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https://github.com/ostris/ai-toolkit.git
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383 lines
15 KiB
Python
383 lines
15 KiB
Python
import os
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import random
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from typing import List
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import cv2
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import numpy as np
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from PIL import Image
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from PIL.ImageOps import exif_transpose
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from torchvision import transforms
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from torch.utils.data import Dataset, DataLoader, ConcatDataset
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from tqdm import tqdm
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import albumentations as A
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from toolkit.config_modules import DatasetConfig
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from toolkit.dataloader_mixins import CaptionMixin
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class ImageDataset(Dataset, CaptionMixin):
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def __init__(self, config):
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self.config = config
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self.name = self.get_config('name', 'dataset')
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self.path = self.get_config('path', required=True)
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self.scale = self.get_config('scale', 1)
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self.random_scale = self.get_config('random_scale', False)
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self.include_prompt = self.get_config('include_prompt', False)
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self.default_prompt = self.get_config('default_prompt', '')
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if self.include_prompt:
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self.caption_type = self.get_config('caption_type', 'txt')
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else:
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self.caption_type = None
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# we always random crop if random scale is enabled
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self.random_crop = self.random_scale if self.random_scale else self.get_config('random_crop', False)
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self.resolution = self.get_config('resolution', 256)
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self.file_list = [os.path.join(self.path, file) for file in os.listdir(self.path) if
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file.lower().endswith(('.jpg', '.jpeg', '.png', '.webp'))]
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# this might take a while
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print(f" - Preprocessing image dimensions")
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new_file_list = []
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bad_count = 0
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for file in tqdm(self.file_list):
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img = Image.open(file)
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if int(min(img.size) * self.scale) >= self.resolution:
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new_file_list.append(file)
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else:
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bad_count += 1
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print(f" - Found {len(self.file_list)} images")
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print(f" - Found {bad_count} images that are too small")
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assert len(self.file_list) > 0, f"no images found in {self.path}"
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self.transform = transforms.Compose([
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transforms.ToTensor(),
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transforms.Normalize([0.5], [0.5]), # normalize to [-1, 1]
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])
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def get_config(self, key, default=None, required=False):
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if key in self.config:
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value = self.config[key]
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return value
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elif required:
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raise ValueError(f'config file error. Missing "config.dataset.{key}" key')
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else:
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return default
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def __len__(self):
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return len(self.file_list)
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def __getitem__(self, index):
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img_path = self.file_list[index]
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img = exif_transpose(Image.open(img_path)).convert('RGB')
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# Downscale the source image first
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img = img.resize((int(img.size[0] * self.scale), int(img.size[1] * self.scale)), Image.BICUBIC)
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min_img_size = min(img.size)
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if self.random_crop:
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if self.random_scale and min_img_size > self.resolution:
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if min_img_size < self.resolution:
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print(
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f"Unexpected values: min_img_size={min_img_size}, self.resolution={self.resolution}, image file={img_path}")
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scale_size = self.resolution
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else:
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scale_size = random.randint(self.resolution, int(min_img_size))
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img = img.resize((scale_size, scale_size), Image.BICUBIC)
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img = transforms.RandomCrop(self.resolution)(img)
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else:
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img = transforms.CenterCrop(min_img_size)(img)
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img = img.resize((self.resolution, self.resolution), Image.BICUBIC)
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img = self.transform(img)
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if self.include_prompt:
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prompt = self.get_caption_item(index)
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return img, prompt
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else:
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return img
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class Augments:
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def __init__(self, **kwargs):
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self.method_name = kwargs.get('method', None)
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self.params = kwargs.get('params', {})
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# convert kwargs enums for cv2
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for key, value in self.params.items():
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if isinstance(value, str):
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# split the string
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split_string = value.split('.')
