Summary of Efficient Fine-tuning and Concept Suppression For Pruned Diffusion Models, by Reza Shirkavand et al.
Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models
by Reza Shirkavand, Peiran Yu, Shangqian Gao, Gowthami Somepalli, Tom Goldstein, Heng Huang
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 paper presents a novel bilevel optimization framework for pruned diffusion generative models to address the issue of undesirable behavior propagation during knowledge distillation. The authors build upon recent advances in diffusion models and propose a unified approach that combines fine-tuning and unlearning processes to selectively suppress the generation of unwanted content. This plug-in framework is compatible with various pruning and concept unlearning methods, enabling efficient and safe deployment of diffusion models in controlled environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to make computer-generated images safer! The current method for making these images smaller and faster to compute has a problem: it can create images that contain copyrighted material or unsafe ideas. This is not what we want. In this paper, researchers introduce a new approach that solves this issue by combining two techniques in one step. They show how this new method works well with different approaches to make the images even better and safer. |
Keywords
» Artificial intelligence » Diffusion » Fine tuning » Knowledge distillation » Optimization » Pruning
