AptAvatar: Fast and Vivid Long-Form Audio-Driven Video Generation for Production-Ready Avatars

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Links are placeholders for now and will be updated as project pages, model cards, community channels, and reports become available.

✨ Key Features

• Endpoint-Anchored Distribution Distillation: It augments vanilla distribution matching with a dedicated Anchor Score Estimator trained on the trajectory-endpoint distribution defined from a frozen pretrained 4-step bridge generator. This provides an attainable endpoint-level anchor for the evolving two-step student.
• Self-Generated History Replay: It reuses cached outputs from earlier generator checkpoints as history conditions during chunk-wise training. This approximates inference-time self-conditioning, mitigating quality degradation from accumulated history errors

🔥 News

• 2026.06.29 - Released the initial AptAvatar repository with open-source inference code.

📋 Todo

• Technical report
• Project page
• Inference code
• Checkpoint release
• Additional demos

🧩 Pipeline

🎬 Examples

🚀 Quick Start

🛠️ Installation

Create and activate the environment:

conda create -n aptavatar python=3.10
conda activate aptavatar

Install PyTorch:

pip install torch==2.8.0 torchvision==0.23.0 --index-url https://download.pytorch.org/whl/cu128

Install project dependencies:

pip install -r requirements.txt

Install FlashAttention:

pip install ninja
pip install flash_attn==2.8.0.post2 --no-build-isolation

[!NOTE] If flash_attn_3 is installed in your environment, please uninstall it, as it conflicts with the FlashAttention-3 kernels bundled in xformers:

pip uninstall flash_attn_3

Install FFmpeg:

# Ubuntu / Debian
apt-get install ffmpeg

# CentOS / RHEL
yum install ffmpeg ffmpeg-devel

or install through conda:

conda install -c conda-forge ffmpeg==7

📦 Model Preparation

The current repository provides inference code only. After checkpoints are released, place them under the following paths: