🌸 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 😊)

🐋 New terminal, add user to docker group

sudo adduser ubuntu docker

sudo su - ubuntu

🐍 Install newer Python

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

🚀 Install Dagger CLI

( cd /usr/local ; curl -L https://dl.dagger.io/dagger/install.sh | sudo sh )

⚙️ Check out repo and configure it

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.

🚂 Train some LoRAs!

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!

🏃 Observe the dagger cache making things faster

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!

💸 Remember to shut down your GPU

If you like keeping your money!