mirror of
https://github.com/Wan-Video/Wan2.1.git
synced 2025-11-04 06:15:17 +00:00
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
|
|
# 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"]
|