- May 28, 2025: 🔥 HunyuanVideo-Avatar is available in Cloud-Native-Build (CNB) HunyuanVideo-Avatar.
- May 28, 2025: 👋 We release the inference code and model weights of HunyuanVideo-Avatar. Download.
- HunyuanVideo-Avatar
- Inference
- Checkpoints
- ComfyUI
- HunyuanVideo-Avatar 🌅
- 🔥🔥🔥 News!!
- 📑 Open-source Plan
- Contents
- Abstract
- HunyuanVideo-Avatar Overall Architecture
- 🎉 HunyuanVideo-Avatar Key Features
- 📈 Comparisons
- 📜 Requirements
- 🛠️ Dependencies and Installation
- 🧱 Download Pretrained Models
- 🚀 Parallel Inference on Multiple GPUs
- 🔑 Single-gpu Inference
- Run a Gradio Server
- 🔗 BibTeX
- Acknowledgements
Recent years have witnessed significant progress in audio-driven human animation. However, critical challenges remain in (i) generating highly dynamic videos while preserving character consistency, (ii) achieving precise emotion alignment between characters and audio, and (iii) enabling multi-character audio-driven animation. To address these challenges, we propose HunyuanVideo-Avatar, a multimodal diffusion transformer (MM-DiT)-based model capable of simultaneously generating dynamic, emotion-controllable, and multi-character dialogue videos. Concretely, HunyuanVideo-Avatar introduces three key innovations: (i) A character image injection module is designed to replace the conventional addition-based character conditioning scheme, eliminating the inherent condition mismatch between training and inference. This ensures the dynamic motion and strong character consistency; (ii) An Audio Emotion Module (AEM) is introduced to extract and transfer the emotional cues from an emotion reference image to the target generated video, enabling fine-grained and accurate emotion style control; (iii) A Face-Aware Audio Adapter (FAA) is proposed to isolate the audio-driven character with latent-level face mask, enabling independent audio injection via cross-attention for multi-character scenarios. These innovations empower HunyuanVideo-Avatar to surpass state-of-the-art methods on benchmark datasets and a newly proposed wild dataset, generating realistic avatars in dynamic, immersive scenarios. The source code and model weights will be released publicly.
We propose HunyuanVideo-Avatar, a multi-modal diffusion transformer(MM-DiT)-based model capable of generating dynamic, emotion-controllable, and multi-character dialogue videos.
HunyuanVideo-Avatar supports animating any input avatar images to high-dynamic and emotion-controllable videos with simple audio conditions. Specifically, it takes as input multi-style avatar images at arbitrary scales and resolutions. The system supports multi-style avatars encompassing photorealistic, cartoon, 3D-rendered, and anthropomorphic characters. Multi-scale generation spanning portrait, upper-body and full-body. It generates videos with high-dynamic foreground and background, achieving superior realistic and naturalness. In addition, the system supports controlling facial emotions of the characters conditioned on input audio.
HunyuanVideo-Avatar supports various downstream tasks and applications. For instance, the system generates talking avatar videos, which could be applied to e-commerce, online streaming, social media video production, etc. In addition, its multi-character animation feature enlarges the application such as video content creation, editing, etc.
- An NVIDIA GPU with CUDA support is required.
- The model is tested on a machine with 8GPUs.
- Minimum: The minimum GPU memory required is 24GB for 720px1280px129f but very slow.
- Recommended: We recommend using a GPU with 80GB of memory for better generation quality.
- Tested operating system: Linux
Begin by cloning the repository:
git clone https://github.com/Tencent/HunyuanVideo-Avatar.git cd HunyuanVideo-Avatar
We recommend CUDA versions 12.4 or 11.8 for the manual installation.
Conda's installation instructions are available here.
# 1. Create conda environment
conda create -n HunyuanVideo-Avatar python==3.10.9
# 2. Activate the environment
conda activate HunyuanVideo-Avatar
# 3. Install PyTorch and other dependencies using conda
# For CUDA 11.8
conda install pytorch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 pytorch-cuda=11.8 -c pytorch -c nvidia
# For CUDA 12.4
conda install pytorch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 pytorch-cuda=12.4 -c pytorch -c nvidia
# 4. Install pip dependencies
python -m pip install -r requirements.txt
# 5. Install flash attention v2 for acceleration (requires CUDA 11.8 or above)
python -m pip install ninja
python -m pip install git+https://github.com/Dao-AILab/flash-attention.git@v2.6.3
In case of running into float point exception(core dump) on the specific GPU type, you may try the following solutions:
# Option 1: Making sure you have installed CUDA 12.4, CUBLAS>=12.4.5.8, and CUDNN>=9.00 (or simply using our CUDA 12 docker image).
pip install nvidia-cublas-cu12==12.4.5.8
export LD_LIBRARY_PATH=/opt/conda/lib/python3.8/site-packages/nvidia/cublas/lib/
# Option 2: Forcing to explicitly use the CUDA 11.8 compiled version of Pytorch and all the other packages
pip uninstall -r requirements.txt # uninstall all packages
pip install torch==2.4.0 --index-url https://download.pytorch.org/whl/cu118
pip install -r requirements.txt
pip install ninja
pip install git+https://github.com/Dao-AILab/flash-attention.git@v2.6.3
Additionally, you can also use HunyuanVideo Docker image. Use the following command to pull and run the docker image.
