Summary of Dreamsampler: Unifying Diffusion Sampling and Score Distillation For Image Manipulation, by Jeongsol Kim et al.
DreamSampler: Unifying Diffusion Sampling and Score Distillation for Image Manipulation
by Jeongsol Kim, Geon Yeong Park, Jong Chul Ye
First submitted to arxiv on: 18 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 The proposed DreamSampler framework combines the strengths of reverse sampling and score distillation in latent diffusion models (LDMs) for image manipulation. This model-agnostic approach leverages regularized latent optimization, allowing for both distillation and reverse sampling with additional guidance for image editing and reconstruction. Compared to existing methods, DreamSampler demonstrates competitive performance in various applications such as image editing and SVG reconstruction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DreamSampler is a new framework that combines two techniques, reverse diffusion sampling and score distillation, to improve image manipulation using latent diffusion models (LDMs). This method works with any LDM architecture and provides better results than existing methods. It’s useful for tasks like editing images and reconstructing SVG files. |
Keywords
* Artificial intelligence * Diffusion * Distillation * Optimization
