AccVideo is an innovative open-source framework designed to accelerate video diffusion models through efficient distillation techniques using synthetic datasets. Developed by aejion and available on GitHub, this tool leverages PyTorch to optimize the training and inference processes for video generation tasks. This guide provides detailed installation instructions for Ubuntu, Windows, and Docker environments, adhering to best practices for security, data persistence, and maintainability.
Prerequisites
Hardware Requirements
- CPU: x86_64/AMD64 or ARM64 architecture (4+ cores recommended)
- RAM: 8GB minimum (16GB+ for large-scale training)
- GPU: NVIDIA GPU with 8GB+ VRAM (RTX 3080 or equivalent recommended)
- Storage: 50GB+ free space (NVMe SSD preferred for dataset handling)
Software Requirements
- Ubuntu: 22.04 LTS or 20.04 LTS
- Windows: Windows 10/11 with WSL2 enabled
- Docker: 24.0+ with Compose Plugin v2.21+
- NVIDIA Drivers: 535+ with CUDA 12.2 toolkit
- Python: 3.10+ with pip package manager
Network Requirements
- Minimum 10Mbps internet connection for model downloads
- Open ports 8000-8010 for local development
- HTTPS access to PyPI and GitHub repositories
Ubuntu Installation
Step 1: System Preparation
sudo apt update && sudo apt upgrade -y
sudo apt install -y build-essential cmake ninja-build git libssl-dev python3-dev python3-venv
Potential Issues:
- Missing
lsb_release:
sudo apt install -y lsb-release
Step 2: CUDA Toolkit Installation
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-ubuntu2204.pin
sudo mv cuda-ubuntu2204.pin /etc/apt/preferences.d/cuda-repository-pin-600
wget https://developer.download.nvidia.com/compute/cuda/12.2.2/local_installers/cuda-repo-ubuntu2204-12-2-local_12.2.2-535.104.05-1_amd64.deb
sudo dpkg -i cuda-repo-ubuntu2204-12-2-local_12.2.2-535.104.05-1_amd64.deb
sudo cp /var/cuda-repo-ubuntu2204-12-2-local/cuda-*-keyring.gpg /usr/share/keyrings/
sudo apt-get update
sudo apt-get -y install cuda-toolkit-12-2
Verify installation:
nvidia-smi
nvcc --version
Step 3: Python Environment Setup
python3 -m venv accvideo-env
source accvideo-env/bin/activate
pip install --upgrade pip setuptools wheel
Step 4: Clone and Install AccVideo
git clone https://github.com/aejion/AccVideo.git
cd AccVideo
pip install -r requirements.txt
pip install -e .
Verification:
python -c "import accvideo; print(accvideo.__version__)"
Windows Installation via WSL2
Step 1: Enable WSL2
wsl --install -d Ubuntu-22.04
wsl --set-version Ubuntu-22.04 2
Step 2: NVIDIA CUDA Setup
curl -O https://developer.download.nvidia.com/compute/cuda/repos/wsl-ubuntu/x86_64/cuda-wsl-ubuntu.pin
sudo mv cuda-wsl-ubuntu.pin /etc/apt/preferences.d/cuda-repository-pin-600
sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/wsl-ubuntu/x86_64/3bf863cc.pub
sudo add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/wsl-ubuntu/x86_64/ /"
sudo apt-get update
sudo apt-get -y install cuda-toolkit-12-2
Step 3: Python Installation
sudo apt install -y python3.10 python3.10-venv
python3.10 -m venv accvideo-env
source accvideo-env/bin/activate
pip install torch==2.1.0+cu121 -f https://download.pytorch.org/whl/torch_stable.html
Docker Installation with Traefik
Step 1: Directory Structure
mkdir -p ~/docker_data/accvideo/{models,config}
Step 2: docker-compose.yml
version: '3.8'
services:
accvideo:
image: pytorch/pytorch:2.1.0-cuda12.1-cudnn8-runtime
restart: unless-stopped
command: bash -c "git clone https://github.com/aejion/AccVideo.git && cd AccVideo && pip install -r requirements.txt && python app.py"
volumes:
- ~/docker_data/accvideo/models:/workspace/models
- ~/docker_data/accvideo/config:/workspace/config
networks:
- proxy
labels:
- "traefik.enable=true"
- "traefik.http.routers.accvideo.rule=Host(`accvideo.yourdomain.com`)"
- "traefik.http.routers.accvideo.entrypoints=https"
- "traefik.http.routers.accvideo.tls.certresolver=leresolver"
- "traefik.http.services.accvideo.loadbalancer.server.port=8000"
networks:
proxy:
external: true
Step 3: Launch Container
docker compose -f ~/docker_data/accvideo/docker-compose.yml up -d
Configuration
Model Parameters
# config/training.yaml
diffusion:
num_timesteps: 1000
beta_schedule: "linear"
loss_type: "hybrid"
distillation:
teacher_model: "stabilityai/stable-diffusion-xl-base-1.0"
student_model: "accvideo/sd-xl-distilled"
temperature: 2.5
kl_weight: 0.8
Environment Variables
export ACCVIDEO_CACHE_DIR="~/accvideo_cache"
export HF_HOME="~/huggingface_cache"
export NVIDIA_VISIBLE_DEVICES="all"
Security Considerations
Least Privilege Execution
sudo useradd -r -s /bin/false accvideo-user
sudo chown -R accvideo-user:accvideo-user ~/docker_data/accvideo
Network Hardening
sudo ufw allow proto tcp from 192.168.1.0/24 to any port 8000
sudo ufw deny out 25
Data Persistence
Backup Strategy
# Daily backup script
tar -czvf accvideo-$(date +%Y%m%d).tar.gz ~/docker_data/accvideo
rclone copy accvideo-*.tar.gz backup:accvideo/archives
Volume Configuration
volumes:
accvideo_models:
driver: local
driver_opts:
type: nfs
o: addr=192.168.1.100,rw
device: ":/mnt/nas/accvideo/models"
Common Issues and Solutions
CUDA Out of Memory
# Add to training script
torch.cuda.empty_cache()
torch.backends.cudnn.benchmark = True
Dependency Conflicts
python -m pip install --upgrade --force-reinstall --no-deps torch torchvision
Maintenance Procedures
Model Updates
docker compose exec accvideo bash -c "cd AccVideo && git pull origin main"
docker compose restart accvideo
Monitoring
watch -n 5 "nvidia-smi && docker stats --no-stream"
This comprehensive guide provides multiple deployment options for AccVideo while emphasizing security and reproducibility. The Docker/Traefik integration enables production-grade deployments, while the native installations cater to development workflows. Regular monitoring of GPU utilization (≥85% during training) and VRAM allocation patterns is recommended for optimal performance.
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