Datasets:

---

michaelu0826

/

ai_toolkit_code

Like 0

import base64

import glob

import hashlib

import json

import math

import os

import random

from collections import OrderedDict

from typing import TYPE_CHECKING, List, Dict, Union

import traceback

import cv2

import numpy as np

import torch

from safetensors.torch import load_file, save_file

from tqdm import tqdm

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection, SiglipImageProcessor

from toolkit.audio.preserve_pitch import time_stretch_preserve_pitch

from toolkit.basic import flush, value_map

from toolkit.buckets import get_bucket_for_image_size, get_resolution

from toolkit.config_modules import ControlTypes

from toolkit.control_generator import ControlGenerator

from toolkit.metadata import get_meta_for_safetensors

from toolkit.models.pixtral_vision import PixtralVisionImagePreprocessorCompatible

from toolkit.prompt_utils import inject_trigger_into_prompt

from torchvision import transforms

from PIL import Image, ImageFilter, ImageOps

from PIL.ImageOps import exif_transpose

import albumentations as A

from toolkit.print import print_acc

from toolkit.accelerator import get_accelerator

from toolkit.prompt_utils import PromptEmbeds

from torchvision.transforms import functional as TF

from toolkit.train_tools import get_torch_dtype

if TYPE_CHECKING:

from toolkit.data_loader import AiToolkitDataset

from toolkit.data_transfer_object.data_loader import FileItemDTO

from toolkit.stable_diffusion_model import StableDiffusion

accelerator = get_accelerator()

class Augments:

def __init__(self, **kwargs):

self.method_name = kwargs.get('method', None)

self.params = kwargs.get('params', {})

# convert kwargs enums for cv2

for key, value in self.params.items():

if isinstance(value, str):

# split the string

split_string = value.split('.')

if len(split_string) == 2 and split_string[0] == 'cv2':

if hasattr(cv2, split_string[1]):

self.params[key] = getattr(cv2, split_string[1].upper())

else:

raise ValueError(f"invalid cv2 enum: {split_string[1]}")

transforms_dict = {

'ColorJitter': transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.03),

'RandomEqualize': transforms.RandomEqualize(p=0.2),

}

img_ext_list = ['.jpg', '.jpeg', '.png', '.webp']

def standardize_images(images):

"""

mean = [0.48145466, 0.4578275, 0.40821073]

std = [0.26862954, 0.26130258, 0.27577711]

# Define the normalization transform

normalize = transforms.Normalize(mean=mean, std=std)

# Apply normalization to each image in the batch

standardized_images = torch.stack([normalize(img) for img in images])

return standardized_images

def clean_caption(caption):

# this doesnt make any sense anymore in a world that is not based on comma seperated tokens

# # remove any newlines

# caption = caption.replace('\n', ', ')

# # remove new lines for all operating systems