Summary of Image Inpainting Via Tractable Steering Of Diffusion Models, by Anji Liu et al.
Image Inpainting via Tractable Steering of Diffusion Models
by Anji Liu, Mathias Niepert, Guy Van den Broeck
First submitted to arxiv on: 28 Nov 2023
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 approach leverages Tractable Probabilistic Models (TPMs) to control the sampling process for constrained image generation tasks like inpainting. Building upon Probabilistic Circuits (PCs), this method scales up PCs to guide the denoising process of diffusion models, improving the quality and semantic coherence of generated images. The approach is tested on three natural image datasets with only a 10% increase in computational overhead. Additionally, this framework allows for more controlled image generation by accepting semantic constraints on specific regions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to generate realistic images by using special models called Tractable Probabilistic Models (TPMs). These models help us make better choices when generating images that need to meet certain conditions, like filling in missing parts. The researchers used something called Probabilistic Circuits (PCs) and made them bigger and more powerful so they could guide the image generation process. They tested it on three types of images and saw big improvements! It also lets us add special instructions for specific parts of the image. |
Keywords
* Artificial intelligence * Image generation
