Single Image Iterative Subject-driven Generation and Editing
Authors: name: Yair Shpitzer, name: Gal Chechik, name: Idan Schwartz

Personalizing image generation and editing is particularly challenging when
we only have a few images of the subject, or even a single image. A common
approach to personalization is concept learning, which can integrate the
subject into existing models relatively quickly, but produces images whose
quality tends to deteriorate quickly when the number of subject images is
small. Quality can be improved by pre-training an encoder, but training
restricts generation to the training distribution, and is time consuming. It is
still an open hard challenge to personalize image generation and editing from a
single image without training. Here, we present SISO, a novel, training-free
approach based on optimizing a similarity score with an input subject image.
More specifically, SISO iteratively generates images and optimizes the model
based on loss of similarity with the given subject image until a satisfactory
level of similarity is achieved, allowing plug-and-play optimization to any
image generator. We evaluated SISO in two tasks, image editing and image
generation, using a diverse data set of personal subjects, and demonstrate
significant improvements over existing methods in image quality, subject
fidelity, and background preservation.