# For CUDA 12.4 (updated to avoid float point exception)
docker pull hunyuanvideo/hunyuanvideo:cuda_12
docker run -itd --gpus all --init --net=host --uts=host --ipc=host --name hunyuanvideo --security-opt=seccomp=unconfined --ulimit=stack=67108864 --ulimit=memlock=-1 --privileged hunyuanvideo/hunyuanvideo:cuda_12
pip install gradio==3.39.0 diffusers==0.33.0 transformers==4.41.2
# For CUDA 11.8
docker pull hunyuanvideo/hunyuanvideo:cuda_11
docker run -itd --gpus all --init --net=host --uts=host --ipc=host --name hunyuanvideo --security-opt=seccomp=unconfined --ulimit=stack=67108864 --ulimit=memlock=-1 --privileged hunyuanvideo/hunyuanvideo:cuda_11
pip install gradio==3.39.0 diffusers==0.33.0 transformers==4.41.2
The details of download pretrained models are shown here.
For example, to generate a video with 8 GPUs, you can use the following command:
cd HunyuanVideo-Avatar
JOBS_DIR=$(dirname $(dirname "$0"))
export PYTHONPATH=./
export MODEL_BASE="./weights"
checkpoint_path=${MODEL_BASE}/ckpts/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt
torchrun --nnodes=1 --nproc_per_node=8 --master_port 29605 hymm_sp/sample_batch.py \
--input 'assets/test.csv' \
--ckpt ${checkpoint_path} \
--sample-n-frames 129 \
--seed 128 \
--image-size 704 \
--cfg-scale 7.5 \
--infer-steps 50 \
--use-deepcache 1 \
--flow-shift-eval-video 5.0 \
--save-path ${OUTPUT_BASEPATH}
For example, to generate a video with 1 GPU, you can use the following command:
cd HunyuanVideo-Avatar
JOBS_DIR=$(dirname $(dirname "$0"))
export PYTHONPATH=./
export MODEL_BASE=./weights
OUTPUT_BASEPATH=./results-single
checkpoint_path=${MODEL_BASE}/ckpts/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states_fp8.pt
export DISABLE_SP=1
CUDA_VISIBLE_DEVICES=0 python3 hymm_sp/sample_gpu_poor.py \
--input 'assets/test.csv' \
--ckpt ${checkpoint_path} \
--sample-n-frames 129 \
--seed 128 \
--image-size 704 \
--cfg-scale 7.5 \
--infer-steps 50 \
--use-deepcache 1 \
--flow-shift-eval-video 5.0 \
--save-path ${OUTPUT_BASEPATH} \
--use-fp8 \
--infer-min
cd HunyuanVideo-Avatar
JOBS_DIR=$(dirname $(dirname "$0"))
export PYTHONPATH=./
export MODEL_BASE=./weights
OUTPUT_BASEPATH=./results-poor
checkpoint_path=${MODEL_BASE}/ckpts/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states_fp8.pt
export CPU_OFFLOAD=1
CUDA_VISIBLE_DEVICES=0 python3 hymm_sp/sample_gpu_poor.py \
--input 'assets/test.csv' \
--ckpt ${checkpoint_path} \
--sample-n-frames 129 \
--seed 128 \
--image-size 704 \
--cfg-scale 7.5 \
--infer-steps 50 \
--use-deepcache 1 \
--flow-shift-eval-video 5.0 \
--save-path ${OUTPUT_BASEPATH} \
--use-fp8 \
--cpu-offload \
--infer-min
cd HunyuanVideo-Avatar
bash ./scripts/run_gradio.sh
If you find HunyuanVideo-Avatar useful for your research and applications, please cite using this BibTeX:
@misc{hu2025HunyuanVideo-Avatar, title={HunyuanVideo-Avatar: High-Fidelity Audio-Driven Human Animation for Multiple Characters}, author={Yi Chen and Sen Liang and Zixiang Zhou and Ziyao Huang and Yifeng Ma and Junshu Tang and Qin Lin and Yuan Zhou and Qinglin Lu}, year={2025}, eprint={2505.20156}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/pdf/2505.20156}, }
We would like to thank the contributors to the HunyuanVideo, SD3, FLUX, Llama, LLaVA, Xtuner, diffusers and HuggingFace repositories, for their open research and exploration.
