2 citations · 2 across the 17 of their papers we have counts for
11 papers · 1 filter
In-Context Sync-LoRA for Portrait Video Editing
Sagi Polaczek, Or Patashnik, Ali Mahdavi-Amiri +1
Editing portrait videos is a challenging task that requires flexible yet precise control over a wide range of modifications, such as appearance changes, expression edits, or the ad…
DeLeaker: Dynamic Inference-Time Reweighting For Semantic Leakage Mitigation in Text-to-Image Models
Mor Ventura, Michael Toker, Or Patashnik +2
Text-to-Image (T2I) models have advanced rapidly, yet they remain vulnerable to semantic leakage, the unintended transfer of semantically related features between distinct entities…
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…
NoisePrints: Distortion-Free Watermarks for Authorship in Private Diffusion Models
Nir Goren, Oren Katzir, Abhinav Nakarmi +3
With the rapid adoption of diffusion models for visual content generation, proving authorship and protecting copyright have become critical. This challenge is particularly importan…
Kontinuous Kontext: Continuous Strength Control for Instruction-based Image Editing
Rishubh Parihar, Or Patashnik, Daniil Ostashev +3
Instruction-based image editing offers a powerful and intuitive way to manipulate images through natural language. Yet, relying solely on text instructions limits fine-grained cont…
Scaling Group Inference for Diverse and High-Quality Generation
Gaurav Parmar, Or Patashnik, Daniil Ostashev +4
Generative models typically sample outputs independently, and recent inference-time guidance and scaling algorithms focus on improving the quality of individual samples. However, i…
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…
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…
Tight Inversion: Image-Conditioned Inversion for Real Image Editing
Edo Kadosh, Nir Goren, Or Patashnik +2
Text-to-image diffusion models offer powerful image editing capabilities. To edit real images, many methods rely on the inversion of the image into Gaussian noise. A common approac…
Dynamic Concepts Personalization from Single Videos
Rameen Abdal, Or Patashnik, Ivan Skorokhodov +5
Personalizing generative text-to-image models has seen remarkable progress, but extending this personalization to text-to-video models presents unique challenges. Unlike static con…