mirror of
https://github.com/Wan-Video/Wan2.1.git
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Add professional-grade Docker setup for local deployment
This commit introduces comprehensive Docker support for running Wan2.1 video generation models locally with GPU acceleration. Changes: - Add Dockerfile with CUDA 12.1 support and optimized layer caching - Add docker-compose.yml for easy container orchestration - Add .dockerignore for efficient Docker builds - Add DOCKER_SETUP.md with detailed setup and troubleshooting guide - Add DOCKER_QUICKSTART.md for rapid deployment - Add docker-run.sh helper script for container management - Update Makefile with Docker management commands Features: - Full GPU support with NVIDIA Docker runtime - Single-GPU and multi-GPU (FSDP + xDiT) configurations - Memory optimization flags for consumer GPUs (8GB+) - Gradio web interface support on port 7860 - Volume mounts for models, outputs, and cache - Comprehensive troubleshooting and optimization guides - Production-ready security best practices The Docker setup supports all Wan2.1 models (T2V, I2V, FLF2V, VACE) and includes both 1.3B (consumer GPU) and 14B (high-end GPU) variants. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
Claude
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.dockerignore
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.dockerignore
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# Git files
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.git
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.gitignore
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.gitattributes
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# Docker files
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Dockerfile
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docker-compose.yml
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.dockerignore
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# Python cache
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__pycache__/
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*.py[cod]
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*$py.class
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*.so
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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# Virtual environments
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venv/
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env/
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ENV/
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.venv
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# IDE files
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.vscode/
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.idea/
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*.swp
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*.swo
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*~
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.DS_Store
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# Model files (download separately)
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models/
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*.pth
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*.pt
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*.bin
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*.safetensors
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*.ckpt
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# Output files
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outputs/
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output/
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*.mp4
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*.avi
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*.mov
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*.png
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*.jpg
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*.jpeg
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# Cache directories
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cache/
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.cache/
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__pycache__/
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# Logs
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*.log
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logs/
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# Documentation (keep lightweight)
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*.md
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!README.md
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!DOCKER_SETUP.md
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# Test files
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tests/
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test/
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*.test.py
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# CI/CD
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.github/
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.gitlab-ci.yml
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.travis.yml
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# Temporary files
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tmp/
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temp/
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*.tmp
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157
DOCKER_QUICKSTART.md
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DOCKER_QUICKSTART.md
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# Wan2.1 Docker Quick Start
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Get Wan2.1 running in Docker in 5 minutes!
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## Prerequisites
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- Docker 20.10+ installed ([Get Docker](https://docs.docker.com/get-docker/))
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- NVIDIA GPU with 8GB+ VRAM (for GPU acceleration)
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- NVIDIA Docker runtime installed ([Install Guide](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html))
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## Quick Start (3 Steps)
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### Step 1: Clone and Navigate
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```bash
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git clone https://github.com/Wan-Video/Wan2.1.git
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cd Wan2.1
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```
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### Step 2: Build and Start
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**Option A: Using the helper script** (Recommended)
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```bash
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./docker-run.sh start
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```
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**Option B: Using Make**
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```bash
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make docker-build
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make docker-up
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```
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**Option C: Using Docker Compose directly**
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```bash
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docker compose up -d wan2-1
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```
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### Step 3: Download Models and Run
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```bash
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# Enter the container
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./docker-run.sh shell
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# OR
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make docker-shell
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# OR
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docker compose exec wan2-1 bash
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# Download a model (1.3B for consumer GPUs)
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pip install "huggingface_hub[cli]"
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huggingface-cli download Wan-AI/Wan2.1-T2V-1.3B --local-dir /app/models/Wan2.1-T2V-1.3B
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# Generate your first video!
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python generate.py \
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--task t2v-1.3B \
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--size 832*480 \
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--ckpt_dir /app/models/Wan2.1-T2V-1.3B \
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--offload_model True \
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--t5_cpu \
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--sample_shift 8 \
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--sample_guide_scale 6 \
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--prompt "A cute cat playing with a ball of yarn"
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# Your video will be in /app/outputs (accessible at ./outputs on your host)
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```
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## Common Commands
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### Container Management
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```bash
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# Start container
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./docker-run.sh start
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# Stop container
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./docker-run.sh stop
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# Restart container
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./docker-run.sh restart
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# View logs
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./docker-run.sh logs
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# Enter shell
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./docker-run.sh shell
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# Check status
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./docker-run.sh status
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```
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### Using Make Commands
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```bash
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make docker-up # Start
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make docker-down # Stop
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make docker-shell # Enter shell
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make docker-logs # View logs
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make docker-status # Check status
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make help # Show all commands
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```
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## Run Gradio Web Interface
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```bash
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# Inside the container
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cd gradio
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python t2v_14B_singleGPU.py --ckpt_dir /app/models/Wan2.1-T2V-1.3B
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# Open browser to: http://localhost:7860
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```
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## Available Models
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| Model | VRAM | Resolution | Download Command |
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|-------|------|------------|------------------|
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| T2V-1.3B | 8GB+ | 480P | `huggingface-cli download Wan-AI/Wan2.1-T2V-1.3B --local-dir /app/models/Wan2.1-T2V-1.3B` |
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| T2V-14B | 24GB+ | 720P | `huggingface-cli download Wan-AI/Wan2.1-T2V-14B --local-dir /app/models/Wan2.1-T2V-14B` |
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| I2V-14B-720P | 24GB+ | 720P | `huggingface-cli download Wan-AI/Wan2.1-I2V-14B-720P --local-dir /app/models/Wan2.1-I2V-14B-720P` |
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| I2V-14B-480P | 16GB+ | 480P | `huggingface-cli download Wan-AI/Wan2.1-I2V-14B-480P --local-dir /app/models/Wan2.1-I2V-14B-480P` |
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## Troubleshooting
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### "CUDA out of memory"
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- Use the 1.3B model with `--offload_model True --t5_cpu`
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- Reduce resolution to 480P
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### "nvidia-smi not found"
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- Ensure NVIDIA Docker runtime is installed
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- Run: `docker run --rm --gpus all nvidia/cuda:12.1.1-base-ubuntu22.04 nvidia-smi`
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### Can't access Gradio interface
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- Check if port 7860 is exposed: `docker ps | grep 7860`
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- Try: `http://127.0.0.1:7860` instead of `localhost`
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## Next Steps
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- Read the full [DOCKER_SETUP.md](DOCKER_SETUP.md) for advanced configuration
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- Check the main [README.md](README.md) for model details
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- Join the [Discord community](https://discord.gg/AKNgpMK4Yj)
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## File Structure
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```
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Wan2.1/
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├── models/ # Downloaded models (created automatically)
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├── outputs/ # Generated videos (accessible from host)
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├── cache/ # Model cache
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├── Dockerfile # Docker image definition
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├── docker-compose.yml # Container orchestration
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├── docker-run.sh # Helper script
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├── Makefile # Make commands
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└── DOCKER_SETUP.md # Detailed documentation
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```
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**Happy Generating!** 🎬
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DOCKER_SETUP.md
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# Wan2.1 Docker Setup Guide
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Professional-grade instructions for running Wan2.1 video generation models in Docker containers with GPU support.
