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README.md
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README.md
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---
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license: Apache License 2.0
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#model-type:
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##如 gpt、phi、llama、chatglm、baichuan 等
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#- gpt
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#domain:
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##如 nlp、cv、audio、multi-modal
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#- nlp
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#language:
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##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa
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#- cn
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#metrics:
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##如 CIDEr、Blue、ROUGE 等
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#- CIDEr
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#tags:
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##各种自定义，包括 pretrained、fine-tuned、instruction-tuned、RL-tuned 等训练方法和其他
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#- pretrained
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#tools:
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##如 vllm、fastchat、llamacpp、AdaSeq 等
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#- vllm
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pipeline_tag: image-to-video
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language:
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- en
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---
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### 当前模型的贡献者未提供更加详细的模型介绍。模型文件和权重，可浏览“模型文件”页面获取。
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#### 您可以通过如下git clone命令，或者ModelScope SDK来下载模型
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<!-- ## **HunyuanVideo-Avatar** -->
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<p align="center">
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<img src="https://cdn-uploads.huggingface.co/production/uploads/646d7592bb95b5d4001e5a04/HDZpvr8F-UaHAHlsF--fh.png" height=100>
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</p>
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<div align="center">
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<a href="https://github.com/Tencent-Hunyuan/HunyuanVideo-Avatar"><img src="https://img.shields.io/static/v1?label=HunyuanVideo-Avatar%20Code&message=Github&color=blue"></a>
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<a href="https://HunyuanVideo-Avatar.github.io/"><img src="https://img.shields.io/static/v1?label=Project%20Page&message=Web&color=green"></a>
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<a href="https://hunyuan.tencent.com/modelSquare/home/play?modelId=126"><img src="https://img.shields.io/static/v1?label=Playground&message=Web&color=green"></a>
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<a href="https://arxiv.org/pdf/2505.20156"><img src="https://img.shields.io/static/v1?label=Tech Report&message=Arxiv&color=red"></a>
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<a href="https://huggingface.co/tencent/HunyuanVideo-Avatar"><img src="https://img.shields.io/static/v1?label=HunyuanVideo-Avatar&message=HuggingFace&color=yellow"></a>
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</div>
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> [**HunyuanVideo-Avatar: High-Fidelity Audio-Driven Human Animation for Multiple Characters**](https://arxiv.org/pdf/2505.20156) <be>
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## **Abstract**
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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.
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## **HunyuanVideo-Avatar Overall Architecture**
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We propose **HunyuanVideo-Avatar**, a multi-modal diffusion transformer(MM-DiT)-based model capable of generating **dynamic**, **emotion-controllable**, and **multi-character dialogue** videos.
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## 🎉 **HunyuanVideo-Avatar Key Features**
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### **High-Dynamic and Emotion-Controllable Video Generation**
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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.
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### **Various Applications**
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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.
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## 🚀 Parallel Inference on Multiple GPUs
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For example, to generate a video with 8 GPUs, you can use the following command:
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SDK下载
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```bash
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#安装ModelScope
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pip install modelscope
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```
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```python
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#SDK模型下载
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from modelscope import snapshot_download
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model_dir = snapshot_download('AI-ModelScope/HunyuanVideo-Avatar')
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```
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Git下载
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```
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#Git模型下载
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git clone https://www.modelscope.cn/AI-ModelScope/HunyuanVideo-Avatar.git
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cd HunyuanVideo-Avatar
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JOBS_DIR=$(dirname $(dirname "$0"))
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export PYTHONPATH=./
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export MODEL_BASE="./weights"
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checkpoint_path=${MODEL_BASE}/ckpts/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt
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torchrun --nnodes=1 --nproc_per_node=8 --master_port 29605 hymm_sp/sample_batch.py \
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--input 'assets/test.csv' \
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--ckpt ${checkpoint_path} \
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--sample-n-frames 129 \
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--seed 128 \
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--image-size 704 \
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--cfg-scale 7.5 \
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--infer-steps 50 \
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--use-deepcache 1 \
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--flow-shift-eval-video 5.0 \
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--save-path ${OUTPUT_BASEPATH}
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```
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<p style="color: lightgrey;">如果您是本模型的贡献者，我们邀请您根据<a href="https://modelscope.cn/docs/ModelScope%E6%A8%A1%E5%9E%8B%E6%8E%A5%E5%85%A5%E6%B5%81%E7%A8%8B%E6%A6%82%E8%A7%88" style="color: lightgrey; text-decoration: underline;">模型贡献文档</a>，及时完善模型卡片内容。</p>
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## 🔑 Single-gpu Inference
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For example, to generate a video with 1 GPU, you can use the following command:
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```bash
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cd HunyuanVideo-Avatar
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JOBS_DIR=$(dirname $(dirname "$0"))
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export PYTHONPATH=./
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export MODEL_BASE=./weights
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OUTPUT_BASEPATH=./results-single
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checkpoint_path=${MODEL_BASE}/ckpts/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states_fp8.pt
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export DISABLE_SP=1
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CUDA_VISIBLE_DEVICES=0 python3 hymm_sp/sample_gpu_poor.py \
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--input 'assets/test.csv' \
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--ckpt ${checkpoint_path} \
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--sample-n-frames 129 \
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--seed 128 \
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--image-size 704 \
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--cfg-scale 7.5 \
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--infer-steps 50 \
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--use-deepcache 1 \
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--flow-shift-eval-video 5.0 \
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--save-path ${OUTPUT_BASEPATH} \
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--use-fp8 \
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--infer-min
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```
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### Run with very low VRAM
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```bash
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cd HunyuanVideo-Avatar
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JOBS_DIR=$(dirname $(dirname "$0"))
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export PYTHONPATH=./
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export MODEL_BASE=./weights
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OUTPUT_BASEPATH=./results-poor
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checkpoint_path=${MODEL_BASE}/ckpts/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states_fp8.pt
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export CPU_OFFLOAD=1
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CUDA_VISIBLE_DEVICES=0 python3 hymm_sp/sample_gpu_poor.py \
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--input 'assets/test.csv' \
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--ckpt ${checkpoint_path} \
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--sample-n-frames 129 \
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--seed 128 \
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--image-size 704 \
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--cfg-scale 7.5 \
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--infer-steps 50 \
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--use-deepcache 1 \
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--flow-shift-eval-video 5.0 \
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--save-path ${OUTPUT_BASEPATH} \
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--use-fp8 \
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--cpu-offload \
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--infer-min
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```
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## Run a Gradio Server
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```bash
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cd HunyuanVideo-Avatar
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bash ./scripts/run_gradio.sh
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```
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## 🔗 BibTeX
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If you find [HunyuanVideo-Avatar](https://arxiv.org/pdf/2505.20156) useful for your research and applications, please cite using this BibTeX:
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```BibTeX
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@misc{hu2025HunyuanVideo-Avatar,
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title={HunyuanVideo-Avatar: High-Fidelity Audio-Driven Human Animation for Multiple Characters},
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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},
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year={2025},
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eprint={2505.20156},
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archivePrefix={arXiv},
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primaryClass={cs.CV},
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url={https://arxiv.org/pdf/2505.20156},
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}
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```
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## Acknowledgements
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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.
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