---
title: HunyuanVideo-Avatar
canonical_url: "https://www.modelscope.cn/models/Tencent-Hunyuan/HunyuanVideo-Avatar"
md_url: "https://www.modelscope.cn/models/Tencent-Hunyuan/HunyuanVideo-Avatar.md"
repository: Tencent-Hunyuan/HunyuanVideo-Avatar
last_updated: 2025-06-25
pipeline_tag: image-to-video
tasks:
- image-to-video
parameters: 8.8B
tensor_type:
- F16
- F32
- I64
library_name:
- safetensors
- tensorflow
- pytorch
frameworks:
- pytorch
language:
- en
downloads: 2114
stars: 8
---
# HunyuanVideo-Avatar
> HunyuanVideo-Avatar - Tencent-Hunyuan 在 ModelScope 开源的模型。HunyuanVideo-Avatar: High-Fidelity Audio-Driven Human Animation for Multiple Characters
- **Repository**: Tencent-Hunyuan/HunyuanVideo-Avatar
- **Tasks**: image-to-video
- **Parameters**: 8.8B
- **Downloads**: 2114
- **Stars**: 8
- **Last updated**: 2025-06-25
Source: https://www.modelscope.cn/models/Tencent-Hunyuan/HunyuanVideo-Avatar
---
> [**HunyuanVideo-Avatar: High-Fidelity Audio-Driven Human Animation for Multiple Characters**](https://arxiv.org/pdf/2505.20156)
## **Abstract**
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.
## **HunyuanVideo-Avatar Overall Architecture**
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 Key Features**
### **High-Dynamic and Emotion-Controllable Video Generation**
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.
### **Various Applications**
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.
## 🚀 Parallel Inference on Multiple GPUs
For example, to generate a video with 8 GPUs, you can use the following command:
```bash
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 ${OUTPUT_BASEPATH}
```
## 🔑 Single-gpu Inference
For example, to generate a video with 1 GPU, you can use the following command:
```bash
cd HunyuanVideo-Avatar
JOBS_DIR=$(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 ${OUTPUT_BASEPATH} \
--use-fp8 \
--infer-min
```
### Run with very low VRAM
```bash
cd HunyuanVideo-Avatar
JOBS_DIR=$(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
```
## Run a Gradio Server
```bash
cd HunyuanVideo-Avatar
bash ./scripts/run_gradio.sh
```
## 🔗 BibTeX
If you find [HunyuanVideo-Avatar](https://arxiv.org/pdf/2505.20156) useful for your research and applications, please cite using this BibTeX:
```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},
}
```
## Acknowledgements
We would like to thank the contributors to the [HunyuanVideo](https://github.com/Tencent/HunyuanVideo), [SD3](https://huggingface.co/stabilityai/stable-diffusion-3-medium), [FLUX](https://github.com/black-forest-labs/flux), [Llama](https://github.com/meta-llama/llama), [LLaVA](https://github.com/haotian-liu/LLaVA), [Xtuner](https://github.com/InternLM/xtuner), [diffusers](https://github.com/huggingface/diffusers) and [HuggingFace](https://huggingface.co) repositories, for their open research and exploration.
