Summary of Enabling Local Editing in Diffusion Models by Joint and Individual Component Analysis, By Theodoros Kouzelis et al.
Enabling Local Editing in Diffusion Models by Joint and Individual Component Analysis
by Theodoros Kouzelis, Manos Plitsis, Mihalis A. Nicolaou, Yannis Panagakis
First submitted to arxiv on: 29 Aug 2024
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 This paper addresses the challenge of local image manipulation in Diffusion Models (DMs) by introducing an unsupervised method to factorize the latent semantics learned by the denoising network. The approach leverages the Jacobian of the denoising network to establish a relation between regions of interest and their corresponding subspaces in the latent space, enabling semantically consistent edits. By disentangling joint and individual components of these subspaces, the method can identify latent directions that facilitate local image manipulation. Experimental results on various datasets demonstrate that this approach produces semantic edits with better fidelity compared to state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to make changes to specific parts of images using a type of artificial intelligence called Diffusion Models. Currently, these models are good at making new images or changing the overall look and feel of an image. But they’re not very good at making precise changes to small areas of an image. This paper presents a way to overcome this limitation by analyzing the internal workings of the model and identifying patterns that can be used to make targeted edits. The method is demonstrated on several different types of images, with impressive results. |
Keywords
» Artificial intelligence » Diffusion » Latent space » Semantics » Unsupervised
