MilesQLi

Install Disco Diffusion v5 for Windows

NOTE: Pytorch3d no longer has to be compiled i have stripped out the function we use to make this a lot easier and also so we do not have to use WSL2 with linux and can now run directly on your windows system.

Comments section is not checked often for issues please join the disco diffusion discord for assistance

https://discord.gg/mK4AneuycS

You may now use the official disco diffusion notebook with this tutorial as it has been uodated to reflect the changes here for better cross platform support

MilesQLi

import yara

#Path to the folder containing downloaded files in the first part
rules_path = 'path/to/the/folder/containing/downloaded/rules'

#Read files
peid_rules = yara.compile(rules_path + 'peid.yar')
packer_rules = yara.compile(rules_path + 'packer.yar')
crypto_rules = yara.compile(rules_path + 'crypto.yar')

MilesQLi

#first, let's define the list of packers/cryptors we want to detect
packers = ['AHTeam', 'Armadillo', 'Stelth', 'yodas', 'ASProtect', 'ACProtect', 'PEnguinCrypt', 
 'UPX', 'Safeguard', 'VMProtect', 'Vprotect', 'WinLicense', 'Themida', 'WinZip', 'WWPACK',
 'Y0da', 'Pepack', 'Upack', 'TSULoader'
 'SVKP', 'Simple', 'StarForce', 'SeauSFX', 'RPCrypt', 'Ramnit', 
 'RLPack', 'ProCrypt', 'Petite', 'PEShield', 'Perplex',
 'PELock', 'PECompact', 'PEBundle', 'RLPack', 'NsPack', 'Neolite', 
 'Mpress', 'MEW', 'MaskPE', 'ImpRec', 'kkrunchy', 'Gentee', 'FSG', 'Epack', 
 'DAStub', 'Crunch', 'CCG', 'Boomerang', 'ASPAck', 'Obsidium','Ciphator',
 'Phoenix', 'Thoreador', 'QinYingShieldLicense', 'Stones', 'CrypKey', 'VPacker',

MilesQLi

#don't forget this
import pefile

#first, let's get the list of sections names used by packers/cryptors
packers_sections = {
        #The packer/protector/tools section names/keywords
        '.aspack': 'Aspack packer',
        '.adata': 'Aspack packer/Armadillo packer',
        'ASPack': 'Aspack packer',
        '.ASPack': 'ASPAck Protector',

MilesQLi

The American Inferno

Chapter II

In the end, what has a man but his thoughts? I would postulate further, what has a man to stand for, but his thoughts? His actions, perhaps? I know precious little of actions. Lions, donkeys, hyenas. They all act. So is that what we are? No. We are more and less than the beasts. We are thoughts. We are actions and the reflections upon those actions. Yet, we are also not merely reflections. We are not mirrors.

That is the preserve of spirits, of the gods. We are actions and the thoughts upon actions. Neither one nor other. We are free neither from action, nor from thought. Our humanity can only be understood if we embrace both the animal and the god within us. As humans, we must nourish both, yet America is a land of action. A place fixated not on ideas, not on the redemptive power of thought but on the obliteration of the intellect.

It is a place wherein mankind has attempted to deny half of his being, and in pursuing freedom has attempted to split himself. Much like the

MilesQLi

"""
A series of speed tests on pytorch LSTMs.
- LSTM is fastest (no surprise)
- When you have to go timestep-by-timestep, LSTMCell is faster than LSTM
- Iterating using chunks is slightly faster than __iter__ or indexing depending on setup

**Results**

My Ubuntu server:
    OS: posix, pytorch version: 0.4.0a0+67bbf58

MilesQLi

#!/usr/bin/env python3
"""
To run: python3 nb2to3.py notebook-or-directory
"""
# Authors: Thomas Kluyver, Fernando Perez
# See: https://gist.github.com/takluyver/c8839593c615bb2f6e80

import argparse
import pathlib
from nbformat import read, write

MilesQLi

var totalForkCount;
var curForkCount=0;
var errorForkArray = new Array();

function initProcess(){
	var user = window.location.pathname.split('/')[1];
	var pageNo = 1;
	var hrefArray = new Array();
	while(hrefArray.length % 30 == 0){
		var resp = sendSyncAjax("https://github.com/"+user+"?tab=repositories&page="+pageNo);

MilesQLi

import theano
from theano import tensor as T
from theano.tensor.nnet import conv
from skimage import io
import numpy
import pylab

rng = numpy.random.RandomState(23455)

# instantiate 4D tensor for input