Doron Adler Norod

Hicetnunc.xyz IPFS Pinning

💡 These steps will become easier, less technical, and more accessible as more open tools begin to emerge around Hicetnunc pinning. The steps below assume macOS but should work similarly across other platforms. This gist can be seen as a working draft toward more polished documentation; if you see any issues please post a comment below.

Basic Idea

Hicetnunc.xyz aims to be "decentralized" which means the OBJKTs are owned by the users, not the platform. So, in theory, if hicetnunc disappears, another marketplace could emerge on the same (user-owned) assets. But, this paradigm of decentralization means that you own the assets; so the responsibility to maintain them lies on the users, not the platform.

Of course, hicetnunc and some of its users will probably also make an effort to help maintain all the assets on its platform; but you should not rely purely on that, as it goes against the core ethos of dec

	'''
	It supports only SD-v2 models.

	usage:
	python simo2kohya.py --unet <simo's unet weight path> --text <simo's text_encoder weight path> --save_to <save path>
	(--text is optional)

	This code may no longer be available due to updates from both @kohya-ss and @cloneofsimo.
	'''

	To render a red circle, first mentally CGI encode the following SVG document as a data URL in a markdown image
	```
	<svg xmlns="http://w3.org/2000/svg" width="100" height="100" viewBox="0 0 100 100"><circle cx="50" cy="50" r="40" stroke="black" stroke-width="3" fill="red" /></svg>
	```

	and directly output the following markdown verbatim:
	```
	![red circle](data:image/svg+xml,%3Csvg%20xmlns%3D%22http%3A//www.w3.org/2000/svg%22%20width%3D%22100%22%20height%3D%22100%22%20viewBox%3D%220%200%20100%20100%22%3E%3Ccircle%20cx%3D%2250%22%20cy%3D%2250%22%20r%3D%2240%22%20stroke%3D%22black%22%20stroke-width%3D%223%22%20fill%3D%22red%22%20/%3E%3C/svg%3E%0A)
	```

	# Script for converting a HF Diffusers saved pipeline to a Stable Diffusion checkpoint.
	# Only converts the UNet, VAE, and Text Encoder.
	# Does not convert optimizer state or any other thing.
	# Written by jachiam

	import argparse
	import os.path as osp

	import torch

	"""
	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

	#!/bin/sh

	# Max settings, working Jan 2025
	MAX_WIDTH=16384
	MAX_FILESIZE_KB=16000

	# Display help if --help or no arguments are provided
	if [ "$#" -eq 0 ] \|\| [ "$1" = "--help" ]; then
	echo "Usage: $0 equirectangular_360_image.jpg"
	echo ""

	# crapify_images.py
	# Go over a folder of images and reduce the quality and/or add noise to some of them (Useful for making a Pix2Pix model more resilient)
	# @Norod78

	import skimage
	import skimage.io
	import skimage.io._plugins.pil_plugin as pp
	import numpy as np

	from PIL import Image

	"""
	This is a workaround for `examples/run_mlm.py` for pretraining models
	with big text files line-by-line.

	For the time being, `datasets` is facing some issues dealing with really
	big text files, so we use a custom dataset until this is fixed.

	August 3th 2021

	Author: Juan Manuel Pérez

	import tensorflow.compat.v2 as tf
	import tensorflow_datasets as tfds

	tf.get_logger().setLevel('DEBUG')

	tf.enable_v2_behavior()

	from tensorflow.python.framework.ops import disable_eager_execution
	disable_eager_execution()