One of the main differences you'll notice right away is that the training script also loads a [`~transformers.CLIPImageProcessor`] - in addition to a scheduler and tokenizer - for preprocessing images and a [`~transformers.CLIPVisionModelWithProjection`] model for encoding the images:  
```py
noise_scheduler = DDPMScheduler(beta_schedule="squaredcos_cap_v2", prediction_type="sample")
image_processor = CLIPImageProcessor.from_pretrained(
args.pretrained_prior_model_name_or_path, subfolder="image_processor"