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>
158 lines
3.8 KiB
Markdown
158 lines
3.8 KiB
Markdown
# 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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