Summary of One-step Diffusion Distillation Via Deep Equilibrium Models, by Zhengyang Geng and Ashwini Pokle and J. Zico Kolter
One-Step Diffusion Distillation via Deep Equilibrium Models
by Zhengyang Geng, Ashwini Pokle, J. Zico Kolter
First submitted to arxiv on: 12 Dec 2023
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 research paper introduces a novel approach for distilling diffusion models into faster networks, tackling challenges faced by existing methods. The proposed method leverages the Deep Equilibrium (DEQ) model, specifically the Generative Equilibrium Transformer (GET), to enable fully offline training with noise/image pairs from the diffusion model. This achieves superior performance compared to one-step methods on comparable training budgets, while maintaining a balance between computational cost and image quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way is found to make computer models that create images faster and better! Usually, these models need many tries to get good results, but this method makes it happen in just one go. It uses a special kind of model called the Generative Equilibrium Transformer (GET) and works with noise and image pairs to train it. This means we can make really cool pictures without needing so much computing power. |
Keywords
* Artificial intelligence * Diffusion * Diffusion model * Transformer
