ReGain: Restoring Subject Fidelity in Personalization on Synthetic Images
ReGain downscales inflated guidance so DreamBooth on synthetic images recovers much of real-photo subject fidelity.
DreamBooth personalization on diffusion-generated subject images degrades fidelity, producing oversaturated color and excess high-frequency detail. The authors trace this to classifier-free guidance: the angle and difference norm between conditional and unconditional noise predictions inflate, especially at high frequencies and nearby prompts. ReGain is a training-free sampling correction that downscales frequency bands whose guidance is inflated relative to the base model. On Stable Diffusion v1.5 it closes 51-64% of the gap to a real-photo personalization on DINO, DINOv2, and CLIP-I, and it also helps SDXL and SD 3.5 without hurting text alignment.