Summary of An Ordinary Differential Equation Sampler with Stochastic Start For Diffusion Bridge Models, by Yuang Wang et al.
An Ordinary Differential Equation Sampler with Stochastic Start for Diffusion Bridge Models
by Yuang Wang, Pengfei Jin, Li Zhang, Quanzheng Li, Zhiqiang Chen, Dufan Wu
First submitted to arxiv on: 28 Dec 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 research proposes a novel high-order Ordinary Differential Equation (ODE) sampler for diffusion bridge models to improve their inference speed. The existing Stochastic Differential Equation (SDE) samplers used by these models result in slower processing times compared to ODE-based solvers. To address this limitation, the authors introduce a stochastic start to the reverse process using a posterior sampling approach, ensuring a smooth transition from corrupted images to the generative trajectory while reducing discretization errors. The proposed method is fully compatible with pre-trained diffusion bridge models and requires no additional training. The sampler outperforms state-of-the-art methods in both visual quality and Frechet Inception Distance (FID) on various image restoration and translation tasks, including super-resolution, JPEG restoration, Edges-to-Handbags, and DIODE-Outdoor. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Diffusion bridge models can generate images based on corrupted pictures. However, these models often use samplers that make them slow. The researchers created a new sampler to speed up the process without needing more training. They introduced a way to start the reverse process smoothly and reduce errors. This method works well with pre-trained models and outperforms others in image restoration and translation tasks. |
Keywords
» Artificial intelligence » Diffusion » Inference » Super resolution » Translation
