* Install wsl2 under windows 11 22h2 and then install ubuntu

* Update ubuntu

sudo apt update
sudo apt upgrade

* Close and reopen terminal window

* Make a place for downloads
cd ~
mkdir Downloads
cd Downloads

* Download & Install Anaconda

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

* Accept license and defaults until

Do you wish the installer to initialize Anaconda3
by running conda init? [yes|no]
[no] >>> yes

* close and reopen terminal window

* Update anaconda
conda update -n base -c defaults conda


* Nvidia CUDA toolkit for Ubuntu WSL

cd ~/Downloads
wget https://developer.download.nvidia.com/compute/cuda/repos/wsl-ubuntu/x86_64/cuda-wsl-ubuntu.pin
sudo mv cuda-wsl-ubuntu.pin /etc/apt/preferences.d/cuda-repository-pin-600
wget https://developer.download.nvidia.com/compute/cuda/11.6.2/local_installers/cuda-repo-wsl-ubuntu-11-6-local_11.6.2-1_amd64.deb
sudo dpkg -i cuda-repo-wsl-ubuntu-11-6-local_11.6.2-1_amd64.deb
sudo apt-key add /var/cuda-repo-wsl-ubuntu-11-6-local/7fa2af80.pub
sudo apt-get update
sudo apt-get -y install cuda


* New virtual Python environment
conda create --name diffusers python=3.10
conda activate diffusers

* Make a directory for all your github downloads, then download diffusers
mkdir ~/github
cd ~/github
git clone https://github.com/ShivamShrirao/diffusers/
cd diffusers

* Install required packages

pip install .
cd examples/dreambooth
pip install -r requirements.txt
pip install torchaudio --extra-index-url https://download.pytorch.org/whl/cu116
pip install diffusers
pip install deepspeed

* Configure / login

accelerate config

* Use following options

In which compute environment are you running? ([0] This machine, [1] AWS (Amazon SageMaker)): 0
Which type of machine are you using? ([0] No distributed training, [1] multi-CPU, [2] multi-GPU, [3] TPU [4] MPS): 0
Do you want to run your training on CPU only (even if a GPU is available)? [yes/NO]:
Do you want to use DeepSpeed? [yes/NO]: yes
Do you want to specify a json file to a DeepSpeed config? [yes/NO]:
What should be your DeepSpeed's ZeRO optimization stage (0, 1, 2, 3)? [2]:
Where to offload optimizer states? [none/cpu/nvme]: cpu
Where to offload parameters? [none/cpu/nvme]: cpu
How many gradient accumulation steps you're passing in your script? [1]:
Do you want to use gradient clipping? [yes/NO]:
Do you want to enable `deepspeed.zero.Init` when using ZeRO Stage-3 for constructing massive models? [yes/NO]:
How many GPU(s) should be used for distributed training? [1]:
Do you wish to use FP16 or BF16 (mixed precision)? [NO/fp16/bf16]: fp16

huggingface-cli login

* And paste token

mkdir -p training
mkdir -p classes
mkdir -p model

*Enter this so you won't have to restart the computer

export LD_LIBRARY_PATH=/usr/lib/wsl/lib:$LD_LIBRARY_PATH


explorer.exe .

* Copy your class images(person?) to the classes folder. Generate with whatever SD gui you're using with the class i.e. person as the prompt, or let the script generate
* Generate the amount you'll use in --num_class_images (200?) if you run out of vram or if you want the script to run faster
* Copy your training images(you?) to the training folder


* Copy your training file from your Windows desktop (or wherever you saved it):
cp /mnt/c/Users/YOUR_USERNAME/Desktop/my_training.sh ./
chmod +x ./my_training.sh
./my_training.sh

*Example of contents of my_training.sh

export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export INSTANCE_DIR="training"
export CLASS_DIR="classes"
export OUTPUT_DIR="model"

accelerate launch train_dreambooth.py \
  --pretrained_model_name_or_path=$MODEL_NAME \
  --instance_data_dir=$INSTANCE_DIR \
  --class_data_dir=$CLASS_DIR \
  --output_dir=$OUTPUT_DIR \
  --with_prior_preservation --prior_loss_weight=1.0 \
  --instance_prompt="a photo of sks person" \
  --class_prompt="a photo of person" \
  --seed=1337 \
  --resolution=512 \
  --train_batch_size=1 \
  --gradient_accumulation_steps=1 --gradient_checkpointing \
  --learning_rate=5e-6 \
  --lr_scheduler="constant" \
  --lr_warmup_steps=0 \
  --num_class_images=200 \
  --sample_batch_size=1 \
  --max_train_steps=1000 \
  --mixed_precision=fp16


* Convert to .ckpt

python -m pip install pytorch-lightning
explorer.exe .

***Put original sd-v1-4.ckpt in the dreambooth folder - Important

*And donwload and run a different script

wget https://raw.githubusercontent.com/ratwithacompiler/diffusers_stablediff_conversion/main/convert_diffusers_to_sd.py

python ./convert_diffusers_to_sd.py ./model ./sd-v1-4.ckpt ./model.ckpt



* NB: If you get an error like this -
403 Client Error: Forbidden for url: https://huggingface.co/CompVis/stable-diffusion-v1-4/resolve/main/model_index.json

Then:
Make sure you've visited https://huggingface.co/CompVis/stable-diffusion-v1-4 and accepted the licence!
NB. If this is the very first time you've used diffusers, it's about a 6GB download

To train again next time, simply:

* Start Ubuntu, then:
conda activate diffusers
cd ~/github/diffusers/examples/dreambooth
rm -r model
./my_training.sh