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20242026
most citedObject-level Visual Prompts for Compositional Image Generation

1 citations · 3 across the 37 of their papers we have counts for

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cs.GR2026

Untwisting RoPE: Frequency Control for Shared Attention in DiTs

Aryan Mikaeili, Or Patashnik, Andrea Tagliasacchi +2

Positional encodings are essential to transformer-based generative models, yet their behavior in multimodal and attention-sharing settings is not fully understood. In this work, we…

cs.GR2026

LooseRoPE: Content-aware Attention Manipulation for Semantic Harmonization

Etai Sella, Yoav Baron, Hadar Averbuch-Elor +2

Recent diffusion-based image editing methods commonly rely on text or high-level instructions to guide the generation process, offering intuitive but coarse control. In contrast, w…

cs.GR2025

SAEdit: Token-level control for continuous image editing via Sparse AutoEncoder

Ronen Kamenetsky, Sara Dorfman, Daniel Garibi +3

Large-scale text-to-image diffusion models have become the backbone of modern image editing, yet text prompts alone do not offer adequate control over the editing process. Two prop…

cs.GR2025

Zero-Shot Dynamic Concept Personalization with Grid-Based LoRA

Rameen Abdal, Or Patashnik, Ekaterina Deyneka +5

Recent advances in text-to-video generation have enabled high-quality synthesis from text and image prompts. While the personalization of dynamic concepts, which capture subject-sp…

cs.GR2025

EditP23: 3D Editing via Propagation of Image Prompts to Multi-View

Roi Bar-On, Dana Cohen-Bar, Daniel Cohen-Or

We present EditP23, a method for mask-free 3D editing that propagates 2D image edits to multi-view representations in a 3D-consistent manner. In contrast to traditional approaches…

cs.GR2025

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…