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---
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## Table of Contents
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- [Prerequisites](#prerequisites)
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- [System Requirements](#system-requirements)
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- [Installation Steps](#installation-steps)
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- [Quick Start](#quick-start)
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- [Model Download](#model-download)
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- [Running Inference](#running-inference)
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- [Gradio Web Interface](#gradio-web-interface)
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- [Advanced Configuration](#advanced-configuration)
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- [Troubleshooting](#troubleshooting)
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- [Performance Optimization](#performance-optimization)
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---
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## Prerequisites
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### Required Software
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1. **Docker Engine** (version 20.10+)
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- [Installation Guide](https://docs.docker.com/engine/install/)
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2. **NVIDIA Docker Runtime** (for GPU support)
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- Required for GPU acceleration
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- [Installation Guide](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html)
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3. **NVIDIA Drivers** (version 525.60.13+)
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- CUDA 12.1 compatible drivers
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- Check with: `nvidia-smi`
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4. **Docker Compose** (version 2.0+)
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- Typically included with Docker Desktop
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- [Installation Guide](https://docs.docker.com/compose/install/)
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### Optional Software
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- **Git** - For cloning the repository
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- **Make** - For using convenience commands
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- **NVIDIA Container Toolkit** - For multi-GPU support
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---
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## System Requirements
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### Minimum Requirements (T2V-1.3B at 480P)
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- **GPU**: NVIDIA GPU with 8GB+ VRAM (e.g., RTX 4060 Ti)
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- **RAM**: 16GB system memory
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- **Storage**: 50GB free space (for models and cache)
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- **OS**: Linux (Ubuntu 20.04+), Windows 10/11 with WSL2
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### Recommended Requirements (T2V-14B at 720P)
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- **GPU**: NVIDIA GPU with 24GB+ VRAM (e.g., RTX 4090, A5000)
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- **RAM**: 32GB+ system memory
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- **Storage**: 100GB+ free space
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- **OS**: Linux (Ubuntu 22.04+)
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### Multi-GPU Setup (for 8x GPU)
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- **GPUs**: 8x NVIDIA GPUs (A100, H100, etc.)
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- **RAM**: 128GB+ system memory
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- **Storage**: 200GB+ free space
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- **Network**: High-bandwidth GPU interconnect (NVLink preferred)
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---
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## Installation Steps
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### Step 1: Verify Docker and NVIDIA Runtime
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```bash
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# Check Docker installation
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docker --version
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docker compose version
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# Check NVIDIA driver
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nvidia-smi
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# Test NVIDIA Docker runtime
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docker run --rm --gpus all nvidia/cuda:12.1.1-base-ubuntu22.04 nvidia-smi
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```
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**Expected output**: You should see your GPU(s) listed in the nvidia-smi output.
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### Step 2: Clone the Repository
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```bash
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git clone https://github.com/Wan-Video/Wan2.1.git
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cd Wan2.1
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```
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### Step 3: Create Required Directories
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```bash
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# Create directories for models, outputs, and cache
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mkdir -p models outputs cache examples
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```
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### Step 4: Set Environment Variables (Optional)
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For prompt extension with Dashscope API:
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```bash
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# Create a .env file
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cat > .env << EOF
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DASH_API_KEY=your_dashscope_api_key_here
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DASH_API_URL=https://dashscope.aliyuncs.com/api/v1
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EOF
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```
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For international Alibaba Cloud users:
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```bash
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DASH_API_URL=https://dashscope-intl.aliyuncs.com/api/v1
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```
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### Step 5: Build the Docker Image
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```bash
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# Build using Docker Compose (recommended)
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docker compose build
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# OR build manually
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docker build -t wan2.1:latest .
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```
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**Build time**: Approximately 10-20 minutes depending on your internet connection.
