You can use your own preprocessing on top of keras preprocessing and data loader, or you can use a custom code for all together.
According to https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/image/ImageDataGenerator ,

Deprecated: tf.keras.preprocessing.image.ImageDataGenerator is not recommended for new code. Prefer loading images with tf.keras.utils.image_dataset_from_directory and transforming the output tf.data.Dataset with preprocessing layers

You can do mini batch training depending on available VRAM, even with a batch size of 1. I assume you are referring to VRAM as RAM, as we hardly do deep learning with CPU for image datasets.

example: you can use data_augmentation pipeline step to have control over preprocessing like this (I used this code with older TF version (2.4.0 or 2.9.0.dev may be) and might need to change function locations for new version as above)

train_ds = tensorflow.keras.preprocessing.image_dataset_from_directory(
    image_directory,
    labels='inferred', 
    label_mode='int',
    class_names=classify_names,     
    validation_split=0.3,
    subset="training",
    shuffle=shuffle_value,
    seed=seed_value,
    image_size=image_size,
    batch_size=batch_size,
)

data_augmentation = tensorflow.keras.Sequential(
    [
        tensorflow.keras.layers.experimental.preprocessing.RandomFlip("horizontal"),
        tensorflow.keras.layers.experimental.preprocessing.RandomRotation(0.1), 
    ]
)

augmented_train_ds = train_ds.map( lambda x, y: (data_augmentation(x, training=True), y))