Summary of Dice: Discrete Inversion Enabling Controllable Editing For Multinomial Diffusion and Masked Generative Models, by Xiaoxiao He et al.
DICE: Discrete Inversion Enabling Controllable Editing for Multinomial Diffusion and Masked Generative Models
by Xiaoxiao He, Ligong Han, Quan Dao, Song Wen, Minhao Bai, Di Liu, Han Zhang, Martin Renqiang Min, Felix Juefei-Xu, Chaowei Tan, Bo Liu, Kang Li, Hongdong Li, Junzhou Huang, Faez Ahmed, Akash Srivastava, Dimitris Metaxas
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 paper introduces DICE (Discrete Inversion for Controllable Editing), a novel approach to enable precise inversion and controlled editing of discrete diffusion models. The method, which includes multinomial diffusion and masked generative models, is the first to accurately reconstruct and flexibly edit discrete data without predefined masks or attention manipulation. The authors demonstrate the effectiveness of DICE across image and text domains, evaluating it on models such as VQ-Diffusion, Paella, and RoBERTa. Results show that DICE preserves high data fidelity while enhancing editing capabilities, offering new opportunities for fine-grained content manipulation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Discrete diffusion models are really good at making images or writing texts that look like they were written by a human. But there’s one problem: it’s hard to change specific parts of what was created. Imagine you drew a picture and wanted to change the color of just one tree, but not the rest of the picture. That’s difficult with current methods. This paper introduces DICE, which helps solve this problem by allowing us to precisely control what we edit in a discrete diffusion model. It works for both pictures and texts, and it’s really good at preserving the quality of what was created while letting us make changes. |
Keywords
» Artificial intelligence » Attention » Diffusion » Diffusion model
