activity
20202026
most citedAn Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion

472 citations · 609 across the 28 of their papers we have counts for

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Showing 2025Show all

5 papers · 1 filter

cs.CV2025

Policy Optimized Text-to-Image Pipeline Design

Uri Gadot, Rinon Gal, Yftah Ziser +2

Text-to-image generation has evolved beyond single monolithic models to complex multi-component pipelines. These combine fine-tuned generators, adapters, upscaling blocks and even…

cs.CL2025

Padding Tone: A Mechanistic Analysis of Padding Tokens in T2I Models

Michael Toker, Ido Galil, Hadas Orgad +4

Text-to-image (T2I) diffusion models rely on encoded prompts to guide the image generation process. Typically, these prompts are extended to a fixed length by adding padding tokens…

cs.CV2025

IP-Composer: Semantic Composition of Visual Concepts

Sara Dorfman, Dana Cohen-Bar, Rinon Gal +1

Content creators often draw inspiration from multiple visual sources, combining distinct elements to craft new compositions. Modern computational approaches now aim to emulate this…

cs.CV2025

ImageRAG: Dynamic Image Retrieval for Reference-Guided Image Generation

Rotem Shalev-Arkushin, Rinon Gal, Amit H. Bermano +1

Diffusion models enable high-quality and diverse visual content synthesis. However, they struggle to generate rare or unseen concepts. To address this challenge, we explore the usa…

cs.CV2025

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…