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---
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## Quick Start
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### Option 1: Using Docker Compose (Recommended)
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```bash
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# Start the container with GPU support
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docker compose up -d wan2-1
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# Check container status
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docker compose ps
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# View logs
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docker compose logs -f wan2-1
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# Access the container shell
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docker compose exec wan2-1 bash
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```
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### Option 2: Using Docker Run
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```bash
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docker run -it --gpus all \
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--name wan2.1-container \
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-v $(pwd)/models:/app/models \
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-v $(pwd)/outputs:/app/outputs \
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-v $(pwd)/cache:/app/cache \
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-p 7860:7860 \
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--shm-size=16g \
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wan2.1:latest bash
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```
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### For CPU-only Mode
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```bash
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# Using Docker Compose
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docker compose --profile cpu up -d wan2-1-cpu
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# Using Docker Run
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docker run -it \
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--name wan2.1-cpu \
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-e CUDA_VISIBLE_DEVICES="" \
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-v $(pwd)/models:/app/models \
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-v $(pwd)/outputs:/app/outputs \
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-v $(pwd)/cache:/app/cache \
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-p 7860:7860 \
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wan2.1:latest bash
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```
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---
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## Model Download
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Download models **before** running inference. Models should be placed in the `./models` directory.
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### Using Hugging Face CLI (Inside Container)
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```bash
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# Enter the container
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docker compose exec wan2-1 bash
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# Download T2V-14B model
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pip install "huggingface_hub[cli]"
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huggingface-cli download Wan-AI/Wan2.1-T2V-14B --local-dir /app/models/Wan2.1-T2V-14B
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# Download T2V-1.3B model
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huggingface-cli download Wan-AI/Wan2.1-T2V-1.3B --local-dir /app/models/Wan2.1-T2V-1.3B
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# Download I2V-14B-720P model
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huggingface-cli download Wan-AI/Wan2.1-I2V-14B-720P --local-dir /app/models/Wan2.1-I2V-14B-720P
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# Download I2V-14B-480P model
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huggingface-cli download Wan-AI/Wan2.1-I2V-14B-480P --local-dir /app/models/Wan2.1-I2V-14B-480P
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# Download FLF2V-14B model
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huggingface-cli download Wan-AI/Wan2.1-FLF2V-14B-720P --local-dir /app/models/Wan2.1-FLF2V-14B-720P
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# Download VACE models
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huggingface-cli download Wan-AI/Wan2.1-VACE-1.3B --local-dir /app/models/Wan2.1-VACE-1.3B
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huggingface-cli download Wan-AI/Wan2.1-VACE-14B --local-dir /app/models/Wan2.1-VACE-14B
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```
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### Using ModelScope (Alternative for Chinese Users)
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```bash
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pip install modelscope
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modelscope download Wan-AI/Wan2.1-T2V-14B --local_dir /app/models/Wan2.1-T2V-14B
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```
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### Download from Host Machine
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You can also download models on your host machine and they will be accessible in the container:
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```bash
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# On host machine (outside Docker)
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cd Wan2.1/models
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huggingface-cli download Wan-AI/Wan2.1-T2V-1.3B --local-dir ./Wan2.1-T2V-1.3B
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```
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---
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## Running Inference
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|
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All commands below should be run **inside the container**.
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|
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### Text-to-Video Generation
|
||||
|
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#### 1.3B Model (480P) - Consumer GPU Friendly
|
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|
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```bash
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python generate.py \
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--task t2v-1.3B \
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--size 832*480 \
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||||
--ckpt_dir /app/models/Wan2.1-T2V-1.3B \
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--offload_model True \
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--t5_cpu \
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--sample_shift 8 \
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--sample_guide_scale 6 \
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||||
--prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."
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```
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|
||||
#### 14B Model (720P) - High-End GPU
|
||||
|
||||
```bash
|
||||
python generate.py \
|
||||
--task t2v-14B \
|
||||
--size 1280*720 \
|
||||
--ckpt_dir /app/models/Wan2.1-T2V-14B \
|
||||
--prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."
|
||||
```
|
||||
|
||||
#### With Prompt Extension (Better Quality)
|
||||
|
||||
```bash
|
||||
# Using local Qwen model
|
||||
python generate.py \
|
||||
--task t2v-14B \
|
||||
--size 1280*720 \
|
||||
--ckpt_dir /app/models/Wan2.1-T2V-14B \
|
||||
--use_prompt_extend \
|
||||
--prompt_extend_method 'local_qwen' \
|
||||
--prompt "A beautiful sunset over the ocean"
|
||||
|
||||
# Using Dashscope API (requires DASH_API_KEY)
|
||||
DASH_API_KEY=your_key python generate.py \
|
||||
--task t2v-14B \
|
||||
--size 1280*720 \
|
||||
--ckpt_dir /app/models/Wan2.1-T2V-14B \
|
||||
--use_prompt_extend \
|
||||
--prompt_extend_method 'dashscope' \
|
||||
--prompt "A beautiful sunset over the ocean"
|
||||
```
|
||||
|
||||
### Image-to-Video Generation
|
||||
|
||||
```bash
|
||||
python generate.py \
|
||||
--task i2v-14B \
|
||||
--size 1280*720 \
|
||||
--ckpt_dir /app/models/Wan2.1-I2V-14B-720P \
|
||||
--image /app/examples/i2v_input.JPG \
|
||||
--prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard."
