Summary of Code: Confident Ordinary Differential Editing, by Bastien Van Delft et al.
CODE: Confident Ordinary Differential Editing
by Bastien van Delft, Tommaso Martorella, Alexandre Alahi
First submitted to arxiv on: 22 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 authors introduce Confident Ordinary Differential Editing (CODE), a novel approach for image synthesis that effectively handles Out-of-Distribution (OoD) guidance images. By utilizing a diffusion model as a generative prior and score-based updates along the probability-flow Ordinary Differential Equation (ODE) trajectory, CODE enhances images without requiring task-specific training, handcrafted modules, or assumptions about corruptions. This method is compatible with any diffusion model and operates in a fully blind manner, relying solely on a pre-trained generative model. The authors’ contributions are twofold: introducing a novel ODE-based editing method providing enhanced control, realism, and fidelity, as well as a confidence interval-based clipping method improving CODE’s effectiveness by disregarding certain pixels or information. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CODE is a new way to edit images that works even when the input image is noisy or doesn’t look like what you want. This is important because it can be hard to make pictures look good when they’re not perfect. The authors use something called a diffusion model, which is like a recipe book for making images. They add some special math tricks to this recipe book and call it CODE. It’s like a superpower that lets them take any image and make it better without knowing what the original picture was supposed to look like. |
Keywords
» Artificial intelligence » Diffusion model » Generative model » Image synthesis » Probability
