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[ACM Computing Surveys] The collection of awesome papers on alignment of diffusion models.
| Date | Stars |
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| 2026-07-31 | 430 |
| 2026-08-02 | 430 |
| 2026-08-06 | 430 |
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# Awesome Alignment of Diffusion Models The collection of awesome papers on the alignment of diffusion models. If you are interested in the alignment of diffusion models, please refer to our survey paper "[Alignment of Diffusion Models: Fundamentals, Challenges, and Future](https://arxiv.org/pdf/2409.07253)", which is the first survey on this topic to our knowledge. We hope to enjoy the adventure of exploring alignment and diffusion models with more researchers. We try to include recent papers in time, which will be soon added in future revision of our survey paper. Corrections and suggestions are welcomed. [](https://github.com/chetanraj/awesome-github-badges) [](https://github.com/zeke-xie/awesome-alignment-of-diffusion-models) [](https://opensource.org/licenses/MIT) ## Alignment Techniques of Diffusion Models ### RLHF/RLAIF * ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation. NeurIPS 2023, [[pdf]](https://arxiv.org/pdf/2304.05977) * DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models, NeurIPS 2023, [[pdf]](https://arxiv.org/pdf/2305.16381) * Aligning Text-to-Image Models using Human Feedback. arXiv 2023, [[pdf]](https://arxiv.org/pdf/2302.12192) * Aligning Text-to-Image Diffusion Models with Reward Backpropagation. arXiv 2023, [[pdf]](https://arxiv.org/pdf/2310.03739v2) * Training Diffusion Models with Reinforcement Learning. ICLR 2024, [[pdf]](https://arxiv.org/abs/2305.13301) * Directly Fine-Tuning Diffusion Models on Differentiable Rewards. ICLR 2024, [[pdf]](https://arxiv.org/pdf/2309.17400) * CoMat: Aligning Text-to-Image Diffusion Model with Image-to-Text Concept Matching. NeurIPS 2024, [[pdf]](https://arxiv.org/pdf/2404.03653) * PRDP: Proximal Reward Difference Prediction for Large-Scale Reward Finetuning of Diffusion Models. CVPR 2024, [[pdf]](https://arxiv.org/pdf/2402.08714) * Confronting Reward Overoptimization for Diffusion Models: A Perspective of Inductive and Primacy Biases. ICML 2024, [[pdf]](https://arxiv.org/pdf/2402.08552) * Feedback Efficient Online Fine-Tuning of Diffusion Models. ICML 2024, [[pdf]](https://arxiv.org/pdf/2402.16359) * Deep Reward Supervisions for Tuning Text-to-Image Diffusion Models. ECCV 2024, [[pdf]](https://arxiv.org/abs/2405.00760) * Fine-Tuning of Continuous-Time Diffusion Models as Entropy-Regularized Control. arXiv 2024, [[pdf]](https://arxiv.org/pdf/2402.15194) * Understanding Reinforcement Learning-Based Fine-Tuning of Diffusion Models: A Tutorial and Review. arXiv 2024, [[pdf]](https://arxiv.org/pdf/2407.13734) * Aligning Few-Step Diffusion Models with Dense Reward Difference Learning. arXiv 2024, [[pdf]](https://arxiv.org/pdf/2411.11727) * Reward Fine-Tuning Two-Step Diffusion Models via Learning Differentiable Latent-Space Surrogate Reward. arXiv 2024, [[pdf]](https://arxiv.org/pdf/2411.15247) * Information Theoretic Text-to-Image Alignment. ICLR 2025, [[pdf]](https://arxiv.org/pdf/2405.20759) * Improving Long-Text Alignment for Text-to-Image Diffusion Models. ICLR 2025, [[pdf]](https://arxiv.org/pdf/2410.11817) * Efficient Diversity-Preserving Diffusion Alignment via Gradient-Informed GFlowNets. ICLR 2025, [[pdf]](https://arxiv.org/pdf/2412.07775) * Towards Better Alignment: Training Diffusion Models with Reinforcement Learning Against Sparse Rewards. CVPR 2025, [[pdf]](https://arxiv.org/pdf/2501.06655) * Focus-N-Fix: Region-Aware Fine-Tuning for Text-to-Image Generation. arXiv 2025, [[pdf]](https://arxiv.org/pdf/2501.06481) * Diffusion Model as a Noise-Aware Latent Reward Model for Step-Level Preference Optimization. arXiv 2025, [[pdf]](https://arxiv.org/abs/2502.01051) * ADT: Tuning Diffusion Models with Adversarial Supervision. arXiv 2025, [[pdf]](https://arx
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matched fp:cbe4e610605d774b, topic:alignment, name:alignment, desc:alignment
matched fp:cbe4e610605d774b, topic:diffusion-models