Elucidating the SNR-t Bias of Diffusion Probabilistic Models
Summary
This paper identifies a Signal-to-Noise Ratio timestep (SNR-t) bias in diffusion probabilistic models during inference, where SNR-timestep alignment from training is disrupted at inference time. The authors propose a differential correction method that decomposes samples into frequency components and corrects each separately, improving generation quality across models like IDDPM, ADM, DDIM, EDM, and FLUX with minimal computational overhead.
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Paper page - Elucidating the SNR-t Bias of Diffusion Probabilistic Models
Source: https://huggingface.co/papers/2604.16044
Abstract
Diffusion probabilistic models suffer from SNR-timestep bias during inference, which is addressed through a differential correction method that processes frequency components separately, improving generation quality across multiple models with minimal computational cost.
Diffusion Probabilistic Modelshave demonstrated remarkable performance across a wide range of generative tasks. However, we have observed that these models often suffer from a Signal-to-Noise Ratio-timestep (SNR-t) bias. This bias refers to the misalignment between the SNR of thedenoising sampleand its corresponding timestep during theinference phase. Specifically, during training, the SNR of a sample is strictly coupled with its timestep. However, this correspondence is disrupted during inference, leading toerror accumulationand impairing thegeneration quality. We provide comprehensive empirical evidence and theoretical analysis to substantiate this phenomenon and propose a simple yet effectivedifferential correctionmethod to mitigate the SNR-t bias. Recognizing that diffusion models typically reconstruct low-frequency componentsbefore focusing on high-frequency details during thereverse denoising process, we decompose samples into variousfrequency componentsand applydifferential correctionto each component individually. Extensive experiments show that our approach significantly improves thegeneration qualityof various diffusion models (IDDPM, ADM, DDIM, A-DPM, EA-DPM, EDM, PFGM++, and FLUX) on datasets of various resolutions with negligible computational overhead. The code is at https://github.com/AMAP-ML/DCW.
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