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README.md

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+<!-- ## **HunyuanVideo-Avatar** -->
+
+<p align="center">
+  <img src="https://cdn-uploads.huggingface.co/production/uploads/646d7592bb95b5d4001e5a04/HDZpvr8F-UaHAHlsF--fh.png"  height=100>
+</p>
+ 
+<div align="center">
+  <a href="https://github.com/Tencent/HunyuanVideo-Avatar"><img src="https://img.shields.io/static/v1?label=HunyuanVideo-Avatar%20Code&message=Github&color=blue"></a>
+  <a href="https://HunyuanVideo-Avatar.github.io/"><img src="https://img.shields.io/static/v1?label=Project%20Page&message=Web&color=green"></a>
+  <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>
+  <a href="https://arxiv.org/pdf/2505.20156"><img src="https://img.shields.io/static/v1?label=Tech Report&message=Arxiv&color=red"></a>
+  <a href="https://huggingface.co/tencent/HunyuanVideo-Avatar"><img src="https://img.shields.io/static/v1?label=HunyuanVideo-Avatar&message=HuggingFace&color=yellow"></a>
+</div>
+
+
+
+![image](https://cdn-uploads.huggingface.co/production/uploads/646d7592bb95b5d4001e5a04/SAQAlLLsEzC1fURoL89_C.png)
+
+> [**Tencent HunyuanVideo-Avatar: Dynamic and Consistent Audio-Driven Human Animation for Multiple Characters**](https://arxiv.org/pdf/2505.20156) <be>
+
+## **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**
+
+![image](https://cdn-uploads.huggingface.co/production/uploads/646d7592bb95b5d4001e5a04/SAQAlLLsEzC1fURoL89_C.png)
+
+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**
+
+![image](https://cdn-uploads.huggingface.co/production/uploads/646d7592bb95b5d4001e5a04/RVM42NLlvlwABiQNlTLdd.png)
+
+### **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.