472 citations · 624 across the 37 of their papers we have counts for
9 papers · 1 filter
Sharp-It: A Multi-view to Multi-view Diffusion Model for 3D Synthesis and Manipulation
Yiftach Edelstein, Or Patashnik, Dana Cohen-Bar +1
Advancements in text-to-image diffusion models have led to significant progress in fast 3D content creation. One common approach is to generate a set of multi-view images of an obj…
Omni-ID: Holistic Identity Representation Designed for Generative Tasks
Guocheng Qian, Kuan-Chieh Wang, Or Patashnik +5
We introduce Omni-ID, a novel facial representation designed specifically for generative tasks. Omni-ID encodes holistic information about an individual's appearance across diverse…
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),…
MaterialFusion: Enhancing Inverse Rendering with Material Diffusion Priors
Yehonathan Litman, Or Patashnik, Kangle Deng +4
Recent works in inverse rendering have shown promise in using multi-view images of an object to recover shape, albedo, and materials. However, the recovered components often fail t…
TurboEdit: Text-Based Image Editing Using Few-Step Diffusion Models
Gilad Deutch, Rinon Gal, Daniel Garibi +2
Diffusion models have opened the path to a wide range of text-based image editing frameworks. However, these typically build on the multi-step nature of the diffusion backwards pro…
LCM-Lookahead for Encoder-based Text-to-Image Personalization
Rinon Gal, Or Lichter, Elad Richardson +4
Recent advancements in diffusion models have introduced fast sampling methods that can effectively produce high-quality images in just one or a few denoising steps. Interestingly,…
Be Yourself: Bounded Attention for Multi-Subject Text-to-Image Generation
Omer Dahary, Or Patashnik, Kfir Aberman +1
Text-to-image diffusion models have an unprecedented ability to generate diverse and high-quality images. However, they often struggle to faithfully capture the intended semantics…
ReNoise: Real Image Inversion Through Iterative Noising
Daniel Garibi, Or Patashnik, Andrey Voynov +2
Recent advancements in text-guided diffusion models have unlocked powerful image manipulation capabilities. However, applying these methods to real images necessitates the inversio…
Consolidating Attention Features for Multi-view Image Editing
Or Patashnik, Rinon Gal, Daniel Cohen-Or +2
Large-scale text-to-image models enable a wide range of image editing techniques, using text prompts or even spatial controls. However, applying these editing methods to multi-view…