DreamBooth

Key Insight

DreamBooth personalizes a diffusion model by fine-tuning the whole network on just 3–5 photos of a subject, binding it to a rare trigger word so you can later prompt "a photo of [V] dog on the moon." Because every weight is updated the likeness is excellent, but the saved file is the size of the full model — the opposite trade-off from a lightweight LoRA. The danger is catastrophic forgetting: train so hard on five images that the model forgets what every other dog looks like, which is exactly why DreamBooth adds a prior-preservation loss — extra training on the model's own generic class images so the broad concept survives while the specific subject is learned.