5 papers · 1 filter
NIV: Neural Axis Variations for Variable Font Generation
Nadav Benedek, Ariel Shamir, Ohad Fried
Variable fonts enable continuous variation of glyph geometry along semantic design axes such as weight, width, slant, and optical size. However, constructing a variable font from a…
Optimal Transport Flow Matching by Design
Shimon Malnick, Matan Rusanovsky, Ohad Fried +1
Flow matching models learn to transport samples from a simple prior distribution to a complex data distribution. When prior-data pairs are coupled via optimal transport (OT), the l…
The Amazing Stability of Flow Matching
Rania Briq, Michael Kamp, Ohad Fried +2
The success of deep generative models in generating high-quality and diverse samples is often attributed to particular architectures and large training datasets. In this paper, we…
Tiled Diffusion
Or Madar, Ohad Fried
Image tiling -- the seamless connection of disparate images to create a coherent visual field -- is crucial for applications such as texture creation, video game asset development,…
Memories of Forgotten Concepts
Matan Rusanovsky, Shimon Malnick, Amir Jevnisek +2
Diffusion models dominate the space of text-to-image generation, yet they may produce undesirable outputs, including explicit content or private data. To mitigate this, concept abl…