What is this?
This site displays computer-generated pixel art sprites. A machine learning model generated sprites resembling those from popular video games.
I am in no way affiliated, associated, endorsed, sponsored, or approved by The Pokemon Company, Niantic, Square Enix, or any other companies associated with the sprites on this site. This is a non-profit parody site for entertainment purposes only.
I claim no legal ownership or rights to any of the images generated by this model. However, all silhouettes and characters depicted are property of their respective owners.
What does ‘paramon’ mean?
The name is derived from the word pareidolia, which is our tendency to ascribe meaningful interpretations to visual stimuli, even when there is none.
The sprites generated on this site are very fuzzy and noisy, which allows a lot of degrees of freedom in interpreting them. This is similar to how cloud watching and Rochscach tests work.
So while the sprites are not as sleek as real pixel art sprites, they are still fun for visual interpretation.
Why is AI so bad at generating pixel art?
Fun question! Per Gwern:
“pixel art is by design an ultra-impoverished representation of the real world, which is parasitic on your photographic understanding of objects. it contains only enough detail to trigger associations. this makes it exceptionally hard for a from-scratch mind to learn”
Pixel art is a low-dimensional, artistic representation of an underlying concept. Your mind automatically tries to interpret this underlying concept.
This is also what causes misread sprites: the same pixel sprite can have multiple valid-looking interpretations, depending on how you read it and what your expectations are.
Can I use this model/these images for commercial purposes (NFTs, video games, etc.)?
I personally won’t stop you, but you should be aware that copyright laws exist. I’m not sure what counts as “transformative use” with respect to the original games and sprites. All risks are your own.
Also, I think there are enough low-effort NFTs in the world already.
Model: Denoising Diffusion by the wonderful Lucidrains
- 96×96 images
- 96 channels
- Added class embeddings for conditional generation
- Large custom Pokémon dataset found here
- Model trained for only 7 days on an RTX 3090 GPU
- I might train it longer soon, but I felt like overfitting started creeping in.
- Lucidrains for his open source DL implementations
- Nshepperd for starting this project
- Nearcyan for allowing me to use his javascipt from TADNE
- RiversHaveWings for original Guided diffusion – CLIP code