madebyollin

List of good image generator training logs

A list of public training logs from neural network image generation models, since I think they're interesting.

The Criteria

• Publicly accessible link
• Losses plotted every so often
• Samples generated every so often
• Nontrivial dataset (i.e. not MNIST - 64x64 output RGB or better)

madebyollin

# ------------------------------------------------------------------
# EDIT: I eventually found a faster way to run SD on macOS, via MPSGraph (~0.8s / step on M1 Pro):
#   https://github.com/madebyollin/maple-diffusion
# The original CoreML-related code & discussion is preserved below :)
# ------------------------------------------------------------------

# you too can run stable diffusion on the apple silicon GPU (no ANE sadly)
#
# quick test portraits (each took 50 steps x 2s / step ~= 100s on my M1 Pro):
# * https://i.imgur.com/5ywISvm.png

madebyollin

function addCompressor(el) {
    var AudioContext = window.AudioContext || window.webkitAudioContext;
    var context = new AudioContext();
    var source = context.createMediaElementSource(el);
    var compressor = context.createDynamicsCompressor();
    var makeupGain = context.createGain();
    
    // Set compressor params here
    compressor.threshold.value = -40;
    compressor.knee.value = 40;

madebyollin

#!/usr/bin/env python
filename = "output.txt"
with open(filename, 'w') as fout:
    x = 0
    for x in range(5):
        fout.write(""" def function_number{}(): \n
""".format(x))

madebyollin

#!/usr/bin/env python
import numpy as np
import cv2
from scipy.signal import convolve2d
from skimage import color, data, restoration
import console

# read input
frame = cv2.imread("input.jpg").astype(np.float32) / 255.0

madebyollin

#!/usr/bin/env python3
"""
To use:
1. install/set-up the google cloud api and dependencies listed on https://github.com/GoogleCloudPlatform/python-docs-samples/tree/master/texttospeech/cloud-client
2. install pandoc and pypandoc, also tqdm
3. create and download a service_account.json ("Service account key") from https://console.cloud.google.com/apis/credentials
4. run GOOGLE_APPLICATION_CREDENTIALS=service_account.json python make_audiobook.py book_name.epub
"""
import re
import sys