ed_model_name_or_path, subfolder="vae", revision=args.revision)
unet = UNet2DConditionModel.from_pretrained(
    args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
) The special placeholder token is added next to the tokenizer, and the embedding is readjusted to account for the new token. Then, the script creates a dataset from the TextualInversionDataset:   Copied train_dataset = TextualInversionDataset(
    data_root