Summary of Generalized Consistency Trajectory Models For Image Manipulation, by Beomsu Kim and Jaemin Kim and Jeongsol Kim and Jong Chul Ye
Generalized Consistency Trajectory Models for Image Manipulation
by Beomsu Kim, Jaemin Kim, Jeongsol Kim, Jong Chul Ye
First submitted to arxiv on: 19 Mar 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 This research paper proposes Generalized Consistency Trajectory Models (GCTMs) to unlock the full potential of consistency trajectory models (CTMs). CTMs excel in translating between any time points along the probability flow ODE (PFODE) and score inference with a single function evaluation. However, they only allow translation from Gaussian noise to data. The proposed GCTMs can translate between arbitrary distributions via ODEs. The authors discuss the design space of GCTMs and demonstrate their efficacy in various image manipulation tasks such as image-to-image translation, restoration, and editing. They highlight the fine-grained control over the generation process by injecting guidance terms into each denoising step. This work has potential applications in unconditional generation, image editing, and restoration. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to improve computer-generated images. Right now, we can make changes to pictures, but it’s not very efficient or flexible. The researchers are proposing a new method called Generalized Consistency Trajectory Models (GCTMs) that allows for more control and flexibility in image manipulation tasks like translation, restoration, and editing. This could have big implications for how we create and edit images in the future. |
Keywords
* Artificial intelligence * Inference * Probability * Translation
