DreamBooth is an innovative method that allows for the customization of text-to-image models like Stable Diffusion using just a few images of a subject. DreamBooth enables the generation of new, contextually varied images of the subject in a range of scenes, poses, and viewpoints, expanding the creative possibilities of generative models.
The data format for DreamBooth training is simple. All you need is images of a concept (e.g. a person) and a concept token.
Collect 3-5 high-quality images of the subject you wish to personalize. These images should vary slightly in pose or background to provide the model with a diverse learning set. You can select more images if you want to train a more robust model.
Choose a base model from the Hugging Face Hub that is compatible with your needs. It’s essential to select a model that supports the image size of your training data. Models available on the hub often have specific requirements or capabilities, so ensure the model you choose can accommodate the dimensions of your images.
The concept token is a crucial element in DreamBooth training. This token acts as a unique identifier for your subject within the model. Typically, you will use a simple, descriptive keyword like prompt in the parameters section of your training setup. This token will be used to generate new images of your subject by the model.
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