Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.
![Screenshot 2023-12-18 at 10 40 27 PM](https://private-user-images.githubusercontent.com/3837836/291468646-4c30ad72-76ee-4939-a5fb-16b570d38cf2.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.kE9GjfJ6DphMSccjJvBMdReSDaYdmqM6oWy12c19P6E)
""" | |
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 |
import logging | |
import cv2 | |
import PySpin | |
logger = logging.getLogger(__name__) | |
class Camera(object): | |
def __init__(self, cam): |
# Automated AMI Backups | |
# | |
# @author Bobby Kozora | |
# | |
# This script will search for all instances having a tag with the name "backup" | |
# and value "Backup" on it. As soon as we have the instances list, we loop | |
# through each instance | |
# and create an AMI of it. Also, it will look for a "Retention" tag key which | |
# will be used as a retention policy number in days. If there is no tag with | |
# that name, it will use a 7 days default value for each AMI. |
""" | |
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) |