9 papers · 1 filter
Latent Space Jam: Layout Guided Video Synthesis
Asaf Joseph, Moshe Mandel, Ohad Fried
Adding control to video generation often requires training, which demands additional computational resources, data, or architectural changes. We propose a training-free approach th…
SoftCache: Adaptive Step Size Caching for Diffusion Transformers
Shimon Malnick, Nadav Orenstein, Ohad Fried +1
Diffusion and flow models generate a sample over iterative steps, each a full pass through a large network, and that pass dominates generation cost. Training-free caching accelerat…
Tracing Generated Samples to Training-Data Clusters in Flow-Matching Models
Rania Briq, Ohad Fried, Michael Kamp +1
Understanding which training samples influence a generated image is an important problem in generative modeling. In flow matching, training samples influence the generated image th…
Gefen: Optimized Stochastic Optimizer
Nadav Benedek, Tomer Koren, Ohad Fried
AdamW is a default optimizer for deep learning, but its moment states add two parameter-sized buffers to training memory, increasing the cost of large-scale pretraining. We propose…
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…
Exploring and Exploiting Stability in Latent Flow Matching
Rania Briq, Michael Kamp, Ohad Fried +2
In this work, we show that Latent Flow-Matching (LFM) models are robust to different types of perturbations, including data reduction and model capacity shrinkage. We characterize…
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…
Copy-Trasform-Paste: Zero-Shot Object-Object Alignment Guided by Vision-Language and Geometric Constraints
Rotem Gatenyo, Ohad Fried
We study zero-shot 3D alignment of two given meshes, using a text prompt describing their spatial relation -- an essential capability for content creation and scene assembly. Earli…