nateraw

"""
Built on top of this gist by @karpathy:
https://gist.github.com/karpathy/00103b0037c5aaea32fe1da1af553355

stable diffusion dreaming over text prompts
creates hypnotic moving videos by smoothly walking randomly through the sample space

example way to run this script:
$ python stable_diffusion_walk.py --prompts "['blueberry spaghetti', 'strawberry spaghetti']" --seeds 243,523 --name berry_good_spaghetti

quasiben

0x00b1

quasiben

Zekfad

Quick examples

• feat: new feature
• fix(scope): bug in scope
• feat!: breaking change / feat(scope)!: rework API
• chore(deps): update dependencies

Commit types

• build: Changes that affect the build system or external dependencies (example scopes: gulp, broccoli, npm)
• ci: Changes to CI configuration files and scripts (example scopes: Travis, Circle, BrowserStack, SauceLabs)
• chore: Changes which doesn't change source code or tests e.g. changes to the build process, auxiliary tools, libraries

bioimage-analysis

from qtpy.QtWidgets import (
    QPushButton,
    QComboBox,
    QTabWidget,
    QHBoxLayout,
    QFileDialog,
    QDialogButtonBox,
    QWidget,
    QSlider,
)

Bomberus

import sys, asyncio, random, threading, datetime

from qtpy.QtWidgets import QApplication, QProgressBar
from asyncqt import QEventLoop, QThreadExecutor

app = QApplication(sys.argv)
loop = QEventLoop(app)
asyncio.set_event_loop(loop)

progress = QProgressBar()

NicoKiaru

/**
 *  GROOVY SCRIPT COMPUTING ON THE FLY EDGES OF A LABEL IMAGE
 *  -----
 * 	 - Computes a sample label image
 *   - Computes lazily with a disk cached cell img an image which finds the border of the labels in 3D
 *   - Displays both images in BigDataViewer
 *
 *  Code to create the label image mainly copied from:
 *  - https://imagej.net/ImgLib2_Examples#Example_8_-_Working_with_sparse_data
 *  - And https://github.com/imglib/imglib2-cache-examples

ksugar

# code picked up from https://github.com/keras-team/keras/blob/master/keras/backend/tensorflow_backend.py
# Just used tf.nn.weighted_cross_entropy_with_logits instead of tf.nn.sigmoid_cross_entropy_with_logits with input pos_weight in calculation
import tensorflow as tf
from keras import backend as K


""" Weighted binary crossentropy between an output tensor and a target tensor.
# Arguments
    pos_weight: A coefficient to use on the positive examples.
# Returns

giswqs

#!/bin/bash

# change the package name to the existing PyPi package you would like to build and adjust the Python versions
pkg='whitebox'
array=( 3.5 3.6 3.7 )

echo "Building conda package ..."
cd ~
conda skeleton pypi $pkg
cd $pkg