rch_dtype=weight_dtype
    ).eval() Kandinsky uses a PriorTransformer to generate the image embeddings, so you’ll want to setup the optimizer to learn the prior mode’s parameters.   Copied prior = PriorTransformer.from_pretrained(args.pretrained_prior_model_name_or_path, subfolder="prior")
prior.train()
optimizer = optimizer_cls(
    prior.parameters(),
    lr=args.learning_rate,
    betas=(args.adam_beta1, args.adam_beta2),
    weight_decay=ar