Class-frequency Guided Noise Schedule for Diffusion Models

• URL: http://arxiv.org/abs/2606.27696v1
• Date: Fri, 26 Jun 2026 03:43:23 GMT
• Title: Class-frequency Guided Noise Schedule for Diffusion Models
• Abstract summary: We first examine the correlations between class frequency and the multi-scale noise schedule within diffusion models.<n>We propose the textitClass-frequency Guided (CFRG) noise schedule, leveraging the insight that low-frequency classes should be endowed with larger-scale noises.
• Score: 83.19018650759415
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In this paper, we are the first to examine the correlations between class frequency and the multi-scale noise schedule within diffusion models. For score-based generative models, low-density regions often lead to inaccurately estimated scores, thereby compromising the generation quality. Although the multi-scale noise schedule can alleviate this issue during the diffusion process, low-frequency classes still face the challenge of large low-density regions, resulting in more inaccurate estimated scores than high-frequency classes. Furthermore, high-frequency classes tend to dominate the score space, causing a convergence of most data points towards generating samples from these classes. Consequently, samples generated within low-frequency classes exhibit suboptimal quality and limited diversity. To address this challenge, we propose the \textit{Class-frequency Guided (CFRG)} noise schedule, leveraging the insight that low-frequency classes should be endowed with larger-scale noises. To illustrate the effectiveness of our method, we conduct experiments on various tasks, including image generation, image classification, and text-to-image generation, using imbalanced datasets, \textit{i.e.}, CIFAR-100-LT, and ImageNet-LT. By employing the CFRG noise schedule, we achieve substantial improvements over baselines, manifesting the crucial role of frequency statistics in noise schedule design.

Related papers

• FAHCD-Net: Frequency-Adaptive Heatmap-Conditional Diffusion Networks for Robust Facial Landmark Detection [4.9271715782923335]
  We propose Frequency-Adaptive Heatmap-Conditional Diffusion Network (FAHCD-Net)<n>FAHCD-Net integrates a Frequency-Adaptive Heatmap-Conditional Diffusion (FAHCD) model with a Smoothness Regularization (SR) loss in a cascaded framework.<n>Experiments on popular benchmarks demonstrate the effectiveness and robustness of the proposed method.
  arXiv  Detail & Related papers  (2026-09-15T08:36:20Z)
• FrequencyCT: Frequency domain pseudo-label generation for self-supervised low-dose CT denoising [8.709309633329585]
  FrequencyCT is the first zero-shot self-supervised method for pseudo-label generation in the frequency domain for low-dose CT denoising.<n>Phase-preserving amplitude modulation and mask perturbation in the high-frequency region generate pseudo-label data for self-supervision.
  arXiv  Detail & Related papers  (2026-05-11T13:55:03Z)
• Information-Guided Noise Allocation for Efficient Diffusion Training [50.49440376762109]
  InfoNoise is a principled data-adaptive training noise schedule that replaces schedule design with an information-guided noise sampling distribution.<n>Across natural-image benchmarks, InfoNoise matches or surpasses tuned EDM-style schedules, in some cases with a substantial training speedup.<n>Overall, InfoNoise makes noise scheduling data-adaptive, reducing the need for per-dataset schedule design as diffusion models expand across domains.
  arXiv  Detail & Related papers  (2026-02-20T22:45:01Z)
• Mitigating the Noise Shift for Denoising Generative Models via Noise Awareness Guidance [54.88271057438763]
  Noise Awareness Guidance (NAG) is a correction method that explicitly steers sampling trajectories to remain consistent with the pre-defined noise schedule.<n>NAG consistently mitigates noise shift and substantially improves the generation quality of mainstream diffusion models.
  arXiv  Detail & Related papers  (2025-10-14T13:31:34Z)
• Noise Conditional Variational Score Distillation [60.38982038894823]
  Noise Conditional Variational Score Distillation (NCVSD) is a novel method for distilling pretrained diffusion models into generative denoisers.<n>By integrating this insight into the Variational Score Distillation framework, we enable scalable learning of generative denoisers.
  arXiv  Detail & Related papers  (2025-06-11T06:01:39Z)
• Improved Noise Schedule for Diffusion Training [51.849746576387375]
  We propose a novel approach to design the noise schedule for enhancing the training of diffusion models.<n>We empirically demonstrate the superiority of our noise schedule over the standard cosine schedule.
  arXiv  Detail & Related papers  (2024-07-03T17:34:55Z)
• Blue noise for diffusion models [50.99852321110366]
  We introduce a novel and general class of diffusion models taking correlated noise within and across images into account. Our framework allows introducing correlation across images within a single mini-batch to improve gradient flow. We perform both qualitative and quantitative evaluations on a variety of datasets using our method.
  arXiv  Detail & Related papers  (2024-02-07T14:59:25Z)
• DuDGAN: Improving Class-Conditional GANs via Dual-Diffusion [2.458437232470188]
  Class-conditional image generation using generative adversarial networks (GANs) has been investigated through various techniques. We propose a novel approach for class-conditional image generation using GANs called DuDGAN, which incorporates a dual diffusion-based noise injection process. Our method outperforms state-of-the-art conditional GAN models for image generation in terms of performance.
  arXiv  Detail & Related papers  (2023-05-24T07:59:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.