8 papers
The Many Senses of Visual Similarity: A Text-Prompted Image Perceptual Metric
Sheng-Yu Wang, Yotam Nitzan, Aaron Hertzmann +4
Human visual similarity judgments are context-dependent. For example, two images may be similar in shape but distinct in color. Existing perceptual similarity metrics, however, col…
HiLo-Token: Input-Adaptive High-Low Frequency Token Compression for Efficient Image Editing
Haoran You, Yotam Nitzan, Lingzhi Zhang +7
Creative image editing tools, such as Photoshop's Remove or Generative Fill buttons, are central to everyday customer use and account for a major share of traffic in Photoshop and…
Learning an Image Editing Model without Image Editing Pairs
Nupur Kumari, Sheng-Yu Wang, Nanxuan Zhao +7
Recent image editing models have achieved impressive results while following natural language editing instructions, but they rely on supervised fine-tuning with large datasets of i…
ParetoSlider: Diffusion Models Post-Training for Continuous Reward Control
Shelly Golan, Michael Finkelson, Ariel Bereslavsky +2
Reinforcement Learning (RL) post-training has become the standard for aligning generative models with human preferences, yet most methods rely on a single scalar reward. When multi…
Self-Evaluation Unlocks Any-Step Text-to-Image Generation
Xin Yu, Xiaojuan Qi, Zhengqi Li +6
We introduce the Self-Evaluating Model (Self-E), a novel, from-scratch training approach for text-to-image generation that supports any-step inference. Self-E learns from data simi…
VLM-Guided Adaptive Negative Prompting for Creative Generation
Shelly Golan, Yotam Nitzan, Zongze Wu +1
Creative generation is the synthesis of new, surprising, and valuable samples that reflect user intent yet cannot be envisioned in advance. This task aims to extend human imaginati…