Merge pull request #9 from felix-yuxiang/master

Add one blog post by Felix

Author: lrjconan

_posts/2025-08-18-diff-distill.md

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+@misc{lipman_flow_2023,
+	title = {Flow Matching for Generative Modeling},
+	url = {http://arxiv.org/abs/2210.02747},
+	doi = {10.48550/arXiv.2210.02747},
+	abstract = {We introduce a new paradigm for generative modeling built on Continuous Normalizing Flows ({CNFs}), allowing us to train {CNFs} at unprecedented scale. Specifically, we present the notion of Flow Matching ({FM}), a simulation-free approach for training {CNFs} based on regressing vector fields of fixed conditional probability paths. Flow Matching is compatible with a general family of Gaussian probability paths for transforming between noise and data samples -- which subsumes existing diffusion paths as specific instances. Interestingly, we find that employing {FM} with diffusion paths results in a more robust and stable alternative for training diffusion models. Furthermore, Flow Matching opens the door to training {CNFs} with other, non-diffusion probability paths. An instance of particular interest is using Optimal Transport ({OT}) displacement interpolation to define the conditional probability paths. These paths are more efficient than diffusion paths, provide faster training and sampling, and result in better generalization. Training {CNFs} using Flow Matching on {ImageNet} leads to consistently better performance than alternative diffusion-based methods in terms of both likelihood and sample quality, and allows fast and reliable sample generation using off-the-shelf numerical {ODE} solvers.},
+	number = {{arXiv}:2210.02747},
+	publisher = {{arXiv}},
+	author = {Lipman, Yaron and Chen, Ricky T. Q. and Ben-Hamu, Heli and Nickel, Maximilian and Le, Matt},
+	urldate = {2024-07-05},
+	date = {2023-02-08},
+	eprinttype = {arxiv},
+	eprint = {2210.02747 [cs, stat]}
+}
+
+@article{albergo2023stochastic,
+  title={Stochastic interpolants: A unifying framework for flows and diffusions},
+  author={Albergo, Michael S and Boffi, Nicholas M and Vanden-Eijnden, Eric},
+  journal={arXiv preprint arXiv:2303.08797},
+  year={2023}
+}
+
+@article{tong2023improving,
+  title={Improving and generalizing flow-based generative models with minibatch optimal transport},
+  author={Tong, Alexander and Fatras, Kilian and Malkin, Nikolay and Huguet, Guillaume and Zhang, Yanlei and Rector-Brooks, Jarrid and Wolf, Guy and Bengio, Yoshua},
+  journal={arXiv preprint arXiv:2302.00482},
+  year={2023}
+}
+
+@article{liu2022flow,
+  title={Flow straight and fast: Learning to generate and transfer data with rectified flow},
+  author={Liu, Xingchao and Gong, Chengyue and Liu, Qiang},
+  journal={arXiv preprint arXiv:2209.03003},
+  year={2022}
+}
+
+@article{hu2021lora,
+  title={Lora: Low-rank adaptation of large language models. arXiv 2021},
+  author={Hu, Edward J and Shen, Yelong and Wallis, Phillip and Allen-Zhu, Zeyuan and Li, Yuanzhi and Wang, Shean and Wang, Lu and Chen, Weizhu},
+  journal={arXiv preprint arXiv:2106.09685},
+  volume={10},
+  year={2021}
+}
+
+@article{micikevicius2017mixed,
+  title={Mixed precision training},
+  author={Micikevicius, Paulius and Narang, Sharan and Alben, Jonah and Diamos, Gregory and Elsen, Erich and Garcia, David and Ginsburg, Boris and Houston, Michael and Kuchaiev, Oleksii and Venkatesh, Ganesh and others},
+  journal={arXiv preprint arXiv:1710.03740},
+  year={2017}
+}
+
+@inproceedings{fu2025moflowonestep,
+  author    = {Fu, Yuxiang and Yan, Qi and Wang, Lele and Li, Ke and Liao, Renjie},
+  title     = {MoFlow: One-Step Flow Matching for Human Trajectory Forecasting via Implicit Maximum Likelihood Estimation based Distillation},
+  journal   = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+  year      = {2025},
+}
+
+@misc{lipman2024flowmatchingguidecode,
+      title={Flow Matching Guide and Code}, 
+      author={Yaron Lipman and Marton Havasi and Peter Holderrieth and Neta Shaul and Matt Le and Brian Karrer and Ricky T. Q. Chen and David Lopez-Paz and Heli Ben-Hamu and Itai Gat},
+      year={2024},
+      eprint={2412.06264},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2412.06264}, 
+}
+
+@article{boffi2025build,
+  title={How to build a consistency model: Learning flow maps via self-distillation},
