Summary of Adjointdeis: Efficient Gradients For Diffusion Models, by Zander W. Blasingame and Chen Liu
AdjointDEIS: Efficient Gradients for Diffusion Models
by Zander W. Blasingame, Chen Liu
First submitted to arxiv on: 23 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Dynamical Systems (math.DS); Machine Learning (stat.ML)
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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 family of bespoke ODE solvers, called AdjointDEIS, to optimize the latents and parameters of diffusion models with respect to differentiable metrics. This is achieved by solving continuous adjoint equations using exponential integrators, which simplify to a simple ODE for diffusion SDEs. The proposed method addresses memory-intensive issues in naive backpropagation techniques and provides convergence order guarantees. AdjointDEIS is demonstrated to be effective in guided generation tasks, such as face morphing, with adversarial attacks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a big problem in computer science called “optimization” using special formulas for “diffusion models”. These formulas are like recipes that allow computers to make new and interesting things, like fake faces. The researchers created a new way to solve these recipes quickly and efficiently, which is important because it will help computers generate even more realistic fake images. |
Keywords
» Artificial intelligence » Backpropagation » Diffusion » Optimization
