Summary of Zigma: a Dit-style Zigzag Mamba Diffusion Model, by Vincent Tao Hu et al.
ZigMa: A DiT-style Zigzag Mamba Diffusion Model
by Vincent Tao Hu, Stefan Andreas Baumann, Ming Gui, Olga Grebenkova, Pingchuan Ma, Johannes Schusterbauer, Björn Ommer
First submitted to arxiv on: 20 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); 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 A novel State-Space Model called Mamba is leveraged to generate visual data, addressing scalability and quadratic complexity issues in diffusion models. A critical oversight in current Mamba-based vision methods is identified: the lack of consideration for spatial continuity. The proposed Zigzag Mamba method outperforms baselines in terms of speed and memory utilization while demonstrating improved performance. This approach is integrated with the Stochastic Interpolant framework to investigate scalability on large-resolution visual datasets, including FacesHQ, UCF101, MultiModal-CelebA-HQ, and MS COCO. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Visual data generation using State-Space Models is made possible by leveraging Mamba’s long sequence modeling capabilities. The problem with current methods is that they don’t consider spatial continuity in the scan scheme of Mamba. A new method called Zigzag Mamba is introduced, which is simple to use and doesn’t require any extra parameters. It performs better than previous methods and uses less memory and time. This approach is combined with the Stochastic Interpolant framework to test how well it works on large images. |
Keywords
* Artificial intelligence * Diffusion
