mlsanityData to 7 Oct 2026

Diffusion Rejection Sampling

arXiv 2405.17880 ↗First posted 28 May 2024cs.LGcited by 11

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

Recent advances in powerful pre-trained diffusion models encourage the development of methods to improve the sampling performance under well-trained diffusion models. This paper introduces Diffusion Rejection Sampling (DiffRS), which uses a rejection sampling scheme that aligns the sampling transition kernels with the true ones at each timestep. The proposed method can be viewed as a mechanism that evaluates the quality of samples at each intermediate timestep and refines them with varying effort depending on the sample. Theoretical analysis shows that DiffRS can achieve a tighter bound on sampling error compared to pre-trained models. Empirical results demonstrate the state-of-the-art performance of DiffRS on the benchmark datasets and the effectiveness of DiffRS for fast diffusion samplers and large-scale text-to-image diffusion models. Our code is available at https://github.com/aailabkaist/DiffRS.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 49 of its 54 references that are in the map. First posted on arXiv 28 May 2024.

Builds on

49 of 54 references.

Closest ancestors · 31

Same direction, newest first

  1. 2024Training Unbiased Diffusion Models From Biased Dataset2 Mar
  2. 2024Label-Noise Robust Diffusion Models27 Feb
  3. 2023DPM-Solver-v3: Improved Diffusion ODE Solver with Empirical Model Statistics20 Oct
  4. 2023Elucidating the Exposure Bias in Diffusion Models29 Aug
  5. 2023Restart Sampling for Improving Generative Processes26 Jun
  6. 2023A Complete Recipe for Diffusion Generative Models3 Mar
  7. 2023Consistency Models2 Mar
  8. 2023Understanding Diffusion Objectives as the ELBO with Simple Data Augmentation1 Mar
  9. 2023Stable Target Field for Reduced Variance Score Estimation in Diffusion Models1 Feb
  10. 2023Don't Play Favorites: Minority Guidance for Diffusion Models29 Jan
  11. 2022Scalable Diffusion Models with Transformers19 Dec
  12. 2022Refining Generative Process with Discriminator Guidance in Score-based Diffusion Models28 Nov
19 more
  1. 2022DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models2 Nov
  2. 2022FP-Diffusion: Improving Score-based Diffusion Models by Enforcing the Underlying Score Fokker-Planck Equation9 Oct
  3. 2022On Distillation of Guided Diffusion Models6 Oct
  4. 2022Classifier-Free Diffusion Guidance26 Jul
  5. 2022DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps2 Jun
  6. 2022Elucidating the Design Space of Diffusion-Based Generative Models1 Jun
  7. 2022Maximum Likelihood Training of Implicit Nonlinear Diffusion Models27 May
  8. 2022Fast Sampling of Diffusion Models with Exponential Integrator29 Apr
  9. 2021High-Resolution Image Synthesis with Latent Diffusion Models20 Dec
  10. 2021Score-Based Generative Modeling with Critically-Damped Langevin Diffusion14 Dec
  11. 2021Score-based Generative Modeling in Latent Space10 Jun
  12. 2021Soft Truncation: A Universal Training Technique of Score-based Diffusion Model for High Precision Score Estimation10 Jun
  13. 2021Cascaded Diffusion Models for High Fidelity Image Generation30 May
  14. 2021Gotta Go Fast When Generating Data with Score-Based Models28 May
  15. 2021Diffusion Models Beat GANs on Image Synthesis11 May
  16. 2021Improved Denoising Diffusion Probabilistic Models18 Feb
  17. 2020Score-Based Generative Modeling through Stochastic Differential Equations26 Nov
  18. 2020Denoising Diffusion Implicit Models6 Oct
  19. 2020Denoising Diffusion Probabilistic Models19 Jun

Other references · 18

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  2. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  3. 2022LAION-5B: An open large-scale dataset for training next generation image-text modelscited by 4,131
  4. 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Modelscited by 3,595
  5. 2016Improved Techniques for Training GANscited by 2,938
  6. 2021CLIPScore: A Reference-free Evaluation Metric for Image Captioningcited by 2,388
  7. 2022Video Diffusion Modelscited by 2,280
  8. 2019StarGAN v2: Diverse Image Synthesis for Multiple Domainscited by 1,095
  9. 2019Improved Precision and Recall Metric for Assessing Generative Modelscited by 925
  10. 2022MCVD: Masked Conditional Video Diffusion for Prediction, Generation, and Interpolationcited by 307
  11. 2021Generating Images with Sparse Representationscited by 262
  12. 2022Scalable Adaptive Computation for Iterative Generationcited by 190
6 more
  1. 2020Telescoping Density-Ratio Estimationcited by 108
  2. 2022Poisson Flow Generative Modelscited by 95
  3. 2023PFGM++: Unlocking the Potential of Physics-Inspired Generative Modelscited by 73
  4. 2018Discriminator Rejection Samplingcited by 48
  5. 2018Metropolis-Hastings Generative Adversarial Networkscited by 40
  6. 2018Variational Rejection Samplingcited by 13

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 8; 2026: 2.

11 papers.

  1. 2025Inference-Time Scaling for Diffusion Models beyond Scaling Denoising Stepscited by 225
  2. 2025Dynamic Classifier-Free Diffusion Guidance via Online Feedbackcited by 11
  3. 2025Preference Optimization by Estimating the Ratio of the Data Distributioncited by 9
  4. 2025Local Manifold Approximation and Projection for Manifold-Aware Diffusion Planningcited by 8
  5. 2025Early Timestep Zero-Shot Candidate Selection for Instruction-Guided Image Editingcited by 5
  6. 2025Diffusion Adaptive Text Embedding for Text-to-Image Diffusion Modelscited by 5
  7. 2024Diffusion Sampling Correction via Approximately 10 Parameterscited by 1
  8. 2025LSRS: Latent Scale Rejection Sampling for Visual Autoregressive Modelingcited by 1
  9. 2026Discrete Diffusion Inference-Time Control with Nested Sequential Monte Carlocited by 1
  10. 2025Improving Compositional Generation with Diffusion Models Using Lift Scorescited by 0
  11. 2026SpecLoR: Spectral Lookahead Rectification for Motion-Coherent Text-to-Video Generationcited by 0

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  1. 2025It Takes Two to Tango: Two Parallel Samplers Improve Quality in Diffusion Models for Limited Steps
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  3. 2022Learning Fast Samplers for Diffusion Models by Differentiating Through Sample Quality
  4. 2023Patch Diffusion: Faster and More Data-Efficient Training of Diffusion Models
  5. 2025Single-Step Consistent Diffusion Samplers
  6. 2024Multistep Distillation of Diffusion Models via Moment Matching
  7. 2025LowDiff: Efficient Diffusion Sampling with Low-Resolution Condition
  8. 2023Sampler Scheduler for Diffusion Models