997 citations · 2.4k across the 65 of their papers we have counts for
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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…
Turbo-DDCM: Fast and Flexible Zero-Shot Diffusion-Based Image Compression
Amit Vaisman, Guy Ohayon, Hila Manor +2
While zero-shot diffusion-based compression methods have seen significant progress in recent years, they remain notoriously slow and computationally demanding. This paper presents…
FlowOpt: Fast Optimization Through Whole Flow Processes for Training-Free Editing
Or Ronai, Vladimir Kulikov, Tomer Michaeli
The remarkable success of diffusion and flow-matching models has ignited a surge of works on adapting them at test time for controlled generation tasks. Examples range from image e…
When Diffusion Models Memorize: Inductive Biases in Probability Flow of Minimum-Norm Shallow Neural Nets
Chen Zeno, Hila Manor, Greg Ongie +3
While diffusion models generate high-quality images via probability flow, the theoretical understanding of this process remains incomplete. A key question is when probability flow…
InvFusion: Bridging Supervised and Zero-shot Diffusion for Inverse Problems
Noam Elata, Hyungjin Chung, Jong Chul Ye +2
Diffusion Models have demonstrated remarkable capabilities in handling inverse problems, offering high-quality posterior-sampling-based solutions. Despite significant advances, a f…
Compressed Image Generation with Denoising Diffusion Codebook Models
Guy Ohayon, Hila Manor, Tomer Michaeli +1
We present a novel generative approach based on Denoising Diffusion Models (DDMs), which produces high-quality image samples along with their losslessly compressed bit-stream repre…