karpathy

"""
stable diffusion dreaming
creates hypnotic moving videos by smoothly walking randomly through the sample space

example way to run this script:

$ python stablediffusionwalk.py --prompt "blueberry spaghetti" --name blueberry

to stitch together the images, e.g.:
$ ffmpeg -r 10 -f image2 -s 512x512 -i blueberry/frame%06d.jpg -vcodec libx264 -crf 10 -pix_fmt yuv420p blueberry.mp4

rhee

## gen() function for proc.stdout ( run_commands, vdl_run )
## async read from proc.stdout
if True:
    def gen(proc):
        remains = b''  # remaining from prev read
        while proc.poll() is None:
            rlist, _, _ = select([proc.stdout],[],[],500)
            if proc.stdout not in rlist:
                continue
            data = os.read(proc.stdout.fileno(), 16384)

rhee

"""
# https://gist.github.com/rhee/c038393822eabec681092b6941ef1f1e
# fake_ipython.py

Usage:
    from fake_ipython import get_ipython 
"""
from inspect import stack

def get_ipython():

rhee

#!/usr/bin/env python
# coding: utf-8
# // update: 2022-04-15 - single-cell python version
# // gist: https://gist.github.com/rhee/f42aa18c8e3dcb7b6c03c23e750124f7
with open('__nb-convert__.bash', 'w') as fout:
    fout.write('''#!/bin/bash
try_nbconvert() {
    local prefix="$1"; shift;
    if test ! -z "$prefix"
    then

rhee

## monitor GPU usage in jupyter notebook -- rhee.elten@gmail.com
## migrated to: https://gist.github.com/rhee-elten/1a1070e3a812ca863c3b937b5180b2f8

rhee

# -*- coding: utf-8 -*-
# ---
# jupyter:
#   jupytext:
#     text_representation:
#       extension: .py
#       format_name: percent
#       format_version: '1.3'
#       jupytext_version: 1.3.0+dev
#   kernelspec:

rhee

# coding: utf-8
from traceback import print_exc
import os
import stat
import sys
from struct import pack, unpack, pack_into, unpack_from
import mmap
import ctypes

rhee

:
export TF_FORCE_GPU_ALLOW_GROWTH=true

ByungSunBae

# from : https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/graph_editor/examples/edit_graph_example.py
import numpy as np

import tensorflow as tf
from tensorflow.contrib import graph_editor as ge

# create a graph
g = tf.Graph()

with g.as_default():

santisbon

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