Dockerfile

# Use an official PyTorch image with CUDA support as the base image
FROM pytorch/pytorch:1.12.1-cuda11.3-cudnn8-runtime

# Install Git and OpenGL libraries, and libglib2.0
RUN apt-get update && apt-get install -y git libgl1-mesa-glx libglib2.0-0

RUN apt-get update && apt-get install -y ninja-build

# Set up a new user named "user" with user ID 1000
RUN useradd -m -u 1000 user

# Switch to the "user" user
USER user

ENV HOME=/home/user \
    PATH=/home/user/.local/bin:$PATH

# Set the working directory to the user's home directory
WORKDIR $HOME/app

# Clone your repository or add your code to the container
RUN git clone -b dev https://github.com/camenduru/DiffBIR $HOME/app

# Install Python dependencies
RUN pip install -q einops pytorch_lightning gradio omegaconf xformers==0.0.16 transformers lpips opencv-python

# Install open_clip from GitHub
RUN pip install -q git+https://github.com/mlfoundations/open_clip@v2.20.0

USER root
# Update package lists and install aria2 without sudo
RUN apt-get update && apt-get install -y aria2
USER user

# Download checkpoint files using aria2
RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lxq007/DiffBIR/resolve/main/general_full_v1.ckpt -d $HOME/app/models -o general_full_v1.ckpt
RUN aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lxq007/DiffBIR/resolve/main/general_swinir_v1.ckpt -d $HOME/app/models -o general_swinir_v1.ckpt

# Expose any necessary ports (if your application requires it)
# EXPOSE 80

RUN find $HOME/app

# Set the environment variable to specify the GPU device
ENV CUDA_DEVICE_ORDER=PCI_BUS_ID
ENV CUDA_VISIBLE_DEVICES=0

# Define the command to run your application
CMD ["python", "gradio_diffbir.py", "--ckpt", "/home/user/app/models/general_full_v1.ckpt", "--config", "/home/user/app/configs/model/cldm.yaml", "--reload_swinir", "--swinir_ckpt", "/home/user/app/models/general_swinir_v1.ckpt"]