ipeline.from_pretrained(base_model, torch_dtype=torch.float16).
This is especially useful when you don’t want to hardcode the base model identifier during initializing the StableDiffusionPipeline.
Inference for DreamBooth training remains the same. Check
this section for more details. 

Merging LoRA with original model
	
When performing inference, you can merge the trained LoRA weights with the frozen pre-trained model weights, to interpolate b