Summary of Pom: Efficient Image and Video Generation with the Polynomial Mixer, by David Picard et al.
PoM: Efficient Image and Video Generation with the Polynomial Mixer
by David Picard, Nicolas Dufour
First submitted to arxiv on: 19 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 introduces the Polynomial Mixer (PoM), a drop-in replacement for Multi-Head Attention (MHA) in diffusion models. MHA is widely used to generate high-quality images and videos, but it has quadratic memory and compute requirements, making it computationally expensive. PoM addresses this issue by encoding the entire sequence into an explicit state, reducing complexity to linear with respect to the number of tokens. This allows for efficient parallel training and sequential frame generation. The authors demonstrate that PoM is a universal sequence-to-sequence approximator, comparable to MHA. They apply PoM to Diffusion Transformers (DiT) for image and video generation, achieving high-quality results while reducing computational resources. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it possible to generate really good images and videos using less computer power. Right now, we use something called Multi-Head Attention to do this, but it takes a lot of memory and computing power. The authors came up with a new idea called the Polynomial Mixer that solves this problem. It’s like a shortcut that makes the process faster and more efficient. They tested it on some big projects and got great results while using less computer power. |
Keywords
» Artificial intelligence » Diffusion » Multi head attention
