Load adapters            There are several training techniques for personalizing diffusion models to generate images of a specific subject or images in certain styles. Each of these training methods produces a different type of adapter. Some of the adapters generate an entirely new model, while other adapters only modify a smaller set of embeddings or weights. This means the loading process for each adapter is also different. This guide will show you how to load DreamBooth, textual inversion, and LoRA weights. Feel free to browse the Stable Diffusion Conceptualizer, LoRA the Explorer, and the Diffusers Models Gallery for checkpoints and embeddings to use.  DreamBooth DreamBooth finetunes an entire diffusion model on just several images of a subject to generate images of that subject in new styles and settings. This method works by using a special word in the prompt that the model learns to associate with the subject image. Of all the training methods, DreamBooth produces the largest file size (usually a few GBs) because it is a full checkpoint model. Let’s load the herge_style checkpoint, which is trained on just 10 images drawn by Hergé, to generate images in that style. For it to work, you need to include the special word herge_style in your prompt to trigger the checkpoint:   Copied from diffusers import AutoPipelineForText2Image