optimize it:

Lower the "Driving FPS" setting in the WebUI to reduce the computational workload.
You can increase the multiplier (e.g., set to num_frames_needed * 4 or higher) to better match your device's inference speed. https://github.com/GVCLab/PersonaLive/blob/6953d1a8b409f360a3ee1d7325093622b29f1e22/webcam/util.py#L73

📚 Community Contribution

Special thanks to the community for providing helpful setups! 🥂

Windows + RTX 50-Series Guide: Thanks to @dknos for providing a detailed guide on running this project on Windows with Blackwell GPUs.
TensorRT on Windows: If you are trying to convert TensorRT models on Windows, this discussion might be helpful. Special thanks to @MaraScott and @Jeremy8776 for their insights.
ComfyUI: Thanks to @okdalto for helping implement the ComfyUI-PersonaLive support.
Useful Scripts: Thanks to @suruoxi for implementing download_weights.py, and to @andchir for adding audio merging functionality.

🎬 More Results

👀 Visualization results

<table width="100%"> <tr> <td width="50%"> <video src="https://github.com/user-attachments/assets/cdc885ef-5e1c-4139-987a-2fa50fefd6a4" controls="controls" style="max-width: 100%; display: block;"></video> </td> <td width="50%"> <video src="https://github.com/user-attachments/assets/014f7bae-74ce-4f56-8621-24bc76f3c123" controls="controls" style="max-width: 100%; display: block;"></video> </td> </tr> </table> <table width="100%"> <tr> <td width="25%"> <video src="https://github.com/user-attachments/assets/1e6a0809-15d2-4cab-ae8f-8cf1728c6281" controls="controls" style="max-width: 100%; display: block;"></video> </td> <td width="25%"> <video src="https://github.com/user-attachments/assets/d9cf265d-9db0-4f83-81da-be967bbd5f26" controls="controls" style="max-width: 100%; display: block;"></video> </td> <td width="25%"> <video src="https://github.com/user-attachments/assets/86235139-b63e-4f26-b09c-d218466e8e24" controls="controls" style="max-width: 100%; display: block;"></video> </td> <td width="25%"> <video src="https://g

PersonaLive

Install / Use

README