mlsanityData to 7 Oct 2026

Cascaded Diffusion Models for High Fidelity Image Generation

arXiv 2106.15282 ↗First posted 30 May 2021cs.CV cs.AI cs.LGcited by 1,155

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

We show that cascaded diffusion models are capable of generating high fidelity images on the class-conditional ImageNet generation benchmark, without any assistance from auxiliary image classifiers to boost sample quality. A cascaded diffusion model comprises a pipeline of multiple diffusion models that generate images of increasing resolution, beginning with a standard diffusion model at the lowest resolution, followed by one or more super-resolution diffusion models that successively upsample the image and add higher resolution details. We find that the sample quality of a cascading pipeline relies crucially on conditioning augmentation, our proposed method of data augmentation of the lower resolution conditioning inputs to the super-resolution models. Our experiments show that conditioning augmentation prevents compounding error during sampling in a cascaded model, helping us to train cascading pipelines achieving FID scores of 1.48 at 64x64, 3.52 at 128x128 and 4.88 at 256x256 resolutions, outperforming BigGAN-deep, and classification accuracy scores of 63.02% (top-1) and 84.06% (top-5) at 256x256, outperforming VQ-VAE-2.

Key facts. Cited by 1,155 papers in the map (data through 7 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 34 of its 37 references that are in the map. First posted on arXiv 30 May 2021.

Builds on

34 of 37 references.

Closest ancestors · 6

Same direction, newest first

  1. 2021Diffusion Models Beat GANs on Image Synthesis11 May
  2. 2021Improved Denoising Diffusion Probabilistic Models18 Feb
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equations26 Nov
  4. 2020Denoising Diffusion Implicit Models6 Oct
  5. 2020Denoising Diffusion Probabilistic Models19 Jun
  6. 2020Improved Techniques for Training Score-Based Generative Models16 Jun

Other references · 28

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  3. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  4. 2017Neural Discrete Representation Learningcited by 4,650
  5. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
  6. 2013Auto-Encoding Variational Bayescited by 3,980
  7. 2016Improved Techniques for Training GANscited by 2,938
  8. 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,609
  9. 2016Density estimation using Real NVPcited by 2,029
  10. 2016WaveNet: A Generative Model for Raw Audiocited by 1,710
  11. 2018Glow: Generative Flow with Invertible 1x1 Convolutionscited by 1,539
  12. 2021Image Super-Resolution via Iterative Refinementcited by 1,506
16 more
  1. 2019Generating Diverse High-Fidelity Images with VQ-VAE-2cited by 1,376
  2. 2015LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loopcited by 1,350
  3. 2020DiffWave: A Versatile Diffusion Model for Audio Synthesiscited by 1,339
  4. 2016Conditional Image Generation with PixelCNN Decoderscited by 859
  5. 2016Pixel Recurrent Neural Networkscited by 814
  6. 2015Scheduled Sampling for Sequence Prediction with Recurrent Neural Networkscited by 779
  7. 2020WaveGrad: Estimating Gradients for Waveform Generationcited by 620
  8. 2015Sequence Level Training with Recurrent Neural Networkscited by 568
  9. 2017PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modificationscited by 415
  10. 2019Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Designcited by 258
  11. 2019Classification Accuracy Score for Conditional Generative Modelscited by 134
  12. 2020Learning Energy-Based Models by Diffusion Recovery Likelihoodcited by 122
  13. 2020Adversarial score matching and improved sampling for image generationcited by 95
  14. 2018Generating High Fidelity Images with Subscale Pixel Networks and Multidimensional Upscalingcited by 74
  15. 2019LOGAN: Latent Optimisation for Generative Adversarial Networkscited by 42
  16. 2019Hierarchical Autoregressive Image Models with Auxiliary Decoderscited by 17

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 13; 2022: 121; 2023: 352; 2024: 329; 2025: 231; 2026: 109.

1,155 papers.

