Omri Avrahami
YOU?
Author Swipe
View article: Click2Mask: Local Editing with Dynamic Mask Generation
Click2Mask: Local Editing with Dynamic Mask Generation Open
Recent advancements in generative models have revolutionized image generation and editing, making these tasks accessible to non-experts. This paper focuses on local image editing, particularly the task of adding new content to a loosely sp…
View article: Stable Flow: Vital Layers for Training-Free Image Editing
Stable Flow: Vital Layers for Training-Free Image Editing Open
Diffusion models have revolutionized the field of content synthesis and editing. Recent models have replaced the traditional UNet architecture with the Diffusion Transformer (DiT), and employed flow-matching for improved training and sampl…
View article: Click2Mask: Local Editing with Dynamic Mask Generation
Click2Mask: Local Editing with Dynamic Mask Generation Open
Recent advancements in generative models have revolutionized image generation and editing, making these tasks accessible to non-experts. This paper focuses on local image editing, particularly the task of adding new content to a loosely sp…
View article: The Chosen One: Consistent Characters in Text-to-Image Diffusion Models
The Chosen One: Consistent Characters in Text-to-Image Diffusion Models Open
Recent advances in text-to-image generation models have unlocked vast potential for visual creativity. However, the users that use these models struggle with the generation of consistent characters, a crucial aspect for numerous real-world…
View article: DiffUHaul: A Training-Free Method for Object Dragging in Images
DiffUHaul: A Training-Free Method for Object Dragging in Images Open
Text-to-image diffusion models have proven effective for solving many image editing tasks. However, the seemingly straightforward task of seamlessly relocating objects within a scene remains surprisingly challenging. Existing methods addre…
View article: PALP: Prompt Aligned Personalization of Text-to-Image Models
PALP: Prompt Aligned Personalization of Text-to-Image Models Open
Content creators often aim to create personalized images using personal subjects that go beyond the capabilities of conventional text-to-image models. Additionally, they may want the resulting image to encompass a specific location, style,…
View article: Break-A-Scene: Extracting Multiple Concepts from a Single Image
Break-A-Scene: Extracting Multiple Concepts from a Single Image Open
Text-to-image model personalization aims to introduce a user-provided concept to the model, allowing its synthesis in diverse contexts. However, current methods primarily focus on the case of learning a single concept from multiple images …
View article: The Chosen One: Consistent Characters in Text-to-Image Diffusion Models
The Chosen One: Consistent Characters in Text-to-Image Diffusion Models Open
Recent advances in text-to-image generation models have unlocked vast potential for visual creativity. However, the users that use these models struggle with the generation of consistent characters, a crucial aspect for numerous real-world…
View article: Blended Latent Diffusion
Blended Latent Diffusion Open
The tremendous progress in neural image generation, coupled with the emergence of seemingly omnipotent vision-language models has finally enabled text-based interfaces for creating and editing images. Handling generic images requires a div…
View article: Blended-NeRF: Zero-Shot Object Generation and Blending in Existing Neural Radiance Fields
Blended-NeRF: Zero-Shot Object Generation and Blending in Existing Neural Radiance Fields Open
Editing a local region or a specific object in a 3D scene represented by a NeRF or consistently blending a new realistic object into the scene is challenging, mainly due to the implicit nature of the scene representation. We present Blende…
View article: Break-A-Scene: Extracting Multiple Concepts from a Single Image
Break-A-Scene: Extracting Multiple Concepts from a Single Image Open
Text-to-image model personalization aims to introduce a user-provided concept to the model, allowing its synthesis in diverse contexts. However, current methods primarily focus on the case of learning a single concept from multiple images …
View article: SpaText: Spatio-Textual Representation for Controllable Image Generation
SpaText: Spatio-Textual Representation for Controllable Image Generation Open
Recent text-to-image diffusion models are able to generate convincing results of unprecedented quality. However, it is nearly impossible to control the shapes of different regions/objects or their layout in a fine-grained fashion. Previous…
View article: Blended Latent Diffusion
Blended Latent Diffusion Open
The tremendous progress in neural image generation, coupled with the emergence of seemingly omnipotent vision-language models has finally enabled text-based interfaces for creating and editing images. Handling generic images requires a div…
View article: Blended Diffusion for Text-driven Editing of Natural Images
Blended Diffusion for Text-driven Editing of Natural Images Open
Natural language offers a highly intuitive interface for image editing. In\nthis paper, we introduce the first solution for performing local (region-based)\nedits in generic natural images, based on a natural language description along\nwi…
View article: Ownership and Creativity in Generative Models
Ownership and Creativity in Generative Models Open
Machine learning generated content such as image artworks, textual poems and music become prominent in recent years. These tools attract much attention from the media, artists, researchers, and investors. Because these tools are data-drive…
View article: Blended Diffusion for Text-driven Editing of Natural Images
Blended Diffusion for Text-driven Editing of Natural Images Open
Natural language offers a highly intuitive interface for image editing. In this paper, we introduce the first solution for performing local (region-based) edits in generic natural images, based on a natural language description along with …
View article: GAN Cocktail: mixing GANs without dataset access
GAN Cocktail: mixing GANs without dataset access Open
Today's generative models are capable of synthesizing high-fidelity images, but each model specializes on a specific target domain. This raises the need for model merging: combining two or more pretrained generative models into a single un…