FramePack-Docker-CUDA: A Dockerfile repository from akitaonrails

FramePack Docker CUDA

Quick Start with Docker Compose (Recommended)

git clone https://github.com/TSavo/FramePack-Docker-CUDA.git
cd FramePack-Docker-CUDA

# Optional: Copy and customize environment settings
cp .env.template .env

# Start the application
docker compose up --build

Manual Docker Setup

git clone https://github.com/TSavo/FramePack-Docker-CUDA.git
cd FramePack-Docker-CUDA
mkdir outputs
mkdir hf_download

# Build the image
docker build -t framepack-torch26-cu124:latest .

# Run mapping the directories outside:
docker run -it --rm --gpus all -p 7860:7860 \
  -v ./outputs:/app/outputs \
  -v ./hf_download:/app/hf_download \
  framepack-torch26-cu124:latest

The first time it runs, it will download all necessary HunyuanVideo, Flux and other neccessary models. It will be more than 30GB, so be patient, but they will be cached on the external mapped directory.

When it finishes access http://localhost:7860 and that's it!

Enhanced Features

This enhanced version includes several performance optimizations:

Multi-stage Docker builds for better layer caching and faster rebuilds
Flash Attention 2 for faster transformer inference
SageAttention for optimized attention mechanisms
xFormers for memory-efficient transformers
Triton 3.2.0 for GPU kernel optimization
PyTorch 2.6.0 with CUDA 12.4 support
Optimized dependency management with proper version pinning
Build parallelism control via MAX_JOBS argument (default: 4)

Build with custom parallelism:

# Docker Compose
MAX_JOBS=8 docker compose up --build

# Manual Docker
docker build --build-arg MAX_JOBS=8 -t framepack-torch26-cu124:latest .

Docker Compose Benefits