Summary of Flowedit: Inversion-free Text-based Editing Using Pre-trained Flow Models, by Vladimir Kulikov and Matan Kleiner and Inbar Huberman-spiegelglas and Tomer Michaeli
FlowEdit: Inversion-Free Text-Based Editing Using Pre-Trained Flow Models
by Vladimir Kulikov, Matan Kleiner, Inbar Huberman-Spiegelglas, Tomer Michaeli
First submitted to arxiv on: 11 Dec 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 proposed method, FlowEdit, is an innovative text-based editing technique for pre-trained text-to-image (T2I) flow models that eliminates the need for inversion or optimization. By constructing an ordinary differential equation (ODE) that directly maps between the source and target distributions, FlowEdit achieves a lower transport cost compared to traditional inversion approaches. This model-agnostic method is demonstrated to produce state-of-the-art results on Stable Diffusion 3 and FLUX datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FlowEdit is a new way to edit real images using text-to-image models. Usually, you need to invert the image into its noise map, but that’s not always enough. This method is special because it doesn’t require inverting or optimizing the model. Instead, it uses an equation that directly changes the source image to the target image based on the text prompts. This makes it work with different models and produces great results. |
Keywords
» Artificial intelligence » Diffusion » Optimization
