added Dockerfile for easier execution without installing dependencies

Dockerfile

@@ -0,0 +1,92 @@
+# Start with a base Ubuntu image
+FROM ubuntu:18.04
+
+# Set environment variables
+ENV DEBIAN_FRONTEND=noninteractive
+ENV PATH="/usr/local/cuda/bin:${PATH}"
+ENV LD_LIBRARY_PATH="/usr/local/cuda/lib64:${LD_LIBRARY_PATH}"
+
+# Install prerequisites
+RUN apt-get update && apt-get install -y \
+    wget \
+    curl \
+    gnupg \
+    software-properties-common \
+    build-essential \
+    python3.8 \
+    python3.8-dev \
+    python3.8-distutils \
+    python3-pip \
+    zlib1g-dev \
+    libjpeg-dev \
+    libpng-dev \
+    libtiff-dev \
+    libopenjp2-7-dev \
+    libwebp-dev \
+    libx11-dev \
+    libgl1-mesa-glx \
+    libegl1-mesa \
+    libxrandr-dev \
+    libxss-dev \
+    libxcursor-dev \
+    libxi-dev \
+    libxtst-dev \
+    libffi-dev \
+    gcc \
+    g++ \
+    make
+
+# Add the CUDA repository pin
+RUN wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-ubuntu1804.pin && \
+    mv cuda-ubuntu1804.pin /etc/apt/preferences.d/cuda-repository-pin-600
+
+# Download and install the CUDA 10.2 local repository package
+RUN wget https://developer.download.nvidia.com/compute/cuda/10.2/Prod/local_installers/cuda-repo-ubuntu1804-10-2-local-10.2.89-440.33.01_1.0-1_amd64.deb && \
+    dpkg -i cuda-repo-ubuntu1804-10-2-local-10.2.89-440.33.01_1.0-1_amd64.deb && \
+    apt-key add /var/cuda-repo-10-2-local-10.2.89-440.33.01/7fa2af80.pub && \
+    apt-get update
+
+# Install CUDA 10.2
+RUN apt-get install -y cuda
+
+# Add the toolchain repository for GCC 8
+RUN add-apt-repository ppa:ubuntu-toolchain-r/test && apt-get update && apt-get install -y \
+    gcc-8 g++-8
+
+# Set GCC 8 as the default
+RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-8 100 && \
+    update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-8 100
+
+# Update the default Python to Python 3.8
+RUN update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.8 1 && \
+    update-alternatives --set python3 /usr/bin/python3.8
+
+# Upgrade pip and install necessary Python packages
+RUN python3 -m pip install --upgrade pip && \
+    pip3 install --no-cache-dir \
+    setuptools \
+    wheel \
+    numpy \
+    Cython \
+    pillow
+
+# Install PyTorch and related packages
+RUN pip3 install --no-cache-dir \
+    torch==1.9.1+cu111 \
+    torchvision==0.10.1+cu111 \
+    torchaudio==0.9.1 -f https://download.pytorch.org/whl/torch_stable.html
+
+WORKDIR /datasetdm
+RUN apt-get install git -y
+RUN ln -s /usr/bin/python3 /usr/bin/python
+RUN pip install --upgrade pip==20.3
+
+COPY . .
+RUN pip install setuptools==59.5.0
+RUN pip install -r requirements.txt
+
+
+ENV PYTHONPATH=/datasetdm:$PYTHONPATH
+# Set default command
+CMD ["bash"]
+

README.md

@@ -92,6 +92,21 @@ There may be some errors (such as https://github.com/showlab/DatasetDM/issues/11
 
 Alternatively, you can directly utilize our diffuser, as there have been some modifications in ```./model/diffusers/models/unet_blocks.py```.
 
+### Installation (Docker)
+
+First, build the image using the Docker CLI:  
+
+```
+docker build -t datasetdm .
+```  
+
+Then, run the container with access to all GPUs:  
+
+```
+docker run --gpus all -it datasetdm
+```  
+
+This will start an interactive bash session where you can execute all inference and training commands. To load your datasets, simply use a bind-mount to link the corresponding folders. 
 
 ### Dataset Prepare