title: Iterative Object Count Optimization for Text-to-image Diffusion Models
summary: We address a persistent challenge in text-to-image models: accurately
  generating a specified number of objects. Current models, which learn from
  image-text pairs, inherently struggle with counting, as training data cannot
  depict every possible number of objects for any given object. To solve this, we
  propose optimizing the generated image based on a counting loss derived from a
  counting model that aggregates an object\'s potential. Employing an
  out-of-the-box counting model is challenging for two reasons: first, the model
  requires a scaling hyperparameter for the potential aggregation that varies
  depending on the viewpoint of the objects, and second, classifier guidance
  techniques require modified models that operate on noisy intermediate diffusion
  steps. To address these challenges, we propose an iterated online training mode
  that improves the accuracy of inferred images while altering the text
  conditioning embedding and dynamically adjusting hyperparameters. Our method
  offers three key advantages: (i) it can consider non-derivable counting
  techniques based on detection models, (ii) it is a zero-shot plug-and-play
  solution facilitating rapid changes to the counting techniques and image
  generation methods, and (iii) the optimized counting token can be reused to
  generate accurate images without additional optimization. We evaluate the
  generation of various objects and show significant improvements in accuracy.
  The project page is available at https://ozzafar.github.io/count_token.
paper_authors._id: 66c767219e496a77e83e1954
paper_authors.name: Oz Zafar
paper_authors._id: 66c767219e496a77e83e1955
paper_authors.name: Lior Wolf
paper_authors._id: 66c767219e496a77e83e1956
paper_authors.name: Idan Schwartz
paper_title: Iterative Object Count Optimization for Text-to-image Diffusion Models
paper_summary: We address a persistent challenge in text-to-image models: accurately
  generating a specified number of objects. Current models, which learn from
  image-text pairs, inherently struggle with counting, as training data cannot
  depict every possible number of objects for any given object. To solve this, we
  propose optimizing the generated image based on a counting loss derived from a
  counting model that aggregates an object\'s potential. Employing an
  out-of-the-box counting model is challenging for two reasons: first, the model
  requires a scaling hyperparameter for the potential aggregation that varies
  depending on the viewpoint of the objects, and second, classifier guidance
  techniques require modified models that operate on noisy intermediate diffusion
  steps. To address these challenges, we propose an iterated online training mode
  that improves the accuracy of inferred images while altering the text
  conditioning embedding and dynamically adjusting hyperparameters. Our method
  offers three key advantages: (i) it can consider non-derivable counting
  techniques based on detection models, (ii) it is a zero-shot plug-and-play
  solution facilitating rapid changes to the counting techniques and image
  generation methods, and (iii) the optimized counting token can be reused to
  generate accurate images without additional optimization. We evaluate the
  generation of various objects and show significant improvements in accuracy.
  The project page is available at https://ozzafar.github.io/count_token.
paper_ai_summary: A novel approach improves text-to-image generation accuracy by integrating counting techniques and dynamically adjusting hyperparameters during iterative online training.
paper_ai_keywords: text-to-image models counting model counting loss potential aggregation viewpoint classifier guidance diffusion steps text conditioning embedding zero-shot plug-and-play counting token