arenasys

import numpy as np
import cv2

def NormalComposition(srcRGBA, dstRGBA):
    srcA = np.dstack((srcRGBA[:,:,3]/255,)*3)
    srcRGB = srcRGBA[:,:,:3]
    dstA = np.dstack((dstRGBA[:,:,3]/255,)*3)
    dstRGB = dstRGBA[:,:,:3]

    outA = srcA + dstA*(1-srcA)

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import lark

class WeightedTree(lark.Tree):
    pass

prompt_grammar = lark.Lark(r"""
!start: (prompt | /[][():]/+)*
prompt: (emphasis | deemphasis | numeric | scheduled | alternate | plain | WHITESPACE)*
emphasis: "(" prompt ")"
deemphasis: "[" prompt "]"

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import torch

allowed_keys = [
"encoder",
"decoder",
"quant_conv",
"post_quant_conv"]

def extract_vae(model, half=False):
    out = {}

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#!/usr/bin/python

import torch
import safetensors
import safetensors.torch
import os

MODEL_KEYS = [
    "model.",
    "first_stage_model",

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import argparse, os, sys, glob
import urllib.request
import statistics

# put this script in stable-diffusion-webui and run it: python squarize.py
#   the model will be downloaded and the squarize folder will be created.

# all images in squarize/input get processed and inpainted, ending up in squarize/output

# *stable-diffusion-webui needs to be working

arenasys

#!/usr/bin/python
import os
import re
import json
import random
from PIL import Image, ImageDraw, ImageOps
from multiprocessing import Pool
import subprocess
import sys
import urllib.request