Aller au contenu
Jefferson Gonçalves
← Retour aux projets

FramePack Docker CUDA

#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-torch28-cu128: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-torch28-cu128: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 3.4.0 for GPU kernel optimization
  • PyTorch 2.8.0 with CUDA 12.8 support (Blackwell / RTX 50xx, sm_120)
  • 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-torch28-cu128:latest .

#Docker Compose Benefits

  • Shared model cache: Models downloaded once can be reused across container rebuilds
  • Easy configuration: Environment variables in .env file
  • Volume management: Persistent storage for models and outputs
  • GPU support: Automatic GPU passthrough configuration
  • Service management: Easy start/stop/restart of the application

#Troubleshooting

  • ERROR: Missing required model: .../sd_xl_base_1.0.safetensors — this was a stale pre-flight check in entrypoint.sh that referenced models FramePack doesn't use, and it blocked startup. It has been removed. Rebuild the image (docker compose up --build) to pick up the fix. FramePack downloads its own models to hf_download on first run.
  • Blackwell / RTX 50xx: this image is built for CUDA 12.8 / cu128 (PyTorch 2.8.0), which is required for sm_120 GPUs. Older cu124-based images will not run on these cards.

Nouvelle version disponible.