DraconicDragon

import argparse
import gzip
import logging
from pathlib import Path

import piexif
from PIL import Image

# ─── Configuration ─────────────────────────────────────────────────────────────
EXTRACT_STEALTH_METADATA = False  # Extract stealth metadata from embedded bits

DraconicDragon

# Before running the script, put it in the directory you want it to convert PNGs into WebPs
# Then run it, it will go through every image in the same directory the script is in (no single image mode sorry, too lazy)
# and save it in a subdirectory. It will skip any existing WebPs so if you want to replace the existing webps with new ones
# like with stealth enabled this time, if you had it disabled before, then youll have to delete the existing webps.

# There are some boolean values you can change as well as some other stuff in the "Configuration" part
# like a save as animated webp option which is useful for sharing the webp with a1111 style metadata in usercomment over discord
# because discord won't remove the metadata if it detects the webp as animated webp

# What this script doesn't do:

DraconicDragon

from transformers import CLIPTokenizer

# Load the tokenizer from the local directory
tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")

# Your input text
text = input("Input text:\n")

# Tokenize the text
tokens = tokenizer(text, return_tensors="pt")

DraconicDragon

# Define path
$downloadsPath = "$env:USERPROFILE\Downloads"
$user = "$env:USERNAME"
$identity = "$env:USERDOMAIN\$user"

# Get current ACL
$acl = Get-Acl $downloadsPath

# Remove all existing rules to start clean
$acl.Access | ForEach-Object { $acl.RemoveAccessRule($_) }

DraconicDragon

import requests
import torch
from PIL import Image
from transformers import AutoModelForCausalLM, AutoProcessor

device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32

model_id = r"microsoft/Florence-2-base" # can also use path/to/huggingface/hub/florence2-repo-name/snapshots/hash or something like that
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch_dtype, trust_remote_code=True).to(device)

DraconicDragon

import sys
import re
class GreenOutput:
    # regex pattern to match URLs, IP and ports
    url_pattern = re.compile(
        r'((http|https|ftp|ftps):\/\/'  # protocol
        r'(\d{1,3}\.){3}\d{1,3}'        # ip
        r'(?::\d+)?'                    # port number if existing
        r'(?:[\/\w.,@?^=%&:/~+#-]*)?'   # rest of URL
        r'|'                            # OR

DraconicDragon

import os
import tkinter as tk
from tkinter import filedialog

import piexif
import piexif.helper
from PIL import Image


class PNGtoJPGConverterApp:

DraconicDragon

:root {
    --sidebar-w-collapsed: 36px; /*used for  collapsable sidebar*/
    --sidebar-h-extended: 240px;
    --memberlist-collapsed: 62px;
    --memberlist-expanded: 240px;
    --memberlist-transition: 0.35s ease-in-out;
}

/*vc text is correct with channel sidebar and main chat margin*/
.chat_f8f70f { margin-left: 0px !important; }

DraconicDragon

XAML Chessboard with Gridview, ItemsPanel, ItemsTemplate

XAML

<GridView
    Width="Auto"
    Height="Auto"
    ItemsSource="{x:Bind Chessboard}">
    <GridView.ItemsPanel>
        <ItemsPanelTemplate>
            <ItemsWrapGrid MaximumRowsOrColumns="8" Orientation="Horizontal" />

DraconicDragon

// https://dev.exiv2.org/projects/exiv2/wiki/The_Metadata_in_PNG_files
// tEXt chunk: text that can be represented in ISO/IEC 8859-1.
// use exif package instead for jpeg, etc
import 'dart:io';
import 'dart:typed_data';
import 'package:png_chunks_extract/png_chunks_extract.dart' as pngExtract; // i thank that this exists.

void main() {
  File file = File('path/to/image.png');
  final data = file.readAsBytesSync();