madebyollin

Notes / Links about Stable Diffusion VAE

Stable Diffusion's VAE is a neural network that encodes images into a compressed "latent" format and decodes them back. The encoder performs 48x lossy compression, and the decoder generates new detail to fill in the gaps.

(Calling this model a "VAE" is sort of a misnomer - it's an encoder with some very slight KL regularization, and a conditional GAN decoder)

This document is a big pile of various links with more info.

paniq

# forays into
    Perfect Spatial Hashing (Lefebvre & Hoppe)
    http://hhoppe.com/perfecthash.pdf

    how it works:

    There are two parts: a slow encoding step, and a fast decoding step.

    Encoding

ditzel

using System;
using System.Collections;
using System.Collections.Generic;
using UnityEngine;
using UnityEngine.Profiling;

public class KdTree<T> : IEnumerable<T>, IEnumerable where T : Component
{
    protected KdNode _root;
    protected KdNode _last;

raulqf

How to install OpenCV 3.4.1 with CUDA on Ubuntu distro

First of all install update and upgrade your system:

    $ sudo apt-get update
    $ sudo apt-get upgrade

Then, install required libraries:

shagunsodhani

Conditional Generative Adversarial Nets

Introduction

• Conditional version of Generative Adversarial Nets (GAN) where both generator and discriminator are conditioned on some data y (class label or data from some other modality).
• Link to the paper

Architecture

• Feed y into both the generator and discriminator as additional input layers such that y and input are combined in a joint hidden representation.

sklam

# GL interoperability example, by Peter Berrington.
# Draws a rotating teapot, using cuda to invert the RGB value
# each frame

from OpenGL.GL import *
from OpenGL.GLUT import *
from OpenGL.GLU import *
from OpenGL.GL.ARB.vertex_buffer_object import *
from OpenGL.GL.ARB.pixel_buffer_object import *

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)