997 citations · 2.4k across the 65 of their papers we have counts for
77 papers
H-DDCM: Hadamard Codebooks for Fast Generative Image and Video Compression
Or Ronai, Vladimir Kulikov, Tomer Michaeli
Training-free diffusion-based compression methods repurpose the generative priors of a diffusion or a flow model to compress data without retraining them as task-specific codecs. R…
Text-to-Image Models Need Less from Text Encoders Than You Think
Nurit Spingarn, Noa Cohen, Tamar Rott Shaham +1
Text-to-image models rely on text prompts as their primary interface to human intent. Prompts are encoded by a text encoder into embeddings that condition the image generation proc…
Versatile Editing of Video Content, Actions, and Dynamics without Training
Vladimir Kulikov, Roni Paiss, Andrey Voynov +3
Controlled video generation has seen drastic improvements in recent years. However, editing actions and dynamic events, or inserting contents that should affect the behaviors of ot…
Spanning the Visual Analogy Space with a Weight Basis of LoRAs
Hila Manor, Rinon Gal, Haggai Maron +2
Visual analogy learning enables image editing via demonstration rather than textual description, allowing users to specify complex transformations difficult to articulate in words.…
Illumination Angular Spectrum Encoding for Controlling the Functionality of Diffractive Networks
Matan Kleiner, Lior Michaeli, Tomer Michaeli
Diffractive neural networks have recently emerged as a promising framework for all-optical computing. However, these networks are typically trained for a single task, limiting thei…
MineTheGap: Automatic Mining of Biases in Text-to-Image Models
Noa Cohen, Nurit Spingarn-Eliezer, Inbar Huberman-Spiegelglas +1
Text-to-Image (TTI) models generate images based on text prompts, which often leave certain aspects of the desired image ambiguous. When faced with these ambiguities, TTI models ha…