CoffeeVampir3

def load_cfg_from_json(json_file):
    with open(json_file, "r", encoding="utf-8") as reader:
        text = reader.read()
    return json.loads(text)

def load_cfg(model_id, cfg_path):
    hf_config = load_cfg_from_json(cfg_path)
    if 'pretrained_cfg' not in hf_config:
        # old form, pull pretrain_cfg out of the base dict
        pretrained_cfg = hf_config

CoffeeVampir3

#!/bin/bash

# Config Start

# Configurations
ckpt="/home/blackroot/Desktop/SD/sd-scripts/models/model.safetensors" # base checkpoint to finetune
image_dir="/home/blackroot/Desktop/SD/sd-scripts/train_data" # folder containing folders with repeats_conceptname
reg_dir="" #optional, just point this to an empty folder if you don't care
output="/home/blackroot/Desktop/SD/sd-scripts/outputs" # safetensors output folder

CoffeeVampir3

def per_polygon_translate(generate_fn, input):
    prompt = (
f"""::JAPANESE TEXT::
むし、 ある ところ に
[[0.0, 8.0], [252.0, 8.0], [252.0, 27.0], [0.0, 26.0]]
おじいさん と おばあさん が いました。
[[0.0, 33.0], [289.0, 32.0], [289.0, 50.0], [0.0, 50.0]]
おじいさん が 山（やま） へ 木（き） を きり に いけば、
[[0.0, 57.0], [416.0, 56.0], [417.0, 74.0], [0.0, 75.0]]
おばあさん は 川（かわ）へ せんたく に でかけます。

CoffeeVampir3

from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline
from diffusers import EulerAncestralDiscreteScheduler
import torch
import torch.nn.functional
import gc
from PIL import Image

from mechanisms.tokenizer_utils import encode_from_pipe

@torch.no_grad()

CoffeeVampir3

import re, os, random, csv

class WildCLIP:
    @classmethod
    def INPUT_TYPES(s):
        return {
        "required": {
            "text": ("STRING", {"multiline": True}), "clip": ("CLIP", )
        }
        }

CoffeeVampir3

// Fill out your copyright notice in the Description page of Project Settings.


#include "Ai/Behaviours/NecroAiBasicCombatant.h"

#include "NavigationSystem.h"
#include "NecroGameplayTags.h"
#include "AbilitySystem/NecroAbilitySystem.h"
#include "ActorInterfaces/CombatInterface.h"
#include "GameFramework/Character.h"

CoffeeVampir3

CoffeeVampir3

import json

def filter_images_by_score_interval(jsonl_file_path, target_interval, output_file_path):
    min_score = None
    matching_entries = []
    
    # Find the minimum score in the file
    with open(jsonl_file_path, 'r') as file:
        for line in file:
            json_object = json.loads(line)

CoffeeVampir3

from transformers import AutoModelForCausalLM, CodeGenTokenizerFast as Tokenizer
from moondream import Moondream
from PIL import Image
from collections import Counter
import glob
import os

model = Moondream.from_pretrained("/home/blackroot/Desktop/moond/moondream1").to("cuda")
tokenizer = Tokenizer.from_pretrained("/home/blackroot/Desktop/moond/moondream1/tokenizer")

CoffeeVampir3

#!/bin/bash

# Config Start

# Configurations
ckpt="checkpoint path here" # base checkpoint to finetune
image_dir="training path here, should have images as subfolder" # folder containing folders with repeats_conceptname
reg_dir="" #optional, just point this to an empty folder if you don't care
output="output path here" # safetensors output folder