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>
70 lines
2.0 KiB
Docker
70 lines
2.0 KiB
Docker
# Wan2.1 Docker Image
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# Professional-grade Docker setup for Wan2.1 video generation models
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# Supports both CPU and GPU (NVIDIA CUDA) environments
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FROM nvidia/cuda:12.1.1-cudnn8-devel-ubuntu22.04
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# Prevent interactive prompts during build
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ENV DEBIAN_FRONTEND=noninteractive
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# Set working directory
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WORKDIR /app
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# Install system dependencies
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RUN apt-get update && apt-get install -y \
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python3.10 \
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python3-pip \
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python3.10-dev \
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git \
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wget \
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curl \
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ffmpeg \
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libsm6 \
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libxext6 \
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libxrender-dev \
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libgomp1 \
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libgl1-mesa-glx \
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&& rm -rf /var/lib/apt/lists/*
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# Upgrade pip and install build tools
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RUN pip3 install --no-cache-dir --upgrade pip setuptools wheel
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# Set Python 3.10 as default
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RUN update-alternatives --install /usr/bin/python python /usr/bin/python3.10 1 && \
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update-alternatives --install /usr/bin/pip pip /usr/bin/pip3 1
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# Copy requirements first for better layer caching
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COPY requirements.txt /app/requirements.txt
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# Install PyTorch with CUDA support
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RUN pip install --no-cache-dir torch==2.4.0 torchvision==0.19.0 --index-url https://download.pytorch.org/whl/cu121
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# Install flash-attention separately (can be problematic)
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RUN pip install --no-cache-dir flash-attn --no-build-isolation || echo "Flash attention installation failed, continuing..."
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# Install remaining Python dependencies
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RUN pip install --no-cache-dir -r requirements.txt
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# Copy application code
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COPY . /app/
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# Create directories for models and outputs
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RUN mkdir -p /app/models /app/outputs /app/cache
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# Set environment variables
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ENV PYTHONUNBUFFERED=1
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ENV TORCH_HOME=/app/cache
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ENV HF_HOME=/app/cache/huggingface
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ENV TRANSFORMERS_CACHE=/app/cache/transformers
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ENV CUDA_VISIBLE_DEVICES=0
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# Expose port for Gradio
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EXPOSE 7860
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# Health check
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HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
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CMD python -c "import torch; print('CUDA available:', torch.cuda.is_available())" || exit 1
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# Default command (can be overridden)
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CMD ["/bin/bash"]
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