Dockerfile

# Use an official PyTorch image WITH CUDA development headers required for compiling
FROM pytorch/pytorch:2.1.2-cuda12.1-cudnn8-devel

# Prevent interactive prompts during apt-get installations
ENV DEBIAN_FRONTEND=noninteractive

# Tell PyTorch which GPU architectures to compile for explicitly
ENV TORCH_CUDA_ARCH_LIST="7.5 8.0 8.6 8.9 9.0"

# 🛑 THE FIX: Throttle the compiler to prevent Out Of Memory (OOMKilled) crashes
ENV MAX_JOBS=2

# Set the working directory
WORKDIR /app

# Install system dependencies for C++/CUDA extensions, 3D processing, and SSH access
RUN apt-get update && apt-get install -y \
    git \
    build-essential \
    ninja-build \
    libgl1 \
    libglib2.0-0 \
    tmate \
    openssh-client \
    && rm -rf /var/lib/apt/lists/*

# Clone the official Hunyuan3D-2 repository
RUN git clone https://github.com/Tencent/Hunyuan3D-2.git /app/Hunyuan3D-2

# Install the base Python requirements from the Tencent repository FIRST
RUN pip install --no-cache-dir -r /app/Hunyuan3D-2/requirements.txt

# Copy your local requirements and install them
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Explicitly force NumPy to stay below 2.0 right before compiling
RUN pip install --no-cache-dir "numpy<2"

# Compile the Custom Rasterizer (Required for Textures)
WORKDIR /app/Hunyuan3D-2/hy3dgen/texgen/custom_rasterizer
RUN python setup.py install

# Compile the Differentiable Renderer (Required for Textures)
WORKDIR /app/Hunyuan3D-2/hy3dgen/texgen/differentiable_renderer
RUN python setup.py install

# Move back to the root app directory
WORKDIR /app

# Copy your API script into the container
COPY app.py .

# Create a directory to store generated outputs temporarily
RUN mkdir -p /app/outputs

# Expose the default Hugging Face Spaces port
EXPOSE 7860

# Command to start the FastAPI application
CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]