Summary of Vipaint: Image Inpainting with Pre-trained Diffusion Models Via Variational Inference, by Sakshi Agarwal et al.
VIPaint: Image Inpainting with Pre-Trained Diffusion Models via Variational Inference
by Sakshi Agarwal, Gabe Hoope, Erik B. Sudderth
First submitted to arxiv on: 28 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 hierarchical variational inference algorithm, called VIPaint, efficiently conditions latent diffusion models on masked or partial images. By analytically marginalizing missing features and optimizing a non-Gaussian Markov approximation of the true diffusion posterior, VIPaint outperforms previous approaches in both plausibility and diversity of imputations. This method is easily generalized to other inverse problems like deblurring and superresolution. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way has been found to generate realistic images from noisy data. The old methods were not very good at filling in missing parts, especially for large areas. To solve this problem, a special algorithm called VIPaint was developed. It uses a clever trick to efficiently remove noise and fill in the gaps. This method works well not only for image generation but also for other problems like removing blur or enhancing resolution. |
Keywords
» Artificial intelligence » Diffusion » Image generation » Inference
