5 citations · 6 across the 7 of their papers we have counts for
9 papers
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…
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…
On-the-fly Repulsion in the Contextual Space for Rich Diversity in Diffusion Transformers
Omer Dahary, Benaya Koren, Daniel Garibi +1
Modern Text-to-Image (T2I) diffusion models have achieved remarkable semantic alignment, yet they often suffer from a significant lack of variety, converging on a narrow set of vis…
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…
Navigating with Annealing Guidance Scale in Diffusion Space
Shai Yehezkel, Omer Dahary, Andrey Voynov +1
Denoising diffusion models excel at generating high-quality images conditioned on text prompts, yet their effectiveness heavily relies on careful guidance during the sampling proce…
Image Generation from Contextually-Contradictory Prompts
Saar Huberman, Or Patashnik, Omer Dahary +2
Text-to-image diffusion models excel at generating high-quality, diverse images from natural language prompts. However, they often fail to produce semantically accurate results whe…
Be Decisive: Noise-Induced Layouts for Multi-Subject Generation
Omer Dahary, Yehonathan Cohen, Or Patashnik +2
Generating multiple distinct subjects remains a challenge for existing text-to-image diffusion models. Complex prompts often lead to subject leakage, causing inaccuracies in quanti…
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…
Digital Gimbal: End-to-end Deep Image Stabilization with Learnable Exposure Times
Omer Dahary, Matan Jacoby, Alex M. Bronstein
Mechanical image stabilization using actuated gimbals enables capturing long-exposure shots without suffering from blur due to camera motion. These devices, however, are often phys…