Summary of Dimsum: Diffusion Mamba — a Scalable and Unified Spatial-frequency Method For Image Generation, by Hao Phung et al.
DiMSUM: Diffusion Mamba – A Scalable and Unified Spatial-Frequency Method for Image Generation
by Hao Phung, Quan Dao, Trung Dao, Hoang Phan, Dimitris Metaxas, Anh Tran
First submitted to arxiv on: 6 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper introduces a novel state-space architecture for diffusion models that effectively harnesses spatial and frequency information to enhance the inductive bias towards local features in input images. The method, which builds upon Mamba, a recurrent neural network, integrates wavelet transformation to better capture long-range relations of frequencies by disentangling them into wavelet subbands. This is achieved through a cross-attention fusion layer that combines both spatial and frequency information to optimize the order awareness of state-space models. Additionally, the paper introduces a globally-shared transformer to supercharge Mamba’s performance, allowing it to capture global relationships. The method is evaluated on standard benchmarks and achieves superior results compared to DiT and DIFFUSSM, with faster training convergence and high-quality outputs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way for computers to generate images by combining different types of information. It uses something called Mamba, which is a special kind of neural network that can process sequences of data. The problem with this approach is that it doesn’t work well when dealing with image data, because images have lots of details and patterns that are hard to capture. To solve this issue, the researchers added a new step called wavelet transformation, which breaks down the data into different frequency bands. This helps the computer to better understand the relationships between different parts of the image. The result is a more accurate and detailed image generation. |
Keywords
» Artificial intelligence » Cross attention » Diffusion » Image generation » Neural network » Transformer
