🌸 Fine-tuning Stable Diffusion using LoRA with Dagger
This example shows how to create brand assets for merchandise from some screenshots of a brand's website:
It's implemented as a Stable Diffusion LoRA pipeline using Dagger: pipelines as (python) code.
- Go to lambdalabs.com, or any other GPU provider of your choice (the instructions below were tested on Lambda)
- Get an instance (e.g. A100 or A10). Min GPU memory is 16GB, tested on 24GB
- Hit up Jupyter (or SSH in, but Jupyter makes viewing the outputs easier 😊)
sudo adduser ubuntu docker
sudo su - ubuntu
sudo add-apt-repository ppa:deadsnakes/ppa
Press enter to install the PPA.
sudo apt install -y socat python3.10-venv
python3.10 -m virtualenv venv
. venv/bin/activate
curl -sS https://bootstrap.pypa.io/get-pip.py | python3.10
( cd /usr/local ; curl -L https://dl.dagger.io/dagger/install.sh | sudo sh )
git clone https://github.com/lukemarsden/dagger-stable-diffusion-lora
cd dagger-stable-diffusion-lora
pip install -r requirements.txt
cp config.yml.sample config.yml
Now open dagger-stable-diffusion-lora/config.yml in the Jupyter editor and change anything you like.
Back in the first terminal, run:
export _EXPERIMENTAL_DAGGER_INTERACTIVE_TUI=1
dagger run python lora.py
Now go and have lunch while you pull docker images & train some LoRAs 😊
...
If you're curious to watch the progress, in another terminal tab, do sudo docker ps and sudo docker logs -f <container_id> for some of the running jobs.
Welcome back, check out output/inference to see the results!
Now uncomment some of the prompts and/or brands in the config.yml and re-run, note how the Dagger cache saves you from having to redo any work that it's already done!
If you like keeping your money!