# HOWTO

This repo provide all the required config to run the Dreambooth version found in this note: https://note.com/kohya_ss/n/nee3ed1649fb6


## Required Dependencies

Python 3.10.6 and Git:

- Python 3.10.6: https://www.python.org/ftp/python/3.10.6/python-3.10.6-amd64.exe
- git: https://git-scm.com/download/win

Give unrestricted script access to powershell so venv can work:

- Open an administrator powershell window
- Type `Set-ExecutionPolicy Unrestricted` and answer A
- Close admin powershell window

## Installation

Open a regular Powershell terminal and type the following inside:

```powershell
git clone https://github.com/bmaltais/kohya_ss.git
cd kohya_ss
python -m venv --system-site-packages venv
.\venv\Scripts\activate

pip install torch==1.12.1+cu116 torchvision==0.13.1+cu116 --extra-index-url https://download.pytorch.org/whl/cu116

pip install --upgrade diffusers
pip install -r requirements.txt
pip install OmegaConf
pip install pytorch_lightning

pip install -U -I --no-deps https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/f/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl

# Setup bitsandbytes with Adam8bit support for windows: https://note.com/kohya_ss/n/n47f654dc161e
pip install bitsandbytes==0.35.0
cp .\bitsandbytes_windows\*.dll .\venv\Lib\site-packages\bitsandbytes\
cp .\bitsandbytes_windows\cextension.py .\venv\Lib\site-packages\bitsandbytes\cextension.py
cp .\bitsandbytes_windows\main.py .\venv\Lib\site-packages\bitsandbytes\cuda_setup\main.py

accelerate config:
- 0
- 0
- NO
- NO
- All
- fp16
```

## Folders configuration

Refer to the note to understand how to create the folde structure. In short it should look like:

```
<wathever top folder name>
|- reg_<class>
    |- <repeat count>_<prompt>
|- train_<class>
   |- <repeat count>_<prompt>
```

Example for `sks dog`

```
my_sks_dog_dreambooth
|- reg_dog
    |- 1_sks dog
|- train_dog
    |- 20_sks dog
```

## Execution

Edit and paste the following in a Powershell terminal:

```powershell
accelerate launch --num_cpu_threads_per_process 6 train_db_fixed_v6.py `
    --pretrained_model_name_or_path="D:\models\last.ckpt" `
    --train_data_dir="D:\dreambooth\train_bernard\train_man" `
    --reg_data_dir="D:\dreambooth\train_bernard\reg_man" `
    --output_dir="D:\dreambooth\train_bernard" `
    --prior_loss_weight=1.0 `
    --resolution=512 `
    --train_batch_size=1 `
    --learning_rate=1e-6 `
    --max_train_steps=2100 `
    --use_8bit_adam `
    --xformers `
    --mixed_precision="fp16" `
    --cache_latents `
    --gradient_checkpointing `
    --save_every_n_epochs=1 

## Finetuning

If you would rather use model finetuning rather than the dreambooth method you can use a command similat to the following. The advantage of fine tuning is that you do not need to worry about regularization images... but you need to provide captions for every images. The caption will be used to train the model. You can use auto1111 to preprocess your training images and add either BLIP or danbooru captions to them. You then need to edit those to add the name of the model and correct any wrong description.

```
accelerate launch --num_cpu_threads_per_process 6 train_db_fixed_v6-ber.py `
    --pretrained_model_name_or_path="D:\models\v1-5-pruned-mse-vae.ckpt" `
    --train_data_dir="D:\dreambooth\source\alet_et_bernard\landscape-pp" `
    --output_dir="D:\dreambooth\train_alex_and_bernard" `
    --resolution="640,448" `
    --train_batch_size=8 `
    --learning_rate=1e-6 `
    --max_train_steps=550 `
    --use_8bit_adam `
    --xformers `
    --mixed_precision="fp16" `
    --cache_latents `
    --save_every_n_epochs=1 `
    --fine_tuning `
    --fine_tuning_repeat=200 `
    --seed=23 `
    --save_half
```

Refer to this url for more details about finetuning: https://note.com/kohya_ss/n/n1269f1e1a54e