EchoMimicV3

memescreamer/EchoMimicV3

EchoMimicV3 is a machine learning model from memescreamer. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for diffusers. The card lists the license as apache-2.0.

<h1 align='center'EchoMimicV3: 1.3B Parameters are All You Need for Unified Multi-Modal and Multi-Task Human Animation</h1

Downloads · 30 days

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Public

Updated Feb 24, 2026

Parameters

1.7B

7.1 GB on disk

Likes

Public

Hugging Face

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.safetensors7.1 GB · 100%

At a glance

Library: diffusers
License: apache-2.0
Access: Public
Created: Feb 24, 2026
Updated: Feb 24, 2026
SHA: 5d611d77

Model card

From the Hugging Face model README

<h1 align='center'>EchoMimicV3: 1.3B Parameters are All You Need for Unified Multi-Modal and Multi-Task Human Animation</h1> <div align='center'> <a href='https://github.com/mengrang' target='_blank'>Rang Meng</a><sup>1</sup>&emsp; <a href='https://github.com/' target='_blank'>Yan Wang</a>&emsp; <a href='https://github.com/' target='_blank'>Weipeng Wu</a>&emsp; <a href='https://github.com/' target='_blank'>Ruobing Zheng</a>&emsp; <a href='https://lymhust.github.io/' target='_blank'>Yuming Li</a><sup>2</sup>&emsp; <a href='https://openreview.net/profile?id=~Chenguang_Ma3' target='_blank'>Chenguang Ma</a><sup>2</sup> </div> <div align='center'> Terminal Technology Department, Alipay, Ant Group. </div> <p align='center'> <sup>1</sup>Core Contributor&emsp; <sup>2</sup>Corresponding Authors </p> <div align='center'> <a href='https://antgroup.github.io/ai/echomimic_v3/'><img src='https://img.shields.io/badge/Project-Page-blue'></a>     <a href='https://arxiv.org/abs/2507.03905'><img src='https://img.shields.io/badge/Paper-Arxiv-red'></a>   </div>

🚀 EchoMimic Series

EchoMimicV3: 1.3B Parameters are All You Need for Unified Multi-Modal and Multi-Task Human Animation. GitHub
EchoMimicV2: Towards Striking, Simplified, and Semi-Body Human Animation. GitHub
EchoMimicV1: Lifelike Audio-Driven Portrait Animations through Editable Landmark Conditioning. GitHub

📣 Updates

[2025.07.08] 🔥 Our paper is in public on arxiv.

🌅 Gallery

<p align="center"> <img src="asset/echomimicv3.jpg" height=700> </p> <table class="center"> <tr> <td width=100% style="border: none"> <video controls loop src="https://github.com/user-attachments/assets/f33edb30-66b1-484b-8be0-a5df20a44f3b" muted="false"></video> </td> </tr> </table> For more demo videos, please refer to the project page.

Quick Start

Environment Setup

Tested System Environment: Centos 7.2/Ubuntu 22.04, Cuda >= 12.1
Tested GPUs: A100(80G) / RTX4090D (24G) / V100(16G)
Tested Python Version: 3.10 / 3.11

🛠️Installation

1. Create a conda environment and install pytorch, xformers

conda create -n echomimic_v3 python=3.10
conda activate echomimic_v3

2. Other dependencies

pip install -r requirements.txt

🧱Model Preparation

Models	Download Link	Notes
Wan2.1-Fun-1.3B-InP	🤗 Huggingface	Base model
wav2vec2-base	🤗 Huggingface	Audio encoder
EchoMimicV3	🤗 Huggingface	Our weights

-- The weights is organized as follows.

./models/
├── Wan2.1-Fun-1.3B-InP
├── wav2vec2-base-960h
└── transformer
    └── diffusion_pytorch_model.safetensors
### 🔑 Quick Inference

python infer.py

> Tips
> - Audio CFG: Audio CFG works optimally between 2~3. Increase the audio CFG value for better lip synchronization, while decreasing the audio CFG value can improve the visual quality.
> - Text CFG: Text CFG works optimally between 4~6. Increase the text CFG value for better prompt following, while decreasing the text CFG value can improve the visual quality.
> - TeaCache: The optimal range for `--teacache_thresh` is between 0~0.1.
> - Sampling steps: 5 steps for talking head, 15~25 steps for talking body. 
> - Long video generation: If you want to generate a video longer than 138 frames, you can use Long Video CFG.


## 📝 TODO List
| Status | Milestone                                                                |     
|:--------:|:-------------------------------------------------------------------------|
|    2025.08.08    | The inference code of EchoMimicV3 meet everyone on GitHub   | 
|    🚀    | Preview version Pretrained models trained on English and Chinese on HuggingFace | 
|    🚀    | Preview version Pretrained models trained on English and Chinese on ModelScope   | 
|    🚀    | 720P Pretrained models trained on English and Chinese on HuggingFace | 
|    🚀    | 720P Pretrained models trained on English and Chinese on ModelScope   | 
|    🚀    | The training code of EchoMimicV3 meet everyone on GitHub   | 



## &#x1F4D2; Citation

If you find our work useful for your research, please consider citing the paper :

@misc{meng2025echomimicv3, title={EchoMimicV3: 1.3B Parameters are All You Need for Unified Multi-Modal and Multi-Task Human Animation}, author={Rang Meng, Yan Wang, Weipeng Wu, Ruobing Zheng, Yuming Li, Chenguang Ma}, year={2025}, eprint={2507.03905}, archivePrefix={arXiv} }


## &#x1F31F; Star History
[![Star History Chart](https://api.star-history.com/svg?repos=antgroup/echomimic_v3&type=Date)](https://star-history.com/#antgroup/echomimic_v3&Date)

Library: diffusers
License: apache-2.0
Created: Feb 24, 2026
Updated: Feb 24, 2026