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if len(split_string) == 2 and split_string[0] == 'cv2':
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if hasattr(cv2, split_string[1]):
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self.params[key] = getattr(cv2, split_string[1].upper())
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else:
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raise ValueError(f"invalid cv2 enum: {split_string[1]}")
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class AugmentedImageDataset(ImageDataset):
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def __init__(self, config):
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super().__init__(config)
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self.augmentations = self.get_config('augmentations', [])
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self.augmentations = [Augments(**aug) for aug in self.augmentations]
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augmentation_list = []
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for aug in self.augmentations:
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# make sure method name is valid
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assert hasattr(A, aug.method_name), f"invalid augmentation method: {aug.method_name}"
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# get the method
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method = getattr(A, aug.method_name)
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# add the method to the list
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augmentation_list.append(method(**aug.params))
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self.aug_transform = A.Compose(augmentation_list)
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self.original_transform = self.transform
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# replace transform so we get raw pil image
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self.transform = transforms.Compose([])
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def __getitem__(self, index):
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# get the original image
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# image is a PIL image, convert to bgr
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pil_image = super().__getitem__(index)
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open_cv_image = np.array(pil_image)
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# Convert RGB to BGR
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open_cv_image = open_cv_image[:, :, ::-1].copy()
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# apply augmentations
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augmented = self.aug_transform(image=open_cv_image)["image"]
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# convert back to RGB tensor
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augmented = cv2.cvtColor(augmented, cv2.COLOR_BGR2RGB)
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# convert to PIL image
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augmented = Image.fromarray(augmented)
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# return both # return image as 0 - 1 tensor
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return transforms.ToTensor()(pil_image), transforms.ToTensor()(augmented)
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class PairedImageDataset(Dataset):
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def __init__(self, config):
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super().__init__()
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self.config = config
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self.size = self.get_config('size', 512)
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self.path = self.get_config('path', None)
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self.pos_folder = self.get_config('pos_folder', None)
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self.neg_folder = self.get_config('neg_folder', None)
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self.default_prompt = self.get_config('default_prompt', '')
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self.network_weight = self.get_config('network_weight', 1.0)
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self.pos_weight = self.get_config('pos_weight', self.network_weight)
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self.neg_weight = self.get_config('neg_weight', self.network_weight)
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supported_exts = ('.jpg', '.jpeg', '.png', '.webp', '.JPEG', '.JPG', '.PNG', '.WEBP')
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if self.pos_folder is not None and self.neg_folder is not None:
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# find matching files
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self.pos_file_list = [os.path.join(self.pos_folder, file) for file in os.listdir(self.pos_folder) if
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file.lower().endswith(supported_exts)]
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self.neg_file_list = [os.path.join(self.neg_folder, file) for file in os.listdir(self.neg_folder) if
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file.lower().endswith(supported_exts)]
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matched_files = []
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for pos_file in self.pos_file_list:
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pos_file_no_ext = os.path.splitext(pos_file)[0]
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for neg_file in self.neg_file_list:
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neg_file_no_ext = os.path.splitext(neg_file)[0]
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if os.path.basename(pos_file_no_ext) == os.path.basename(neg_file_no_ext):
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matched_files.append((neg_file, pos_file))
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break
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# remove duplicates
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matched_files = [t for t in (set(tuple(i) for i in matched_files))]
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self.file_list = matched_files
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print(f" - Found {len(self.file_list)} matching pairs")
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else:
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self.file_list = [os.path.join(self.path, file) for file in os.listdir(self.path) if
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file.lower().endswith(supported_exts)]
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print(f" - Found {len(self.file_list)} images")
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self.transform = transforms.Compose([
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transforms.ToTensor(),
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transforms.Normalize([0.5], [0.5]), # normalize to [-1, 1]
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])
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def get_all_prompts(self):
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prompts = []
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for index in range(len(self.file_list)):
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prompts.append(self.get_prompt_item(index))
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# remove duplicates
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prompts = list(set(prompts))
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return prompts
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def __len__(self):
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return len(self.file_list)
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def get_config(self, key, default=None, required=False):
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if key in self.config:
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value = self.config[key]
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return value
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elif required:
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raise ValueError(f'config file error. Missing "config.dataset.{key}" key')
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else:
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return default
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def get_prompt_item(self, index):
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img_path_or_tuple = self.file_list[index]
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if isinstance(img_path_or_tuple, tuple):
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# check if either has a prompt file
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path_no_ext = os.path.splitext(img_path_or_tuple[0])[0]
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prompt_path = path_no_ext + '.txt'
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if not os.path.exists(prompt_path):
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path_no_ext = os.path.splitext(img_path_or_tuple[1])[0]
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prompt_path = path_no_ext + '.txt'
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else:
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img_path = img_path_or_tuple
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# see if prompt file exists
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path_no_ext = os.path.splitext(img_path)[0]
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prompt_path = path_no_ext + '.txt'
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if os.path.exists(prompt_path):
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with open(prompt_path, 'r', encoding='utf-8') as f:
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prompt = f.read()
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# remove any newlines
