diffusers中的dreambooth的微调和lora微调_dreambooth和lora区别

train_dreambooth.py

代码：

accelerator = Accelerator()->
 
# Generate class image if prior oreservation is enabled
if args.with_prior_preservation:
    if cur_class_images<args.num_class_images:
        pipeline = DiffusionPipline.from_pretrained()->
        sample_dataset = PromptDataset(args.class_prompt, num_new_images)
        sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)
        for example in sample_dataloader:
            images = pipeline(example['prompt']).images
 
tokenizer = AutoTokenizer.from_pretrained(,"tokenizer")->
text_encoder_cls = import_model_class_from_model_name_or_path()->
noise_scheduler = DDPMScheduler.from_pretrained(,"scheduler")->
text_encoder = text_encoder_cls.from_pretrained(,"text_encoder")->
vae = AutoencoderKL.from_pretrained(,"vae")->
unet &