242 citations · 342 across the 14 of their papers we have counts for
15 papers
Accelerating Text-to-Video Generation with Calibrated Sparse Attention
Shai Yehezkel, Shahar Yadin, Noam Elata +2
Recent diffusion models enable high-quality video generation, but suffer from slow runtimes. The large transformer-based backbones used in these models are bottlenecked by spatiote…
Novel View Synthesis with Pixel-Space Diffusion Models
Noam Elata, Bahjat Kawar, Yaron Ostrovsky-Berman +2
Synthesizing a novel view from a single input image is a challenging task. Traditionally, this task was approached by estimating scene depth, warping, and inpainting, with machine…
Simple ReFlow: Improved Techniques for Fast Flow Models
Beomsu Kim, Yu-Guan Hsieh, Michal Klein +4
Diffusion and flow-matching models achieve remarkable generative performance but at the cost of many sampling steps, this slows inference and limits applicability to time-critical…
Nested Diffusion Processes for Anytime Image Generation
Noam Elata, Bahjat Kawar, Tomer Michaeli +1
Diffusion models are the current state-of-the-art in image generation, synthesizing high-quality images by breaking down the generation process into many fine-grained denoising ste…
GSURE-Based Diffusion Model Training with Corrupted Data
Bahjat Kawar, Noam Elata, Tomer Michaeli +1
Diffusion models have demonstrated impressive results in both data generation and downstream tasks such as inverse problems, text-based editing, classification, and more. However,…
Editing Implicit Assumptions in Text-to-Image Diffusion Models
Hadas Orgad, Bahjat Kawar, Yonatan Belinkov
Text-to-image diffusion models often make implicit assumptions about the world when generating images. While some assumptions are useful (e.g., the sky is blue), they can also be o…
Image Denoising: The Deep Learning Revolution and Beyond -- A Survey Paper --
Michael Elad, Bahjat Kawar, Gregory Vaksman
Image denoising (removal of additive white Gaussian noise from an image) is one of the oldest and most studied problems in image processing. An extensive work over several decades…
Imagic: Text-Based Real Image Editing with Diffusion Models
Bahjat Kawar, Shiran Zada, Oran Lang +5
Text-conditioned image editing has recently attracted considerable interest. However, most methods are currently either limited to specific editing types (e.g., object overlay, sty…
JPEG Artifact Correction using Denoising Diffusion Restoration Models
Bahjat Kawar, Jiaming Song, Stefano Ermon +1
Diffusion models can be used as learned priors for solving various inverse problems. However, most existing approaches are restricted to linear inverse problems, limiting their app…
Enhancing Diffusion-Based Image Synthesis with Robust Classifier Guidance
Bahjat Kawar, Roy Ganz, Michael Elad
Denoising diffusion probabilistic models (DDPMs) are a recent family of generative models that achieve state-of-the-art results. In order to obtain class-conditional generation, it…
Threat Model-Agnostic Adversarial Defense using Diffusion Models
Tsachi Blau, Roy Ganz, Bahjat Kawar +2
Deep Neural Networks (DNNs) are highly sensitive to imperceptible malicious perturbations, known as adversarial attacks. Following the discovery of this vulnerability in real-world…
Do Perceptually Aligned Gradients Imply Adversarial Robustness?
Roy Ganz, Bahjat Kawar, Michael Elad
Adversarially robust classifiers possess a trait that non-robust models do not -- Perceptually Aligned Gradients (PAG). Their gradients with respect to the input align well with hu…
Denoising Diffusion Restoration Models
Bahjat Kawar, Michael Elad, Stefano Ermon +1
Many interesting tasks in image restoration can be cast as linear inverse problems. A recent family of approaches for solving these problems uses stochastic algorithms that sample…
SNIPS: Solving Noisy Inverse Problems Stochastically
Bahjat Kawar, Gregory Vaksman, Michael Elad
In this work we introduce a novel stochastic algorithm dubbed SNIPS, which draws samples from the posterior distribution of any linear inverse problem, where the observation is ass…
Stochastic Image Denoising by Sampling from the Posterior Distribution
Bahjat Kawar, Gregory Vaksman, Michael Elad
Image denoising is a well-known and well studied problem, commonly targeting a minimization of the mean squared error (MSE) between the outcome and the original image. Unfortunatel…