Summary of Diffusion Bridge Implicit Models, by Kaiwen Zheng et al.
Diffusion Bridge Implicit Models
by Kaiwen Zheng, Guande He, Jianfei Chen, Fan Bao, Jun Zhu
First submitted to arxiv on: 24 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 Denoising diffusion bridge models (DDBMs) are a powerful variant of diffusion models for interpolating between two arbitrary paired distributions given as endpoints. This paper presents a novel approach to fast sampling of DDBMs without extra training, building upon established recipes in diffusion models. The authors generalize DDBMs via non-Markovian diffusion bridges defined on discretized timesteps, resulting in generative processes ranging from stochastic to deterministic. These diffusion bridge implicit models (DBIMs) are up to 25 times faster than the vanilla sampler of DDBMs and induce a novel ordinary differential equation (ODE) that inspires high-order numerical solvers. DBIMs maintain generation diversity through bootstrapping noise, enabling faithful encoding, reconstruction, and semantic interpolation in image translation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Denoising diffusion bridge models are a new way to connect two groups of things together. This paper shows how to make this process faster without needing extra training. They do this by adding some extra rules to the model that make it go from random to predictable. This makes it 25 times faster than before! It also gives us a simple way to solve tricky math problems and helps keep the results diverse. |
Keywords
» Artificial intelligence » Bootstrapping » Diffusion » Translation
