Summary of Sit: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers, by Nanye Ma et al.
SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers
by Nanye Ma, Mark Goldstein, Michael S. Albergo, Nicholas M. Boffi, Eric Vanden-Eijnden, Saining Xie
First submitted to arxiv on: 16 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 Scalable Interpolant Transformers (SiT) is a family of generative models that leverages the backbone of Diffusion Transformers (DiT). The interpolant framework enables connecting two distributions in a more flexible manner than standard diffusion models. This allows for exploring various design choices impacting generative models built on dynamical transport, such as learning in discrete or continuous time, objective functions, interpolants, and deterministic or stochastic sampling. SiT surpasses DiT uniformly across model sizes on the conditional ImageNet 256×256 and 512×512 benchmark using the same model structure, number of parameters, and GFLOPs. The paper explores various diffusion coefficients that can be tuned separately from learning, achieving an FID-50K score of 2.06 and 2.62, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Scalable Interpolant Transformers (SiT) is a new way to create fake images that’s better than before. It works by connecting two distributions in a special way. This lets us try different ideas for making the images more realistic. SiT does this job really well and can make fake images that look almost as good as real ones. |
Keywords
* Artificial intelligence * Diffusion
