https://github.com/xrsrke/stable-diffusion-from-scratch
Implementation of Stable Diffusion from scratch [WORK IN PROGRESS]
https://github.com/xrsrke/stable-diffusion-from-scratch
deep-learning machine-learning stable-diffusion
Last synced: 6 months ago
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Implementation of Stable Diffusion from scratch [WORK IN PROGRESS]
- Host: GitHub
- URL: https://github.com/xrsrke/stable-diffusion-from-scratch
- Owner: xrsrke
- License: mit
- Created: 2022-11-24T23:59:16.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-02-18T08:07:06.000Z (over 2 years ago)
- Last Synced: 2023-03-02T04:20:23.605Z (about 2 years ago)
- Topics: deep-learning, machine-learning, stable-diffusion
- Language: Jupyter Notebook
- Homepage: https://xrsrke.github.io/stable-diffusion-from-scratch/
- Size: 30 MB
- Stars: 10
- Watchers: 3
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
Implementation of Stable Diffusion
================
------------------------------------------------------------------------
This repository contains Stable Diffusion’s implementation in PyTorch.
## Updates
This isn't actually an implementation of stable diffusion from scratch. I cancelled this project and started working on [RLHF](https://github.com/xrsrke/instructGOOSE)
## Install
``` sh
pip install -r requirements.txt
pip install -e .
```
## Usage
``` python
from PIL import Image
from foundation.stable_diffusion import StableDiffusion
prompt = "holy young female battle robot flying award winning, portrait bust symmetry faded tetrachromacycolors arctic background tim hildebrandt wayne barlowe bruce pennington donato giancola larry elmore masterpiece trending on artstation cinematic composition beautiful lighting hyper detailed!!! 8 k oil on canva"
model = StableDiffusion(n_inference_steps=30)
images = model.generate(prompt)
pil_images = [Image.fromarray(image) for image in images]
pil_images[0]
```
## Paper Reviews
### CLIP - Multimodal Neurons
### VAE
Variational Autoencoder (VAE) model with KL loss from the paper
Auto-Encoding Variational Bayes by Diederik P. Kingma and Max Wellin
### UNET
### Diffusion Process
## Contributing
This repository is still a work in progress.
Currently, no downloads
and no executables are provided.
I welcome many contributors who can help.
## License
Licensed under the MIT license.