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Noise Estimation for Generative Diffusion Models

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arxiv 2104.02600 v2 pith:MC732QXB submitted 2021-04-06 cs.LG cs.CV

Robin San-Roman , Eliya Nachmani , Lior Wolf This is my paper · ORCID
classification cs.LGcs.CV
keywords numberdiffusionmodelsnoisestepsgenerativeparameterssmall
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Generative diffusion models have emerged as leading models in speech and image generation. However, in order to perform well with a small number of denoising steps, a costly tuning of the set of noise parameters is needed. In this work, we present a simple and versatile learning scheme that can step-by-step adjust those noise parameters, for any given number of steps, while the previous work needs to retune for each number separately. Furthermore, without modifying the weights of the diffusion model, we are able to significantly improve the synthesis results, for a small number of steps. Our approach comes at a negligible computation cost.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Scale-Adaptive Generative Flows for Multiscale Scientific Data

    stat.ML 2025-09 conditional novelty 6.0 of 10

    For generative flows on multiscale scientific fields, the noise spectrum should be at least as rough as the data's, and a scale-adaptive schedule can tame the terminal-time stiffness of rougher noise.

  2. FeRA: Frequency-Energy Constrained Routing for Effective Diffusion Adaptation Fine-Tuning

    cs.CV 2025-11 conditional novelty 5.0 of 10

    A frequency-energy router that blends LoRA experts according to the latent's bandwise energy improves diffusion fine-tuning quality and style consistency across multiple backbones.

  3. fastWDM3D: Fast and Accurate 3D Healthy Tissue Inpainting

    eess.IV 2025-07 conditional novelty 5.0 of 10

    fastWDM3D, a wavelet diffusion model with a variance-preserving noise schedule and reconstruction losses, achieves high-quality 3D brain inpainting in two steps and about 1.81 seconds per image.

  4. ControlMambaIR: Conditional Controls with State-Space Model for Image Restoration

    cs.CV 2025-06 reject novelty 4.0 of 10

    A diffusion image restoration model with a Mamba condition network reports low LPIPS/FID on several benchmarks, but the PSNR losses and internal inconsistencies undermine the stated performance claims.

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