|
||||
```
|
||||
|
||||
### First-Last-Frame-to-Video
|
||||
|
||||
```bash
|
||||
python generate.py \
|
||||
--task flf2v-14B \
|
||||
--size 1280*720 \
|
||||
--ckpt_dir /app/models/Wan2.1-FLF2V-14B-720P \
|
||||
--first_frame /app/examples/flf2v_input_first_frame.png \
|
||||
--last_frame /app/examples/flf2v_input_last_frame.png \
|
||||
--prompt "CG animation style, a small blue bird takes off from the ground"
|
||||
```
|
||||
|
||||
### Text-to-Image Generation
|
||||
|
||||
```bash
|
||||
python generate.py \
|
||||
--task t2i-14B \
|
||||
--size 1024*1024 \
|
||||
--ckpt_dir /app/models/Wan2.1-T2V-14B \
|
||||
--prompt "A serene mountain landscape at dawn"
|
||||
```
|
||||
|
||||
### VACE (Video Creation and Editing)
|
||||
|
||||
```bash
|
||||
python generate.py \
|
||||
--task vace-1.3B \
|
||||
--size 832*480 \
|
||||
--ckpt_dir /app/models/Wan2.1-VACE-1.3B \
|
||||
--src_ref_images /app/examples/girl.png,/app/examples/snake.png \
|
||||
--prompt "Your detailed prompt here"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Gradio Web Interface
|
||||
|
||||
### Start Gradio Interface
|
||||
|
||||
#### Text-to-Video (14B)
|
||||
|
||||
```bash
|
||||
cd gradio
|
||||
python t2v_14B_singleGPU.py \
|
||||
--ckpt_dir /app/models/Wan2.1-T2V-14B \
|
||||
--prompt_extend_method 'local_qwen'
|
||||
```
|
||||
|
||||
#### Image-to-Video (14B)
|
||||
|
||||
```bash
|
||||
cd gradio
|
||||
python i2v_14B_singleGPU.py \
|
||||
--ckpt_dir_720p /app/models/Wan2.1-I2V-14B-720P \
|
||||
--prompt_extend_method 'local_qwen'
|
||||
```
|
||||
|
||||
#### VACE (All-in-One)
|
||||
|
||||
```bash
|
||||
cd gradio
|
||||
python vace.py --ckpt_dir /app/models/Wan2.1-VACE-1.3B
|
||||
```
|
||||
|
||||
### Access the Web Interface
|
||||
|
||||
1. Open your web browser
|
||||
2. Navigate to: `http://localhost:7860`
|
||||
3. Use the intuitive interface to generate videos
|
||||
|
||||
### For Remote Access
|
||||
|
||||
If running on a remote server:
|
||||
|
||||
```bash
|
||||
# Start with public URL (Gradio share feature)
|
||||
python gradio/t2v_14B_singleGPU.py \
|
||||
--ckpt_dir /app/models/Wan2.1-T2V-14B \
|
||||
--server_name 0.0.0.0 \
|
||||
--server_port 7860 \
|
||||
--share
|
||||
```
|
||||
|
||||
Then access via: `http://your-server-ip:7860`
|
||||
|
||||
---
|
||||
|
||||
## Advanced Configuration
|
||||
|
||||
### Multi-GPU Inference (FSDP + xDiT)
|
||||
|
||||
For 8-GPU setup using Ulysses or Ring attention strategies:
|
||||
|
||||
```bash
|
||||
# Install xDiT
|
||||
pip install "xfuser>=0.4.1"
|
||||
|
||||
# Run with Ulysses strategy (8 GPUs)
|
||||
torchrun --nproc_per_node=8 generate.py \
|
||||
--task t2v-14B \
|
||||
--size 1280*720 \
|
||||
--ckpt_dir /app/models/Wan2.1-T2V-14B \
|
||||
--dit_fsdp \
|
||||
--t5_fsdp \
|
||||
--ulysses_size 8 \
|
||||
--prompt "Your prompt here"
|
||||
|
||||
# Run with Ring strategy (for sequence parallelism)
|
||||
torchrun --nproc_per_node=8 generate.py \
|
||||
--task t2v-14B \
|
||||
--size 1280*720 \
|
||||
--ckpt_dir /app/models/Wan2.1-T2V-14B \
|
||||
--dit_fsdp \
|
||||
--t5_fsdp \
|
||||
--ring_size 8 \
|
||||
--prompt "Your prompt here"
|
||||
```
|
||||
|
||||
### Memory Optimization Flags
|
||||
|
||||
For limited VRAM:
|
||||
|
||||
```bash
|
||||
python generate.py \
|
||||
--task t2v-1.3B \
|
||||
--size 832*480 \
|
||||
--ckpt_dir /app/models/Wan2.1-T2V-1.3B \
|
||||
--offload_model True \ # Offload model to CPU when not in use
|
||||
--t5_cpu \ # Keep T5 encoder on CPU
|
||||
--sample_shift 8 \
|
||||
--sample_guide_scale 6 \
|
||||
--prompt "Your prompt"
|
||||
```
|
||||
|
||||
### Custom Output Directory
|
||||
|
||||
```bash
|
||||
python generate.py \
|
||||
--task t2v-14B \
|
||||
--size 1280*720 \
|
||||
--ckpt_dir /app/models/Wan2.1-T2V-14B \
|
||||
--output_dir /app/outputs/my_generation \
|
||||
--prompt "Your prompt"
|
||||
```
|
||||
|
||||
### Batch Generation
|
||||
|
||||
Generate multiple variations:
|
||||
|
||||
```bash
|
||||
python generate.py \
|
||||
--task t2v-14B \
|
||||
--size 1280*720 \
|
||||
--ckpt_dir /app/models/Wan2.1-T2V-14B \
|
||||
--base_seed 0 \
|
||||
--num_samples 4 \ # Generate 4 variations
|
||||
--prompt "Your prompt"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### Issue: "CUDA out of memory"
|
||||
|
||||
**Solutions:**
|
||||
1. Use smaller model (1.3B instead of 14B)
|
||||
2. Reduce resolution (480P instead of 720P)
|
||||
3. Enable memory optimization flags:
|
||||
```bash
|
||||
--offload_model True --t5_cpu
|
||||
```
|
||||
4. Increase Docker shared memory:
|
||||
```bash
|
||||
docker run --shm-size=32g ...
