nateraw

"""
Built on top of this gist by @karpathy:
https://gist.github.com/karpathy/00103b0037c5aaea32fe1da1af553355

stable diffusion dreaming over text prompts
creates hypnotic moving videos by smoothly walking randomly through the sample space

example way to run this script:
$ python stable_diffusion_walk.py --prompts "['blueberry spaghetti', 'strawberry spaghetti']" --seeds 243,523 --name berry_good_spaghetti

osanseviero

/*
 * Converts table from [[Header0, Header1, Header2], [Column0Val0, Column1Val0, Column2Val0], ...]
 * to {Header0: [ColumnVal0, ...], Header1: [Column1Val0, ...], Header2: [Column2Val0, ...]}
 */
function convertTableToData(table) {
    transposed = table[0].map((_, colIndex) => table.map(row => row[colIndex]));
    result = {}
    for(var i = 0; i < transposed.length; i++) {
      header = transposed[i][0]
      result[header] = transposed[i].slice(1, 4).map(String);

awni

"""
Author: Awni Hannun

This is an example CTC decoder written in Python. The code is
intended to be a simple example and is not designed to be
especially efficient.

The algorithm is a prefix beam search for a model trained
with the CTC loss function.

yrevar

{0: 'tench, Tinca tinca',
 1: 'goldfish, Carassius auratus',
 2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias',
 3: 'tiger shark, Galeocerdo cuvieri',
 4: 'hammerhead, hammerhead shark',
 5: 'electric ray, crampfish, numbfish, torpedo',
 6: 'stingray',
 7: 'cock',
 8: 'hen',
 9: 'ostrich, Struthio camelus',