Noise Estimation for Generative Diffusion Models

• URL: http://arxiv.org/abs/2104.02600v1
• Date: Tue, 6 Apr 2021 15:46:16 GMT
• Title: Noise Estimation for Generative Diffusion Models
• Authors: Robin San-Roman, Eliya Nachmani, Lior Wolf
• Abstract summary: In this work, we present a simple and versatile learning scheme that can adjust the noise parameters for any given number of steps. Our approach comes at a negligible computation cost.
• Score: 91.22679787578438
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative diffusion models have emerged as leading models in speech and image generation. However, in order to perform well with a small number of denoising steps, a costly tuning of the set of noise parameters is needed. In this work, we present a simple and versatile learning scheme that can step-by-step adjust those noise parameters, for any given number of steps, while the previous work needs to retune for each number separately. Furthermore, without modifying the weights of the diffusion model, we are able to significantly improve the synthesis results, for a small number of steps. Our approach comes at a negligible computation cost.

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