mlsanityData to 7 Oct 2026

PaGoDA: Progressive Growing of a One-Step Generator from a Low-Resolution Diffusion Teacher

arXiv 2405.14822 ↗First posted 23 May 2024cs.CV cs.AI cs.LGcited by 27

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

The diffusion model performs remarkable in generating high-dimensional content but is computationally intensive, especially during training. We propose Progressive Growing of Diffusion Autoencoder (PaGoDA), a novel pipeline that reduces the training costs through three stages: training diffusion on downsampled data, distilling the pretrained diffusion, and progressive super-resolution. With the proposed pipeline, PaGoDA achieves a 64× reduced cost in training its diffusion model on 8x downsampled data; while at the inference, with the single-step, it performs state-of-the-art on ImageNet across all resolutions from 64x64 to 512x512, and text-to-image. PaGoDA's pipeline can be applied directly in the latent space, adding compression alongside the pre-trained autoencoder in Latent Diffusion Models (e.g., Stable Diffusion). The code is available at https://github.com/sony/pagoda.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 68 of its 86 references that are in the map. First posted on arXiv 23 May 2024.

Builds on

68 of 86 references.

Closest ancestors · 27

Same direction, newest first

  1. 2024Fast High-Resolution Image Synthesis with Latent Adversarial Diffusion Distillation18 Mar
  2. 2024Scaling Rectified Flow Transformers for High-Resolution Image Synthesis5 Mar
  3. 2024SCott: Accelerating Diffusion Models with Stochastic Consistency Distillation3 Mar
  4. 2024Contractive Diffusion Probabilistic Models23 Jan
  5. 2023Analyzing and Improving the Training Dynamics of Diffusion Models5 Dec
  6. 2023One-step Diffusion with Distribution Matching Distillation30 Nov
  7. 2023Adversarial Diffusion Distillation28 Nov
  8. 2023Manifold Preserving Guided Diffusion28 Nov
  9. 2023Wasserstein Convergence Guarantees for a General Class of Score-Based Generative Models18 Nov
  10. 2023Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference6 Oct
  11. 2023Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion1 Oct
  12. 2023PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis30 Sep
15 more
  1. 2023Consistency Models2 Mar
  2. 2023Understanding Diffusion Objectives as the ELBO with Simple Data Augmentation1 Mar
  3. 2023Simple diffusion: End-to-end diffusion for high resolution images26 Jan
  4. 2022Scalable Diffusion Models with Transformers19 Dec
  5. 2022On Distillation of Guided Diffusion Models6 Oct
  6. 2022Classifier-Free Diffusion Guidance26 Jul
  7. 2022DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps2 Jun
  8. 2022Elucidating the Design Space of Diffusion-Based Generative Models1 Jun
  9. 2021High-Resolution Image Synthesis with Latent Diffusion Models20 Dec
  10. 2021Cascaded Diffusion Models for High Fidelity Image Generation30 May
  11. 2021Diffusion Models Beat GANs on Image Synthesis11 May
  12. 2021Knowledge Distillation in Iterative Generative Models for Improved Sampling Speed7 Jan
  13. 2020Score-Based Generative Modeling through Stochastic Differential Equations26 Nov
  14. 2020Denoising Diffusion Implicit Models6 Oct
  15. 2020Denoising Diffusion Probabilistic Models19 Jun

Other references · 41

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  4. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  5. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  6. 2023Visual Instruction Tuningcited by 7,792
  7. 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,764
  8. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,893
  9. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  10. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
  11. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  12. 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,466
29 more
  1. 2020Training data-efficient image transformers & distillation through attentioncited by 4,560
  2. 2022LAION-5B: An open large-scale dataset for training next generation image-text modelscited by 4,131
  3. 2020Taming Transformers for High-Resolution Image Synthesiscited by 3,229
  4. 2016Wide Residual Networkscited by 3,057
  5. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,967
  6. 2016Improved Techniques for Training GANscited by 2,938
  7. 2022Diffusion Posterior Sampling for General Noisy Inverse Problemscited by 1,375
  8. 2021Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Conceptscited by 1,209
  9. 2019Improved Precision and Recall Metric for Assessing Generative Modelscited by 925
  10. 2022eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoiserscited by 921
  11. 2019On the Variance of the Adaptive Learning Rate and Beyondcited by 763
  12. 2018Which Training Methods for GANs do actually Converge?cited by 672
  13. 2020Scaling Laws for Autoregressive Generative Modelingcited by 510
  14. 2023Scaling up GANs for Text-to-Image Synthesiscited by 492
  15. 2022StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasetscited by 489
  16. 20218-bit Optimizers via Block-wise Quantizationcited by 395
  17. 2023InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generationcited by 387
  18. 2020Differentiable Augmentation for Data-Efficient GAN Trainingcited by 361
  19. 2023StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image Synthesiscited by 212
  20. 2022Scalable Adaptive Computation for Iterative Generationcited by 190
  21. 2023UFOGen: You Forward Once Large Scale Text-to-Image Generation via Diffusion GANscited by 165
  22. 2021LAFITE: Towards Language-Free Training for Text-to-Image Generationcited by 126
  23. 2018The Mechanics of n-Player Differentiable Gamescited by 118
  24. 2017The Numerics of GANscited by 104
  25. 2017Gradient descent GAN optimization is locally stablecited by 65
  26. 2020A Contrastive Learning Approach for Training Variational Autoencoder Priorscited by 65
  27. 2020Training Generative Adversarial Networks by Solving Ordinary Differential Equationscited by 21
  28. 2018Global Convergence to the Equilibrium of GANs using Variational Inequalitiescited by 15
  29. 2018A Solvable High-Dimensional Model of GANcited by 13

