7 papers · 1 filter
Bootstrap Your Generator: Unpaired Visual Editing with Flow Matching
Yoad Tewel, Yuval Atzmon, Gal Chechik +1
Modern generative models possess a deep understanding of visual content, yet training them for image editing typically requires massive datasets of paired examples. This limits sca…
Motion by Queries: Identity-Motion Trade-offs in Text-to-Video Generation
Yuval Atzmon, Rinon Gal, Yoad Tewel +2
Text-to-video diffusion models have shown remarkable progress in generating coherent video clips from textual descriptions. However, the interplay between motion, structure, and id…
Lightning-Fast Image Inversion and Editing for Text-to-Image Diffusion Models
Dvir Samuel, Barak Meiri, Haggai Maron +5
Diffusion inversion is the problem of taking an image and a text prompt that describes it and finding a noise latent that would generate the exact same image. Most current determin…
Add-it: Training-Free Object Insertion in Images With Pretrained Diffusion Models
Yoad Tewel, Rinon Gal, Dvir Samuel +3
Adding Object into images based on text instructions is a challenging task in semantic image editing, requiring a balance between preserving the original scene and seamlessly integ…
Make It Count: Text-to-Image Generation with an Accurate Number of Objects
Lital Binyamin, Yoad Tewel, Hilit Segev +3
Despite the unprecedented success of text-to-image diffusion models, controlling the number of depicted objects using text is surprisingly hard. This is important for various appli…
Key-Locked Rank One Editing for Text-to-Image Personalization
Yoad Tewel, Rinon Gal, Gal Chechik +1
Text-to-image models (T2I) offer a new level of flexibility by allowing users to guide the creative process through natural language. However, personalizing these models to align w…