Summary of Belm: Bidirectional Explicit Linear Multi-step Sampler For Exact Inversion in Diffusion Models, by Fangyikang Wang et al.
BELM: Bidirectional Explicit Linear Multi-step Sampler for Exact Inversion in Diffusion Models
by Fangyikang Wang, Hubery Yin, Yuejiang Dong, Huminhao Zhu, Chao Zhang, Hanbin Zhao, Hui Qian, Chen Li
First submitted to arxiv on: 9 Oct 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 The paper proposes a new approach to solving the inverse problem in diffusion models, which is crucial for various tasks. The authors introduce a generic formulation called Bidirectional Explicit Linear Multi-step (BELM) samplers, which includes all previously proposed exact inversion samplers as special cases. BELM is derived from the variable-stepsize-variable-formula linear multi-step method and features a bidirectional explicit constraint that ensures mathematically exact inversion. The paper also investigates the Local Truncation Error (LTE) within the BELM framework and shows that existing heuristic designs of exact inversion samplers yield sub-optimal LTE. To address this, the authors propose an Optimal BELM (O-BELM) sampler through LTE minimization and demonstrate its stability and global convergence properties. Comprehensive experiments validate O-BELM’s ability to establish exact inversion while achieving high-quality sampling. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a big problem in making computers generate realistic images. They create a new way to find the original noise that was used to make an image, which is important for many tasks like image editing and generation. The authors show how their method works by comparing it to existing methods and doing experiments to test its performance. |
Keywords
» Artificial intelligence » Diffusion