+  author={Boffi, Nicholas M and Albergo, Michael S and Vanden-Eijnden, Eric},
+  journal={arXiv preprint arXiv:2505.18825},
+  year={2025}
+}
+
+@article{geng2025mean,
+  title={Mean flows for one-step generative modeling},
+  author={Geng, Zhengyang and Deng, Mingyang and Bai, Xingjian and Kolter, J Zico and He, Kaiming},
+  journal={arXiv preprint arXiv:2505.13447},
+  year={2025}
+}
+
+@article{peng2025flow,
+  title={Flow-Anchored Consistency Models},
+  author={Peng, Yansong and Zhu, Kai and Liu, Yu and Wu, Pingyu and Li, Hebei and Sun, Xiaoyan and Wu, Feng},
+  journal={arXiv preprint arXiv:2507.03738},
+  year={2025}
+}
+
+@article{guo2025splitmeanflow,
+  title={SplitMeanFlow: Interval Splitting Consistency in Few-Step Generative Modeling},
+  author={Guo, Yi and Wang, Wei and Yuan, Zhihang and Cao, Rong and Chen, Kuan and Chen, Zhengyang and Huo, Yuanyuan and Zhang, Yang and Wang, Yuping and Liu, Shouda and others},
+  journal={arXiv preprint arXiv:2507.16884},
+  year={2025}
+}
+
+@article{ho2020denoising,
+  title={Denoising diffusion probabilistic models},
+  author={Ho, Jonathan and Jain, Ajay and Abbeel, Pieter},
+  journal={Advances in neural information processing systems},
+  volume={33},
+  pages={6840--6851},
+  year={2020}
+}
+
+@article{song2020score,
+  title={Score-based generative modeling through stochastic differential equations},
+  author={Song, Yang and Sohl-Dickstein, Jascha and Kingma, Diederik P and Kumar, Abhishek and Ermon, Stefano and Poole, Ben},
+  journal={arXiv preprint arXiv:2011.13456},
+  year={2020}
+}
+
+@article{lu2024simplifying,
+  title={Simplifying, stabilizing and scaling continuous-time consistency models},
+  author={Lu, Cheng and Song, Yang},
+  journal={arXiv preprint arXiv:2410.11081},
+  year={2024}
+}
+
+@article{kim2023consistency,
+  title={Consistency trajectory models: Learning probability flow ode trajectory of diffusion},
+  author={Kim, Dongjun and Lai, Chieh-Hsin and Liao, Wei-Hsiang and Murata, Naoki and Takida, Yuhta and Uesaka, Toshimitsu and He, Yutong and Mitsufuji, Yuki and Ermon, Stefano},
+  journal={arXiv preprint arXiv:2310.02279},
+  year={2023}
+}
+
+@article{sabour2025align,
+  title={Align Your Flow: Scaling Continuous-Time Flow Map Distillation},
+  author={Sabour, Amirmojtaba and Fidler, Sanja and Kreis, Karsten},
+  journal={arXiv preprint arXiv:2506.14603},
+  year={2025}
+}
+
+@article{frans2024one,
+  title={One step diffusion via shortcut models},
+  author={Frans, Kevin and Hafner, Danijar and Levine, Sergey and Abbeel, Pieter},
+  journal={arXiv preprint arXiv:2410.12557},
+  year={2024}
+}
+
+@article{zhou2025inductive,
+  title={Inductive moment matching},
+  author={Zhou, Linqi and Ermon, Stefano and Song, Jiaming},
+  journal={arXiv preprint arXiv:2503.07565},
+  year={2025}
+} 
+
+@article{yin2024improved,
+  title={Improved distribution matching distillation for fast image synthesis},
+  author={Yin, Tianwei and Gharbi, Micha{\"e}l and Park, Taesung and Zhang, Richard and Shechtman, Eli and Durand, Fredo and Freeman, Bill},
+  journal={Advances in neural information processing systems},
+  volume={37},
+  pages={47455--47487},
+  year={2024}
+}
+
+@article{song2020denoising,
+  title={Denoising diffusion implicit models},
+  author={Song, Jiaming and Meng, Chenlin and Ermon, Stefano},
+  journal={arXiv preprint arXiv:2010.02502},
+  year={2020}
+}
+
+@article{wang2025uni,
+  title={Uni-Instruct: One-step Diffusion Model through Unified Diffusion Divergence Instruction},
+  author={Wang, Yifei and Bai, Weimin and Zhang, Colin and Zhang, Debing and Luo, Weijian and Sun, He},
+  journal={arXiv preprint arXiv:2505.20755},
+  year={2025}
+}
+
+@inproceedings{zhou2024score,
+  title={Score identity Distillation: Exponentially Fast Distillation of Pretrained Diffusion Models for One-Step Generation},
+  author={Mingyuan Zhou and Huangjie Zheng and Zhendong Wang and Mingzhang Yin and Hai Huang},
+  booktitle={International Conference on Machine Learning},
+  url={https://arxiv.org/abs/2404.04057},
+  year={2024}
+}
+
+@article{xu2025one,
+  title={One-step Diffusion Models with $ f $-Divergence Distribution Matching},
+  author={Xu, Yilun and Nie, Weili and Vahdat, Arash},
+  journal={arXiv preprint arXiv:2502.15681},
+  year={2025}
+}