mlsanityData to 7 Oct 2026

Unifying GANs and Score-Based Diffusion as Generative Particle Models

arXiv 2305.16150 ↗First posted 25 May 2023cs.LG cs.CV cs.NEcited by 21

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

Particle-based deep generative models, such as gradient flows and score-based diffusion models, have recently gained traction thanks to their striking performance. Their principle of displacing particle distributions using differential equations is conventionally seen as opposed to the previously widespread generative adversarial networks (GANs), which involve training a pushforward generator network. In this paper we challenge this interpretation, and propose a novel framework that unifies particle and adversarial generative models by framing generator training as a generalization of particle models. This suggests that a generator is an optional addition to any such generative model. Consequently, integrating a generator into a score-based diffusion model and training a GAN without a generator naturally emerge from our framework. We empirically test the viability of these original models as proofs of concepts of potential applications of our framework.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 58 of its 83 references that are in the map. First posted on arXiv 25 May 2023.

Builds on

58 of 83 references.

Closest ancestors · 5

Same direction, newest first

  1. 2022Elucidating the Design Space of Diffusion-Based Generative Models1 Jun
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Models20 Dec
  3. 2021Tackling the Generative Learning Trilemma with Denoising Diffusion GANs15 Dec
  4. 2021Diffusion Models Beat GANs on Image Synthesis11 May
  5. 2020Score-Based Generative Modeling through Stochastic Differential Equations26 Nov

Other references · 53

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  4. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  5. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
  6. 2014Deep Learning Face Attributes in the Wildcited by 3,679
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  9. 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,609
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  11. 2017Improved Training of Wasserstein GANscited by 2,223
  12. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,048
38 more
  1. 2022Imagen Video: High Definition Video Generation with Diffusion Modelscited by 1,765
  2. 2018Spectral Normalization for Generative Adversarial Networkscited by 1,618
  3. 2022Diffusion-LM Improves Controllable Text Generationcited by 1,006
  4. 2018Self-Attention Generative Adversarial Networkscited by 809
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  7. 2016f-GAN: Training Generative Neural Samplers using Variational Divergence Minimizationcited by 504
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  9. 2019Sliced Score Matching: A Scalable Approach to Density and Score Estimationcited by 381
  10. 2022MCVD: Masked Conditional Video Diffusion for Prediction, Generation, and Interpolationcited by 307
  11. 2016NIPS 2016 Tutorial: Generative Adversarial Networkscited by 284
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  14. 2022Diffusion-GAN: Training GANs with Diffusioncited by 203
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  16. 2021Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesiscited by 140
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50 most cited of 53 All 53 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 6; 2024: 10; 2025: 4; 2026: 1.

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  1. 2023One-step Diffusion with Distribution Matching Distillationcited by 978
  2. 2023Adversarial Diffusion Distillationcited by 741
  3. 2024Improved Distribution Matching Distillation for Fast Image Synthesiscited by 648
  4. 2024From Slow Bidirectional to Fast Autoregressive Video Diffusion Modelscited by 401
  5. 2023SwiftBrush: One-Step Text-to-Image Diffusion Model with Variational Score Distillationcited by 109
  6. 2023On the Design Fundamentals of Diffusion Models: A Surveycited by 59
  7. 2024Autoregressive Diffusion Transformer for Text-to-Speech Synthesiscited by 34
  8. 2025Distributional Diffusion Models with Scoring Rulescited by 25
  9. 2024Deep MMD Gradient Flow without adversarial trainingcited by 18
  10. 2025Flash-DMD: Towards High-Fidelity Few-Step Image Generation with Efficient Distillation and Joint Reinforcement Learningcited by 17
  11. 2024Turbo3D: Ultra-fast Text-to-3D Generationcited by 14
  12. 2024Synthetic Face Datasets Generation via Latent Space Exploration from Brownian Identity Diffusioncited by 12
9 more
  1. 2023Differentially Private Gradient Flow based on the Sliced Wasserstein Distancecited by 4
  2. 2024Inclusive KL Gradient Flows: Otto-Wasserstein, Fisher-Rao-Gaussian, and Local-Estimator Dynamicscited by 4
  3. 2025Training Latent Diffusion Models with Interacting Particle Algorithmscited by 4
  4. 2024Statistical and Geometrical properties of regularized Kernel Kullback-Leibler divergencecited by 3
  5. 2025Score-based Idempotent Distillation of Diffusion Modelscited by 2
  6. 2026Guiding Distribution Matching Distillation with Gradient-Based Reinforcement Learningcited by 2
  7. 2024Distribution Backtracking Builds A Faster Convergence Trajectory for Diffusion Distillationcited by 1
  8. 2023Insights into Closed-form IPM-GAN Discriminator Guidance for Diffusion Modelingcited by 0
  9. 2024EchoAlign: Bridging Generative and Discriminative Learning under Noisy Labelscited by 0

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  2. 2023DiffFlow: A Unified SDE Framework for Score-Based Diffusion Models and Generative Adversarial Networks
  3. 2024A Score-Based Density Formula, with Applications in Diffusion Generative Models
  4. 2023Diffusion-Model-Assisted Supervised Learning of Generative Models for Density Estimation
  5. 2025Generative AI in Depth: A Survey of Recent Advances, Model Variants, and Real-World Applications
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