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We explore a new class of diffusion models based on the transformer architecture.
Estimation of non-normalized statistical models by score matching
Aapo Hyvärinen and Peter Dayan · 2005
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
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Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
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Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
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Earlier work this paper cites.
Gaussian error linear units (gelus)
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Earlier work this paper cites.
Improved techniques for training GANs
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, Xi Chen, and Xi Chen · 2016
Earlier work this paper cites.
Conditional image generation with pixelcnn decoders
Aaron Van den Oord, Nal Kalchbrenner, Lasse Espeholt, Oriol Vinyals, Alex Graves, et al · 2016
Earlier work this paper cites.
Accurate, large minibatch sgd: Training imagenet in 1 hour
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Earlier work this paper cites.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
Earlier work this paper cites.
Image-to-image translation with conditional adversarial networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros · 2017
Earlier work this paper cites.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
Earlier work this paper cites.
Tim Salimans, Andrej Karpathy, Xi Chen, and Diederik P Kingma · 2017
Earlier work this paper cites.
Neural discrete representation learning
Aaron Van Den Oord, Oriol Vinyals, et al · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
JAX: composable transformations of Python+NumPy programs, 2018
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang · 2018
Earlier work this paper cites.
Image transformer
Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Lukasz Kaiser, Noam Shazeer, Alexander Ku, and Dustin Tran · 2018
Earlier work this paper cites.
Film: Visual reasoning with a general conditioning layer
Ethan Perez, Florian Strub, Harm De Vries, Vincent Dumoulin, and Aaron Courville · 2018
Earlier work this paper cites.
Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever · 2018
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Large scale GAN training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2019
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Generating long sequences with sparse transformers
Rewon Child, Scott Gray, Alec Radford, and Ilya Sutskever · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2021
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Offline reinforcement learning as one big sequence modeling problem
Michael Janner, Qiyang Li, and Sergey Levine · 2021
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Generating images with sparse representations
Charlie Nash, Jacob Menick, Sander Dieleman, and Peter W Battaglia · 2021
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Glide: Towards photorealistic image generation and editing with text-guided diffusion models
Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen · 2021
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Improved denoising diffusion probabilistic models
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
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Tuomas Kynkäänniemi, Tero Karras, Samuli Laine, Jaakko Lehtinen, and Timo Aila · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
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On network design spaces for visual recognition
Ilija Radosavovic, Justin Johnson, Saining Xie, Wan-Yen Lo, and Piotr Dollár · 2019
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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Language models are few-shot learners
Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Generative pretraining from pixels
Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, and Ilya Sutskever · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Zero-shot text-to-image generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever · 2021
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Early convolutions help transformers see better
Tete Xiao, Piotr Dollar, Mannat Singh, Eric Mintun, Trevor Darrell, and Ross Girshick · 2021
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Maskgit: Masked generative image transformer
Huiwen Chang, Han Zhang, Lu Jiang, Ce Liu, and William T Freeman · 2022
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Vector quantized diffusion model for text-to-image synthesis
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Scalable adaptive computation for iterative generation
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Elucidating the design space of diffusion-based generative models
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On aliased resizing and surprising subtleties in gan evaluation
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Learning to learn with generative models of neural network checkpoints
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Hierarchical text-conditional image generation with clip latents
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High-resolution image synthesis with latent diffusion models
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Photorealistic text-to-image diffusion models with deep language understanding
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Stylegan-xl: Scaling stylegan to large diverse datasets
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How to train your ViT? data, augmentation, and regularization in vision transformers
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Scaling autoregressive models for content-rich text-to-image generation
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