472 citations · 624 across the 37 of their papers we have counts for
36 papers · 1 filter
Stuck on Shuffle: On Attribute-Object Binding in Vision-Language Models
Netanel Y. Tamir, Michael Green, Issar Tzachor +4
Retrieval is a fundamental task in multimodal systems: given an image or text query, the goal is to find the most relevant images or texts in a collection, often using a shared emb…
Learning to Read the Contextual Tokens in Diffusion Transformers
Omer Dahary, Etai Sella, Hadar Averbuch-Elor +2
Multimodal Diffusion Transformers (MM-DiTs) jointly process visual and textual representations throughout generation. These models repeatedly update the text tokens through multimo…
Analysis-by-Proxy: Localization Signals in VLMs Operating as Condition Encoders
Yoav Baron, Sara Dorfman, Roni Paiss +2
Vision-Language Models (VLMs) are increasingly utilized as the conditioning backbone for diffusion-based image editing due to their remarkable multimodal reasoning capabilities. Wh…
Token-to-Token Alignment of Text Embeddings for Semantic Blending
Saar Huberman, Ron Mokady, Or Patashnik +1
In modern generative models, images are specified and controlled through text prompts. In practice, images are generated from sequences of tokens derived from these prompts. Howeve…
Keep The Essentials: Efficient Reference Conditioned Generation via Token Dropping
Rishubh Parihar, Ayush Raina, R. Venkatesh Babu +1
Reference-based diffusion models enable highly controllable image generation by leveraging elements from input images to guide prompt-driven synthesis. However, these models are co…
Semantic Browsing: Controllable Diversity for Image Generation
Sara Dorfman, Maya Vishnevsky, Omer Dahary +2
Modern text-to-image models excel in visual fidelity and prompt adherence. However, this strict adherence comes at the cost of diversity: generated samples tend to collapse into a…
HDR Video Generation via Latent Alignment with Logarithmic Encoding
Naomi Ken Korem, Mohamed Oumoumad, Harel Cain +8
High dynamic range (HDR) imagery offers a rich and faithful representation of scene radiance, but remains challenging for generative models due to its mismatch with the bounded, pe…
Visual Diffusion Models are Geometric Solvers
Nir Goren, Shai Yehezkel, Omer Dahary +3
In this paper we show that visual diffusion models can serve as effective geometric solvers: they can directly reason about geometric problems by working in pixel space. We first d…
Object-level Visual Prompts for Compositional Image Generation
Gaurav Parmar, Or Patashnik, Kuan-Chieh Wang +5
We introduce a method for composing object-level visual prompts within a text-to-image diffusion model. Our approach addresses the task of generating semantically coherent composit…
Nested Attention: Semantic-aware Attention Values for Concept Personalization
Or Patashnik, Rinon Gal, Daniil Ostashev +3
Personalizing text-to-image models to generate images of specific subjects across diverse scenes and styles is a rapidly advancing field. Current approaches often face challenges i…
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…
CLiC: Concept Learning in Context
Mehdi Safaee, Aryan Mikaeili, Or Patashnik +2
This paper addresses the challenge of learning a local visual pattern of an object from one image, and generating images depicting objects with that pattern. Learning a localized c…
Cross-Image Attention for Zero-Shot Appearance Transfer
Yuval Alaluf, Daniel Garibi, Or Patashnik +2
Recent advancements in text-to-image generative models have demonstrated a remarkable ability to capture a deep semantic understanding of images. In this work, we leverage this sem…
Noise-Free Score Distillation
Oren Katzir, Or Patashnik, Daniel Cohen-Or +1
Score Distillation Sampling (SDS) has emerged as the de facto approach for text-to-content generation in non-image domains. In this paper, we reexamine the SDS process and introduc…
Localizing Object-level Shape Variations with Text-to-Image Diffusion Models
Or Patashnik, Daniel Garibi, Idan Azuri +2
Text-to-image models give rise to workflows which often begin with an exploration step, where users sift through a large collection of generated images. The global nature of the te…
Latent-NeRF for Shape-Guided Generation of 3D Shapes and Textures
Gal Metzer, Elad Richardson, Or Patashnik +2
Text-guided image generation has progressed rapidly in recent years, inspiring major breakthroughs in text-guided shape generation. Recently, it has been shown that using score dis…