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We investigate the approximation and estimation rates of conditional diffusion transformers (DiTs) with classifier-free guidance.
A connection between score matching and denoising autoencoders
Pascal Vincent · 2011
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Neural networks and rational functions
Matus Telgarsky · 2017
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On the minimax optimality and superiority of deep neural network learning over sparse parameter spaces
Satoshi Hayakawa and Taiji Suzuki · 2019
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Nonparametric regression using deep neural networks with relu activation function
Johannes Schmidt-Hieber · 2020
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Are transformers universal approximators of sequence-to-sequence functions?
Chulhee Yun, Srinadh Bhojanapalli, Ankit Singh Rawat, Sashank Reddi, and Sanjiv Kumar · 2020
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Conditional image generation with score-based diffusion models
Georgios Batzolis, Jan Stanczuk, Carola-Bibiane Schönlieb, and Christian Etmann · 2021
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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Provable memorization via deep neural networks using sub-linear parameters
Sejun Park, Jaeho Lee, Chulhee Yun, and Jinwoo Shin · 2021
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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2021
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Score-based generative modeling in latent space
Arash Vahdat, Karsten Kreis, and Jan Kautz · 2021
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A short note on an inequality between kl and tv
Clément L Canonne · 2022
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Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions
Sitan Chen, Sinho Chewi, Jerry Li, Yuanzhi Li, Adil Salim, and Anru R Zhang · 2022
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Inductive biases and variable creation in self-attention mechanisms
Benjamin L Edelman, Surbhi Goel, Sham Kakade, and Cyril Zhang · 2022
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Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2022
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Provable memorization capacity of transformers
Junghwan Kim, Michelle Kim, and Barzan Mozafari · 2022
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Convergence for score-based generative modeling with polynomial complexity
Holden Lee, Jianfeng Lu, and Yixin Tan · 2022
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Scalable diffusion models with transformers. 2023 ieee
William S Peebles and Saining Xie · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Photorealistic text-to-image diffusion models with deep language understanding
Scalable diffusion models with transformers
William Peebles and Saining Xie · 2023
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Reward-directed conditional diffusion: Provable distribution estimation and reward improvement
Hui Yuan, Kaixuan Huang, Chengzhuo Ni, Minshuo Chen, and Mengdi Wang · 2023
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Nearly d d -linear convergence bounds for diffusion models via stochastic localization
Joe Benton, Valentin De Bortoli, Arnaud Doucet, and George Deligiannidis · 2024
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Learning mixtures of gaussians using diffusion models
Khashayar Gatmiry, Jonathan Kelner, and Holden Lee · 2024
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Jiuxiang Gu, Chenyang Li, Yingyu Liang, Zhenmei Shi, and Zhao Song · 2024
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Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al · 2022
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Mcvd-masked conditional video diffusion for prediction, generation, and interpolation
Vikram Voleti, Alexia Jolicoeur-Martineau, and Chris Pal · 2022
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Sumformer: Universal approximation for efficient transformers
Silas Alberti, Niclas Dern, Laura Thesing, and Gitta Kutyniok · 2023
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All are worth words: A vit backbone for diffusion models
Fan Bao, Shen Nie, Kaiwen Xue, Yue Cao, Chongxuan Li, Hang Su, and Jun Zhu · 2023
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Rethinking conditional diffusion sampling with progressive guidance
Anh-Dung Dinh, Daochang Liu, and Chang Xu · 2023
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Gaussian mixture solvers for diffusion models
Hanzhong Guo, Cheng Lu, Fan Bao, Tianyu Pang, Shuicheng Yan, Chao Du, and Chongxuan Li · 2023
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Convergence of score-based generative modeling for general data distributions
Holden Lee, Jianfeng Lu, and Yixin Tan · 2023
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Gradient guidance for diffusion models: An optimization perspective
Yingqing Guo, Hui Yuan, Yukang Yang, Minshuo Chen, and Mengdi Wang · 2024
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Understanding scaling laws with statistical and approximation theory for transformer neural networks on intrinsically low-dimensional data
Alexander Havrilla and Wenjing Liao · 2024
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Are transformers with one layer self-attention using low-rank weight matrices universal approximators?
Tokio Kajitsuka and Issei Sato · 2024
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Sora: A review on background, technology, limitations, and opportunities of large vision models
Yixin Liu, Kai Zhang, Yuan Li, Zhiling Yan, Chujie Gao, Ruoxi Chen, Zhengqing Yuan, Yue Huang, Hanchi Sun, Jianfeng Gao, et al · 2024
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Linear convergence of diffusion models under the manifold hypothesis
Peter Potaptchik, Iskander Azangulov, and George Deligiannidis · 2024
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Score-based diffusion models via stochastic differential equations–a technical tutorial
Wenpin Tang and Hanyang Zhao · 2024
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Tfg: Unified training-free guidance for diffusion models
Haotian Ye, Haowei Lin, Jiaqi Han, Minkai Xu, Sheng Liu, Yitao Liang, Jianzhu Ma, James Zou, and Stefano Ermon · 2024
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Reward-directed conditional diffusion: Provable distribution estimation and reward improvement
Hui Yuan, Kaixuan Huang, Chengzhuo Ni, Minshuo Chen, and Mengdi Wang · 2024
Closest in time.