title: Single Image Iterative Subject-driven Generation and Editing
summary: 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.
paper_authors._id: 67dd02594aa37abf77af416b
paper_authors.name: Yair Shpitzer
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-21T11:40:09.783Z
paper_authors._id: 63eb8b1113a3eb9b0dc89d8c
paper_authors.avatarUrl: /avatars/d9cb7bdf4f3d2218f7d84120a00054bb.svg
paper_authors.fullname: Yair Shpitzer
paper_authors.type: user
paper_authors.user: yairshp
paper_authors._id: 67dd02594aa37abf77af416c
paper_authors.name: Gal Chechik
paper_authors._id: 67dd02594aa37abf77af416d
paper_authors.name: Idan Schwartz
paper_title: Single Image Iterative Subject-driven Generation and Editing
paper_summary: 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.
paper_ai_summary: SISO, a training-free method, enhances image generation and editing quality from single images by iteratively optimizing similarity between generated and subject images.
paper_ai_keywords: concept learning encoder iterative generation similarity score plug-and-play optimization image editing image quality subject fidelity background preservation