illuminatianon

Z‑Image Turbo is a bit different from “classic” Stable Diffusion, so a lot of old prompting habits don’t quite apply. I’ll walk through how to prompt it deeply and safely, with special focus on controlling content (nudity, stereotypes, unwanted artifacts) even though the model does not support traditional negative prompts at all. ([Hugging Face][1])

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1. How Z‑Image Turbo thinks (and why “negative” prompts don’t work)

Key facts that matter for prompting:

Geoyi

How to install virtualenv:

Install pip first

sudo apt-get install python3-pip

Then install virtualenv using pip3

sudo pip3 install virtualenv 

aneesh-joshi

import tensorflow as tf
import numpy as np

corpus_raw = 'He is the king . The king is royal . She is the royal  queen '

# convert to lower case
corpus_raw = corpus_raw.lower()

words = []
for word in corpus_raw.split():

nikitametha

This is a guide on how to install Caffe for Ubuntu 16.04 and above, without GPU support (No CUDA required).

Prerequisites:

OpenCV

sudo apt-get install libopencv-dev python-opencv

OpenBLAS OR Atlas

kylehounslow

from __future__ import print_function
import requests
import json
import cv2

addr = 'http://localhost:5000'
test_url = addr + '/api/test'

# prepare headers for http request
content_type = 'image/jpeg'

shagunsodhani

GloVe: Global Vectors for Word Representation

Introduction

• Introduces a new global log-bilinear regression model which combines the benefits of both global matrix factorization and local context window methods.

Global Matrix Factorization Methods

• Decompose large matrices into low-rank approximations.

tomokishii

#
#  mnist_cnn_bn.py   date. 5/21/2016
#                    date. 6/2/2017 check TF 1.1 compatibility
#

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os

kukuruza

from math import sqrt

def put_kernels_on_grid (kernel, pad = 1):

  '''Visualize conv. filters as an image (mostly for the 1st layer).
  Arranges filters into a grid, with some paddings between adjacent filters.

  Args:
    kernel:            tensor of shape [Y, X, NumChannels, NumKernels]
    pad:               number of black pixels around each filter (between them)

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)

grenade

#!/bin/bash

mkdir -p ~/.ssh

# generate new personal ed25519 ssh keys
ssh-keygen -o -a 100 -t ed25519 -f ~/.ssh/id_ed25519 -C "rob thijssen <rthijssen@gmail.com>"
ssh-keygen -o -a 100 -t ed25519 -f ~/.ssh/id_robtn -C "rob thijssen <rob@rob.tn>"

# generate new host cert authority (host_ca) ed25519 ssh key
# used for signing host keys and creating host certs