[2603.23463] InverFill: One-Step Inversion for Enhanced Few-Step Diffusion Inpainting

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.23463 (cs) [Submitted on 24 Mar 2026] Title:InverFill: One-Step Inversion for Enhanced Few-Step Diffusion Inpainting Authors:Duc Vu, Kien Nguyen, Trong-Tung Nguyen, Ngan Nguyen, Phong Nguyen, Khoi Nguyen, Cuong Pham, Anh Tran View a PDF of the paper titled InverFill: One-Step Inversion for Enhanced Few-Step Diffusion Inpainting, by Duc Vu and 7 other authors View PDF HTML (experimental) Abstract:Recent diffusion-based models achieve photorealism in image inpainting but require many sampling steps, limiting practical use. Few-step text-to-image models offer faster generation, but naively applying them to inpainting yields poor harmonization and artifacts between the background and inpainted region. We trace this cause to random Gaussian noise initialization, which under low function evaluations causes semantic misalignment and reduced fidelity. To overcome this, we propose InverFill, a one-step inversion method tailored for inpainting that injects semantic information from the input masked image into the initial noise, enabling high-fidelity few-step inpainting. Instead of training inpainting models, InverFill leverages few-step text-to-image models in a blended sampling pipeline with semantically aligned noise as input, significantly improving vanilla blended sampling and even matching specialized inpainting models at low NFEs. Moreover, InverFill does not require real-image supervision and only adds min...