Summary of Bespoke Non-stationary Solvers For Fast Sampling Of Diffusion and Flow Models, by Neta Shaul et al.
Bespoke Non-Stationary Solvers for Fast Sampling of Diffusion and Flow Models
by Neta Shaul, Uriel Singer, Ricky T. Q. Chen, Matthew Le, Ali Thabet, Albert Pumarola, Yaron Lipman
First submitted to arxiv on: 2 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 paper introduces Bespoke Non-Stationary (BNS) Solvers, a solver distillation approach that improves the sample efficiency of Diffusion and Flow models. The BNS solvers are based on a family of non-stationary solvers that outperform existing numerical ODE solvers in terms of sample approximation performance, measured by PSNR. Compared to model distillation, BNS solvers have a tiny parameter space (<200 parameters), fast optimization (two orders of magnitude faster), and maintain the diversity of samples. They also nearly close the gap with standard distillation methods like Progressive Distillation in the low-medium NFE regime. For example, the BNS solver achieves 45 PSNR/1.76 FID using 16 NFE in class-conditional ImageNet-64. The paper demonstrates the effectiveness of BNS solvers for conditional image generation, text-to-image generation, and text-2-audio generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary BNS Solvers are a new way to make computer programs that create images or audio better. They use a special kind of math called “solver distillation” to help the program learn faster and be more accurate. This makes it possible for the program to generate better images or audio with less effort. The new solvers also keep the generated samples diverse, which is important because it means they won’t all look the same. The paper shows how this works by testing the BNS Solvers on different tasks like generating images from text or converting text into audio. |
Keywords
* Artificial intelligence * Diffusion * Distillation * Image generation * Optimization
