Summary of Turboedit: Instant Text-based Image Editing, by Zongze Wu et al.
TurboEdit: Instant text-based image editing
by Zongze Wu, Nicholas Kolkin, Jonathan Brandt, Richard Zhang, Eli Shechtman
First submitted to arxiv on: 14 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 a novel approach to precise image inversion and disentangled image editing using few-step diffusion models. The proposed encoder-based iterative inversion technique conditions the inversion network on the input image and the reconstructed image from the previous step, allowing for correction towards the input image. This enables realistic text-guided image edits in real-time, requiring only 8 number of functional evaluations (NFEs) in inversion and 4 NFEs per edit. The method outperforms state-of-the-art multi-step diffusion editing techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine having a magic tool that can change the color of someone’s shirt or make their hair longer just by typing a few sentences. This paper is all about making that kind of technology possible for images. It develops a new way to edit pictures using a special kind of computer model called a “few-step diffusion model.” This model allows for fast and realistic changes to an image, like changing the color of someone’s shirt or their hair style, just by typing what you want to change. |
Keywords
* Artificial intelligence * Diffusion * Diffusion model * Encoder
