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The high-quality image synthesis offered by the Denoising Diffusion Probabilistic Model may be stepped up using different neural network frameworks instead of the U-Net implementation originally provided. With the availability of these options comes a need to classify them on the basis of their performance, and the necessity to select the one that yields the best results for further work in the field. Thus, we present a discussion in the effectiveness of these models and a relative determination of which one gives a better performance in this comparative study of quality of image synthesis using the three variations of the traditional U-Net model—U-Net, U-Net2+ and U-Net3+.

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Effectiveness of Integration of U-Net Variants in Denoising Diffusion Models

  • 会议论文

  • 发表日期：

    2025年

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  • Biprajit Ghoshal,
  • Sayan Mondal,
  • Mainak Bandyopadhyay

摘要

The high-quality image synthesis offered by the Denoising Diffusion Probabilistic Model may be stepped up using different neural network frameworks instead of the U-Net implementation originally provided. With the availability of these options comes a need to classify them on the basis of their performance, and the necessity to select the one that yields the best results for further work in the field. Thus, we present a discussion in the effectiveness of these models and a relative determination of which one gives a better performance in this comparative study of quality of image synthesis using the three variations of the traditional U-Net model—U-Net, U-Net2+ and U-Net3+.