akashsara/fusion-dance
Pixel VQ-VAEs for Improved Pixel Art Representation
This project helps artists, game developers, or researchers who work with pixel art to create, analyze, or process it more effectively using machine learning. It takes in collections of pixel art images and learns their underlying patterns to produce improved digital representations. The primary users are those who need specialized tools for generating or modifying pixel art with AI.
No commits in the last 6 months.
Use this if you need to generate new pixel art, analyze existing pixel art, or improve the quality of pixel art assets using advanced AI techniques.
Not ideal if you primarily work with realistic images, photographs, or non-pixelated art styles, as it's specifically optimized for the unique characteristics of pixel art.
Stars
17
Forks
2
Language
Jupyter Notebook
License
—
Last pushed
Feb 11, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/akashsara/fusion-dance"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
Naresh1318/Adversarial_Autoencoder
A wizard's guide to Adversarial Autoencoders
mseitzer/pytorch-fid
Compute FID scores with PyTorch.
acids-ircam/RAVE
Official implementation of the RAVE model: a Realtime Audio Variational autoEncoder
ratschlab/aestetik
AESTETIK: Convolutional autoencoder for learning spot representations from spatial...
jaanli/variational-autoencoder
Variational autoencoder implemented in tensorflow and pytorch (including inverse autoregressive flow)