|
||||
```
|
||||
|
||||
### Issue: "nvidia-smi not found" inside container
|
||||
|
||||
**Solutions:**
|
||||
1. Verify NVIDIA Docker runtime is installed on host
|
||||
2. Check Docker daemon configuration:
|
||||
```bash
|
||||
# Edit /etc/docker/daemon.json
|
||||
{
|
||||
"runtimes": {
|
||||
"nvidia": {
|
||||
"path": "nvidia-container-runtime",
|
||||
"runtimeArgs": []
|
||||
}
|
||||
},
|
||||
"default-runtime": "nvidia"
|
||||
}
|
||||
```
|
||||
3. Restart Docker daemon:
|
||||
```bash
|
||||
sudo systemctl restart docker
|
||||
```
|
||||
|
||||
### Issue: "Flash attention installation failed"
|
||||
|
||||
**Solution:**
|
||||
Flash attention is optional. The Dockerfile continues even if it fails. For better performance, install manually:
|
||||
|
||||
```bash
|
||||
# Inside container
|
||||
pip install flash-attn --no-build-isolation
|
||||
```
|
||||
|
||||
### Issue: Model download fails
|
||||
|
||||
**Solutions:**
|
||||
1. Check internet connection
|
||||
2. Use mirror sites (ModelScope for Chinese users)
|
||||
3. Download models on host machine and mount them
|
||||
4. Increase Docker download timeout
|
||||
|
||||
### Issue: "RuntimeError: CUDA error: device-side assert triggered"
|
||||
|
||||
**Solutions:**
|
||||
1. Check CUDA compatibility:
|
||||
```bash
|
||||
python -c "import torch; print(torch.cuda.is_available())"
|
||||
```
|
||||
2. Update NVIDIA drivers
|
||||
3. Rebuild Docker image with matching CUDA version
|
||||
|
||||
### Issue: Gradio interface not accessible
|
||||
|
||||
**Solutions:**
|
||||
1. Check if port is exposed:
|
||||
```bash
|
||||
docker ps | grep 7860
|
||||
```
|
||||
2. Ensure firewall allows port 7860
|
||||
3. Try binding to all interfaces:
|
||||
```bash
|
||||
python gradio/app.py --server_name 0.0.0.0
|
||||
```
|
||||
|
||||
### Issue: Permission denied errors
|
||||
|
||||
**Solution:**
|
||||
```bash
|
||||
# Fix ownership of mounted volumes
|
||||
sudo chown -R $(id -u):$(id -g) models outputs cache
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Performance Optimization
|
||||
|
||||
### 1. Use SSD Storage
|
||||
- Store models and cache on SSD for faster loading
|
||||
- Use NVMe for best performance
|
||||
|
||||
### 2. Increase Shared Memory
|
||||
```bash
|
||||
# In docker-compose.yml
|
||||
shm_size: '32gb'
|
||||
```
|
||||
|
||||
### 3. Use Mixed Precision
|
||||
- The model uses bfloat16 by default (optimal for modern GPUs)
|
||||
|
||||
### 4. Enable Xformers (if available)
|
||||
```bash
|
||||
pip install xformers
|
||||
```
|
||||
|
||||
### 5. Multi-GPU Best Practices
|
||||
- Use NVLink/NVSwitch for GPU communication
|
||||
- Balance model sharding with Ulysses + Ring strategies
|
||||
- Monitor GPU utilization: `watch -n 1 nvidia-smi`
|
||||
|
||||
### 6. Optimize Inference Parameters
|
||||
```bash
|
||||
# For T2V-1.3B
|
||||
--sample_shift 8 \ # Adjust 8-12 based on quality
|
||||
--sample_guide_scale 6 # Lower = faster, higher = better quality
|
||||
|
||||
# For T2V-14B
|
||||
--sample_guide_scale 5.0 # Default recommended
|
||||
```
|
||||
|
||||
### 7. Use Persistent Cache
|
||||
```bash
|
||||
# Models and transformers will be cached in ./cache
|
||||
# Reusing the cache speeds up subsequent runs
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Container Management
|
||||
|
||||
### Stop Container
|
||||
```bash
|
||||
docker compose down
|
||||
```
|
||||
|
||||
### Restart Container
|
||||
```bash
|
||||
docker compose restart wan2-1
|
||||
```
|
||||
|
||||
### View Logs
|
||||
```bash
|
||||
docker compose logs -f wan2-1
|
||||
```
|
||||
|
||||
### Clean Up
|
||||
```bash
|
||||
# Remove containers
|
||||
docker compose down -v
|
||||
|
||||
# Remove images
|
||||
docker rmi wan2.1:latest
|
||||
|
||||
# Clean up Docker system
|
||||
docker system prune -a
|
||||
```
|
||||
|
||||
### Update Container
|
||||
```bash
|
||||
# Pull latest code
|
||||
git pull origin main
|
||||
|
||||
# Rebuild image
|
||||
docker compose build --no-cache
|
||||
|
||||
# Restart containers
|
||||
docker compose up -d
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Security Best Practices
|
||||
|
||||
1. **Do not commit API keys** to version control
|
||||
2. **Use .env files** for sensitive environment variables
|
||||
3. **Limit container privileges**: Avoid running as root
|
||||
4. **Keep Docker updated** for security patches
|
||||
5. **Scan images** for vulnerabilities:
|
||||
```bash
|
||||
docker scan wan2.1:latest
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Support and Resources
|
||||
|
||||
- **GitHub Issues**: [https://github.com/Wan-Video/Wan2.1/issues](https://github.com/Wan-Video/Wan2.1/issues)
|
||||
- **Discord**: [Join the community](https://discord.gg/AKNgpMK4Yj)
|
||||
- **Technical Report**: [arXiv:2503.20314](https://arxiv.org/abs/2503.20314)
|
||||
- **Docker Documentation**: [https://docs.docker.com/](https://docs.docker.com/)
|
||||
- **NVIDIA Container Toolkit**: [https://github.com/NVIDIA/nvidia-docker](https://github.com/NVIDIA/nvidia-docker)
|
||||
|
||||
---
|
||||
|
||||
## License
|
||||
|
||||
This Docker setup follows the same Apache 2.0 License as the Wan2.1 project. See [LICENSE.txt](LICENSE.txt) for details.
