  --real_prior=True \  Training script A lot of the code in the Custom Diffusion training script is similar to the DreamBooth script. This guide instead focuses on the code that is relevant to Custom Diffusion. The Custom Diffusion training script has two dataset classes: CustomDiffusionDataset: preprocesses the images, class images, and prompts for training PromptDataset: prepares the prompts for generating class images Next, the modifier_token is added to the tokenizer, converted to token ids, and the token embeddings are resized to account for the new modifier_token. Then the modifier_token embeddings are initialized with the embeddings of the initializer_token. All parameters in the text encoder are frozen, except for the token embeddings since this is what the model is trying to learn to associate with the concepts.   Copied params_to_freeze = itertools.chain(