3 papers
cs.CV2026
Efficient Zero-Shot Inpainting with Decoupled Diffusion Guidance
Badr Moufad, Navid Bagheri Shouraki, Alain Oliviero Durmus +4
Diffusion models have emerged as powerful priors for image editing tasks such as inpainting and local modification, where the objective is to generate realistic content that remain…
cs.CV2026
When Test-Time Guidance Is Enough: Fast Image and Video Editing with Diffusion Guidance
Ahmed Ghorbel, Badr Moufad, Navid Bagheri Shouraki +5
Text-driven image and video editing can be naturally cast as inpainting problems, where masked regions are reconstructed to remain consistent with both the observed content and the…
stat.ML2026
Fast and Robust Likelihood-Guided Diffusion Posterior Sampling with Amortized Variational Inference
Léon Zheng, Thomas Hirtz, Yazid Janati +1
Zero-shot diffusion posterior sampling offers a flexible framework for inverse problems by accommodating arbitrary degradation operators at test time, but incurs high computational…