SMUsamaShah

These are some of the Stable Diffusion references people have made listing hundreds of artist styles.

Flux

1. https://cheatsheet.strea.ly/

Stable Diffusion

1. https://supagruen.github.io/StableDiffusion-CheatSheet/
2. https://rentry.org/artists_sd-v1-4 backed up as single page on http://xosh.org/sd_1.4_list_of_artists/

salanki

# On how to build the pixelstreaming images yourself: https://adamrehn.com/articles/pixel-streaming-in-linux-containers/
# and https://github.com/adamrehn/ue4-example-dockerfiles/tree/master/pixel-streaming. All images used in this YAML are public.
# Developed for Bare Metal Managed Kubernetes GPUs from http://www.coreweave.com
# Will work locally without any additional components if POD CIDRs are reachable from the clients computer. To work from the Internet, a STUN/TURN server needs to be setup. Hit me up for more info on that. 

apiVersion: serving.knative.dev/v1
kind: Service
metadata:
  name: ue4-temple
spec:

lukeplausin

import logging
import time
import yaml
from jaeger_client import Config
import os
import datetime

import logging
from logging import StreamHandler

pekkavaa

import numpy as np
import matplotlib.pyplot as plt
from numpy import array
import torch
from torch import Tensor

"""
A dumb triangle rasterizer with PyTorch.
It evaluates the barycentrics for all image pixels for each triangle
and then picks the "colors" (just barycentrics again) for each pixel

htruong

#!/bin/bash

# This script allows you to chroot ("work on") 
# the raspbian sd card as if it's the raspberry pi
# on your Ubuntu desktop/laptop
# just much faster and more convenient

# credits: https://gist.github.com/jkullick/9b02c2061fbdf4a6c4e8a78f1312a689

# make sure you have issued

oseiskar

#!/usr/bin/python
#
#  Copyright 2017 Otto Seiskari
#  Licensed under the Apache License, Version 2.0.
#  See http://www.apache.org/licenses/LICENSE-2.0 for the full text.
#
#  This file is based on
#  https://github.com/swagger-api/swagger-ui/blob/4f1772f6544699bc748299bd65f7ae2112777abc/dist/index.html
#  (Copyright 2017 SmartBear Software, Licensed under Apache 2.0)
#

sausax

# This specifies the deployment process on AWS ElasticBeanstalk for a Django app using npm and Postgres.
#
# The target environment should have access to a Postgres Database through environment variables.
# The environment can be setup using `eb create --database.engine=postgres`
# The necessary environment variables to access the database will be automatically defined on the
# instances of that environment.
#
# In addition, the target environment should define environment variables (django secret key ...).
# They can be manually defined using the AWS ElasticBeanstalk interface on the web.
# They can also be specified when creating the environment from command line, for example: 

jarutis

sudo su

# Java
yum -y install java-1.8.0-openjdk-devel

# Build Esentials (minimal)
yum -y install gcc gcc-c++ kernel-devel make automake autoconf swig git unzip libtool binutils

# Extra Packages for Enterprise Linux (EPEL) (for pip, zeromq3)
yum -y install epel-release

charanpald

# Run some recommendation experiments using MovieLens 100K
import pandas
import numpy
import scipy.sparse
import scipy.sparse.linalg
import matplotlib.pyplot as plt
from sklearn.metrics import mean_absolute_error

data_dir = "data/ml-100k/"
data_shape = (943, 1682)

GilLevi

Gil Levi and Tal Hassner, Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns

Convolutional neural networks for emotion classification from facial images as described in the following work:

Gil Levi and Tal Hassner, Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns, Proc. ACM International Conference on Multimodal Interaction (ICMI), Seattle, Nov. 2015

Project page: http://www.openu.ac.il/home/hassner/projects/cnn_emotions/

If you find our models useful, please add suitable reference to our paper in your work.