repagi

#!/usr/bin/env python3
import os, math, glob, time, json, csv, subprocess, random
from pathlib import Path
import torch
from torch import nn
from PIL import Image
import numpy as np
from tqdm import tqdm
from diffusers import StableDiffusionXLPipeline, DDPMScheduler, EulerAncestralDiscreteScheduler

repagi

#!/usr/bin/env python3
import os, math, glob, time, json, csv, subprocess, random
from pathlib import Path
import torch
from torch import nn
from PIL import Image
import numpy as np
from tqdm import tqdm
from diffusers import StableDiffusionXLPipeline, DDPMScheduler, EulerAncestralDiscreteScheduler

repagi

#!/usr/bin/env python3
import os, glob
from pathlib import Path
import torch
from PIL import Image
import numpy as np
from diffusers import AutoencoderKL

print("="*80)
print("DIAGNÓSTICO DO DATASET")

repagi

#!/bin/bash
set -e

echo "================================================================================"
echo "INSTALAÇÃO E TREINAMENTO LORA COM KOHYA SS"
echo "================================================================================"

cd /workspace

# Clonar Kohya SS se não existir

repagi

#!/usr/bin/env python3
"""
Script simples para treinar LoRA SDXL usando PEFT (Hugging Face)
Baseado na documentação oficial e exemplos testados
"""
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"

import torch
import glob