Summary of Physics Informed Distillation For Diffusion Models, by Joshua Tian Jin Tee et al.
Physics Informed Distillation for Diffusion Models
by Joshua Tian Jin Tee, Kang Zhang, Hee Suk Yoon, Dhananjaya Nagaraja Gowda, Chanwoo Kim, Chang D. Yoo
First submitted to arxiv on: 13 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 introduces Physics Informed Distillation (PID), a novel approach that leverages the connection between diffusion models and Probability Flow Ordinary Differential Equations (ODEs) to distill knowledge from teacher diffusion models into student models. This method, inspired by Physics Informed Neural Networks (PINNs), allows for efficient image generation without sacrificing performance. By framing diffusion models as ODE systems, PID eliminates the need for synthetic dataset generation during the distillation process and demonstrates predictable trends concerning method-specific hyperparameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Physics Informed Distillation is a new way to learn from one type of computer model (diffusion models) and teach it to another (student models). This helps diffusion models generate images faster without losing quality. The idea comes from linking diffusion models to ordinary differential equations, which are mathematical tools that describe how things change over time. By using this link, the new method avoids creating fake data during the learning process, making it easier to use and more efficient. |
Keywords
» Artificial intelligence » Distillation » Image generation » Probability