|
||||
|
||||
---
|
||||
|
||||
**Last Updated**: 2025-10-26
|
||||
**Version**: 1.0.0
|
||||
**Maintainer**: Wan2.1 Community
|
||||
69
Dockerfile
Normal file
69
Dockerfile
Normal file
@ -0,0 +1,69 @@
|
||||
# Wan2.1 Docker Image
|
||||
# Professional-grade Docker setup for Wan2.1 video generation models
|
||||
# Supports both CPU and GPU (NVIDIA CUDA) environments
|
||||
|
||||
FROM nvidia/cuda:12.1.1-cudnn8-devel-ubuntu22.04
|
||||
|
||||
# Prevent interactive prompts during build
|
||||
ENV DEBIAN_FRONTEND=noninteractive
|
||||
|
||||
# Set working directory
|
||||
WORKDIR /app
|
||||
|
||||
# Install system dependencies
|
||||
RUN apt-get update && apt-get install -y \
|
||||
python3.10 \
|
||||
python3-pip \
|
||||
python3.10-dev \
|
||||
git \
|
||||
wget \
|
||||
curl \
|
||||
ffmpeg \
|
||||
libsm6 \
|
||||
libxext6 \
|
||||
libxrender-dev \
|
||||
libgomp1 \
|
||||
libgl1-mesa-glx \
|
||||
&& rm -rf /var/lib/apt/lists/*
|
||||
|
||||
# Upgrade pip and install build tools
|
||||
RUN pip3 install --no-cache-dir --upgrade pip setuptools wheel
|
||||
|
||||
# Set Python 3.10 as default
|
||||
RUN update-alternatives --install /usr/bin/python python /usr/bin/python3.10 1 && \
|
||||
update-alternatives --install /usr/bin/pip pip /usr/bin/pip3 1
|
||||
|
||||
# Copy requirements first for better layer caching
|
||||
COPY requirements.txt /app/requirements.txt
|
||||
|
||||
# Install PyTorch with CUDA support
|
||||
RUN pip install --no-cache-dir torch==2.4.0 torchvision==0.19.0 --index-url https://download.pytorch.org/whl/cu121
|
||||
|
||||
# Install flash-attention separately (can be problematic)
|
||||
RUN pip install --no-cache-dir flash-attn --no-build-isolation || echo "Flash attention installation failed, continuing..."
|
||||
|
||||
# Install remaining Python dependencies
|
||||
RUN pip install --no-cache-dir -r requirements.txt
|
||||
|
||||
# Copy application code
|
||||
COPY . /app/
|
||||
|
||||
# Create directories for models and outputs
|
||||
RUN mkdir -p /app/models /app/outputs /app/cache
|
||||
|
||||
# Set environment variables
|
||||
ENV PYTHONUNBUFFERED=1
|
||||
ENV TORCH_HOME=/app/cache
|
||||
ENV HF_HOME=/app/cache/huggingface
|
||||
ENV TRANSFORMERS_CACHE=/app/cache/transformers
|
||||
ENV CUDA_VISIBLE_DEVICES=0
|
||||
|
||||
# Expose port for Gradio
|
||||
EXPOSE 7860
|
||||
|
||||
# Health check
|
||||
HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
|
||||
CMD python -c "import torch; print('CUDA available:', torch.cuda.is_available())" || exit 1
|
||||
|
||||
# Default command (can be overridden)
|
||||
CMD ["/bin/bash"]
|
||||
55
Makefile
55
Makefile
@ -1,5 +1,58 @@
|
||||
.PHONY: format
|
||||
.PHONY: format docker-build docker-up docker-down docker-shell docker-logs docker-clean help
|
||||
|
||||
# Code formatting
|
||||
format:
|
||||
isort generate.py gradio wan
|
||||
yapf -i -r *.py generate.py gradio wan
|
||||
|
||||
# Docker commands
|
||||
docker-build:
|
||||
@echo "Building Docker image..."
|
||||
docker compose build
|
||||
|
||||
docker-up:
|
||||
@echo "Starting Wan2.1 container..."
|
||||
@mkdir -p models outputs cache
|
||||
docker compose up -d wan2-1
|
||||
|
||||
docker-down:
|
||||
@echo "Stopping Wan2.1 container..."
|
||||
docker compose down
|
||||
|
||||
docker-restart: docker-down docker-up
|
||||
|
||||
docker-shell:
|
||||
@echo "Entering container shell..."
|
||||
docker compose exec wan2-1 bash
|
||||
|
||||
docker-logs:
|
||||
@echo "Showing container logs..."
|
||||
docker compose logs -f wan2-1
|
||||
|
||||
docker-clean:
|
||||
@echo "Cleaning up Docker resources..."
