https://github.com/justinpinkney/stable-diffusion
https://github.com/justinpinkney/stable-diffusion
Last synced: 6 months ago
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- Host: GitHub
- URL: https://github.com/justinpinkney/stable-diffusion
- Owner: justinpinkney
- License: mit
- Fork: true (pesser/stable-diffusion)
- Created: 2022-08-10T20:29:25.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2024-01-18T21:36:17.000Z (over 1 year ago)
- Last Synced: 2024-10-01T11:05:16.898Z (10 months ago)
- Language: Jupyter Notebook
- Size: 72.2 MB
- Stars: 1,444
- Watchers: 24
- Forks: 261
- Open Issues: 42
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Experiments with Stable Diffusion
This repository extends and adds to the [original training repo](https://github.com/pesser/stable-diffusion) for Stable Diffusion. Be careful using this repo, it's by personal Stable Diffusion playground and backwards compatibility breaking changes might happen anytime. So use at your own risk. I recommend sticking to a particular git commit if you are depending on some functionality.
Currently it adds:
- [Image Mixer](#image-mixer)
- [Super Resolution](#super-resolution)
- [Imagic](notebooks/imagic.ipynb)
- [Fine tuning](#fine-tuning)
- [Image variations](#image-variations)
- [Conversion to Huggingface Diffusers](scripts/convert_sd_to_diffusers.py)
## Image Mixer
The model is available on [huggingface spaces](https://huggingface.co/spaces/lambdalabs/image-mixer-demo) or to run locally do the following:
```bash
git clone https://github.com/justinpinkney/stable-diffusion.git
cd stable-diffusion
git checkout 1c8a598f312e54f614d1b9675db0e66382f7e23c
python -m venv .venv --prompt sd
. .venv/bin/activate
pip install -U pip
pip install -r requirements.txt
python scripts/gradio_image_mixer.py
```
Then navigate to the gradio demo link printed in the terminal.
For details on how to use the model outside the app refer to the [`run` function](https://github.com/justinpinkney/stable-diffusion/blob/c1963a36a4f8ce23784c8247fa1af0e34e02b766/scripts/gradio_image_mixer.py#L79) in `gradio_image_mixer.py`
## Super Resolution
I fine tuned a version of Stable Diffusion 1.4 for the task of super-resolution, you can find the [trained model on huggingface hub](https://huggingface.co/lambdalabs/stable-diffusion-super-res) and can run a gradio demo as follows:
```bash
git clone https://github.com/justinpinkney/stable-diffusion.git
cd stable-diffusion
python -m venv .venv --prompt sd
. .venv/bin/activate
pip install -U pip
pip install -r requirements.txt
python scripts/gradio_superres.py
```
Note since I trained this model there is now an 'official' super res model for Stable Diffusion 2 which you might prefer to use.
## Fine tuning
Makes it easy to fine tune Stable Diffusion on your own dataset. For example generating new Pokemon from text!
[](https://replicate.com/lambdal/text-to-pokemon)
[](https://colab.research.google.com/github/LambdaLabsML/lambda-diffusers/blob/main/notebooks/pokemon_demo.ipynb)
[](https://huggingface.co/spaces/lambdalabs/text-to-pokemon)
> Girl with a pearl earring, Cute Obama creature, Donald Trump, Boris Johnson, Totoro, Hello Kitty
For a step by step guide see the [Lambda Labs examples repo](https://github.com/LambdaLabsML/examples).
## Image variations
[](https://47725.gradio.app/)
[](https://colab.research.google.com/drive/1JqNbI_kDq_Gth2MIYdsphgNgyGIJxBgB?usp=sharing)
[](https://huggingface.co/spaces/lambdalabs/stable-diffusion-image-variations)
For more details on the Image Variation model see the [model card](https://huggingface.co/lambdalabs/stable-diffusion-image-conditioned).
- Get access to a Linux machine with a decent NVIDIA GPU (e.g. on [Lambda GPU Cloud](https://lambdalabs.com/service/gpu-cloud))
- Clone this repo
- Make sure PyTorch is installed and then install other requirements: `pip install -r requirements.txt`
- Get model from huggingface hub [lambdalabs/stable-diffusion-image-conditioned](https://huggingface.co/lambdalabs/stable-diffusion-image-conditioned/blob/main/sd-clip-vit-l14-img-embed_ema_only.ckpt)
- Put model in `models/ldm/stable-diffusion-v1/sd-clip-vit-l14-img-embed_ema_only.ckpt`
- Run `scripts/image_variations.py` or `scripts/gradio_variations.py`
All together:
```
git clone https://github.com/justinpinkney/stable-diffusion.git
cd stable-diffusion
mkdir -p models/ldm/stable-diffusion-v1
wget https://huggingface.co/lambdalabs/stable-diffusion-image-conditioned/resolve/main/sd-clip-vit-l14-img-embed_ema_only.ckpt -O models/ldm/stable-diffusion-v1/sd-clip-vit-l14-img-embed_ema_only.ckpt
pip install -r requirements.txt
python scripts/gradio_variations.py
```
Then you should see this:
[](https://twitter.com/Buntworthy/status/1565704770056294400)
Trained by [Justin Pinkney](https://www.justinpinkney.com) ([@Buntworthy](https://twitter.com/Buntworthy)) at [Lambda](https://lambdalabs.com/)