Summary of Learning to Discretize Denoising Diffusion Odes, by Vinh Tong and Trung-dung Hoang and Anji Liu and Guy Van Den Broeck and Mathias Niepert
Learning to Discretize Denoising Diffusion ODEs
by Vinh Tong, Trung-Dung Hoang, Anji Liu, Guy Van den Broeck, Mathias Niepert
First submitted to arxiv on: 24 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 framework called LD3, which aims to optimize the sampling process of Diffusion Probabilistic Models (DPMs) for image synthesis and 3D point cloud generation. DPMs are competitive generative models that require multiple neural function evaluations (NFEs) to transform Gaussian noise samples into images, resulting in higher computational costs compared to single-step generative models like GANs or VAEs. To reduce the number of NFEs while preserving generation quality, LD3 learns the optimal time discretization for sampling, which can be combined with various samplers and improves generation quality without requiring retraining resource-intensive neural networks. The proposed method is evaluated through extensive experiments on 7 pre-trained models, covering unconditional and conditional sampling in both pixel-space and latent-space DPMs, achieving FIDs of 2.38 (10 NFE) and 2.27 (10 NFE) on unconditional CIFAR10 and AFHQv2 within 5-10 minutes of training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LD3 is a new way to make computers generate images faster and better. Currently, these computer models need many calculations to create an image from noise. This takes a lot of time and energy. LD3 helps by finding the best way to break down these calculations into smaller steps. This makes the process faster and more efficient. The researchers tested this method with 7 different computer models and found that it worked well, even when generating images quickly. |
Keywords
» Artificial intelligence » Diffusion » Image synthesis » Latent space
