stable diffusion finetune

Stable Diffusion fine-tuning

This project shows how to fine-tune stable diffusion model on your own dataset.

Note: This script is experimental. The script fine-tunes the whole model and often times the model overfits and runs into issues like catastrophic forgetting. It's recommended to try different hyperparamters to get the best result on your dataset.

Prerequisites

Before running the scripts, make sure to install the library's training dependencies (such as PyTorch and ?Transformers):

git clone https://github.com/huggingface/diffusers
cd diffusers
pip install .

Then run

cd ..
pip install -r requirements.txt

And initialize an ?Accelerate environment with:

accelerate config

Then you should download the pretrained stable diffusion model using model_download.py:

python model_download.py --repo_id runwayml/stable-diffusion-v1-5
# If you cannot connect to huggingface, you should use the following command:
python model_download.py --repo_id runwayml/stable-diffusion-v1-5 --mirror

Making your own datasets

To fine-tune stable diffusion model on your own dataset, you need to prepare your dataset in the following format:

Firstly create the dataset directory in root directory, and you should create three subdirectory jpg, hint, and train. In jpg directory, you should put all the target images in your dataset. In hint directory, you should put all the source images (condition images) in your dataset. In train directory, you should put a metadata.jsonl. The metadata.jsonl should be in the following format:

{"jpg": "./dataset/jpg/<name_of_target_image_1>", "txt": "<prompt_1>", "hint": "./dataset/hint/<name_of_source_image_1>"}
{"jpg": "./dataset/jpg/<name_of_target_image_2>", "txt": "<prompt_2>", "hint": "./dataset/hint/<name_of_source_image_1>"}
…

Dataset example

Here is the structure of the dataset directory:

dataset
├── hint
│   ├── a.png
│   ├── b.png
│   └── c.png
├── jpg
│   ├── a.png
│   ├── b.png
│   └── c.png
└── train
    └── metadata.jsonl

In metadata.jsonl file:

{"jpg": "./dataset/jpg/a.png", "txt": "a", "hint": "./dataset/hint/a.png"}
{"jpg": "./dataset/jpg/b.png", "txt": "b", "hint": "./dataset/hint/b.png"}
{"jpg": "./dataset/jpg/c.png", "txt": "c", "hint": "./dataset/hint/c.png"}

Training

LoRA

To train LoRA model, run:

./train_lora.sh

You can change some hyperparameters in run_lora.sh file. For example, you can change --num_train_epochs to change the number of training epochs.

ControlNet

To train ControlNet model, run:

./train_controlnet.sh

You can change some hyperparameters in run_controlnet.sh file. For example, you can change --num_train_epochs to change the number of training epochs.

Train ControlNet and LoRA at the same time

To train ControlNet and LoRA at the same time, run:

./train_controlnet_and_lora.sh

Note that you should change the output directory of the ControlNet and LoRA model to start your own training.

All in One

Just run:

./train_lora.sh && ./train_controlnet.sh && ./train_controlnet_and_lora.sh

You will get all the models in the controlnet-lora-output directory.

Inference

You can change the path of the model and the condition image in inference.py file. Then run:

python inference.py

And you will get output.png in the root directory.

Acknowledgements

This project is based on diffusers and it's examples.