mirror of
https://github.com/Wan-Video/Wan2.1.git
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0bd40b9bf0
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>
100 lines
2.2 KiB
YAML
100 lines
2.2 KiB
YAML
version: '3.8'
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services:
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wan2-1:
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build:
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context: .
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dockerfile: Dockerfile
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image: wan2.1:latest
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container_name: wan2.1-gpu
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# GPU support - requires NVIDIA Docker runtime
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deploy:
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resources:
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reservations:
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devices:
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- driver: nvidia
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count: all
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capabilities: [gpu]
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# Environment variables
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environment:
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- NVIDIA_VISIBLE_DEVICES=all
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- NVIDIA_DRIVER_CAPABILITIES=compute,utility
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- CUDA_VISIBLE_DEVICES=0
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- PYTHONUNBUFFERED=1
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- TORCH_HOME=/app/cache
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- HF_HOME=/app/cache/huggingface
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- TRANSFORMERS_CACHE=/app/cache/transformers
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# Optional: Set your Dashscope API key for prompt extension
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# - DASH_API_KEY=your_api_key_here
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# - DASH_API_URL=https://dashscope.aliyuncs.com/api/v1
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# Volume mounts
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volumes:
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# Mount models directory (download models here)
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- ./models:/app/models
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# Mount outputs directory
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- ./outputs:/app/outputs
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# Mount cache directory for model downloads
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- ./cache:/app/cache
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# Optional: Mount examples directory if you modify it
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- ./examples:/app/examples
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# Port mapping for Gradio interface
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ports:
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- "7860:7860"
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# Shared memory size (important for DataLoader workers)
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shm_size: '16gb'
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# Keep container running
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stdin_open: true
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tty: true
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# Network mode
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network_mode: bridge
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# Restart policy
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restart: unless-stopped
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# CPU-only service (for systems without GPU)
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wan2-1-cpu:
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build:
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context: .
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dockerfile: Dockerfile
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image: wan2.1:latest
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container_name: wan2.1-cpu
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profiles:
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- cpu
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environment:
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- PYTHONUNBUFFERED=1
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- TORCH_HOME=/app/cache
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- HF_HOME=/app/cache/huggingface
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- TRANSFORMERS_CACHE=/app/cache/transformers
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- CUDA_VISIBLE_DEVICES=""
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volumes:
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- ./models:/app/models
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- ./outputs:/app/outputs
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- ./cache:/app/cache
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- ./examples:/app/examples
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ports:
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- "7860:7860"
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shm_size: '8gb'
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stdin_open: true
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tty: true
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network_mode: bridge
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restart: unless-stopped
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volumes:
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models:
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outputs:
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cache:
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