|
||||
docker compose down -v
|
||||
docker system prune -f
|
||||
|
||||
docker-status:
|
||||
@echo "Container status:"
|
||||
docker compose ps
|
||||
|
||||
# Help command
|
||||
help:
|
||||
@echo "Wan2.1 Makefile Commands:"
|
||||
@echo ""
|
||||
@echo "Code Formatting:"
|
||||
@echo " make format - Format Python code with isort and yapf"
|
||||
@echo ""
|
||||
@echo "Docker Management:"
|
||||
@echo " make docker-build - Build Docker image"
|
||||
@echo " make docker-up - Start container (with GPU support)"
|
||||
@echo " make docker-down - Stop container"
|
||||
@echo " make docker-restart - Restart container"
|
||||
@echo " make docker-shell - Enter container shell"
|
||||
@echo " make docker-logs - View container logs"
|
||||
@echo " make docker-status - Show container status"
|
||||
@echo " make docker-clean - Remove containers and clean up"
|
||||
@echo ""
|
||||
@echo "For detailed Docker setup, see DOCKER_SETUP.md"
|
||||
|
||||
99
docker-compose.yml
Normal file
99
docker-compose.yml
Normal file
@ -0,0 +1,99 @@
|
||||
version: '3.8'
|
||||
|
||||
services:
|
||||
wan2-1:
|
||||
build:
|
||||
context: .
|
||||
dockerfile: Dockerfile
|
||||
image: wan2.1:latest
|
||||
container_name: wan2.1-gpu
|
||||
|
||||
# GPU support - requires NVIDIA Docker runtime
|
||||
deploy:
|
||||
resources:
|
||||
reservations:
|
||||
devices:
|
||||
- driver: nvidia
|
||||
count: all
|
||||
capabilities: [gpu]
|
||||
|
||||
# Environment variables
|
||||
environment:
|
||||
- NVIDIA_VISIBLE_DEVICES=all
|
||||
- NVIDIA_DRIVER_CAPABILITIES=compute,utility
|
||||
- CUDA_VISIBLE_DEVICES=0
|
||||
- PYTHONUNBUFFERED=1
|
||||
- TORCH_HOME=/app/cache
|
||||
- HF_HOME=/app/cache/huggingface
|
||||
- TRANSFORMERS_CACHE=/app/cache/transformers
|
||||
# Optional: Set your Dashscope API key for prompt extension
|
||||
# - DASH_API_KEY=your_api_key_here
|
||||
# - DASH_API_URL=https://dashscope.aliyuncs.com/api/v1
|
||||
|
||||
# Volume mounts
|
||||
volumes:
|
||||
# Mount models directory (download models here)
|
||||
- ./models:/app/models
|
||||
# Mount outputs directory
|
||||
- ./outputs:/app/outputs
|
||||
# Mount cache directory for model downloads
|
||||
- ./cache:/app/cache
|
||||
# Optional: Mount examples directory if you modify it
|
||||
- ./examples:/app/examples
|
||||
|
||||
# Port mapping for Gradio interface
|
||||
ports:
|
||||
- "7860:7860"
|
||||
|
||||
# Shared memory size (important for DataLoader workers)
|
||||
shm_size: '16gb'
|
||||
|
||||
# Keep container running
|
||||
stdin_open: true
|
||||
tty: true
|
||||
|
||||
# Network mode
|
||||
network_mode: bridge
|
||||
|
||||
# Restart policy
|
||||
restart: unless-stopped
|
||||
|
||||
# CPU-only service (for systems without GPU)
|
||||
wan2-1-cpu:
|
||||
build:
|
||||
context: .
|
||||
dockerfile: Dockerfile
|
||||
image: wan2.1:latest
|
||||
container_name: wan2.1-cpu
|
||||
profiles:
|
||||
- cpu
|
||||
|
||||
environment:
|
||||
- PYTHONUNBUFFERED=1
|
||||
- TORCH_HOME=/app/cache
|
||||
- HF_HOME=/app/cache/huggingface
|
||||
- TRANSFORMERS_CACHE=/app/cache/transformers
|
||||
- CUDA_VISIBLE_DEVICES=""
|
||||
|
||||
volumes:
|
||||
- ./models:/app/models
|
||||
- ./outputs:/app/outputs
|
||||
- ./cache:/app/cache
|
||||
- ./examples:/app/examples
|
||||
|
||||
ports:
|
||||
- "7860:7860"
|
||||
|
||||
shm_size: '8gb'
|
||||
|
||||
stdin_open: true
|
||||
tty: true
|
||||
|
||||
network_mode: bridge
|
||||
|
||||
restart: unless-stopped
|
||||
|
||||
volumes:
|
||||
models:
|
||||
outputs:
|
||||
cache:
|
||||
183
docker-run.sh
Executable file
183
docker-run.sh
Executable file
@ -0,0 +1,183 @@
|
||||
#!/bin/bash
|
||||
# Wan2.1 Docker Helper Script
|
||||
# Quick start script for running Wan2.1 in Docker
|
||||
|
||||
set -e
|
||||
|
||||
# Colors for output
|
||||
GREEN='\033[0;32m'
|
||||
BLUE='\033[0;34m'
|
||||
RED='\033[0;31m'
|
||||
YELLOW='\033[1;33m'
|
||||
NC='\033[0m' # No Color
|
||||
|
||||
# Configuration
|
||||
CONTAINER_NAME="wan2.1-gpu"
|
||||
IMAGE_NAME="wan2.1:latest"
|
||||
MODELS_DIR="./models"
|
||||
OUTPUTS_DIR="./outputs"
|
||||
CACHE_DIR="./cache"
|
||||
|
||||
# Print header
|
||||
echo -e "${BLUE}========================================${NC}"
|
||||
echo -e "${BLUE} Wan2.1 Docker Helper Script${NC}"
|
||||
echo -e "${BLUE}========================================${NC}\n"
|
||||
|
||||
# Check if Docker is installed
|
||||
if ! command -v docker &> /dev/null; then
|
||||
echo -e "${RED}Error: Docker is not installed${NC}"
|
||||
echo "Please install Docker first: https://docs.docker.com/engine/install/"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# Check if Docker Compose is installed
|
||||
if ! command -v docker compose &> /dev/null; then
|
||||
echo -e "${RED}Error: Docker Compose is not installed${NC}"
|
||||
echo "Please install Docker Compose: https://docs.docker.com/compose/install/"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# Function to check NVIDIA Docker runtime
|
||||
check_nvidia_runtime() {
|
||||
if ! docker run --rm --gpus all nvidia/cuda:12.1.1-base-ubuntu22.04 nvidia-smi &> /dev/null; then
|
||||
echo -e "${YELLOW}Warning: NVIDIA Docker runtime not available${NC}"
|
||||
echo "GPU acceleration will not be available"
|
||||
echo "Install NVIDIA Container Toolkit: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html"
|
||||
return 1
|
||||
fi
|
||||
return 0
|
||||
}
|
||||
|
||||
# Function to create directories
|
||||
create_directories() {
|
||||
echo -e "${BLUE}Creating required directories...${NC}"
|
||||
mkdir -p "$MODELS_DIR" "$OUTPUTS_DIR" "$CACHE_DIR"
|
||||
echo -e "${GREEN}✓ Directories created${NC}\n"
|
||||
}
|
||||
|
||||
# Function to build Docker image
|
||||
build_image() {
|
||||
echo -e "${BLUE}Building Docker image...${NC}"
|
||||
docker compose build
|
||||
echo -e "${GREEN}✓ Docker image built successfully${NC}\n"
|
||||
}
|
||||
|
||||
# Function to start container
|
||||
start_container() {
|
||||
echo -e "${BLUE}Starting Wan2.1 container...${NC}"
|
||||
|
||||
if check_nvidia_runtime; then
|
||||
echo "Starting with GPU support..."
