mlsanityData to 14 Oct 2026

A Comprehensive Review on Noise Control of Diffusion Model

arXiv 2502.04669 ↗First posted 7 Feb 2025cs.LG cs.AIcited by 4

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

Diffusion models have recently emerged as powerful generative frameworks for producing high-quality images. A pivotal component of these models is the noise schedule, which governs the rate of noise injection during the diffusion process. Since the noise schedule substantially influences sampling quality and training quality, understanding its design and implications is crucial. In this discussion, various noise schedules are examined, and their distinguishing features and performance characteristics are highlighted.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 32 of its 33 references that are in the map. First posted on arXiv 7 Feb 2025.

Builds on

32 of 33 references.

Closest ancestors · 12

Same direction, newest first

  1. 2024Improved Noise Schedule for Diffusion Training3 Jul
  2. 2024Align Your Steps: Optimizing Sampling Schedules in Diffusion Models22 Apr
  3. 2023Diffusion Models With Learned Adaptive Noise20 Dec
  4. 2023Simple diffusion: End-to-end diffusion for high resolution images26 Jan
  5. 2023On the Importance of Noise Scheduling for Diffusion Models26 Jan
  6. 2022Diffusion Models in Vision: A Survey10 Sep
  7. 2022Diffusion Models: A Comprehensive Survey of Methods and Applications2 Sep
  8. 2022Classifier-Free Diffusion Guidance26 Jul
  9. 2022Elucidating the Design Space of Diffusion-Based Generative Models1 Jun
  10. 2021Variational Diffusion Models1 Jul
  11. 2021Improved Denoising Diffusion Probabilistic Models18 Feb
  12. 2020Denoising Diffusion Probabilistic Models19 Jun

Other references · 20

  1. 2023Visual Instruction Tuningcited by 7,869
  2. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  3. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,355
  4. 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,503
  5. 2023Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Modelscited by 702
  6. 2023Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generatorscited by 670
  7. 2023Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Modelscited by 668
  8. 2023Muse: Text-To-Image Generation via Masked Generative Transformerscited by 648
  9. 2020WaveGrad: Estimating Gradients for Waveform Generationcited by 622
  10. 2023Segment Everything Everywhere All at Oncecited by 548
  11. 2023Zero-shot Image-to-Image Translationcited by 505
  12. 2023Emergent Correspondence from Image Diffusioncited by 446
8 more
  1. 2022Scalable Adaptive Computation for Iterative Generationcited by 195
  2. 2022A Generalist Framework for Panoptic Segmentation of Images and Videoscited by 99
  3. 2024Schedule Your Edit: A Simple yet Effective Diffusion Noise Schedule for Image Editingcited by 39
  4. 2024Unveiling Decentralization: A Comprehensive Review of Technologies, Comparison, Challenges in Bitcoin, Ethereum, and Solana Blockchaincited by 6
  5. 2024BundledSLAM: An Accurate Visual SLAM System Using Multiple Camerascited by 4
  6. 2024Advancing Blockchain Scalability: An Introduction to Layer 1 and Layer 2 Solutionscited by 4
  7. 2024Advancements in Translation Accuracy for Stereo Visual-Inertial Initializationcited by 3
  8. 2024Visual SLAM with 3D Gaussian Primitives and Depth Priors Enabling Novel View Synthesiscited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025Don't Settle Too Early: Self-Reflective Remasking for Diffusion Language Modelscited by 37
  2. 2026Wasserstein bounds for denoising diffusion probabilistic models via the F"ollmer processcited by 2
  3. 2025Diffusion-based Surrogate Model for Time-varying Underwater Acoustic Channelscited by 0
  4. 2026Signal-Aware Conditional Diffusion Surrogates for Transonic Wing Pressure Predictioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Spectrally-Guided Diffusion Noise Schedules
  2. 2023Common Diffusion Noise Schedules and Sample Steps are Flawed
  3. 2024Constant Rate Scheduling: A General Framework for Optimizing Diffusion Noise Schedule via Distributional Change
  4. 2024Not All Noises Are Created Equally:Diffusion Noise Selection and Optimization
  5. 2026Noise Schedule Design for Diffusion Models: An Optimal Control Perspective
  6. 2026Class-frequency Guided Noise Schedule for Diffusion Models
  7. 2026Couple to Control: Joint Initial Noise Design in Diffusion Models
  8. 2024Blue noise for diffusion models