Summary of Scaling Diffusion Mamba with Bidirectional Ssms For Efficient Image and Video Generation, by Shentong Mo et al.
Scaling Diffusion Mamba with Bidirectional SSMs for Efficient Image and Video Generation
by Shentong Mo, Yapeng Tian
First submitted to arxiv on: 24 May 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 Mamba architecture has been successful in modeling long sequences efficiently, but its application in image generation remains underexplored. Traditional diffusion transformers (DiT) are effective, but their computational complexity limits their use for high-resolution images. To address this challenge, the authors introduce Diffusion Mamba (DiM), which replaces traditional attention mechanisms with a scalable alternative. DiM achieves rapid inference times and reduced computational load, maintaining linear complexity with respect to sequence length. The architecture outperforms existing diffusion transformers in both image and video generation tasks. This work advances the field of generative models and paves the way for further applications of scalable architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Mamba architecture has been good at modeling long sequences, but it’s not been used much for generating images. The problem is that traditional image generators are too slow and take up too much computer power. To fix this, the researchers created a new generator called Diffusion Mamba (DiM). DiM is faster and uses less computer power than other generators. It’s also better at creating high-quality images and videos. This breakthrough could lead to more powerful image and video generation tools in the future. |
Keywords
» Artificial intelligence » Attention » Diffusion » Image generation » Inference
