752 citations · 1.4k across the 13 of their papers we have counts for
13 papers
Solaris: Towards Interfaces That Are Generated, Not Coded
Yuval Alaluf, Omri Avrahami, Guy Bukchin Leshem +18
Digital interfaces are traditionally implemented through intermediate representations such as code, requiring their appearance and behavior to be specified in advance. We introduce…
Stable Flow: Vital Layers for Training-Free Image Editing
Omri Avrahami, Or Patashnik, Ohad Fried +4
Diffusion models have revolutionized the field of content synthesis and editing. Recent models have replaced the traditional UNet architecture with the Diffusion Transformer (DiT),…
Click2Mask: Local Editing with Dynamic Mask Generation
Omer Regev, Omri Avrahami, Dani Lischinski
Recent advancements in generative models have revolutionized image generation and editing, making these tasks accessible to non-experts. This paper focuses on local image editing,…
DiffUHaul: A Training-Free Method for Object Dragging in Images
Omri Avrahami, Rinon Gal, Gal Chechik +4
Text-to-image diffusion models have proven effective for solving many image editing tasks. However, the seemingly straightforward task of seamlessly relocating objects within a sce…
PALP: Prompt Aligned Personalization of Text-to-Image Models
Moab Arar, Andrey Voynov, Amir Hertz +5
Content creators often aim to create personalized images using personal subjects that go beyond the capabilities of conventional text-to-image models. Additionally, they may want t…
The Chosen One: Consistent Characters in Text-to-Image Diffusion Models
Omri Avrahami, Amir Hertz, Yael Vinker +5
Recent advances in text-to-image generation models have unlocked vast potential for visual creativity. However, the users that use these models struggle with the generation of cons…
Blended-NeRF: Zero-Shot Object Generation and Blending in Existing Neural Radiance Fields
Ori Gordon, Omri Avrahami, Dani Lischinski
Editing a local region or a specific object in a 3D scene represented by a NeRF or consistently blending a new realistic object into the scene is challenging, mainly due to the imp…
Break-A-Scene: Extracting Multiple Concepts from a Single Image
Omri Avrahami, Kfir Aberman, Ohad Fried +2
Text-to-image model personalization aims to introduce a user-provided concept to the model, allowing its synthesis in diverse contexts. However, current methods primarily focus on…
SpaText: Spatio-Textual Representation for Controllable Image Generation
Omri Avrahami, Thomas Hayes, Oran Gafni +6
Recent text-to-image diffusion models are able to generate convincing results of unprecedented quality. However, it is nearly impossible to control the shapes of different regions/…
Blended Latent Diffusion
Omri Avrahami, Ohad Fried, Dani Lischinski
The tremendous progress in neural image generation, coupled with the emergence of seemingly omnipotent vision-language models has finally enabled text-based interfaces for creating…
Ownership and Creativity in Generative Models
Omri Avrahami, Bar Tamir
Machine learning generated content such as image artworks, textual poems and music become prominent in recent years. These tools attract much attention from the media, artists, res…
Blended Diffusion for Text-driven Editing of Natural Images
Omri Avrahami, Dani Lischinski, Ohad Fried
Natural language offers a highly intuitive interface for image editing. In this paper, we introduce the first solution for performing local (region-based) edits in generic natural…
GAN Cocktail: mixing GANs without dataset access
Omri Avrahami, Dani Lischinski, Ohad Fried
Today's generative models are capable of synthesizing high-fidelity images, but each model specializes on a specific target domain. This raises the need for model merging: combinin…