stable-diffusion-finetune/ldm/data/base.py

import os
import numpy as np
from abc import abstractmethod
from torch.utils.data import Dataset, ConcatDataset, ChainDataset, IterableDataset


class Txt2ImgIterableBaseDataset(IterableDataset):
    '''
    Define an interface to make the IterableDatasets for text2img data chainable
    '''
    def __init__(self, num_records=0, valid_ids=None, size=256):
        super().__init__()
        self.num_records = num_records
        self.valid_ids = valid_ids
        self.sample_ids = valid_ids
        self.size = size

        print(f'{self.__class__.__name__} dataset contains {self.__len__()} examples.')

    def __len__(self):
        return self.num_records

    @abstractmethod
    def __iter__(self):
        pass


class PRNGMixin(object):
    """
    Adds a prng property which is a numpy RandomState which gets
    reinitialized whenever the pid changes to avoid synchronized sampling
    behavior when used in conjunction with multiprocessing.
    """
    @property
    def prng(self):
        currentpid = os.getpid()
        if getattr(self, "_initpid", None) != currentpid:
            self._initpid = currentpid
            self._prng = np.random.RandomState()
        return self._prng