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prompt = prompt.replace('\n', ', ')
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# remove new lines for all operating systems
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prompt = prompt.replace('\r', ', ')
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prompt_split = prompt.split(',')
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# remove empty strings
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prompt_split = [p.strip() for p in prompt_split if p.strip()]
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# join back together
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prompt = ', '.join(prompt_split)
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else:
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prompt = self.default_prompt
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return prompt
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def __getitem__(self, index):
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img_path_or_tuple = self.file_list[index]
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if isinstance(img_path_or_tuple, tuple):
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# load both images
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img_path = img_path_or_tuple[0]
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img1 = exif_transpose(Image.open(img_path)).convert('RGB')
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img_path = img_path_or_tuple[1]
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img2 = exif_transpose(Image.open(img_path)).convert('RGB')
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# combine them side by side
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img = Image.new('RGB', (img1.width + img2.width, max(img1.height, img2.height)))
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img.paste(img1, (0, 0))
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img.paste(img2, (img1.width, 0))
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else:
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img_path = img_path_or_tuple
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img = exif_transpose(Image.open(img_path)).convert('RGB')
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prompt = self.get_prompt_item(index)
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height = self.size
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# determine width to keep aspect ratio
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width = int(img.size[0] * height / img.size[1])
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# Downscale the source image first
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img = img.resize((width, height), Image.BICUBIC)
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img = self.transform(img)
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return img, prompt, (self.neg_weight, self.pos_weight)
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class AiToolkitDataset(Dataset, CaptionMixin):
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def __init__(self, dataset_config: 'DatasetConfig'):
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self.dataset_config = dataset_config
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self.folder_path = dataset_config.folder_path
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self.caption_type = dataset_config.caption_type
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self.default_caption = dataset_config.default_caption
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self.random_scale = dataset_config.random_scale
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self.scale = dataset_config.scale
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# we always random crop if random scale is enabled
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self.random_crop = self.random_scale if self.random_scale else dataset_config.random_crop
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self.resolution = dataset_config.resolution
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# get the file list
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self.file_list = [
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os.path.join(self.folder_path, file) for file in os.listdir(self.folder_path) if
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file.lower().endswith(('.jpg', '.jpeg', '.png', '.webp'))
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]
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# this might take a while
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print(f" - Preprocessing image dimensions")
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new_file_list = []
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bad_count = 0
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for file in tqdm(self.file_list):
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img = Image.open(file)
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if int(min(img.size) * self.scale) >= self.resolution:
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new_file_list.append(file)
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else:
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bad_count += 1
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print(f" - Found {len(self.file_list)} images")
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print(f" - Found {bad_count} images that are too small")
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assert len(self.file_list) > 0, f"no images found in {self.folder_path}"
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self.transform = transforms.Compose([
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transforms.ToTensor(),
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transforms.Normalize([0.5], [0.5]), # normalize to [-1, 1]
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])
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def __len__(self):
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return len(self.file_list)
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def __getitem__(self, index):
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img_path = self.file_list[index]
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img = exif_transpose(Image.open(img_path)).convert('RGB')
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# Downscale the source image first
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img = img.resize((int(img.size[0] * self.scale), int(img.size[1] * self.scale)), Image.BICUBIC)
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min_img_size = min(img.size)
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if self.random_crop:
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if self.random_scale and min_img_size > self.resolution:
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if min_img_size < self.resolution:
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print(
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f"Unexpected values: min_img_size={min_img_size}, self.resolution={self.resolution}, image file={img_path}")
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scale_size = self.resolution
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else:
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scale_size = random.randint(self.resolution, int(min_img_size))
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img = img.resize((scale_size, scale_size), Image.BICUBIC)
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img = transforms.RandomCrop(self.resolution)(img)
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else:
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img = transforms.CenterCrop(min_img_size)(img)
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img = img.resize((self.resolution, self.resolution), Image.BICUBIC)
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img = self.transform(img)
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if self.caption_type is not None:
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prompt = self.get_caption_item(index)
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return img, prompt
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else:
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return img
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def get_dataloader_from_datasets(dataset_options, batch_size=1):
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if dataset_options is None or len(dataset_options) == 0:
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return None
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datasets = []
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for dataset_option in dataset_options:
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if isinstance(dataset_option, DatasetConfig):
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config = dataset_option
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else:
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config = DatasetConfig(**dataset_option)
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if config.type == 'image':
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dataset = AiToolkitDataset(config)
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datasets.append(dataset)
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else:
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raise ValueError(f"invalid dataset type: {config.type}")
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concatenated_dataset = ConcatDataset(datasets)
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data_loader = DataLoader(
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concatenated_dataset,
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batch_size=batch_size,
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shuffle=True,
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num_workers=2
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)
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return data_loader
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