Summary of Sfddm: Single-fold Distillation For Diffusion Models, by Chi Hong et al.
SFDDM: Single-fold Distillation for Diffusion models
by Chi Hong, Jiyue Huang, Robert Birke, Dick Epema, Stefanie Roos, Lydia Y. Chen
First submitted to arxiv on: 23 May 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 The paper proposes a novel approach called Single-Fold Distillation Model (SFDDM) to accelerate the inference process of diffusion models while maintaining high-quality synthetic image generation. By retraining the teacher model in a progressive and binary manner, SFDDM compresses the 1024-step original model into a student model of any desired step size. This is achieved by minimizing not only the output distance but also the distribution of hidden variables between the teacher and student models. The proposed algorithm is evaluated on four datasets, demonstrating that the student model can generate high-quality data with inference steps reduced to as little as approximately 1%, achieving semantic consistency and meaningful image interpolation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a new way to make computer-generated images more efficient. It’s like shrinking a big program into a smaller one without losing its ability to create realistic pictures. This is done by copying the knowledge from the original big model into a smaller model, step-by-step. The result is an image generator that can produce high-quality images in much less time. The researchers tested their idea on four sets of data and showed that it works well. |
Keywords
» Artificial intelligence » Distillation » Image generation » Inference » Student model » Teacher model
