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---
license: openrail++
tags:
- stable-diffusion
- text-to-image
widget:
- text: Cartoon, ocean.
example_title: Ocean (Cartoon)
---
# Clean Diffusion 2.0 PoC Model Card
> That's one small step for artists, one giant leap for engineers.
Clean Diffusion 2.0 PoC is Latent Diffusion Model made of public domain images.
Clean Diffusion 2.0 PoC is for the proof of the concept: Stable Diffusion can be made of public domain images.
Therefore, the model can only express the ocean.
If you are Japanese, I recommend Clean Diffusion For Japanese (TBA) instead of Clean Diffusion (For Global).
The model is more powerful than this global version.
# Note
> With great power comes great responsibility.
If you **CANNOT UNDERSTAND THESE WORDS**, I recommend that **YOU SHOULD NOT USE ALL OF DIFFUSION MODELS** what have great powers.
# Getting Started
You would be able to use Clean Diffusion by the following code soon.
```python
from diffusers import StableDiffusionPipeline
import torch
model_id = "alfredplpl/clean-diffusion-2-0-poc"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float32)
pipe = pipe.to("cuda")
prompt = "Cartoon, ocean."
image = pipe(prompt).images[0]
image.save("ocean.png")
```
# Tuning
Clean Diffusion is less powerful than Stable Diffusion.
Therefore, I recommend to tune Clean Diffusion like Stable Diffusion
because Clean Diffusion of the network architecture and Stable Diffusion of the network architecture are same.
And I repeat the words before I explain the detail.
> With great power comes great responsibility.
Please consider the words before you tune Clean Diffusion.
## Textual Inversion
TBA on Colab.
## Dreambooth on Stable Diffusion
TBA on Colab.
## Pure fine-tuning
TBA
# Transparency of Clean Diffusion
I proof that clean diffusion is clean by following explanation.
## Legal information
TBA
### Training
Clean Diffusion is legal and ethical.
Clean Diffusion is MADE IN JAPAN.
Therefore, Clean Diffusion is subject to [Japanese copyright laws](https://en.wikipedia.org/wiki/Copyright_law_of_Japan).
TBA
### Generating
TBA
## Training Images
TBA
### List of works
- [ArtBench](https://github.com/liaopeiyuan/artbench) (public domain is True)
- Popeye the Sailor Meets Sindbad the Sailor
### Tiny training images
I would like to the all training raw images because these images are public domain.
However, these images are huge (70GB+).
Therefore, I have opened the tiny version like this.
[Tiny Images](https://1drv.ms/u/s!ApxVlgxlqLRliLpSC58y5qyAlt52tQ?e=3Yfwbt)
### Training Process of VAE
TBA
## Training text-image pairs
TBA
## Trainning code and config
TBA
# Acknowledgement
> Standing on the shoulders of giants
# Citations
```bibtex
@misc{rombach2021highresolution,
title={High-Resolution Image Synthesis with Latent Diffusion Models},
author={Robin Rombach and Andreas Blattmann and Dominik Lorenz and Patrick Esser and Björn Ommer},
year={2021},
eprint={2112.10752},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
```bibtex
@article{liao2022artbench,
title={The ArtBench Dataset: Benchmarking Generative Models with Artworks},
author={Liao, Peiyuan and Li, Xiuyu and Liu, Xihui and Keutzer, Kurt},
journal={arXiv preprint arXiv:2206.11404},
year={2022}
}
```
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