% Key papers for Distributional control of diffusion (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{neurips2021_diffusion_62107,
  title = {{Diffusion Models Beat GANs on Image Synthesis}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28362},
}

@inproceedings{iclr2021_score_30287,
  title = {{Score-Based Generative Modeling through Stochastic Differential Equations}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/3177},
}

@inproceedings{neurips2023_prolificdreamer_69525,
  title = {{ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/70394},
}

@inproceedings{cvpr2018_learning_13284,
  title = {{Learning Generative ConvNets via Multi-Grid Modeling and Sampling}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Gao_Learning_Generative_ConvNets_CVPR_2018_paper.html},
}

@inproceedings{cvpr2021_soft_17916,
  title = {{Soft-IntroVAE: Analyzing and Improving the Introspective Variational Autoencoder}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{iclr2021_wavegrad_30247,
  title = {{WaveGrad: Estimating Gradients for Waveform Generation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/3220},
}

@inproceedings{icml2017_parallel_36336,
  title = {{Parallel Multiscale Autoregressive Density Estimation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/643},
}

@inproceedings{iclr2021_very_30311,
  title = {{Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2790},
}

@inproceedings{icml2018_autoregressive_36533,
  title = {{Autoregressive Quantile Networks for Generative Modeling}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2416},
}

@inproceedings{iclr2021_adversarial_30498,
  title = {{Adversarial score matching and improved sampling for image generation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/3155},
}

@inproceedings{neurips2022_riemannian_65976,
  title = {{Riemannian Score-Based Generative Modelling}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54590},
}

@inproceedings{iclr2022_diffusion_30931,
  title = {{Diffusion-Based Voice Conversion with Fast Maximum Likelihood Sampling Scheme}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/6240},
}

@inproceedings{icml2023_on_42984,
  title = {{On Provable Copyright Protection for Generative Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/23825},
}

@inproceedings{cvpr2022_polarity_19600,
  title = {{Polarity Sampling: Quality and Diversity Control of Pre-Trained Generative Networks via Singular Values}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{cvpr2024_consistent3d_23018,
  title = {{Consistent3D: Towards Consistent High-Fidelity Text-to-3D Generation with Deterministic Sampling Prior}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30801},
}

@inproceedings{cvpr2023_unite_22472,
  title = {{Unite and Conquer: Plug \& Play Multi-Modal Synthesis Using Diffusion Models}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/23017},
}

@inproceedings{aaai2024_ccdpm_5389,
  title = {{CcDPM: A Continuous Conditional Diffusion Probabilistic Model for Inverse Design}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29647},
}

@inproceedings{cvpr2024_taming_24954,
  title = {{Taming Mode Collapse in Score Distillation for Text-to-3D Generation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/31221},
}

@inproceedings{cvpr2024_interhandgen_23828,
  title = {{InterHandGen: Two-Hand Interaction Generation via Cascaded Reverse Diffusion}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30217},
}

@inproceedings{neurips2022_generative_64466,
  title = {{Generative Visual Prompt: Unifying Distributional Control of Pre-Trained Generative Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/55093},
}

@inproceedings{cvpr2024_gpld3d_23556,
  title = {{GPLD3D: Latent Diffusion of 3D Shape Generative Models by Enforcing Geometric and Physical Priors}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30121},
}

@inproceedings{cvpr2024_fully_23530,
  title = {{Fully Exploiting Every Real Sample: SuperPixel Sample Gradient Model Stealing}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/31823},
}

@inproceedings{iclr2024_text_35112,
  title = {{Text-to-3D with Classifier Score Distillation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17961},
}

@inproceedings{iclr2024_noise_34827,
  title = {{Noise-free Score Distillation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18247},
}

@inproceedings{icml2025_normalizing_45982,
  title = {{Normalizing Flows are Capable Generative Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46564},
}

@inproceedings{neurips2024_score_70423,
  title = {{Score Distillation via Reparametrized DDIM}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96682},
}

@inproceedings{neurips2024_rethinking_71472,
  title = {{Rethinking Score Distillation as a Bridge Between Image Distributions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95788},
}

@inproceedings{aaai2026_rectified_100693,
  title = {{Rectified Noise: A Generative Model Using Positive-incentive Noise}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/42433},
}

@inproceedings{cvpr2025_di_25823,
  title = {{DI-PCG: Diffusion-based Efficient Inverse Procedural Content Generation for High-quality 3D Asset Creation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/34495},
}

@inproceedings{iclr2024_impus_34924,
  title = {{IMPUS: Image Morphing with Perceptually-Uniform Sampling Using Diffusion Models}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18150},
}

@inproceedings{iclr2024_on_34133,
  title = {{On the Stability of Iterative Retraining of Generative Models on their own Data}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18942},
}

@inproceedings{neurips2024_exploring_72972,
  title = {{Exploring Structured Semantic Priors Underlying Diffusion Score for Test-time Adaptation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94444},
}

@inproceedings{iclr2025_u_83734,
  title = {{U-Nets as Belief Propagation: Efficient Classification, Denoising, and Diffusion in Generative Hierarchical Models}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/28085},
}

@inproceedings{aaai2025_bgdb_8213,
  title = {{BGDB: Bernoulli-Gaussian Decision Block with Improved Denoising Diffusion Probabilistic Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34279},
}

@inproceedings{neurips2024_diffaug_72333,
  title = {{DiffAug: A Diffuse-and-Denoise Augmentation for Training Robust Classifiers}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95014},
}

@inproceedings{neurips2024_a_73047,
  title = {{A Versatile Diffusion Transformer with Mixture of Noise Levels for Audiovisual Generation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94376},
}