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 10; 2025: 14; 2026: 3.

27 papers.

Sort:

By area:

  1. 2025PixelFlow: Pixel-Space Generative Models with Flowcited by 91
  2. 2025SANA-Sprint: One-Step Diffusion with Continuous-Time Consistency Distillationcited by 85
  3. 2024Simpler Diffusion (SiD2): 1.5 FID on ImageNet512 with pixel-space diffusioncited by 74
  4. 2025Advantage Weighted Matching: Aligning RL with Pretraining in Diffusion Modelscited by 59
  5. 2024Adversarial Diffusion Compression for Real-World Image Super-Resolutioncited by 49
  6. 2024Changen2: Multi-Temporal Remote Sensing Generative Change Foundation Modelcited by 37
  7. 2024Alleviating Distortion in Image Generation via Multi-Resolution Diffusion Models and Time-Dependent Layer Normalizationcited by 36
  8. 2024Switti: Designing Scale-Wise Transformers for Text-to-Image Synthesiscited by 33
  9. 2024Diff-Instruct++: Training One-step Text-to-image Generator Model to Align with Human Preferencescited by 30
  10. 2025Direct Discriminative Optimization: Your Likelihood-Based Visual Generative Model is Secretly a GAN Discriminatorcited by 22
  11. 2025FlexiDiT: Your Diffusion Transformer Can Easily Generate High-Quality Samples with Less Computecited by 12
  12. 2025Scale-wise Distillation of Diffusion Modelscited by 11
15 more
  1. 2025Autoregressive Distillation of Diffusion Transformerscited by 9
  2. 2025Modular MeanFlow: Towards Stable and Scalable One-Step Generative Modelingcited by 8
  3. 2024See Further When Clear: Curriculum Consistency Modelcited by 6
  4. 2025Geometry-Preserving Encoder/Decoder in Latent Generative Modelscited by 6
  5. 2025Stochastic Interpolants via Conditional Dependent Couplingcited by 6
  6. 2025MeanFlow Transformers with Representation Autoencoderscited by 6
  7. 2024Bellman Diffusion: Generative Modeling as Learning a Linear Operator in the Distribution Spacecited by 4
  8. 2024HERO: Human-Feedback Efficient Reinforcement Learning for Online Diffusion Model Finetuningcited by 4
  9. 2025Latent Stochastic Interpolantscited by 2
  10. 2026FP4 Explore, BF16 Train: Diffusion Reinforcement Learning via Efficient Rollout Scalingcited by 2
  11. 2026Distilling Drifting Transformers with Representation Autoencoderscited by 2
  12. 2026Unpaired Image-to-Image Translation via a Self-Supervised Semantic Bridgecited by 1
  13. 2024Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samplescited by 0
  14. 2025UCD: Unconditional Discriminator Promotes Nash Equilibrium in GANscited by 0
  15. 2025Breaking the Likelihood-Quality Trade-off in Diffusion Models by Merging Pretrained Expertscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024DiffuseHigh: Training-free Progressive High-Resolution Image Synthesis through Structure Guidance
  2. 2025Masked Autoencoders Are Effective Tokenizers for Diffusion Models
  3. 2025DC-AE 1.5: Accelerating Diffusion Model Convergence with Structured Latent Space
  4. 2022f-DM: A Multi-stage Diffusion Model via Progressive Signal Transformation
  5. 2022Progressive Distillation for Fast Sampling of Diffusion Models
  6. 2025Fewer Denoising Steps or Cheaper Per-Step Inference: Towards Compute-Optimal Diffusion Model Deployment
  7. 2025Accelerating Diffusion Model Training under Minimal Budgets: A Condensation-Based Perspective
  8. 2024PQD: Post-training Quantization for Efficient Diffusion Models