install on Nvidia

Before attempting to install make sure all the required dependencies are met.

Automatic Installation

Windows

Run webui-user.bat from Windows Explorer as normal, non-administrator, user.

See Troubleshooting section for what to do if things go wrong.

Linux

To install in the default directory /home/$(whoami)/stable-diffusion-webui/, run:

bash <(wget -qO- https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh)

In order to customize the installation, clone the repository into the desired location, change the required variables in webui-user.sh and run :

bash webui.sh

Almost Automatic Installation and Launch

To install the required packages via pip without creating a virtual environment, run:

python launch.py

Command line arguments may be passed directly, for example:

python launch.py --opt-split-attention --ckpt ../secret/anime9999.ckpt

Manual Installation

Manual installation is very outdated and probably won’t work. check colab in the repo’s readme for instructions.

The following process installs everything manually on both Windows or Linux (the latter requiring dir to be replaced by ls):

# install torch with CUDA support. See https://pytorch.org/get-started/locally/ for more instructions if this fails.
pip install torch --extra-index-url https://download.pytorch.org/whl/cu113

# check if torch supports GPU; this must output "True". You need CUDA 11. installed for this. You might be able to use
# a different version, but this is what I tested.
python -c "import torch; print(torch.cuda.is_available())"

# clone web ui and go into its directory
git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
cd stable-diffusion-webui

# clone repositories for Stable Diffusion and (optionally) CodeFormer
mkdir repositories
git clone https://github.com/CompVis/stable-diffusion.git repositories/stable-diffusion
git clone https://github.com/CompVis/taming-transformers.git repositories/taming-transformers
git clone https://github.com/sczhou/CodeFormer.git repositories/CodeFormer
git clone https://github.com/salesforce/BLIP.git repositories/BLIP

# install requirements of Stable Diffusion
pip install transformers==4.19.2 diffusers invisible-watermark --prefer-binary

# install k-diffusion
pip install git+https://github.com/crowsonkb/k-diffusion.git --prefer-binary

# (optional) install GFPGAN (face restoration)
pip install git+https://github.com/TencentARC/GFPGAN.git --prefer-binary

# (optional) install requirements for CodeFormer (face restoration)
pip install -r repositories/CodeFormer/requirements.txt --prefer-binary

# install requirements of web ui
pip install -r requirements.txt  --prefer-binary

# update numpy to latest version
pip install -U numpy  --prefer-binary

# (outside of command line) put stable diffusion model into web ui directory
# the command below must output something like: 1 File(s) 4,265,380,512 bytes
dir model.ckpt

The installation is finished, to start the web ui, run:

python webui.py

Windows 11 WSL2 instructions

To install under a Linux distro in Windows 11’s WSL2:

# install conda (if not already done)
wget https://repo.anaconda.com/archive/Anaconda3-2022.05-Linux-x86_64.sh
chmod +x Anaconda3-2022.05-Linux-x86_64.sh
./Anaconda3-2022.05-Linux-x86_64.sh

# Clone webui repo
git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
cd stable-diffusion-webui

# Create and activate conda env
conda env create -f environment-wsl2.yaml
conda activate automatic

At this point, the instructions for the Manual installation may be applied starting at step # clone repositories for Stable Diffusion and (optionally) CodeFormer.

Alternative installation on Windows using Conda

• Prerequisites *(Only needed if you do not have them)*. Assumes Chocolatey is installed.

    # install git
    choco install git
    # install conda
    choco install anaconda3
  

  Optional parameters: git, conda

• Install (warning: some files exceed multiple gigabytes, make sure you have space first)
  1. Download as .zip and extract or use git to clone.

        git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
     

  2. Launch the Anaconda prompt. It should be noted that you can use older Python versions, but you may be forced to manually remove features like cache optimization, which will degrade your performance.

        # Navigate to the git directory
        cd "GIT\StableDiffusion"
        # Create environment
        conda create -n StableDiffusion python=3.10.6
        # Activate environment
        conda activate StableDiffusion
        # Validate environment is selected
        conda env list
        # Start local webserver
        webui-user.bat
        # Wait for "Running on local URL:  http://127.0.0.1:7860" and open that URI.
     

  3. *(Optional)* Go to CompVis and download latest model, for example 1.4 and unpack it to ex:

      GIT\StableDiffusion\models\Stable-diffusion
     

     after that restart the server by restarting Anaconda prompt and

      webui-user.bat
     

• Alternative defaults worth trying out:
  1. Try euler a (Ancestral Euler) with higher Sampling Steps ex: 40 or others with 100.
  2. Set “Settings > User interface > Show image creation progress every N sampling steps” to 1 and pick a deterministic Seed value. Can visually see how image defusion happens and record a .gif with ScreenToGif.
  3. Use Restore faces. Generally, better results, but that quality comes at the cost of speed.