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  1. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  2. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
  3. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
  4. 2022Scalable Diffusion Models with Transformerscited by 5,931
  5. 2022Classifier-Free Diffusion Guidancecited by 5,717
  6. 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,466
  7. 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Modelscited by 3,595
  8. 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationcited by 3,286
  9. 2022Elucidating the Design Space of Diffusion-Based Generative Modelscited by 2,994
  10. 2022InstructPix2Pix: Learning to Follow Image Editing Instructionscited by 2,591
  11. 2023Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasetscited by 2,526
  12. 2022Prompt-to-Prompt Image Editing with Cross Attention Controlcited by 2,409
38 more
  1. 2022Video Diffusion Modelscited by 2,280
  2. 2022Imagen Video: High Definition Video Generation with Diffusion Modelscited by 1,765
  3. 2023Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Modelscited by 1,465
  4. 2021Palette: Image-to-Image Diffusion Modelscited by 1,296
  5. 2022Scaling Autoregressive Models for Content-Rich Text-to-Image Generationcited by 1,287
  6. 2022Imagic: Text-Based Real Image Editing with Diffusion Modelscited by 1,172
  7. 2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,107
  8. 2022MaskGIT: Masked Generative Image Transformercited by 1,082
  9. 2022Diffusion Models in Vision: A Surveycited by 1,008
  10. 2024Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Predictioncited by 1,005
  11. 2022DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Modelscited by 924
  12. 2022eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoiserscited by 921
  13. 2022Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translationcited by 897
  14. 2021Vector Quantized Diffusion Model for Text-to-Image Synthesiscited by 773
  15. 2024Autoregressive Model Beats Diffusion: Llama for Scalable Image Generationcited by 760
  16. 2022On Distillation of Guided Diffusion Modelscited by 721
  17. 2024SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformerscited by 707
  18. 2022Point-E: A System for Generating 3D Point Clouds from Complex Promptscited by 694
  19. 2024Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Thinkcited by 666
  20. 2023Language Model Beats Diffusion -- Tokenizer is Key to Visual Generationcited by 633
  21. 2023Structure and Content-Guided Video Synthesis with Diffusion Modelscited by 589
  22. 2022LION: Latent Point Diffusion Models for 3D Shape Generationcited by 552
  23. 2021Tackling the Generative Learning Trilemma with Denoising Diffusion GANscited by 515
  24. 2022All are Worth Words: A ViT Backbone for Diffusion Modelscited by 498
  25. 2023Scaling up GANs for Text-to-Image Synthesiscited by 492
  26. 2022StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasetscited by 489
  27. 2022Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priorscited by 487
  28. 2024Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Modelscited by 467
  29. 2022MagicVideo: Efficient Video Generation With Latent Diffusion Modelscited by 463
  30. 2021Diffusion Autoencoders: Toward a Meaningful and Decodable Representationcited by 447
  31. 2023Simple diffusion: End-to-end diffusion for high resolution imagescited by 413
  32. 2023Analyzing and Improving the Training Dynamics of Diffusion Modelscited by 396
  33. 2023InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generationcited by 387
  34. 2022Latent Video Diffusion Models for High-Fidelity Long Video Generationcited by 385
  35. 2022Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioningcited by 362
  36. 2023Uni-ControlNet: All-in-One Control to Text-to-Image Diffusion Modelscited by 358
  37. 2024Pyramidal Flow Matching for Efficient Video Generative Modelingcited by 354
  38. 2024Retrieval-Augmented Generation for AI-Generated Content: A Surveycited by 352

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  4. 2025Evolutionary Caching to Accelerate Your Off-the-Shelf Diffusion Model
  5. 2026TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution
  6. 2025CaO2: Rectifying Inconsistencies in Diffusion-Based Dataset Distillation
  7. 2023Masked Diffusion Models Are Fast Distribution Learners
  8. 2025H2-Cache: A Novel Hierarchical Dual-Stage Cache for High-Performance Acceleration of Generative Diffusion Models