|
||||
docker compose up -d wan2-1
|
||||
else
|
||||
echo "Starting in CPU-only mode..."
|
||||
docker compose --profile cpu up -d wan2-1-cpu
|
||||
CONTAINER_NAME="wan2.1-cpu"
|
||||
fi
|
||||
|
||||
echo -e "${GREEN}✓ Container started successfully${NC}\n"
|
||||
}
|
||||
|
||||
# Function to show status
|
||||
show_status() {
|
||||
echo -e "${BLUE}Container Status:${NC}"
|
||||
docker compose ps
|
||||
echo ""
|
||||
}
|
||||
|
||||
# Function to show logs
|
||||
show_logs() {
|
||||
echo -e "${BLUE}Showing container logs (Ctrl+C to exit)...${NC}"
|
||||
docker compose logs -f "$CONTAINER_NAME"
|
||||
}
|
||||
|
||||
# Function to enter container
|
||||
enter_container() {
|
||||
echo -e "${BLUE}Entering container shell...${NC}"
|
||||
docker compose exec wan2-1 bash || docker compose exec wan2-1-cpu bash
|
||||
}
|
||||
|
||||
# Function to stop container
|
||||
stop_container() {
|
||||
echo -e "${BLUE}Stopping container...${NC}"
|
||||
docker compose down
|
||||
echo -e "${GREEN}✓ Container stopped${NC}\n"
|
||||
}
|
||||
|
||||
# Function to show help
|
||||
show_help() {
|
||||
cat << EOF
|
||||
Usage: ./docker-run.sh [COMMAND]
|
||||
|
||||
Commands:
|
||||
build Build the Docker image
|
||||
start Start the container
|
||||
stop Stop the container
|
||||
restart Restart the container
|
||||
status Show container status
|
||||
logs Show container logs
|
||||
shell Enter container shell
|
||||
clean Stop container and clean up
|
||||
help Show this help message
|
||||
|
||||
Examples:
|
||||
./docker-run.sh build # Build the image
|
||||
./docker-run.sh start # Start the container
|
||||
./docker-run.sh shell # Enter the container
|
||||
./docker-run.sh logs # View logs
|
||||
|
||||
For detailed documentation, see DOCKER_SETUP.md
|
||||
EOF
|
||||
}
|
||||
|
||||
# Main script logic
|
||||
case "${1:-start}" in
|
||||
build)
|
||||
create_directories
|
||||
build_image
|
||||
;;
|
||||
start)
|
||||
create_directories
|
||||
if ! docker images | grep -q "$IMAGE_NAME"; then
|
||||
build_image
|
||||
fi
|
||||
start_container
|
||||
show_status
|
||||
echo -e "${GREEN}Container is running!${NC}"
|
||||
echo -e "Run ${BLUE}./docker-run.sh shell${NC} to enter the container"
|
||||
echo -e "Run ${BLUE}./docker-run.sh logs${NC} to view logs"
|
||||
;;
|
||||
stop)
|
||||
stop_container
|
||||
;;
|
||||
restart)
|
||||
stop_container
|
||||
start_container
|
||||
;;
|
||||
status)
|
||||
show_status
|
||||
;;
|
||||
logs)
|
||||
show_logs
|
||||
;;
|
||||
shell)
|
||||
enter_container
|
||||
;;
|
||||
clean)
|
||||
echo -e "${YELLOW}This will stop the container and remove volumes${NC}"
|
||||
read -p "Are you sure? (y/N) " -n 1 -r
|
||||
echo
|
||||
if [[ $REPLY =~ ^[Yy]$ ]]; then
|
||||
docker compose down -v
|
||||
echo -e "${GREEN}✓ Cleanup complete${NC}"
|
||||
fi
|
||||
;;
|
||||
help|--help|-h)
|
||||
show_help
|
||||
;;
|
||||
*)
|
||||
echo -e "${RED}Unknown command: $1${NC}"
|
||||
show_help
|
||||
exit 1
|
||||
;;
|
||||
esac
|
||||
Loading…
Reference in New Issue
Block a user