Summary of Renoise: Real Image Inversion Through Iterative Noising, by Daniel Garibi et al.
ReNoise: Real Image Inversion Through Iterative Noising
by Daniel Garibi, Or Patashnik, Andrey Voynov, Hadar Averbuch-Elor, Daniel Cohen-Or
First submitted to arxiv on: 21 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Graphics (cs.GR); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces an inversion method for powerful text-guided diffusion models that unlock image manipulation capabilities. The goal is to invert real images into the domain of these models while maintaining high quality and accuracy. The authors propose ReNoise, a novel iterative renoising mechanism that refines the approximation of predicted points along the forward diffusion trajectory. This approach enhances reconstruction accuracy without increasing computational complexity. The method is evaluated using various sampling algorithms and models, including recent accelerated diffusion models. Results show that ReNoise achieves high accuracy and speed while preserving editability for text-driven image editing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a way to make images more realistic by using special computer programs called diffusion models. These models are good at creating new images from scratch, but it’s hard to make them understand what existing images look like. The authors of this paper came up with a new method that can do this inversion better than before. They use an iterative process that refines the prediction of where a point is in the image. This makes the results more accurate and faster to compute. The authors tested their method on different types of models and showed it works well. |
Keywords
* Artificial intelligence * Diffusion
