Accelerating Guided Diffusion Sampling with Splitting Numerical Methods
- URL: http://arxiv.org/abs/2301.11558v1
- Date: Fri, 27 Jan 2023 06:48:29 GMT
- Title: Accelerating Guided Diffusion Sampling with Splitting Numerical Methods
- Authors: Suttisak Wizadwongsa, Supasorn Suwajanakorn
- Abstract summary: Recent techniques can accelerate unguided sampling by applying high-order numerical methods to the sampling process.
This paper explores the culprit of this problem and provides a solution based on operator splitting methods.
Our proposed method can re-utilize the high-order methods for guided sampling and can generate images with the same quality as a 250-step DDIM baseline.
- Score: 8.689906452450938
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Guided diffusion is a technique for conditioning the output of a diffusion
model at sampling time without retraining the network for each specific task.
One drawback of diffusion models, however, is their slow sampling process.
Recent techniques can accelerate unguided sampling by applying high-order
numerical methods to the sampling process when viewed as differential
equations. On the contrary, we discover that the same techniques do not work
for guided sampling, and little has been explored about its acceleration. This
paper explores the culprit of this problem and provides a solution based on
operator splitting methods, motivated by our key finding that classical
high-order numerical methods are unsuitable for the conditional function. Our
proposed method can re-utilize the high-order methods for guided sampling and
can generate images with the same quality as a 250-step DDIM baseline using
32-58% less sampling time on ImageNet256. We also demonstrate usage on a wide
variety of conditional generation tasks, such as text-to-image generation,
colorization, inpainting, and super-resolution.
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