Summary of Fast Sampling Through the Reuse Of Attention Maps in Diffusion Models, by Rosco Hunter et al.
Fast Sampling Through The Reuse Of Attention Maps In Diffusion Models
by Rosco Hunter, Łukasz Dudziak, Mohamed S. Abdelfattah, Abhinav Mehrotra, Sourav Bhattacharya, Hongkai Wen
First submitted to arxiv on: 13 Dec 2023
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Innovative text-to-image models have revolutionized realistic image synthesis, but their time-consuming sampling process has sparked efforts to reduce latency. Most approaches rely on retraining or fine-tuning additional networks for efficient generation. Our method takes a different route by directly reducing latency without retraining, using ODE theory-inspired strategies to reuse attention maps during sampling. We compare our approach with few-step sampling procedures of similar latency and find that reused images are closer to those produced by the original high-latency model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a super powerful tool for creating realistic images, but it takes a long time to make them. That’s what’s happening in text-to-image models right now. Some smart people are trying to make these tools work faster. Our team thought, “Why not try to speed up the process directly?” We discovered that some steps in the original model are doing similar things, so we can reuse those results instead of repeating them. It turns out this makes the images almost as good as if we had used the original tool all along! |
Keywords
» Artificial intelligence » Attention » Fine tuning » Image synthesis
