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Align Your Steps

A method for optimizing the sampling schedule of a diffusion model to improve the output quality of a generated model.

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Align Your Steps is a method used to optimize the sampling schedule of Diffusion Models (DMs). This method utilizes stochastic calculus to find specific optimal sampling schedules for different solvers, trained DMs, and datasets. It optimizes time discretization, i.e., sampling scheduling, by minimizing the KLUB term, thereby improving output quality with the same computational budget. The method performed well in image, video, and 2D toy data synthesis benchmarks, and the optimized sampling schedule outperformed previous manual schedules in almost all experiments.
Align Your Steps
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Usage Scenario Examples:

  • Improve image quality on image generation benchmarks such as CIFAR10, FFHQ, and ImageNet with optimized sampling schedules
  • In the Stable Diffusion 1.5 model, more detailed text-to-image results are generated through an optimized sampling schedule
  • In the field of video generation, optimized sampling schedules are used to reduce color distortion during video generation

The features of the tool:

  • Stochastic calculus method is used to optimize sampling schedule
  • Customized optimizations for different solvers, trained DMs, and data sets
  • Improve output quality by minimizing KLUB entries
  • Validate effectiveness in multiple image and video generation benchmarks
  • User studies have shown that images generated with optimized sampling schedules are more popular
  • Support for plug-and-play optimized sampling schedules

Steps for Use:

  • Step 1: Understand the fundamentals of the diffusion model and the sampling process
  • Step 2: Select the appropriate optimal sampling schedule based on the solver, DMs, and data set used
  • Step 3: Use the provided quick Start guide and Colab notebook to start using the optimized sampling schedule
  • Step 4: Compare the effect of the optimized sampling schedule with the traditional manual schedule in the experiment
  • Step 5: Adjust and further optimize the sampling schedule according to the experimental results to suit the specific application scenario
  • Step 6: Deploy optimized sampling schedules in real projects to improve model performance

Tool’s Tabs: AI, image generation

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