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20242026
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cs.CV2026

Spanning the Visual Analogy Space with a Weight Basis of LoRAs

Hila Manor, Rinon Gal, Haggai Maron +2

Visual analogy learning enables image editing via demonstration rather than textual description, allowing users to specify complex transformations difficult to articulate in words.…

cs.CV2026

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

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.CV2025

Motion by Queries: Identity-Motion Trade-offs in Text-to-Video Generation

Yuval Atzmon, Rinon Gal, Yoad Tewel +2

Text-to-video diffusion models have shown remarkable progress in generating coherent video clips from textual descriptions. However, the interplay between motion, structure, and id…

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

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…