e=weight_dtype
    ).eval()
    image_encoder = CLIPVisionModelWithProjection.from_pretrained(
        args.pretrained_prior_model_name_or_path, subfolder="image_encoder", torch_dtype=weight_dtype
    ).eval()
unet = UNet2DConditionModel.from_pretrained(args.pretrained_decoder_model_name_or_path, subfolder="unet") Next, the script includes several image transforms and a preprocessing function for applying the transforms to the images and return