Mitigating the Noise Shift for Denoising Generative Models via Noise
  Awareness Guidance
Existing denoising generative models rely on solving discretized reverse-time
SDEs or ODEs. In this paper, we identify a long-overlooked yet pervasive issue
in this family of models: a misalignment between the pre-defined noise level
and the actual noise level encoded in intermediate states during sampling. We
refer to this misalignment as noise shift. Through empirical analysis, we
demonstrate that noise shift is widespread in modern diffusion models and
exhibits a systematic bias, leading to sub-optimal generation due to both
out-of-distribution generalization and inaccurate denoising updates. To address
this problem, we propose Noise Awareness Guidance (NAG), a simple yet effective
correction method that explicitly steers sampling trajectories to remain
consistent with the pre-defined noise schedule. We further introduce a
classifier-free variant of NAG, which jointly trains a noise-conditional and a
noise-unconditional model via noise-condition dropout, thereby eliminating the
need for external classifiers. Extensive experiments, including ImageNet
generation and various supervised fine-tuning tasks, show that NAG consistently
mitigates noise shift and substantially improves the generation quality of
mainstream diffusion models.
68ef5e26007ec82ded771038
Jincheng Zhong
claimed_verified
2025-10-15T15:24:34.707Z
658d7ae79a1397992a56430c
https://cdn-avatars.huggingface.co/v1/production/uploads/658d7ae79a1397992a56430c/2G7hUfbtJl6Sx02U-g412.jpeg
Jincheng Zhong
user
ConnorZhong
68ef5e26007ec82ded771039
Boyuan Jiang
68ef5e26007ec82ded77103a
Xin Tao
68ef5e26007ec82ded77103b
Pengfei Wan
68ef5e26007ec82ded77103c
Kun Gai
68ef5e26007ec82ded77103d
Mingsheng Long
Mitigating the Noise Shift for Denoising Generative Models via Noise
  Awareness Guidance
Existing denoising generative models rely on solving discretized reverse-time
SDEs or ODEs. In this paper, we identify a long-overlooked yet pervasive issue
in this family of models: a misalignment between the pre-defined noise level
and the actual noise level encoded in intermediate states during sampling. We
refer to this misalignment as noise shift. Through empirical analysis, we
demonstrate that noise shift is widespread in modern diffusion models and
exhibits a systematic bias, leading to sub-optimal generation due to both
out-of-distribution generalization and inaccurate denoising updates. To address
this problem, we propose Noise Awareness Guidance (NAG), a simple yet effective
correction method that explicitly steers sampling trajectories to remain
consistent with the pre-defined noise schedule. We further introduce a
classifier-free variant of NAG, which jointly trains a noise-conditional and a
noise-unconditional model via noise-condition dropout, thereby eliminating the
need for external classifiers. Extensive experiments, including ImageNet
generation and various supervised fine-tuning tasks, show that NAG consistently
mitigates noise shift and substantially improves the generation quality of
mainstream diffusion models.
Noise Awareness Guidance (NAG) addresses noise shift in diffusion models by aligning sampling trajectories with the pre-defined noise schedule, improving generation quality.
denoising generative models discretized reverse-time SDEs ODEs noise shift pre-defined noise level out-of-distribution generalization inaccurate denoising updates Noise Awareness Guidance classifier-free variant noise-conditional noise-unconditional model noise-condition dropout ImageNet generation supervised fine-tuning tasks