Is Noise Conditioning Necessary for Denoising Generative Models?

• URL: http://arxiv.org/abs/2502.13129v1
• Date: Tue, 18 Feb 2025 18:53:24 GMT
• Title: Is Noise Conditioning Necessary for Denoising Generative Models?
• Abstract summary: We investigate a variety of denoising-based generative models in the absence of noise conditioning.<n>Most models exhibit graceful degradation, and in some cases, they even perform better without noise conditioning.<n>We introduce a noise-unconditional model that achieves a competitive FID of 2.23 on CIFAR-10, significantly narrowing the gap to leading noise-conditional models.
• Score: 19.2998909343441
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: It is widely believed that noise conditioning is indispensable for denoising diffusion models to work successfully. This work challenges this belief. Motivated by research on blind image denoising, we investigate a variety of denoising-based generative models in the absence of noise conditioning. To our surprise, most models exhibit graceful degradation, and in some cases, they even perform better without noise conditioning. We provide a theoretical analysis of the error caused by removing noise conditioning and demonstrate that our analysis aligns with empirical observations. We further introduce a noise-unconditional model that achieves a competitive FID of 2.23 on CIFAR-10, significantly narrowing the gap to leading noise-conditional models. We hope our findings will inspire the community to revisit the foundations and formulations of denoising generative models.

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